WO2024178585A1

WO2024178585A1 - Prediction of precoding matrix indicator (pmi) and communication based thereon

Info

Publication number: WO2024178585A1
Application number: PCT/CN2023/078620
Authority: WO
Inventors: Ming Li; Xiaolin Pu; Fangxun DU
Original assignee: Telefonaktiebolaget LM Ericsson AB
Current assignee: Telefonaktiebolaget LM Ericsson AB
Priority date: 2023-02-28
Filing date: 2023-02-28
Publication date: 2024-09-06
Anticipated expiration: 2025-08-28

Abstract

The present disclosure is related to a UE, a network node, and methods for A method at a UE for predicting one or more PMIs comprises: predicting one or more PMIs for a time period in the future based on at least one or more historical PMI data. A method at a network node for communicating with a UE based on at least one or more predicted PMIs comprises: receiving, from the UE, a first message indicating one or more predicted PMIs for a time period in the future; and communicating, with the UE, one or more messages during the time period based on at least the one or more predicted PMIs.

Description

PREDICTION OF PRECODING MATRIX INDICATOR (PMI) AND COMMUNICATION BASED THEREON

Technical Field

The present disclosure is related to the field of telecommunication, and in particular, to User Equipments (UEs) , network nodes, and methods for prediction of precoding matrix indicator (PMI) and communication based thereon.

Background

Airplane passenger traffic continues to rise annually, with significant competition among airlines to provide best-in-class in-flight services. Key among these is broadband connectivity. Today′s airplane passengers are increasingly accustomed to broadband connections anywhere, 24/7. They need to stay in touch with family and maintain critical business communications, and they want to access to entertainment in their free time. When they fly, they want broadband connectivity equal to what they experience from terrestrial networks and Wi-Fi hotspots. These expectations are increasing demand for fast, seamless connectivity on an aircraft to the point where an airline′s in-flight broadband capability has become a key competitive advantage.

As a specific type of Radio Access Network (RAN) , Air-To-Ground (ATG) networks aim to provide such in-flight connectivity for airplanes by utilizing ground stations which play a similar role as base stations (BSs) in terrestrial mobile networks. Unlike terrestrial mobile networks, the antennas of the ground stations in an ATG network are up-tilted towards the sky, and the inter-site distances (ISD) of the ground stations are much larger than that of terrestrial mobile networks. Further, the ATG technology is well suited to provide broadband connectivity to continental aircraft flights. It has significant technology and cost advantages over existing and future satellite solutions.

Summary

In order to maintain a robust and reliable connection between an aerial UE (AUE) and the base stations, the AUE and/or the base stations have to measure reference signals (RS) (e.g., Channel State Information Reference Signals (CSI-RSs) or Sounding Reference Signals (SRSs) ) , report the measurements, and determine precoders for transmission based thereon.

However, due to the high travelling speed of the AUE, precoders for spatial multiplexing have to be calculated or otherwise determined more frequently than other types of UEs with a much lower speed, and therefore RS measurements, measurement reporting, and PMI determinations have to be performed more frequently, which may consume too much network bandwidth and processing power and also cause delays in communications.

Therefore, to address or at least partially alleviate the above issues, some embodiments of the present disclosure are provided.

According to a first aspect of the present disclosure, a method at a UE for predicting one or more PMIs is provided. The method comprises: predicting one or more PMIs for a time period in the future based on at least one or more historical PMI data.

In some embodiments, the method further comprises: transmitting, to a network node, a first message indicating the one or more predicted PMIs. In some embodiments, the step of predicting the one or more PMIs comprises: predicting the one or more PMIs for a first time period in the future based on the one or more historical PMI data only.

In some embodiments, the step of predicting the one or more PMIs comprises: predicting the one or more PMIs for a second time period in the future based on at least the one or more historical PMI data and assistance information that is valid during the second time period. In some embodiments, the assistance information comprises at least one of: trajectory information associated with the UE; and velocity information associated with the UE.

In some embodiments, the one or more predicted PMIs are predicted by using at least Kalman filtering on the one or more historical PMI data. In some embodiments, at least one of the first time period and the second time period is determined based on at least one of: a change of movement of the UE; weather; and one or more air conditions. In some embodiments, the first message indicates: the one or more predicted PMIs; and one or more sub time periods in the future, each of which is a time period during which a corresponding predicted PMI will be valid. In some embodiments, a sub time period, TP_i, associated with a predicted PMI is indicated as follows:

where t_i is a time instant indicated by the first message, N is the number of the one or more predicted PMIs indicated by the first message, and t_N+1 is equal to the ending time instant of the first time period or the second time period.

In some embodiments, after the step of transmitting the first message indicating the one or more predicted PMIs, the method further comprises at least one of: preventing one or more measurements for CSI-RS from being performed during the first time period or the second time period; and preventing one or more reports for CSI from being generated and/or transmitted to the network node during the first time period or the second time period. In some embodiments, the one or more reports for CSI are one or more reports for PMI.

In some embodiments, after the step of transmitting the first message indicating the one or more predicted PMIs, the method further comprises: communicating, with the network node, one or more messages during the first time period and/or the second time period by assuming that the network node will follow the one or more predicted PMIs.

According to a second aspect of the present disclosure, a method at a network node for communicating with a UE based on at least one or more predicted PMIs is provided. The method comprises: receiving, from the UE, a first message indicating one or more predicted PMIs for a time period in the future; and communicating, with the UE, one or more messages during the time period based on at least the one or more predicted PMIs.

In some embodiments, the one or more predicted PMIs are predicted by the UE based on at least one or more historical PMI data. In some embodiments, the first message indicates: the one or more predicted PMIs; and one or more sub time periods in the future, each of which is a time period during which a corresponding predicted PMI will be valid. In some embodiments, a sub time period, TP_i, associated with a predicted PMI is indicated as follows:

where t_i is a time instant indicated by the first message, N is the number of the one or more predicted PMIs indicated by the first message, and t_N+1 is equal to the ending time instant of the time period.

In some embodiments, after the step of receiving the first message indicating the one or more predicted PMIs, the method further comprises at least one of: preventing one or more CSI-RSs associated with the UE from being generated and/or transmitted during the time period; and preventing one or more reports for CSI associated with the UE from being received and/or processed during the time period. In some embodiments, the one or more reports for CSI are one or more reports for PMI. In some embodiments, the step of communicating, with the UE, one or more messages during the time period based on at least the one or more predicted PMIs comprises: communicating, with the UE, the one or more messages during the time period by using the one or more predicted PMIs.

According to a third aspect of the present disclosure, a method at a network node for predicting one or more PMIs for a UE is provided. The method comprises: predicting one or more PMIs for a time period in the future based on at least one or more historical PMI data associated with the UE.

In some embodiments, the method further comprises: determining one or more codebooks for the UE based on at least the one or more predicted PMIs; and transmitting, to the UE, a second message indicating the one or more determined codebooks. In some embodiments, the one or more historical PMI data associated with the UE comprises at least one of: PMI data that is determined based on one or more CSI reports that are transmitted from the UE; and PMI data that is determined based on one or more SRSs that are transmitted from the UE.

In some embodiments, the step of predicting the one or more PMIs comprises: predicting the one or more PMIs for a third time period in the future based on only the one or more historical PMI data, which are determined based on one or more CSI reports that are transmitted from the UE. In some embodiments, the step of predicting the one or more PMIs comprises: predicting the one or more PMIs for a fourth time period in the future based on at least the one or more historical PMI data, which are determined based on one or more CSI reports that are transmitted from the UE, and assistance information that is valid during the fourth time period. In some embodiments, the step of predicting the one or more PMIs comprises: predicting the one or more PMIs for a fifth time period in the future based on only the one or more historical PMI data, which are determined based on one or more SRSs that are transmitted from the UE. In some embodiments, the step of predicting the one or more PMIs comprises: predicting the one or more PMIs for a sixth time period in the future based on at least the one or more historical PMI data, which are determined based on one or more SRSs that are transmitted from the UE, and assistance information that is valid during the sixth time period. In some embodiments, the assistance information comprises at least one of: trajectory information associated with the UE; and velocity information associated with the UE.

In some embodiments, the one or more predicted PMIs are predicted by using at least Kalman filtering on the one or more historical PMI data. In some embodiments, at least one of the third time period, the fourth time period, the fifth time period, and the sixth time period is determined based on at least one of: a change of movement of the UE; weather; and one or more air conditions. In some embodiments, the second message indicates: the one or more determined codebooks; and one or more sub time periods in the future, each of which is a time period during which a corresponding determined codebook will be valid.

In some embodiments, a sub time period, TP_j, associated with a determined codebook is indicated as follows:

where t_j is a time instant indicated by the second message, M is the number of the one or more determined codebooks indicated by the second message, t_M+1 is equal to the ending time instant of the third time period, the fourth time period, the fifth time period, or the sixth time period.

In some embodiments, the method further comprises at least one of: preventing one or more CSI-RSs associated with the UE from being generated and/or transmitted during the third time period or the fourth time period; preventing one or more reports for CSI associated with the UE from being received and/or processed during the third time period or the fourth time period; and preventing one or more SRSs associated with the UE from being received and/or processed during the fifth time period or the sixth time period. In some embodiments, the one or more reports for CSI are one or more reports for PMI. In some embodiments, the method further comprises at least one of: transmitting, to the UE, a third message indicating at least one of: that the UE is to provide a periodic PMI reporting with a prolonged or relaxed periodicity; that the UE is to provide a reduced number of aperiodic PMI reporting; and that the UE is to transmit a reduced number of SRSs.

According to a fourth aspect of the present disclosure, a method at a UE for communicating with a network node based on at least one or more predicted codebooks is provided. The method comprises: receiving, from the network node, a second message indicating one or more predicted codebooks for a time period in the future; and communicating, with the network node, one or more messages during the time period based on at least the one or more predicted codebooks.

In some embodiments, the one or more predicted codebooks are determined by the network node at least based on one or more predicted PMIs. In some embodiments, the one or more predicted PMIs are predicted by the network node at least based on one or more historical PMI data associated with the UE. In some embodiments, the one or more historical PMI data associated with the UE comprises at least one of: PMI data that is determined based on one or more CSI reports that are transmitted from the UE to the network node; and PMI data that is determined based on one or more SRSs that are transmitted from the UE to the network node.

In some embodiments, the second message indicates: the one or more predicted codebooks; and one or more sub time periods in the future, each of which is a time period during which a corresponding predicted codebook will be valid. In some embodiments, a sub time period, TP_j, associated with a predicted codebook is indicated as follows:

where t_j is a time instant indicated by the second message, M is the number of the one or more predicted codebooks indicated by the second message, and t_M+1 is equal to the ending time instant of the time period.

In some embodiments, the method further comprises at least one of: preventing one or more measurements for CSI-RSs from being performed during the time period; preventing one or more reports for CSI from being generated and/or transmitted during the time period; and preventing one or more SRSs associated with the UE from being generated and/or transmitted during the time period. In some embodiments, the one or more reports for CSI are one or more reports for PMI.

In some embodiments, the method further comprises: receiving, from the network node, a third message indicating at least one of: that the UE is to provide a periodic PMI reporting with a prolonged or relaxed periodicity; that the UE is to provide a reduced number of aperiodic PMI reporting; and that the UE is to transmit a reduced number of SRSs.

According to a fifth aspect of the present disclosure, a UE is provided. The UE comprises: a processor; a memory storing instructions which, when executed by the processor, cause the processor to perform any of the methods of the first and/or fourth aspects.

According to a sixth aspect of the present disclosure, a UE for predicting one or more PMIs is provided. The UE comprises: a predicting module configured to predict one or more PMIs for a time period in the future based on at least one or more historical PMI data. In some embodiments, the UE comprises one or more further modules, each of which may perform any of the steps of any of the methods of the first aspect.

According to a seventh aspect of the present disclosure, a UE for communicating with a network node based on at least one or more predicted codebooks is provided. The UE comprises: a receiving module configured to receive, from the network node, a second message indicating one or more predicted codebooks for a time period in the future; and a communicating module configured to communicate, with the network node, one or more messages during the time period based on at least the one or more predicted codebooks. In some embodiments, the UE comprises one or more further modules, each of which may perform any of the steps of any of the methods of the fourth aspect.

According to an eighth aspect of the present disclosure, a network node is provided. The network node comprises: a processor; a memory storing instructions which, when executed by the processor, cause the processor to perform any of the methods of the second and/or third aspects.

According to a ninth aspect of the present disclosure, a network node for communicating with a UE based on at least one or more predicted PMIs is provided. The network node comprises: a receiving module configured to receive, from the UE, a first message indicating one or more predicted PMIs for a time period in the future; and a communicating module configured to communicate, with the UE, one or more messages during the time period based on at least the one or more predicted PMIs. In some embodiments, the network node comprises one or more further modules, each of which may perform any of the steps of any of the methods of the second aspect.

According to a tenth aspect of the present disclosure, a network node for predicting one or more PMIs for a UE is provided. The network node comprises: a predicting module configured to predict one or more PMIs for a time period in the future based on at least one or more historical PMI data associated with the UE. In some embodiments, the network node comprises one or more further modules, each of which may perform any of the steps of any of the methods of the third aspect.

According to an eleventh aspect of the present disclosure, a computer program comprising instructions is provided. The instructions, when executed by at least one processor, cause the at least one processor to carry out any of the methods of any of the first, second, third, and fourth aspects.

According to a twelfth aspect of the present disclosure, a carrier containing the computer program of the eleventh aspect is provided. The carrier is one of an electronic signal, optical signal, radio signal, or computer readable storage medium.

According to a thirteenth aspect of the present disclosure, a telecommunication system is provided. The telecommunication system comprises one or more UEs of any of the fifth, sixth, and seventh aspects; and at least one network node of the eighth, ninth, and tenth aspects.

With some embodiments of the present disclosure, a UE can provide robust and reliable PMI report. Further, the UE and/or the gNB are able to avoid performing unnecessary measurements, reduce PMI reporting, reduce CSI-RS/SRS measurements, such that related signaling and power consumption can be reduced. Furthermore, the UE and/or the gNB are able to avoid performing unnecessary RS (e.g., SRS or CSI-RS) transmission to reduce power consumption.

Brief Description of the Drawings

Fig. 1 is a diagram illustrating an exemplary telecommunication network in which UE and gNB may be operated according to an embodiment of the present disclosure.

Fig. 2 is a diagram illustrating an exemplary Type 1 codebook with which prediction of PMI and communication based thereon is applicable according to an embodiment of the present disclosure.

Fig. 3 is a diagram illustrating an exemplary antenna array with which prediction of PMI and communication based thereon is applicable according to an embodiment of the present disclosure.

Fig. 4 is a diagram illustrating exemplary prediction of PMI at a UE according to an embodiment of the present disclosure.

Fig. 5 is a diagram illustrating exemplary prediction of PMI at a network node according to an embodiment of the present disclosure.

Fig. 6 is a flowchart illustrating an exemplary method for PMI prediction and communication based thereon according to an embodiment of the present disclosure.

Fig. 7 is a flow chart illustrating an exemplary method at a UE for predicting one or more PMIs according to an embodiment of the present disclosure.

Fig. 8 is a flow chart illustrating an exemplary method at a network node for communicating with a UE based on at least one or more predicted PMIs according to an embodiment of the present disclosure.

Fig. 9 is a flow chart illustrating an exemplary method at a network node for predicting one or more PMIs according to an embodiment of the present disclosure.

Fig. 10 is a flow chart illustrating an exemplary method at a UE for communicating with a network node based on at least one or more predicted PMIs according to an embodiment of the present disclosure.

Fig 11 schematically shows an embodiment of an arrangement which may be used in UEs or network nodes according to an embodiment of the present disclosure.

Fig 12 is a block diagram of an exemplary UE according to an embodiment of the present disclosure.

Fig 13 is a block diagram of an exemplary network node according to an embodiment of the present disclosure.

Fig 14 is a block diagram of another exemplary network node according to another embodiment of the present disclosure.

Fig 15 is a block diagram of another exemplary UE according to another embodiment of the present disclosure.

Fig 16 shows an example of a communication system in accordance with some embodiments of the present disclosure.

Fig 17 shows an exemplary UE in accordance with some embodiments of the present disclosure.

Fig. 18 shows an exemplary network node in accordance with some embodiments of the present disclosure.

Fig. 19 is a block diagram of an exemplary host, which may be an embodiment of the host of Fig. 16, in accordance with various aspects described herein.

Fig. 20 is a block diagram illustrating an exemplary virtualization environment in which functions implemented by some embodiments may be virtualized.

Fig. 21 shows a communication diagram of an exemplary host communicating via an exemplary network node with an exemplary UE over a partially wireless connection in accordance with some embodiments of the present disclosure.

Detailed Description

Hereinafter, the present disclosure is described with reference to embodiments shown in the attached drawings. However, it is to be understood that those descriptions are just provided for illustrative purpose, rather than limiting the present disclosure. Further, in the following, descriptions of known structures and techniques are omitted so as not to unnecessarily obscure the concept of the present disclosure.

Those skilled in the art will appreciate that the term "exemplary" is used herein to mean "illustrative, " or "serving as an example, " and is not intended to imply that a particular embodiment is preferred over another or that a particular feature is essential. Likewise, the terms "first" , "second" , "third" , "fourth, " and similar terms, are used simply to distinguish one particular instance of an item or feature from another, and do not indicate a particular order or arrangement, unless the context clearly indicates otherwise. Further, the term "step, " as used herein, is meant to be synonymous with "operation" or "action. " Any description herein of a sequence of steps does not imply that these operations must be carried out in a particular order, or even that these operations are carried out in any order at all, unless the context or the details of the described operation clearly indicates otherwise.

Conditional language used herein, such as "can, " "might, " "may, " "e.g., " and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or states. Thus, such conditional language is not generally intended to imply that features, elements and/or states are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without author input or prompting, whether these features, elements and/or states are included or are to be performed in any particular embodiment. Also, the term "or" is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term "or" means one, some, or all of the elements in the list. Further, the term "each, " as used herein, in addition to having its ordinary meaning, can mean any subset of a set of elements to which the term "each" is applied.

The term "based on" is to be read as "based at least in part on. " The term "one embodiment" and "an embodiment" are to be read as "at least one embodiment. " The term "another embodiment" is to be read as "at least one other embodiment. " Other definitions, explicit and implicit, may be included below. In addition, language such as the phrase "at least one of X, Y and Z, " unless specifically stated otherwise, is to be understood with the context as used in general to convey that an item, term, etc. may be either X, Y, or Z, or a combination thereof.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limitation of example embodiments. As used herein, the singular forms "a" , "an" , and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" , "comprising" , "has" , "having" , "includes" and/or "including" , when used herein, specify the presence of stated features, elements, and/or components etc., but do not preclude the presence or addition of one or more other features, elements, components and/or combinations thereof. It will be also understood that the terms "connect (s) , " "connecting" , "connected" , etc. when used herein, just mean that there is an electrical or communicative connection between two elements and they can be connected either directly or indirectly, unless explicitly stated to the contrary.

Of course, the present disclosure may be carried out in other specific ways than those set forth herein without departing from the scope and essential characteristics of the disclosure. One or more of the specific processes discussed below may be carried out in any electronic device comprising one or more appropriately configured processing circuits, which may in some embodiments be embodied in one or more application-specific integrated circuits (ASICs) . In some embodiments, these processing circuits may comprise one or more microprocessors, microcontrollers, and/or digital signal processors programmed with appropriate software and/or firmware to carry out one or more of the operations described above, or variants thereof. In some embodiments, these processing circuits may comprise customized hardware to carry out one or more of the functions described above. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.

Although multiple embodiments of the present disclosure will be illustrated in the accompanying Drawings and described in the following Detailed Description, it should be understood that the disclosure is not limited to the disclosed embodiments, but instead is also capable of numerous rearrangements, modifications, and substitutions without departing from the present disclosure that as will be set forth and defined within the claims.

Further, please note that although the following description of some embodiments of the present disclosure is given in the context of 5G New Radio (NR) , the present disclosure is not limited thereto. In fact, as long as prediction of PMI and communication based thereon is involved, the inventive concept of the present disclosure may be applicable to any appropriate communication architecture, for example, to Global System for Mobile Communications (GSM) /General Packet Radio Service (GPRS) , Enhanced Data Rates for GSM Evolution (EDGE) , Code Division Multiple Access (CDMA) , Wideband CDMA (WCDMA) , Time Division -Synchronous CDMA (TD-SCDMA) , CDMA2000, Worldwide Interoperability for Microwave Access (WiMAX) , Wireless Fidelity (Wi-Fi) , 4th Generation Long Term Evolution (LTE) , LTE-Advance (LTE-A) , or 5G NR, etc. Therefore, one skilled in the arts could readily understand that the terms used herein may also refer to their equivalents in any other infrastructure. For example, the term "User Equipment" or "UE" used herein may refer to a terminal device, a mobile device, a mobile terminal, a mobile station, a user device, a user terminal, a wireless device, a wireless terminal, or any other equivalents. For another example, the term "network node" used herein may refer to a transmission reception point (TRP) , a base station, a base transceiver station, an access point, a hot spot, a NodeB, an Evolved NodeB (eNB) , a gNB, a network element, or any other equivalents.

In some embodiments, the terms "UE" and "AUE" are interchangeably used. However, the term "UE" used in any of the embodiments may also be referred to as AUE.

Further, following 3GPP documents are incorporated herein by reference in their entireties:

- 3GPP TS 38.214 V17.4.0 (2022-12) , Technical Specification, 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; NR; Physical layer procedures for data (Release 17) ; and

- 3GPP TS 38.331 V17.3.0 (2022-12) , Technical Specification, 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; NR; Radio Resource Control (RRC) protocol specification (Release 17) .

An ATG network is basically utilizing the existing mature terrestrial techniques to build one stereoscopic network to provide high quality services for users and/or devices in aircrafts (e.g., airplanes, balloons, unmanned aerial vehicles (UAVs) , etc. ) . As mentioned above, the bore sight direction of ATG BS antennas will point to the air rather than to the ground. Meanwhile with the introduction of 5G technologies into ATG networks, the users on board could experience the adequate data rates as terrestrial network or even better, and therefore the user could have normal online services like online browsing, conference calls, and real time entertainment.

Due to the better propagation condition (almost line-of-sight path) and ATG UE with large antennas, ATG BS coverage range could be up to 200 km as shown in Fig. 1 compared with cell coverage up to hundreds of meters of the legacy terrestrial BS. The ATG BS could have similar output power as terrestrial BS (i.e., Macro BS or Advanced Antenna System (AAS) BS) to avoid high co-existing interference. The ATG UE could be mounted in the airplane and use AAS radio to get much higher output power and antenna gain than a legacy handheld UE. That makes a high throughput for downlink (DL) and uplink (UL) in ATG possible.

In addition, ATG UEs are assumed to have similar capability of getting global navigation satellite system (GNSS) and ephemeris information as non-terrestrial networks (NTN) UE. In that case, high Doppler shift and timing variance could be pre-compensated on UE side, and then ATG BS could also support the mobility of airplane up to 1200 km/h which is much faster than a high speed train (HST) .

Fig. 1 is a diagram illustrating an exemplary telecommunication network 10 in which an AUE (or UE) 100 and multiple RAN nodes (e.g., gNBs) 105-1 through 105-4 (collectively, gNBs 105 hereinafter) may be operated according to an embodiment of the present disclosure. Although the telecommunication network 10 is a network defined in the context of 5G NR, the present disclosure is not limited thereto.

As shown in Fig. 1, the network 10 may comprise one or more UEs 100 and multiple RAN nodes 105, which could be a base station, a Node B, an evolved NodeB (eNB) , a gNB, or an AN node which provides the UE 100 with access to the network. Further, the network 10 may comprise its core network portion that is not shown in Fig. 1.

However, the present disclosure is not limited thereto. In some other embodiments, the network 10 may comprise additional nodes, less nodes, or some variants of the existing nodes shown in Fig. 1. For example, in a network with the 4G architecture, the entities (e.g., an eNB) which perform these functions may be different from those (e.g., the gNB 105) shown in Fig. 1. For another example, in a network with a mixed 4G/5G architecture, some of the entities may be same as those shown in Fig. 1, and others may be different.

Further, although one AUE (or UE) 100 and four gNBs 105 are shown in Fig. 1, the present disclosure is not limited thereto. In some other embodiments, any number of AUEs and/or any number of gNBs may be comprised in the network 10.

As shown in Fig. 1, the UE 100 may be communicatively connected to one or more of the gNB 105 which in turn may be communicatively connected to a corresponding Core Network (CN) and then the Internet, such that the UE 100 may finally communicate its user plane data with other devices outside the network 10, for example, via the gNB 105.

Further, as shown in Fig. 1, the ISD between two gNBs 105 may be up to 200 km as mentioned above while the AUE 100 may fly at an altitude of 10 to 12 km from the ground where the gNBs 105 are located. However, the present disclosure is not limited thereto. For example, when the AUE 100 is a UAV, it may fly at an altitude of tens or hundreds meters from the ground. For another example, the ISD between two gNBs 105 may be up to tens km.

Different from a satellite system which occupies dedicated channels to provide its services, an ATG system could be deployed in same channels as those for a terrestrial network, which could better utilize the existing spectrum at hand.

In addition, an ATG system could provide a much higher data rate than a satellite system due to its closer distance between BS and UE. In addition, a high gain of antenna array equipped on an airplane is also part of the reason to achieve a better performance.

NR support for non-terrestrial networks (NTN) which includes satellite component has been specified in 3GPP specifications since Rel-17. The NR NTN (non-terrestrial networks) specification includes especially Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) with implicit compatibility to support HAPS (high altitude platform station) and ATG (air to ground) scenarios. There are several regional commercial or trial in-flight networks based on hybrid techniques of ATG and satellite communication. Satellite links focus on providing every-where connectivity (e.g., when crossing the sea) , while ATG links focus on providing high-quality data services for all service available areas (e.g., inland and coastline area) .

The atmospheric ducting phenomenon, caused by lower densities at higher altitudes in the Earth′s atmosphere, causes a reduced refractive index, causing the signals to bend back towards the Earth. A signal trapped in the atmospheric duct can reach distances far greater than normal. In Time Division Duplex (TDD) networks with the same UL/DL slot configuration, and in the absence of atmospheric ducting, a guard period is used to avoid the interference between UL and DL transmissions in different cells. However, when the atmospheric ducting phenomenon happens, radio signals can travel a relatively long distance, and the propagation delay exceeds the guard period. Consequently, the DL signals of an aggressor cell can interfere with the UL signals of a victim cell that is far away from the aggressor. Such interference is termed as remote interference. The further the aggressor is to the victim, the more UL symbols of the victim will be impacted. This phenomenon usually occurs during the spring-summer transition in inland areas, summer-autumn transition period and winter in coastal areas.

In the ATG scenario, the propagation channel between AUE and ATG base station is assumed as line of sight (LOS) channel taking benefits from a highly directional antenna implementation and a greater horizontal angle between base station′s antenna and AUE′s antenna, and the atmospheric duct is not a critical issue impacting positioning and spatial characteristics of AUE.

The UE performs measurements on one or more DL reference signals (RS) of one or more cells in different UE activity states e.g. RRC idle state, RRC inactive state, RRC connected state etc. The measured cell may belong to or operate on the same carrier frequency as of the serving cell (e.g. intra-frequency carrier) or it may belong to or operate on different carrier frequency as of the serving cell (e.g. non-serving carrier frequency) . The non-serving carrier may be called as inter-frequency carrier if the serving and measured cells belong to the same Radio Access Technology (RAT) but different carriers. The non-serving carrier may be called as inter-RAT carrier if the serving and measured cells belong to different RATs. Examples of downlink RS are signals in Synchronous Signal (SS) and Physical Broadcast Channel (PBCH) Block (SSB) , CSI-RS, Cell-specific Reference Signal (CRS) , Demodulation Reference Signal (DMRS) , Primary Synchronous Signal (PSS) , Secondary Synchronous Signal (SSS) , Discovery Reference Signal (DRS) , Positioning Reference Signal (PRS) etc. Examples of uplink RS are signals in Sounding Reference Signal (SRS) , DMRS, etc.

Each SSB carries NR-PSS, NR-SSS and NR-PBCH in 4 successive symbols. One or multiple SSBs are transmitted in one SSB burst which is repeated with certain periodicity e.g. 5 ms, 10 ms, 20 ms, 40 ms, 80 ms, or 160 ms. The UE is configured with information about SSB on cells of certain carrier frequency by one or more SS/PBCH block measurement timing configuration (SMTC) configurations. The SMTC configuration comprises parameters such as SMTC periodicity, SMTC occasion length in time or duration, SMTC time offset with regard to reference time (e.g. serving cell′s System Frame Number (SFN) ) etc. Therefore, SMTC occasion may also occur with certain periodicity e.g. 5 ms, 10 ms, 20 ms, 40 ms, 80 ms or 160 ms.

Examples of measurements are cell identification (e.g. Physical Cell Identifier (PCI) acquisition, PSS/SSS detection, cell detection, cell search etc. ) , Reference Signal Received Power (RSRP) , Reference Signal Received Quality (RSRQ) , Synchronous Signal RSRP (SS-RSRP) , SS-RSRQ, Signal to Noise and Interference Ratio (SINR) , RS-SINR, SS-SINR, CSI-RSRP, CSI-RSRQ, Received Signal Strength Indicator (RSSI) , acquisition of System Information (SI) , Cell Global ID (CGI) acquisition, Reference Signal Time Difference (RSTD) , UE RX-TX time difference measurement, Radio Link Monitoring (RLM) , which consists of Out of Synchronization (out of sync) detection and In Synchronization (in-sync) detection etc.

To reduce uplink overhead, 3GPP LTE and 5G networks only require UEs to feedback CSI that is relevant to the upcoming scheduling decisions at the serving BS. The basic principle of the NR CSI feedback is as follows. The BS first configures the UE′s CSI report. This configuration may specify the time-and frequency-resources that can be used by the UE to report CSI as well as what information should be reported. For example, the CSI report can consist of a Channel Quality Indicator (CQI) , Precoding Matrix Indicator (PMI) , CSI-RS Resource Indicator (CRI) , Strongest Layer Indication (SLI) , Rank Indicator (RI) , and/or L1-RSRP.

3GPP Release 15 specifies two types of CSI reports, each utilizing different ways to calculate the PMI (i.e. different "codebooks" )

- Type I CSI feedback; and

- Type II CSI feedback.

For sake of simplicity but without loss of generality, Type I CSI has been designed as a low-feedback overhead technology, primarily for Single User Multiple Input Multiple Output (SU-MIMO) scenarios. The Type I precoder codebooks are based on Discrete Fourier Transform (DFT) vectors, where a spatial layer of the precoder only utilizes a single DFT vector, corresponding to only the strongest angular direction of the channel. Thus, such a codebook may be seen as a spatial down-sampling of the channel.

The term "precoding" is used in various wireless communication systems, e.g. Wi-Fi, 4G, and 5G systems, and the words are sometimes used interchangeably. However they are not identical. Precoding generally refers to the transmitter side and involves the individual control of the amplitudes and phases of the signals sent from the various transmit antennas.

Precoding assumes that channel state information (CSI) is known at the transmitter. Meaning of Codebook under the context of CSI-RS is a set of Precoders (a set of Precoding Matrix) . Putting it other way, Codebook is a kind of matrix (a matrix having complex value elements) that transforms the data bits (e.g., Physical Downlink Shared Channel or PDSCH) to another set of data that maps to each antenna port.

From UE′s perspective, a typical way to know about the precoder information is to send measurement report for PMI (Precoding Matrix Indicator) to gNB and may assume that gNB would use the PMI that it reports. It is not mandatory for gNB to use the PMI that UE reports. It may or may not use the same PMI depending on its own situation. Even if gNB uses a different PMI than what UE reports, UE can decode the downlink data because the downlink data (e.g., PDSCH) carries its own reference signal. However, UE′s feedback is still a useful reference or benchmark. UE can estimate the channel using the DMRS and in many cases this type of channel estimation would be good enough to decode PDSCH.

For NR TDD network, from gNB′s perspective, a typical way is to tell UE to send SRS and pick a specific PMI based on the channel estimation of SRS, and tell UE via Downlink Control Information (DCI) signaling to use a specific PMI. In this case, UE shall use the specific precoding matrix specified by gNB. If assuming that UE and gNB are in such a condition where channel reciprocity is guaranteed (like well calibrated TDD channel) , gNB can use the channel information from SRS for estimating downlink channel quality and selecting downlink beam.

As shown in Fig. 2, multiple beams associated with the Type 1 codebook can be formed by an antenna array (e.g., an antenna array shown in Fig. 3) . The multiple beams may comprise orthogonal DFT beams (indicated by black solid circles) and oversampled DFT beams (indicated by white solid circles) in both horizontal and vertical directions of an antenna panel. Taking Type I Single-Panel Codebook as an example, citing relevant definitions and statements in section 5.2.2.2 in TS 38.214, V17.4.0, the parameters: N₁ and N₂ are determined by the number of antennas in horizontal and vertical directions while O₁ and O₂ indicate DFT Oversampling. For example, in the embodiment shown in Fig. 2, N₁ is equal to 4 and N₂ is equal to 2, resulting in a 4x2 antenna array (e.g., the antenna array shown in Fig. 3) . Further, in the embodiment shown in Fig. 2, O₁ is equal to 4 and O₂ is also equal to 4, resulting in a total of N₁ x O₁ = 16 beams in the horizontal direction and a total of N₂ x O₂ = 8 beams in the vertical direction. However, the present disclosure is not limited thereto. In some other embodiments, other configurations for the beams are also applicable.

The overall functionality of O₁ and O₂ is to determine the sweeping steps of a beam during the beam management (e.g., beam tracking) . O₁ determines the sweeping step in the horizontal direction and O₂ determines the sweeping step in the vertical direction. In other words, the higher O₁ and/or O₂ are, in smaller steps (i.e., finer angles) the beam can be swept.

Table 1 below duplicates Table 5.2.2.2.1-2 for Type 1 Single Panel antenna array shown in Fig. 3 in TS 38.214 v17.4.0 for a single panel antenna array. Further, some additional columns are added to Table 1 to explain the relationships between the parameters.

Table 1: Supported configurations of (N₁, N₂) and (O₁, O₂)

However, the present disclosure is not limited thereto. In some other embodiments, different configurations may be supported additionally or alternatively.

Different from UEs in a legacy terrestrial system, an AUE in an ATG system is typically located or embedded in an aerial device or vehicle such as an aircraft. A typical scenario is that the aerial device generally flies or moves in the air according to planned flight path. In such a case, the fixed trajectory of the AUE may be used as assistance information for the gNB and the UE in selecting/determining codebook and/or PMI in the precoding procedure, and may bring benefit to power consumption of gNB and UE and mitigate signaling overhead between gNB and UE.

As mentioned above, in order to maintain a robust and reliable connection between an AUE and the base stations, the AUE and/or the base stations have to measure RS (e.g., CSI-RS or SRS) , report the measurements, and determine precoders for transmission based thereon. However, due to the high travelling speed of the AUE, precoders for spatial multiplexing have to be calculated or otherwise determined more frequently than other types of UEs with a much lower speed, and therefore RS measurements, measurement reporting, and PMI/codebook determinations have to be performed more frequently, which may consume too much network bandwidth and processing power and also cause delays in communications.

Therefore, some embodiments of the present disclosure provide methods at AUEs and network nodes for prediction of PMI and communication based thereon. For example, a scenario may comprise an AUE (e.g., the AUE 100 shown in Fig. 1) served by a first cell (cell1) , which is managed, served, operated, and/or configured by a first gNB/network node (NW1) (e.g., the gNB 105-1, the gNB 105-2, the gNB 105-3, or the gNB 105-4, or collectively the gNBs 105, shown in Fig. 1) . The AUE (for simplicity also called as a UE) is embedded, housed, installed, fitted, and/or contained in an aerial device, which flies or moves in the air or free space e.g. an aircraft, flying taxi, drone etc. NW1 may be located on the ground or at a fixed location or in the air but immobile.

According to some embodiments, the UE may:

- predict and determine PMI during a time period (Tp1) in terms of received sets of CSI-RS if trajectory information of UE and location information of gNB are not available explicitly; and/or

- predict and determine PMI during a time period (Tp2) in terms of received CSI-RS and trajectory information of UE and/or location information of gNB.

According to some embodiments, the gNB may:

- after acquiring an initial set (s) of PMI feedback from UE, determine and adapt precoder, without request of PMI reporting, during a time period (Tp3) if trajectory information of UE and location information of gNB are not available explicitly; and/or

- after acquiring an initial set (s) of PMI feedback from UE, determine and adapt precoder based on trajectory information of UE and location information of gNB, without request of PMI reporting, during a time period (Tp4) ; and/or

- after determining an initial set (s) of PMI based on SRS from UE, determine and adapt precoder, without request of SRS transmission, during a time period (Tp5) if trajectory information of UE and location information of gNB are not available explicitly; and/or

- after determining an initial set (s) of PMI based on SRS from UE, determine and adapt precoder based on trajectory information of UE and location information of gNB, without request of SRS transmission, during a time period (Tp6) ; and/or

- determine CSI-RS transmission adaptively with PMI feedback from UE; and/or

- prolong periodicity of periodic PMI reporting or reduce the number of aperiodic PMI reporting during Tp3 and Tp4 under respective conditions; and/or

- prolong periodicity of periodic SRS transmission or reduce the number of aperiodic SRS transmission during Tp5 and Tp6 under respective conditions; .

With some embodiments of the present disclosure, the UE can provide robust and reliable PMI report. Further, the UE and/or the gNB are able to avoid performing unnecessary measurements and reduce PMI reporting/codebook indication to save signaling and power consumption. Furthermore, the gNB and/or the UE are able to avoid performing unnecessary RS transmission to save power consumption.

In some embodiments, in addition to current or historical CSI reporting, UE shall be able to report future CSI information. In some embodiments, among all parameters in CSI reporting, PMI is the most critical one with respect to high-speed moving AUE and other parameters can be assumed to be consistent in a long time period due to the LOS channel condition and relatively small amount of AUEs served by one ATG BS. In this manner, in below embodiments, PMI is discussed intensively. However, the present disclosure is not limited thereto. In some other embodiments, parameters other than PMI may be predicted and/or used in a similar manner.

Next, some embodiments will be described in details to explain a method for PMI prediction at UE.

In some embodiments, for sake of simplicity, assuming that gNB configures codebookConfig = Type I, and that the antenna array and codebook are same as shown in Fig. 2. However, please note that the idea disclosed in some embodiments may also be used in other transmission scheme, e.g. type II codebook, UL/DL reciprocity, and etc.

In some embodiments, a UE may report PMI associated with the chosen codebook. The typical format of PMI is {i_1, 1, i_1, 2, i_1, 3, i₂} when the number of layers v ∈ {2, 3, 4} , or {i_1, 1, i_1, 2, i₂} when the number of layersFor sake of simplicity and without loss of generality, only the term "PMI" is used without any explicit format involved hereinafter.

In some embodiments, the prediction may be made based on statistical and/or historical PMI data only and without extra assistance information. For example, the UE may detect CSI-RS transmitted by the gNB, and continuously assess and determine the PMI based on the detected CSI-RS. Since the UE is moving along a certain trajectory or path, the UE may predict a set (s) of PMIs for a time period (e.g., Tp1) , based on PMIs which UE has gotten. Next, an exemplary embodiment will be described with reference to Fig. 4.

Fig. 4 is a diagram illustrating exemplary prediction of PMI at a UE according to an embodiment of the present disclosure. As shown in Fig. 4, the UE may fly or otherwise move along a certain trajectory (indicated by the white solid arrow) continuously, and therefore the beams associated with its detected PMIs during its flight or movement, which are determined for example based on the detected/measured CSI-RSs, are shown as being changed continuously and indicated by the black solid circles.

Using these detected or otherwise determined historical PMIs, a set of PMIs for a time period in the future may be predicted. For example, as shown by the grey solid circles, PMIs for a certain time period in the future may be estimated, predicted, or otherwise determined based on the historical PMI data. In some embodiments, a flying trace (indicated by the grey solid arrow) corresponding to the predicted PMIs may be predicted or otherwise determined also.

In some embodiments, a typical prediction or estimation algorithm may be Kalman filtering. It is an algorithm that uses a series of measurements (e.g., the CSI-RS measurements or SRS measurements) observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe.

In some embodiments, extra assistance information may be valid, which for example may comprise but not limited to the UE′s trajectory and/or velocity. Based on the assistance information, the UE may predict the next PMIs for a time period (e.g., Tp2, which also indicates that assistance information is valid in Tp2 and after that, assistance information shall be refreshed with a new validity time period) . In some embodiments, the assistance information may be used as a reference to correct UE′s predicted PMIs, for example, as indicated by the patterned arrow shown in Fig. 4. In some embodiments, predicted PMI may be an output of a combination function of estimation, e.g. Kalman filtering, based on practically calculated PMI and assistance information.

In some embodiments, the time length of Tp1 and/or Tp2 may depend on one or more factors, comprising but not limited to: change of moving of AUE, weather, air conditions, and so on.

Please note that although the trajectory of the UE is shown in Fig. 4 as a straight path indicated by the arrows, the present disclosure is not limited thereto. In some other embodiments, the trajectory of the UE may have any shape, regular or irregular, convex or concave. For example, the trajectory may be a convex curve, a circle, etc.

Further, the trajectories shown in Fig. 4 are provided only for ease of understanding, but not necessarily mean that the UE does have a real trajectory as shown. In fact, what the UE can determine and really care about is its detected or predicted PMI (or equivalently, the beams indicated by the circles) , rather than its trajectory indicated by the arrows, and therefore the UE may actually have a different trajectory which may be mapped to same beams or PMIs as those for the trajectory shown in Fig. 4.

In some embodiments, different from PMI only indicating a channel state at the current time, the predicted PMI may include a set (s) of PMIs for a validity time duration Tp1 or Tp2 in the future.

In some embodiments, based on PMIs predicted by the UE with variants of implementations, the UE may be able to report future one or more PMIs to the gNB. In some embodiments, to realize it, an example of generalized formation of set (s) of PMI may be provided in Table 2:

Table 2. Set (s) of PMI associated with time reference

In some embodiments, each PMI or PMI index may be associated with a time reference or time reference index. In some embodiments, it can be interpreted as: PMI #1 is valid from time reference #1 until time reference #2; PMI #2 is valid from time reference #2 until time reference #3 and in turn for PMI #3. In some embodiments, the last PMI #N reported is valid from its corresponding time reference #N until the Tp1 or Tp2. In some embodiments, the Tp1 or Tp2 may be reported separately from the set of PMIs reported, for example, as separate Information Elements (IEs) in a same message or as separate messages. In some embodiments, the last PMI #N reported is valid from its corresponding time reference #N until the time reference #1 for the first PMI #1 in the next reporting message. There are many ways to indicate one or more predicted PMIs and their corresponding validity time periods, and therefore the present disclosure is not limited to those described above.

In some embodiments, the time reference may be an absolute time instant, e.g. System Frame Number (SFN) number or an offset to a time instant, e.g. every N ms after UE′s current time instance.

In some embodiments, an example of an RRC message containing the aforementioned PMI with associated time reference defined in CSI-ReportConfig is provided as follows:

In some embodiments, for validityTimingPerPMI-Index, the field counts the number of Coordinated Universal Time (UTC) seconds in 10 ms units since 00: 00: 00 on Gregorian calendar date 1 January, 1900 (midnight between Sunday, December 31, 1899 and Monday, January 1, 1900) .

In some embodiments, Tp1 or Tp2 may be equal to the maximum of time reference.

In some embodiments, the UE does not report CSI or PMI continuously within Tp1 or Tp2, provided that the UE supports the capability of PMI association with time reference and the UE informs the gNB of that it supports and reports PMI association with time reference in CSI reporting.

In some embodiments, the UE does not need to measure reference signals, e.g. CSI-RS within Tp1 or Tp2, provided that the UE supports the capability of PMI association with time reference and the UE informs the gNB of that it supports and reports PMI association with time reference in CSI reporting.

In some embodiments, the UE may assume that the gNB always follows the predicted PMI reported by the UE, and in this case, the gNB does not need to perform reference signal transmission during Tp1 or Tp2.

Next, an exemplary embodiment where the prediction is made at a gNB will be described with reference to Fig. 5.

Fig. 5 is a diagram illustrating prediction of PMI at a network node (e.g., a gNB) according to an embodiment of the present disclosure.

As shown in Fig. 5, a UE may fly or otherwise move along a certain trajectory (indicated by the white solid arrow) continuously, and therefore the beams associated with its PMIs, which are determined by the UE based on the detected/measured CSI-RSs and reported to the network node during its flight or movement, are shown as being changed continuously and indicated by the black solid circles.

Using these reported or otherwise determined historical PMIs, a set of PMIs for a time period in the future may be predicted by the gNB for the UE. For example, as shown by the grey solid circles, PMIs for a certain time period in the future may be estimated, predicted, or otherwise determined based on the historical PMI data. In some embodiments, a flying trace (indicated by the grey solid arrow) corresponding to the predicted PMIs may be predicted or otherwise determined also.

In some embodiments, in response to the set (s) of PMI reported by the UE, the gNB may predict and determine set (s) of precoder for a time period (Tp3) as the grey solid arrow shown in Fig. 5, e.g. with support of Kalman filtering, based on statistics and/or historical PMIs reported by UE. In other words, the gNB does not need to acquire UE′s PMI report within Tp3.

Tn some embodiments, extra assistance information may be valid, which may comprise but not limited to the UE′s trajectory and/or velocity. Based on that, the gNB may predict and determine set (s) of precoders for a time period (Tp4, which also indicates that assistance information is valid in Tp4, and after that, assistance information shall be refreshed with a new validity time period) , for example, indicated by a patterned arrow shown in Fig. 5. In some embodiments, the assistance information may be used as reference to correct UE′s predicted precoder. In one example, predicted precoder may be an output of a combination function of estimation, e.g. Kalman filtering, based on predicted precoder and assistance information.

In some embodiments, the gNB may not need to perform reference signals transmission during Tp3 or Tp4.

In some embodiments, the gNB may signal the UE to prolong or relax periodicity of periodic PMI reporting, or reduce the number of aperiodic PMI reporting during Tp3 or Tp4, for example, through RRC, Medium Access Control -Control Element (MAC-CE) , or DCI command. In some embodiments, the message used by the gNB to instruct the UE to prolong or relax periodicity or reduce the number may be the same message used by the gNB to signal the one or more codebooks. In some embodiments, they can be indicated by different fields or IEs in the same message. In some embodiments, the indication of prolonging or relaxing periodicity or reducing the number may be implicitly indicated by the presence of the one or more predicted codebooks in the message. Further, in some other embodiments, they can be indicated by different messages.

In some embodiments, different from a legacy terrestrial system, the gNB may indicate the codebooks to be used in the predicted precoder within Tp3 or Tp4 to the UE, even if the codebook does not need to be known by UE explicitly. For example, the indication of the codebooks can reduce UE′s workload of channel estimation and facilitate other UE′s operations. In some embodiments, to realize it, an example of generalized formation of set (s) of codebook may be provided in Table 3:

Table 3. Set (s) of codebook associated with time reference

In some embodiments, each codebook or codebook index may be associated with a time reference or time reference index. In some embodiments, it can be interpreted as: codebook #1 is valid from time reference #1 until time reference #2; codebook #2 is valid from time reference #2 until time reference #3 and in turn for codebook #3. In some embodiments, the last codebook #M indicated is valid from its corresponding time reference #M until the Tp3 or Tp4. In some embodiments, the Tp3 or Tp4 may be indicated separately from the set of codebooks indicated, for example, as separate IEs in a same message or as separate messages. In some embodiments, the last codebook #M indicated is valid from its corresponding time reference #M until the time reference #1 for the first codebook #1 in the next indicating message. There are many ways to indicate one or more predicted codebooks and their corresponding validity time periods, and therefore the present disclosure is not limited to those described above.

In some embodiments, the time reference may be an absolute time instant, e.g. an SFN number or an offset to a time instant, e.g. every M ms after UE′s current time instance.

In some embodiments, an example of RRC message containing the aforementioned codebook with associated time reference is listed as following:

In some embodiments, for validityTimingPercodebook-Index, the field counts the number of UTC seconds in 10 ms units since 00: 00: 00 on Gregorian calendar date 1 January, 1900 (midnight between Sunday, December 31, 1899 and Monday, January 1, 1900) .

In some embodiments, Tp3 or Tp4 may be equal to the maximum of time reference.

Following above methods of determining and adapting precoders and PMIs by UE and gNB may be used standalone or in any combination thereof. As described below with reference to Fig. 6, the UE may report predicted set (s) of PMI in Tp1 or Tp2 to the gNB, and meanwhile the gNB may apply set (s) of precoder to the UE and the validity time is Tp3 or Tp4.

Fig. 6 is a flowchart illustrating an exemplary method for PMI prediction and communication based thereon according to an embodiment of the present disclosure. As shown in Fig. 6, the gNB 105 and the UE 100 may optionally communicate assistance information from one to the other at step S610. For example, when the UE 100 is aware of the assistance information (e.g., when the UE is installed in an airplane and notified of its trajectory and/or velocity information by its speed meter and/or the navigation system) , it may provide the assistance information to the gNB 105 at step S610. For another example, when the gNB 105 is aware of the assistance information (e.g., the gNB is notified of the trajectory and/or velocity information of the airplane by a server in the air traffic bureau that is monitoring the airplane) , it may provide the assistance information to the UE 100 at step S610. For yet another example, both of the gNB 105 and the UE 100 may transmit a part or whole of the assistance information to each other, for example, to make sure they have same assistance information for PMI prediction and communication based thereon.

At step S620, the gNB 105 may transmit one or more CSI-RSs to the UE for measurements, and the UE 100 may measure the CSI-RSs and determine one or more PMIs based thereon. With some historical PMIs determined in this way, the UE 100 may begin its prediction of the PMIs without further CSI-RS measurements.

At step S630, the UE may estimate, predict, or otherwise determine one or more PMIs for a time period in the future, for example, in a manner similar to that described with reference to Fig. 4.

At step S640, the UE may report the one or more predicted PMIs to the gNB 105. In some embodiments, the reported PMIs may comprise the predicted PMIs only, as shown in Table 2. In some other embodiments, the reported PMIs may comprise both the predicted PMIs and PMIs determined based on the CSI-RS measurements.

At step S650, upon reception of the reported PMIs, no matter whether they are predicted PMIs or PMIs determined based on the CSI-RS measurements, the gNB 105 may estimate, predict, or otherwise determine one or more precoders based on the reported PMIs, for example, in a manner similar to that described with reference to Fig. 5. In some embodiments, the prediction can be made based on the PMIs determined based on the CSI-RS measurements only. In some embodiments, the prediction can be made based on both the predicted PMIs and the PMIs determined based on the CSI-RS measurements.

At step S660, once the one or more precoders are predicted, the gNB 105 may apply the precoder corresponding to the correct time period for its DL transmission.

At step S670, the gNB 105 may perform its PDSCH transmission based on the predicted precoders during corresponding time periods.

Further, in some embodiments, the gNB 105 may provide the one or more predicted precoders along with their valid time periods to the UE 100, for example, as shown in Table 3.

In some embodiments, in terms of UL/DL reciprocity based transmission scheme, similar methods can be applied. For example, with respect to reception of the UE′s reference signals, e.g. SRS, the gNB may predict and determine DL beamforming coefficients for a time period (Tp5) , e.g. with support of Kalman filtering. In other words, the UE needs not to transmit reference signal to the gNB in Tp5.

In some embodiments, extra assistance information is valid, which may comprise but not limited to the UE′s trajectory and velocity. Based on that, the gNB may predict and determine beamforming coefficients for a time period (Tp6) . The assistance information may be used as reference to correct UE′s reported PMI. In some embodiments, predicted beamforming coefficients may be an output of a function of estimation, e.g. Kalman filtering, based on received UE′s reference signal and assistance information.

With the above embodiments of the present disclosure, a UE can provide robust and reliable PMI report. Further, the UE and/or the gNB are able to avoid performing unnecessary measurements, reduce PMI reporting, reduce SRS measurements, such that related signaling and power consumption can be reduced. Furthermore, the UE and/or the gNB are able to avoid performing unnecessary RS (e.g., SRS or CSI-RS) transmission to reduce power consumption.

Fig. 7 is a flow chart of an exemplary method 700 at a UE for predicting one or more PMIs according to an embodiment of the present disclosure. The method 700 may be performed at a user equipment (e.g., the UE 100) . The method 700 may comprise a step S710. However, the present disclosure is not limited thereto. In some other embodiments, the method 700 may comprise more steps, different steps, or any combination thereof. Further the steps of the method 700 may be performed in a different order than that described herein when multiple steps are involved. Further, in some embodiments, a step in the method 700 may be split into multiple sub-steps and performed by different entities, and/or multiple steps in the method 700 may be combined into a single step.

The method 700 may begin at step S710 where one or more PMIs for a time period in the future may be predicted based on at least one or more historical PMI data.

In some embodiments, the method 700 may further comprise: transmitting, to a network node, a first message indicating the one or more predicted PMIs. In some embodiments, the step of predicting the one or more PMIs may comprise: predicting the one or more PMIs for a first time period in the future based on the one or more historical PMI data only. In some embodiments, the step of predicting the one or more PMIs may comprise: predicting the one or more PMIs for a second time period in the future based on at least the one or more historical PMI data and assistance information that is valid during the second time period. In some embodiments, the assistance information may comprise at least one of: trajectory information associated with the UE; and velocity information associated with the UE.

In some embodiments, the one or more predicted PMIs may be predicted by using at least Kalman filtering on the one or more historical PMI data. In some embodiments, at least one of the first time period and the second time period may be determined based on at least one of: a change of movement of the UE; weather; and one or more air conditions. In some embodiments, the first message may indicate: the one or more predicted PMIs; and one or more sub time periods in the future, each of which is a time period during which a corresponding predicted PMI will be valid. In some embodiments, a sub time period, TP_i, associated with a predicted PMI may be indicated as follows:

where t_i may be a time instant indicated by the first message, N may be the number of the one or more predicted PMIs indicated by the first message, and t_N+1 may be equal to the ending time instant of the first time period or the second time period.

In some embodiments, after the step of transmitting the first message indicating the one or more predicted PMIs, the method 700 may further comprise at least one of: preventing one or more measurements for CSI-RS from being performed during the first time period or the second time period; and preventing one or more reports for CSI from being generated and/or transmitted to the network node during the first time period or the second time period. In some embodiments, the one or more reports for CSI are one or more reports for PMI.

In some embodiments, after the step of transmitting the first message indicating the one or more predicted PMIs, the method 700 may further comprise: communicating, with the network node, one or more messages during the first time period and/or the second time period by assuming that the network node will follow the one or more predicted PMIs.

Fig. 8 is a flow chart of an exemplary method 800 at a network node for communicating with a UE based on at least one or more predicted PMIs according to an embodiment of the present disclosure. The method 800 may be performed at a network node (e.g., the gNB 105) . The method 800 may comprise steps S810 and S820. However, the present disclosure is not limited thereto. In some other embodiments, the method 800 may comprise more steps, less steps, different steps, or any combination thereof. Further the steps of the method 800 may be performed in a different order than that described herein when multiple steps are involved. Further, in some embodiments, a step in the method 800 may be split into multiple sub-steps and performed by different entities, and/or multiple steps in the method 800 may be combined into a single step.

The method 800 may begin at step S810 where a first message indicating one or more predicted PMIs for a time period in the future may be received from the UE.

At step S820, one or more messages may be communicated with the UE during the time period based on at least the one or more predicted PMIs.

In some embodiments, the one or more predicted PMIs may be predicted by the UE based on at least one or more historical PMI data. In some embodiments, the first message may indicate: the one or more predicted PMIs; and one or more sub time periods in the future, each of which is a time period during which a corresponding predicted PMI will be valid. In some embodiments, a sub time period, TP_i, associated with a predicted PMI may be indicated as follows:

where t_i may be a time instant indicated by the first message, N may be the number of the one or more predicted PMIs indicated by the first message, and t_N+1 may be equal to the ending time instant of the time period.

In some embodiments, after the step of receiving the first message indicating the one or more predicted PMIs, the method 800 may further comprise at least one of: preventing one or more CSI-RSs associated with the UE from being generated and/or transmitted during the time period; and preventing one or more reports for CSI associated with the UE from being received and/or processed during the time period. In some embodiments, the one or more reports for CSI may be one or more reports for PMI. In some embodiments, the step of communicating, with the UE, one or more messages during the time period based on at least the one or more predicted PMIs may comprise: communicating, with the UE, the one or more messages during the time period by using the one or more predicted PMIs.

Fig. 9 is a flow chart of an exemplary method 900 at a network node for predicting one or more PMIs for a UE according to an embodiment of the present disclosure. The method 900 may be performed at a network node (e.g., the gNB 105) . The method 900 may comprise a step S910. However, the present disclosure is not limited thereto. In some other embodiments, the method 900 may comprise more steps, different steps, or any combination thereof. Further the steps of the method 900 may be performed in a different order than that described herein when multiple steps are involved. Further, in some embodiments, a step in the method 900 may be split into multiple sub-steps and performed by different entities, and/or multiple steps in the method 900 may be combined into a single step.

The method 900 may begin at step S910 where one or more PMIs for a time period in the future may be predicted based on at least one or more historical PMI data associated with the UE.

In some embodiments, the method 900 may further comprise: determining one or more codebooks for the UE based on at least the one or more predicted PMIs; and transmitting, to the UE, a second message indicating the one or more determined codebooks. In some embodiments, the one or more historical PMI data associated with the UE may comprise at least one of: PMI data that is determined based on one or more CSI reports that are transmitted from the UE; and PMI data that is determined based on one or more SRSs that are transmitted from the UE.

In some embodiments, the step of predicting the one or more PMIs may comprise: predicting the one or more PMIs for a third time period in the future based on only the one or more historical PMI data, which may be determined based on one or more CSI reports that are transmitted from the UE. In some embodiments, the step of predicting the one or more PMIs may comprise: predicting the one or more PMIs for a fourth time period in the future based on at least the one or more historical PMI data, which may be determined based on one or more CSI reports that are transmitted from the UE, and assistance information that is valid during the fourth time period. In some embodiments, the step of predicting the one or more PMIs may comprise: predicting the one or more PMIs for a fifth time period in the future based on only the one or more historical PMI data, which may be determined based on one or more SRSs that are transmitted from the UE. In some embodiments, the step of predicting the one or more PMIs may comprise: predicting the one or more PMIs for a sixth time period in the future based on at least the one or more historical PMI data, which may be determined based on one or more SRSs that are transmitted from the UE, and assistance information that is valid during the sixth time period. In some embodiments, the assistance information may comprise at least one of: trajectory information associated with the UE; and velocity information associated with the UE.

In some embodiments, the one or more predicted PMIs may be predicted by using at least Kalman filtering on the one or more historical PMI data. In some embodiments, at least one of the third time period, the fourth time period, the fifth time period, and the sixth time period may be determined based on at least one of: a change of movement of the UE; weather; and one or more air conditions. In some embodiments, the second message may indicate: the one or more determined codebooks; and one or more sub time periods in the future, each of which may be a time period during which a corresponding determined codebook will be valid.

In some embodiments, a sub time period, TP_j, associated with a determined codebook may be indicated as follows:

where t_j may be a time instant indicated by the second message, M may be the number of the one or more determined codebooks indicated by the second message, t_M+1 may be equal to the ending time instant of the third time period, the fourth time period, the fifth time period, or the sixth time period.

In some embodiments, the method 900 may further comprise at least one of: preventing one or more CSI-RSs associated with the UE from being generated and/or transmitted during the third time period or the fourth time period; preventing one or more reports for CSI associated with the UE from being received and/or processed during the third time period or the fourth time period; and preventing one or more SRSs associated with the UE from being received and/or processed during the fifth time period or the sixth time period. In some embodiments, the one or more reports for CSI may be one or more reports for PMI. In some embodiments, the method 900 may further comprise at least one of: transmitting, to the UE, a third message indicating at least one of: that the UE is to provide a periodic PMI reporting with a prolonged or relaxed periodicity; that the UE is to provide a reduced number of aperiodic PMI reporting; and that the UE is to transmit a reduced number of SRSs.

Fig. 10 is a flow chart of an exemplary method 1000 at a UE for communicating with a network node based on at least one or more predicted codebooks according to an embodiment of the present disclosure. The method 1000 may be performed at a user equipment (e.g., the UE 100) . The method 1000 may comprise steps S1010 and S1020. However, the present disclosure is not limited thereto. In some other embodiments, the method 1000 may comprise more steps, less steps, different steps, or any combination thereof. Further the steps of the method 1000 may be performed in a different order than that described herein when multiple steps are involved. Further, in some embodiments, a step in the method 1000 may be split into multiple sub-steps and performed by different entities, and/or multiple steps in the method 1000 may be combined into a single step.

The method 1000 may begin at step S1010 where a second message indicating one or more predicted codebooks for a time period in the future may be received from the network node.

At step S1020, one or more messages may be communicated with the network node during the time period based on at least the one or more predicted codebooks.

Tn some embodiments, the one or more predicted codebooks may be determined by the network node at least based on one or more predicted PMIs. In some embodiments, the one or more predicted PMIs may be predicted by the network node at least based on one or more historical PMI data associated with the UE. In some embodiments, the one or more historical PMI data associated with the UE may comprise at least one of: PMI data that is determined based on one or more CSI reports that are transmitted from the UE to the network node; and PMI data that is determined based on one or more SRSs that are transmitted from the UE to the network node.

In some embodiments, the second message may indicate: the one or more predicted codebooks; and one or more sub time periods in the future, each of which may be a time period during which a corresponding predicted codebook will be valid. In some embodiments, a sub time period, TP_j, associated with a predicted codebook may be indicated as follows:

where t_j may be a time instant indicated by the second message, M may be the number of the one or more predicted codebooks indicated by the second message, and t_M+1 may be equal to the ending time instant of the time period.

In some embodiments, the method 1000 may further comprise at least one of: preventing one or more measurements for CSI-RSs from being performed during the time period; preventing one or more reports for CSI from being generated and/or transmitted during the time period; and preventing one or more SRSs associated with the UE from being generated and/or transmitted during the time period. In some embodiments, the one or more reports for CSI may be one or more reports for PMI.

In some embodiments, the method 1000 may further comprise: receiving, from the network node, a third message indicating at least one of: that the UE is to provide a periodic PMI reporting with a prolonged or relaxed periodicity; that the UE is to provide a reduced number of aperiodic PMI reporting; and that the UE is to transmit a reduced number of SRSs.

Fig. 11 schematically shows an embodiment of an arrangement 1100 which may be used in a user equipment (e.g., the UE 100) or a network node (e.g., the gNB 105) according to an embodiment of the present disclosure. Comprised in the arrangement 1100 are a processing unit 1106, e.g., with a Digital Signal Processor (DSP) or a Central Processing Unit (CPU) . The processing unit 1106 may be a single unit or a plurality of units to perform different actions of procedures described herein. The arrangement 1100 may also comprise an input unit 1102 for receiving signals from other entities, and an output unit 1104 for providing signal (s) to other entities. The input unit 1102 and the output unit 1104 may be arranged as an integrated entity or as separate entities.

Furthermore, the arrangement 1100 may comprise at least one computer program product 1108 in the form of a non-volatile or volatile memory, e.g., an Electrically Erasable Programmable Read-Only Memory (EEPROM) , a flash memory and/or a hard drive. The computer program product 1108 comprises a computer program 1110, which comprises code/computer readable instructions, which when executed by the processing unit 1106 in the arrangement 1100 causes the arrangement 1100 and/or the UE/network node in which it is comprised to perform the actions, e.g., of the procedure described earlier in conjunction with Fig. 4 through Fig. 10 or any other variant.

The computer program 1110 may be configured as a computer program code structured in a computer program module 1110A. Hence, in an exemplifying embodiment when the arrangement 1100 is used in a UE for predicting one or more PMIs, the code in the computer program of the arrangement 1100 includes: a module 1110A configured to predict one or more PMIs for a time period in the future based on at least one or more historical PMI data.

Additionally or alternatively, the computer program 1110 may be further configured as a computer program code structured in computer program modules 1110B and 1110C. Hence, in an exemplifying embodiment when the arrangement 1100 is used in a network node for communicating with a UE based on at least one or more predicted PMIs, the code in the computer program of the arrangement 1100 includes: a module 1110B configured to receive, from the UE, a first message indicating one or more predicted PMIs for a time period in the future; and a module 1110C configured to communicate, with the UE, one or more messages during the time period based on at least the one or more predicted PMIs.

Additionally or alternatively, the computer program 1110 may be further configured as a computer program code structured in a computer program module 1110D. Hence, in an exemplifying embodiment when the arrangement 1100 is used in a network node predicting one or more PMIs for a UE, the code in the computer program of the arrangement 1100 includes: a module 1110D configured to predict one or more PMIs for a time period in the future based on at least one or more historical PMI data associated with the UE.

Additionally or alternatively, the computer program 1110 may be further configured as a computer program code structured in computer program modules 1110E and 1110F. Hence, in an exemplifying embodiment when the arrangement 1100 is used in a UE for communicating with a network node based on at least one or more predicted codebooks, the code in the computer program of the arrangement 1100 includes: a module 1110E configured to receive, from the network node, a second message indicating one or more predicted codebooks for a time period in the future; and a module 1110F configured to communicate, with the network node, one or more messages during the time period based on at least the one or more predicted codebooks.

The computer program modules could essentially perform the actions of the flow illustrated in Fig. 4 through Fig. 10, to emulate the UE or the network node. In other words, when the different computer program modules are executed in the processing unit 1106, they may correspond to different modules in the UE or the network node.

Although the code means in the embodiments disclosed above in conjunction with Fig. 11 are implemented as computer program modules which when executed in the processing unit causes the arrangement to perform the actions described above in conjunction with the figures mentioned above, at least one of the code means may in alternative embodiments be implemented at least partly as hardware circuits.

The processor may be a single CPU (Central processing unit) , but could also comprise two or more processing units. For example, the processor may include general purpose microprocessors; instruction set processors and/or related chips sets and/or special purpose microprocessors such as Application Specific Integrated Circuit (ASICs) . The processor may also comprise board memory for caching purposes. The computer program may be carried by a computer program product connected to the processor. The computer program product may comprise a computer readable medium on which the computer program is stored. For example, the computer program product may be a flash memory, a Random-access memory (RAM) , a Read-Only Memory (ROM) , or an EEPROM, and the computer program modules described above could in alternative embodiments be distributed on different computer program products in the form of memories within the UE and/or the network node.

Correspondingly to the method 700 as described above, an exemplary user equipment for predicting one or more PMIs is provided. Fig. 12 is a block diagram of a UE 1200 according to an embodiment of the present disclosure. The UE 1200 may be, e.g., the UE 100 in some embodiments.

The UE 1200 may be configured to perform the method 700 as described above in connection with Fig. 7. As shown in Fig. 12, the UE 1200 may comprise: a predicting module 1210 configured to predict one or more PMIs for a time period in the future based on at least one or more historical PMI data.

The above module 1210 may be implemented as a pure hardware solution or as a combination of software and hardware, e.g., by one or more of: a processor or a micro-processor and adequate software and memory for storing of the software, a Programmable Logic Device (PLD) or other electronic component (s) or processing circuitry configured to perform the actions described above, and illustrated, e.g., in Fig. 7. Further, the UE 1200 may comprise one or more further modules, each of which may perform any of the steps of the method 700 described with reference to Fig. 7.

Correspondingly to the method 800 as described above, an exemplary network node for communicating with a UE based on at least one or more predicted PMIs is provided. Fig. 13 is a block diagram of an exemplary network node 1300 according to an embodiment of the present disclosure. The network node 1300 may be, e.g., the gNB 105 in some embodiments.

The network node 1300 may be configured to perform the method 800 as described above in connection with Fig. 8. As shown in Fig. 13, the network node 1300 may comprise a receiving module 1310 configured to receive, from the UE, a first message indicating one or more predicted PMIs for a time period in the future; and a communicating module 1320 configured to communicate, with the UE, one or more messages during the time period based on at least the one or more predicted PMIs

The above modules 1310 and 1320 may be implemented as a pure hardware solution or as a combination of software and hardware, e.g., by one or more of: a processor or a micro-processor and adequate software and memory for storing of the software, a PLD or other electronic component (s) or processing circuitry configured to perform the actions described above, and illustrated, e.g., in Fig. 8. Further, the network node 1300 may comprise one or more further modules, each of which may perform any of the steps of the method 800 described with reference to Fig. 8.

Correspondingly to the method 900 as described above, an exemplary network node for predicting one or more PMIs for a UE is provided. Fig. 14 is a block diagram of an exemplary network node 1400 according to an embodiment of the present disclosure. The network node 1400 may be, e.g., the gNB 105 in some embodiments.

The network node 1400 may be configured to perform the method 900 as described above in connection with Fig. 9. As shown in Fig. 14, the network node 1400 may comprise a predicting module 1410 configured to predict one or more PMIs for a time period in the future based on at least one or more historical PMI data associated with the UE.

The above module 1410 may be implemented as a pure hardware solution or as a combination of software and hardware, e.g., by one or more of: a processor or a micro-processor and adequate software and memory for storing of the software, a PLD or other electronic component (s) or processing circuitry configured to perform the actions described above, and illustrated, e.g., in Fig. 9. Further, the network node 1400 may comprise one or more further modules, each of which may perform any of the steps of the method 900 described with reference to Fig. 9.

Correspondingly to the method 1000 as described above, an exemplary user equipment for communicating with a network node based on at least one or more predicted codebooks is provided. Fig. 15 is a block diagram of a UE 1500 according to an embodiment of the present disclosure. The UE 1500 may be, e.g., the UE 100 in some embodiments.

The UE 1500 may be configured to perform the method 1000 as described above in connection with Fig. 10. As shown in Fig. 15, the UE 1500 may comprise: a receiving module 1510 configured to receive, from the network node, a second message indicating one or more predicted codebooks for a time period in the future; and a communicating module 1520 configured to communicate, with the network node, one or more messages during the time period based on at least the one or more predicted codebooks

The above modules 1510 and 1520 may be implemented as a pure hardware solution or as a combination of software and hardware, e.g., by one or more of: a processor or a micro-processor and adequate software and memory for storing of the software, a PLD or other electronic component (s) or processing circuitry configured to perform the actions described above, and illustrated, e.g., in Fig. 10. Further, the UE 1500 may comprise one or more further modules, each of which may perform any of the steps of the method 1000 described with reference to Fig. 10.

Fig. 16 shows an example of a communication system QQ100 in accordance with some embodiments.

In the example, the communication system QQ100 includes a telecommunication network QQ102 that includes an access network QQ104, such as a radio access network (RAN) , and a core network QQ106, which includes one or more core network nodes QQ108. The access network QQ104 includes one or more access network nodes, such as network nodes QQ110a and QQ110b (one or more of which may be generally referred to as network nodes QQ110) , or any other similar 3rd Generation Partnership Project (3GPP) access node or non-3GPP access point. The network nodes QQ110 facilitate direct or indirect connection of user equipment (UE) , such as by connecting UEs QQ112a, QQ112b, QQ112c, and QQ112d (one or more of which may be generally referred to as UEs QQ112) to the core network QQ106 over one or more wireless connections.

Example wireless communications over a wireless connection include transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system QQ100 may include any number of wired or wireless networks, network nodes, UEs, and/or any other components or systems that may facilitate or participate in the communication of data and/or signals whether via wired or wireless connections. The communication system QQ100 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system.

The UEs QQ112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and/or operable to communicate wirelessly with the network nodes QQ110 and other communication devices. Similarly, the network nodes QQ110 are arranged, capable, configured, and/or operable to communicate directly or indirectly with the UEs QQ112 and/or with other network nodes or equipment in the telecommunication network QQ102 to enable and/or provide network access, such as wireless network access, and/or to perform other functions, such as administration in the telecommunication network QQ102.

In the depicted example, the core network QQ106 connects the network nodes QQ110 to one or more hosts, such as host QQ116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network QQ106 includes one more core network nodes (e.g., core network node QQ108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and/or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node QQ108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC) , Mobility Management Entity (MME) , Home Subscriber Server (HSS) , Access and Mobility Management Function (AMF) , Session Management Function (SMF) , Authentication Server Function (AUSF) , Subscription Identifier De-concealing function (SIDF) , Unified Data Management (UDM) , Security Edge Protection Proxy (SEPP) , Network Exposure Function (NEF) , and/or a User Plane Function (UPF) .

The host QQ116 may be under the ownership or control of a service provider other than an operator or provider of the access network QQ104 and/or the telecommunication network QQ102, and may be operated by the service provider or on behalf of the service provider. The host QQ116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio/video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

As a whole, the communication system QQ100 of Fig. 16 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM) ; Universal Mobile Telecommunications System (UMTS) ; Long Term Evolution (LTE) , and/or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G) ; wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (Wi-Fi) ; and/or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax) , Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and/or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

In some examples, the telecommunication network QQ102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network QQ102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network QQ102. For example, the telecommunications network QQ102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and/or Massive Machine Type Communication (mvTC) /Massive IoT services to yet further UEs.

In some examples, the UEs QQ112 are configured to transmit and/or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network QQ104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network QQ104. Additionally, a UE may be configured for operating in single-or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC) , such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio -Dual Connectivity (EN-DC) .

In the example, the hub QQ114 communicates with the access network QQ104 to facilitate indirect communication between one or more UEs (e.g., UE QQ112c and/or QQ112d) and network nodes (e.g., network node QQ110b) . In some examples, the hub QQ114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub QQ114 may be a broadband router enabling access to the core network QQ106 for the UEs. As another example, the hub QQ114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes QQ110, or by executable code, script, process, or other instructions in the hub QQ114. As another example, the hub QQ114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub QQ114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub QQ114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub QQ114 then provides to the UE either directly, after performing local processing, and/or after adding additional local content. In still another example, the hub QQ114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy IoT devices.

The hub QQ114 may have a constant/persistent or intermittent connection to the network node QQ110b. The hub QQ114 may also allow for a different communication scheme and/or schedule between the hub QQ114 and UEs (e.g., UE QQ112c and/or QQ112d) , and between the hub QQ114 and the core network QQ106. In other examples, the hub QQ114 is connected to the core network QQ106 and/or one or more UEs via a wired connection. Moreover, the hub QQ114 may be configured to connect to an M2M service provider over the access network QQ104 and/or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes QQ110 while still connected via the hub QQ114 via a wired or wireless connection. In some embodiments, the hub QQ114 may be a dedicated hub -that is, a hub whose primary function is to route communications to/from the UEs from/to the network node QQ110b. In other embodiments, the hub QQ114 may be a non-dedicated hub -that is, a device which is capable of operating to route communications between the UEs and network node QQ110b, but which is additionally capable of operating as a communication start and/or end point for certain data channels.

Fig. 17 shows a UE QQ200 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA) , wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE) , laptop-mounted equipment (LME) , smart device, wireless customer-premise equipment (CPE) , vehicle-mounted or vehicle embedded/integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP) , including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE.

A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC) , vehicle-to-vehicle (V2V) , vehicle-to-infrastructure (V2I) , or vehicle-to-everything (V2X) . In other examples, a UE may not necessarily have a user in the sense of a human user who owns and/or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller) . Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter) .

The UE QQ200 includes processing circuitry QQ202 that is operatively coupled via a bus QQ204 to an input/output interface QQ206, a power source QQ208, a memory QQ210, a communication interface QQ212, and/or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Fig. 17. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

The processing circuitry QQ202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory QQ210. The processing circuitry QQ202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs) , application specific integrated circuits (ASICs) , etc. ) ; programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP) , together with appropriate software; or any combination of the above. For example, the processing circuitry QQ202 may include multiple central processing units (CPUs) .

In the example, the input/output interface QQ206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and/or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE QQ200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc. ) , a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

In some embodiments, the power source QQ208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet) , photovoltaic device, or power cell, may be used. The power source QQ208 may further include power circuitry for delivering power from the power source QQ208 itself, and/or an external power source, to the various parts of the UE QQ200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source QQ208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source QQ208 to make the power suitable for the respective components of the UE QQ200 to which power is supplied.

The memory QQ210 may be or be configured to include memory such as random access memory (RAM) , read-only memory (ROM) , programmable read-only memory (PROM) , erasable programmable read-only memory (EPROM) , electrically erasable programmable read-only memory (EEPROM) , magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory QQ210 includes one or more application programs QQ214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data QQ216. The memory QQ210 may store, for use by the UE QQ200, any of a variety of various operating systems or combinations of operating systems.

The memory QQ210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID) , flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM) , synchronous dynamic random access memory (SDRAM) , external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs) , such as a USIM and/or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC) , integrated UICC (iUICC) or a removable UICC commonly known as ′SIM card. ′ The memory QQ210 may allow the UE QQ200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory QQ210, which may be or comprise a device-readable storage medium.

The processing circuitry QQ202 may be configured to communicate with an access network or other network using the communication interface QQ212. The communication interface QQ212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna QQ222. The communication interface QQ212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network) . Each transceiver may include a transmitter QQ218 and/or a receiver QQ220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth) . Moreover, the transmitter QQ218 and receiver QQ220 may be coupled to one or more antennas (e.g., antenna QQ222) and may share circuit components, software or firmware, or alternatively be implemented separately.

In the illustrated embodiment, communication functions of the communication interface QQ212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and/or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA) , Wideband Code Division Multiple Access (WCDMA) , GSM, LTE, New Radio (NR) , UMTS, WiMax, Ethernet, transmission control protocol/internet protocol (TCP/IP) , synchronous optical networking (SONET) , Asynchronous Transfer Mode (ATM) , QUIC, Hypertext Transfer Protocol (HTTP) , and so forth.

Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface QQ212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature) , random (e.g., to even out the load from reporting from several sensors) , in response to a triggering event (e.g., when moisture is detected an alert is sent) , in response to a request (e.g., a user initiated request) , or a continuous stream (e.g., a live video feed of a patient) .

As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

A UE, when in the form of an Internet of Things (IoT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an IoT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door/window sensor, a flood/moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR) , a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal-or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV) , and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an IoT device comprises circuitry and/or software in dependence of the intended application of the IoT device in addition to other components as described in relation to the UE QQ200 shown in Fig. 17.

As yet another specific example, in an IoT scenario, a UE may represent a machine or other device that performs monitoring and/or measurements, and transmits the results of such monitoring and/or measurements to another UE and/or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation.

In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone′s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone′s speed. The first and/or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

Fig. 18 shows a network node QQ300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and/or operable to communicate directly or indirectly with a UE and/or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points) , base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs) ) .

Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and/or remote radio units (RRUs) , sometimes referred to as Remote Radio Heads (RRHs) . Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS) .

Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs) , base transceiver stations (BTSs) , transmission points, transmission nodes, multi- cell/multicast coordination entities (MCEs) , Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs) ) , and/or Minimization of Drive Tests (MDTs) .

The network node QQ300 includes a processing circuitry QQ302, a memory QQ304, a communication interface QQ306, and a power source QQ308. The network node QQ300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc. ) , which may each have their own respective components. In certain scenarios in which the network node QQ300 comprises multiple separate components (e.g., BTS and BSC components) , one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node QQ300 may be configured to support multiple radio access technologies (RATs) . In such embodiments, some components may be duplicated (e.g., separate memory QQ304 for different RATs) and some components may be reused (e.g., a same antenna QQ310 may be shared by different RATs) . The network node QQ300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node QQ300, for example GSM, WCDMA, LTE, NR, Wi-Fi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node QQ300.

The processing circuitry QQ302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and/or encoded logic operable to provide, either alone or in conjunction with other network node QQ300 components, such as the memory QQ304, to provide network node QQ300 functionality.

In some embodiments, the processing circuitry QQ302 includes a system on a chip (SOC) . In some embodiments, the processing circuitry QQ302 includes one or more of radio frequency (RF) transceiver circuitry QQ312 and baseband processing circuitry QQ314. In some embodiments, the radio frequency (RF) transceiver circuitry QQ312 and the baseband processing circuitry QQ314 may be on separate chips (or sets of chips) , boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry QQ312 and baseband processing circuitry QQ314 may be on the same chip or set of chips, boards, or units.

The memory QQ304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM) , read-only memory (ROM) , mass storage media (for example, a hard disk) , removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD) ) , and/or any other volatile or non-volatile, non-transitory device-readable and/or computer-executable memory devices that store information, data, and/or instructions that may be used by the processing circuitry QQ302. The memory QQ304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and/or other instructions capable of being executed by the processing circuitry QQ302 and utilized by the network node QQ300. The memory QQ304 may be used to store any calculations made by the processing circuitry QQ302 and/or any data received via the communication interface QQ306. In some embodiments, the processing circuitry QQ302 and memory QQ304 is integrated.

The communication interface QQ306 is used in wired or wireless communication of signaling and/or data between a network node, access network, and/or UE. As illustrated, the communication interface QQ306 comprises port (s) /terminal (s) QQ316 to send and receive data, for example to and from a network over a wired connection. The communication interface QQ306 also includes radio front-end circuitry QQ318 that may be coupled to, or in certain embodiments a part of, the antenna QQ310. Radio front-end circuitry QQ318 comprises filters QQ320 and amplifiers QQ322. The radio front-end circuitry QQ318 may be connected to an antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry may be configured to condition signals communicated between antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry QQ318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry QQ318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters QQ320 and/or amplifiers QQ322. The radio signal may then be transmitted via the antenna QQ310. Similarly, when receiving data, the antenna QQ310 may collect radio signals which are then converted into digital data by the radio front-end circuitry QQ318. The digital data may be passed to the processing circuitry QQ302. In other embodiments, the communication interface may comprise different components and/or different combinations of components.

In certain alternative embodiments, the network node QQ300 does not include separate radio front-end circuitry QQ318, instead, the processing circuitry QQ302 includes radio front-end circuitry and is connected to the antenna QQ310. Similarly, in some embodiments, all or some of the RF transceiver circuitry QQ312 is part of the communication interface QQ306. In still other embodiments, the communication interface QQ306 includes one or more ports or terminals QQ316, the radio front-end circuitry QQ318, and the RF transceiver circuitry QQ312, as part of a radio unit (not shown) , and the communication interface QQ306 communicates with the baseband processing circuitry QQ314, which is part of a digital unit (not shown) .

The antenna QQ310 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. The antenna QQ310 may be coupled to the radio front-end circuitry QQ318 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In certain embodiments, the antenna QQ310 is separate from the network node QQ300 and connectable to the network node QQ300 through an interface or port.

The antenna QQ310, communication interface QQ306, and/or the processing circuitry QQ302 may be configured to perform any receiving operations and/or certain obtaining operations described herein as being performed by the network node. Any information, data and/or signals may be received from a UE, another network node and/or any other network equipment. Similarly, the antenna QQ310, the communication interface QQ306, and/or the processing circuitry QQ302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and/or signals may be transmitted to a UE, another network node and/or any other network equipment.

The power source QQ308 provides power to the various components of network node QQ300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component) . The power source QQ308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node QQ300 with power for performing the functionality described herein. For example, the network node QQ300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source QQ308. As a further example, the power source QQ308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

Embodiments of the network node QQ300 may include additional components beyond those shown in Fig. 18 for providing certain aspects of the network node′s functionality, including any of the functionality described herein and/or any functionality necessary to support the subject matter described herein. For example, the network node QQ300 may include user interface equipment to allow input of information into the network node QQ300 and to allow output of information from the network node QQ300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node QQ300.

Fig. 19 is a block diagram of a host QQ400, which may be an embodiment of the host QQ116 of Fig. 16, in accordance with various aspects described herein. As used herein, the host QQ400 may be or comprise various combinations hardware and/or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host QQ400 may provide one or more services to one or more UEs.

The host QQ400 includes processing circuitry QQ402 that is operatively coupled via a bus QQ404 to an input/output interface QQ406, a network interface QQ408, a power source QQ410, and a memory QQ412. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Fig. 17 and Fig. 18, such that the descriptions thereof are generally applicable to the corresponding components of host QQ400.

The memory QQ412 may include one or more computer programs including one or more host application programs QQ414 and data QQ416, which may include user data, e.g., data generated by a UE for the host QQ400 or data generated by the host QQ400 for a UE. Embodiments of the host QQ400 may utilize only a subset or all of the components shown. The host application programs QQ414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC) , High Efficiency Video Coding (HEVC) , Advanced Video Coding (AVC) , MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC) , MPEG, G. 711) , including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems) . The host application programs QQ414 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host QQ400 may select and/or indicate a different host for over-the-top services for a UE. The host application programs QQ414 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP) , Real-Time Streaming Protocol (RTSP) , Dynamic Adaptive Streaming over HTTP (MPEG-DASH) , etc.

Fig. 20 is a block diagram illustrating a virtualization environment QQ500 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments QQ500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host) , then the node may be entirely virtualized.

Applications QQ502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc. ) are run in the virtualization environment QQ500 to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein.

Hardware QQ504 includes processing circuitry, memory that stores software and/or instructions executable by hardware processing circuitry, and/or other hardware devices as described herein, such as a network interface, input/output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers QQ506 (also referred to as hypervisors or virtual machine monitors (VMMs) ) , provide VMs QQ508a and QQ508b (one or more of which may be generally referred to as VMs QQ508) , and/or perform any of the functions, features and/or benefits described in relation with some embodiments described herein. The virtualization layer QQ506 may present a virtual operating platform that appears like networking hardware to the VMs QQ508.

The VMs QQ508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer QQ506. Different embodiments of the instance of a virtual appliance QQ502 may be implemented on one or more of VMs QQ508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV) . NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

In the context of NFV, a VM QQ508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs QQ508, and that part of hardware QQ504 that executes that VM, be it hardware dedicated to that VM and/or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs QQ508 on top of the hardware QQ504 and corresponds to the application QQ502.

Hardware QQ504 may be implemented in a standalone network node with generic or specific components. Hardware QQ504 may implement some functions via virtualization. Alternatively, hardware QQ504 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration QQ510, which, among others, oversees lifecycle management of applications QQ502. In some embodiments, hardware QQ504 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system QQ512 which may alternatively be used for communication between hardware nodes and radio units.

Fig. 21 shows a communication diagram of a host QQ602 communicating via a network node QQ604 with a UE QQ606 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE QQ112a of Fig. 16 and/or UE QQ200 of Fig. 17) , network node (such as network node QQ110a of Fig. 16 and/or network node QQ300 of Fig. 18) , and host (such as host QQ116 of Fig. 16 and/or host QQ400 of Fig. 19) discussed in the preceding paragraphs will now be described with reference to Fig. 21.

Like host QQ400, embodiments of host QQ602 include hardware, such as a communication interface, processing circuitry, and memory. The host QQ602 also includes software, which is stored in or accessible by the host QQ602 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE QQ606 connecting via an over-the-top (OTT) connection QQ650 extending between the UE QQ606 and host QQ602. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection QQ650.

The network node QQ604 includes hardware enabling it to communicate with the host QQ602 and UE QQ606. The connection QQ660 may be direct or pass through a core network (like core network QQ106 of Fig. 16) and/or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

The UE QQ606 includes hardware and software, which is stored in or accessible by UE QQ606 and executable by the UE′s processing circuitry. The software includes a client application, such as a web browser or operator-specific "app" that may be operable to provide a service to a human or non-human user via UE QQ606 with the support of the host QQ602. In the host QQ602, an executing host application may communicate with the executing client application via the OTT connection QQ650 terminating at the UE QQ606 and host QQ602. In providing the service to the user, the UE′s client application may receive request data from the host′s host application and provide user data in response to the request data. The OTT connection QQ650 may transfer both the request data and the user data. The UE′s client application may interact with the user to generate the user data that it provides to the host application through the OTT connection QQ650.

The OTT connection QQ650 may extend via a connection QQ660 between the host QQ602 and the network node QQ604 and via a wireless connection QQ670 between the network node QQ604 and the UE QQ606 to provide the connection between the host QQ602 and the UE QQ606. The connection QQ660 and wireless connection QQ670, over which the OTT connection QQ650 may be provided, have been drawn abstractly to illustrate the communication between the host QQ602 and the UE QQ606 via the network node QQ604, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

As an example of transmitting data via the OTT connection QQ650, in step QQ608, the host QQ602 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE QQ606. In other embodiments, the user data is associated with a UE QQ606 that shares data with the host QQ602 without explicit human interaction. In step QQ610, the host QQ602 initiates a transmission carrying the user data towards the UE QQ606. The host QQ602 may initiate the transmission responsive to a request transmitted by the UE QQ606. The request may be caused by human interaction with the UE QQ606 or by operation of the client application executing on the UE QQ606. The transmission may pass via the network node QQ604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step QQ612, the network node QQ604 transmits to the UE QQ606 the user data that was carried in the transmission that the host QQ602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step QQ614, the UE QQ606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE QQ606 associated with the host application executed by the host QQ602.

In some examples, the UE QQ606 executes a client application which provides user data to the host QQ602. The user data may be provided in reaction or response to the data received from the host QQ602. Accordingly, in step QQ616, the UE QQ606 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input/output interface of the UE QQ606. Regardless of the specific manner in which the user data was provided, the UE QQ606 initiates, in step QQ618, transmission of the user data towards the host QQ602 via the network node QQ604. In step QQ620, in accordance with the teachings of the embodiments described throughout this disclosure, the network node QQ604 receives user data from the UE QQ606 and initiates transmission of the received user data towards the host QQ602. In step QQ622, the host QQ602 receives the user data carried in the transmission initiated by the UE QQ606.

One or more of the various embodiments improve the performance of OTT services provided to the UE QQ606 using the OTT connection QQ650, in which the wireless connection QQ670 forms the last segment. More precisely, the teachings of these embodiments may improve the data rate, latency, power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, improved content resolution, better responsiveness, extended battery lifetime.

In an example scenario, factory status information may be collected and analyzed by the host QQ602. As another example, the host QQ602 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host QQ602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights) . As another example, the host QQ602 may store surveillance video uploaded by a UE. As another example, the host QQ602 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host QQ602 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices) , or any other function of collecting, retrieving, storing, analyzing and/or transmitting data.

In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection QQ650 between the host QQ602 and UE QQ606, in response to variations in the measurement results. The measurement procedure and/or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host QQ602 and/or UE QQ606. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection QQ650 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection QQ650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node QQ604. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host QQ602. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ′dummy′ messages, using the OTT connection QQ650 while monitoring propagation times, errors, etc.

Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and/or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and/or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and/or by end users and a wireless network generally.

The present disclosure is described above with reference to the embodiments thereof. However, those embodiments are provided just for illustrative purpose, rather than limiting the present disclosure. The scope of the disclosure is defined by the attached claims as well as equivalents thereof. Those skilled in the art can make various alternations and modifications without departing from the scope of the disclosure, which all fall into the scope of the disclosure.

Abbreviation Explanation

ATG Air-to-ground

BLER Block error rate

BWP Bandwidth part

CP Cyclic prefix

CSI-RS Channel state information reference signals

CSSF Carrier-specific scaling factor

DCI Downlink control information

DL Downlink

eMBB Evolved mobile broadband

FDD Frequency division duplex

FR1 Frequency range 1

FR2 Frequency range 2

FR3 Frequency range 3

gNB Next generation Node B (5G base station)

HARQ Hybrid automatic repeat request

MAC Medium access control

NACK Negative acknowledgement

NR New radio (5G)

PBCH Physical broadcast channel

PDCCH Physical downlink control channel

PDSCH Physical downlink shared channel

PRS Positioning reference signals

PUCCH Physical uplink control channel

PUSCH Physical uplink shared channel

RAT Radio access technology

SCS Subcarrier spacing

SRS Sounding reference signal

SSB Synchronization signal and PBCH block

TDD Time division duplex

UE User equipment

UL Uplink

Claims

A method (700) at a User Equipment (UE) (100) for predicting one or more Precoding Matrix Indicators (PMIs) , the method (700) comprising:

predicting (S630, S710) one or more PMIs for a time period in the future based on at least one or more historical PMI data.
The method (700) of claim 1, further comprising:

transmitting (S640) , to a network node (105) , a first message indicating the one or more predicted PMIs.
The method (700) of claim 1 or 2, wherein the step of predicting (S630, S710) the one or more PMIs comprises:

predicting the one or more PMIs for a first time period in the future based on the one or more historical PMI data only.
The method (700) of claim 1 or 2, wherein the step of predicting (S630, S710) the one or more PMIs comprises:

predicting the one or more PMIs for a second time period in the future based on at least the one or more historical PMI data and assistance information that is valid during the second time period.
The method (700) of claim 4, wherein the assistance information comprises at least one of:

- trajectory information associated with the UE (100) ; and

- velocity information associated with the UE (100) .
The method (700) of any of claims 1 to 5, wherein the one or more predicted PMIs are predicted by using at least Kalman filtering on the one or more historical PMI data.
The method (700) of any of claims 3 to 6, wherein at least one of the first time period and the second time period is determined based on at least one of:

- a change of movement of the UE (100) ;

- weather; and

- one or more air conditions.
The method (700) of any of claims 2 to 7, wherein the first message indicates:

- the one or more predicted PMIs; and

- one or more sub time periods in the future, each of which is a time period during which a corresponding predicted PMI will be valid.
The method (700) of claim 8, wherein a sub time period, TP_i, associated with a predicted PMI is indicated as follows:

where t_i is a time instant indicated by the first message, N is the number of the one or more predicted PMIs indicated by the first message, and t_N+1 is equal to the ending time instant of the first time period or the second time period.
The method (700) of any of claims 2 to 9, wherein after the step of transmitting (S640) the first message indicating the one or more predicted PMIs, the method (700) further comprises at least one of:

preventing one or more measurements for Channel State Information Reference Signal (CSI-RS) from being performed during the first time period or the second time period; and

preventing one or more reports for CSI from being generated and/or transmitted to the network node (105) during the first time period or the second time period.
The method (700) of claim 10, wherein the one or more reports for CSI are one or more reports for PMI.
The method (700) of any of claims 2 to 11, wherein after the step of transmitting (S640) the first message indicating the one or more predicted PMIs, the method (700) further comprises:

communicating (S670) , with the network node (105) , one or more messages during the first time period and/or the second time period by assuming that the network node (105) will follow the one or more predicted PMIs.
A method (800) at a network node (105) for communicating with a UE (100) based on at least one or more predicted PMIs, the method (800) comprising:

receiving (S640, S810) , from the UE (100) , a first message indicating one or more predicted PMIs for a time period in the future; and

communicating (S670, S820) , with the UE (100) , one or more messages during the time period based on at least the one or more predicted PMIs.
The method (800) of claim 13, wherein the one or more predicted PMIs are predicted by the UE (100) based on at least one or more historical PMI data.
The method (800) of claim 13 or 14, wherein the first message indicates:

- the one or more predicted PMIs; and

- one or more sub time periods in the future, each of which is a time period during which a corresponding predicted PMI will be valid.
The method (800) of claim 15, wherein a sub time period, TP_i, associated with a predicted PMI is indicated as follows:

where t_i is a time instant indicated by the first message, N is the number of the one or more predicted PMIs indicated by the first message, and t_N+1 is equal to the ending time instant of the time period.
The method (800) of any of claims 13 to 16, wherein after the step of receiving (S640, S810) the first message indicating the one or more predicted PMIs, the method (800) further comprises at least one of:

preventing one or more CSI-RSs associated with the UE (100) from being generated and/or transmitted during the time period; and

preventing one or more reports for CSI associated with the UE (100) from being received and/or processed during the time period.
The method (800) of claim 17, wherein the one or more reports for CSI are one or more reports for PMI.
The method (800) of any of claims 13 to 18, wherein the step of communicating (S670, S820) , with the UE (100) , one or more messages during the time period based on at least the one or more predicted PMIs comprises:

communicating, with the UE (100) , the one or more messages during the time period by using the one or more predicted PMIs.
A method (900) at a network node (105) for predicting one or more PMIs for a UE (100) , the method (900) comprising:

predicting (S910) one or more PMIs for a time period in the future based on at least one or more historical PMI data associated with the UE (100) .
The method (900) of claim 20, further comprising:

determining (S650) one or more codebooks for the UE (100) based on at least the one or more predicted PMIs; and

transmitting, to the UE (100) , a second message indicating the one or more determined codebooks.
The method (900) of claim 20 or 21, wherein the one or more historical PMI data associated with the UE (100) comprises at least one of:

- PMI data that is determined based on one or more CSI reports that are transmitted from the UE (100) ; and

- PMI data that is determined based on one or more Sounding Reference Signals (SRSs) that are transmitted from the UE (100) .
The method (900) of any of claims 20 to 22, wherein the step of predicting (S910) the one or more PMIs comprises:

predicting the one or more PMIs for a third time period in the future based on only the one or more historical PMI data, which are determined based on one or more CSI reports that are transmitted from the UE (100) .
The method (900) of any of claims 20 to 22, wherein the step of predicting (S910) the one or more PMIs comprises:

predicting the one or more PMIs for a fourth time period in the future based on at least the one or more historical PMI data, which are determined based on one or more CSI reports that are transmitted from the UE (100) , and assistance information that is valid during the fourth time period.
The method (900) of any of claims 20 to 22, wherein the step of predicting (S910) the one or more PMIs comprises:

predicting the one or more PMIs for a fifth time period in the future based on only the one or more historical PMI data, which are determined based on one or more SRSs that are transmitted from the UE (100) .
The method (900) of any of claims 20 to 22, wherein the step of predicting (S910) the one or more PMIs comprises:

predicting the one or more PMIs for a sixth time period in the future based on at least the one or more historical PMI data, which are determined based on one or more SRSs that are transmitted from the UE (100) , and assistance information that is valid during the sixth time period.
The method (900) of any of claims 24 or 26, wherein the assistance information comprises at least one of:

- trajectory information associated with the UE (100) ; and

- velocity information associated with the UE (100) .
The method (900) of any of claims 20 to 27, wherein the one or more predicted PMIs are predicted by using at least Kalman filtering on the one or more historical PMI data.
The method (900) of any of claims 23 to 28, wherein at least one of the third time period, the fourth time period, the fifth time period, and the sixth time period is determined based on at least one of:

- a change of movement of the UE (100) ;

- weather; and

- one or more air conditions.
The method (900) of any of claims 21 to 29, wherein the second message indicates:

- the one or more determined codebooks; and

- one or more sub time periods in the future, each of which is a time period during which a corresponding determined codebook will be valid.
The method (900) of claim 30, wherein a sub time period, TP_j, associated with a determined codebook is indicated as follows:

where t_j is a time instant indicated by the second message, M is the number of the one or more determined codebooks indicated by the second message, t_M+1 is equal to the ending time instant of the third time period, the fourth time period, the fifth time period, or the sixth time period.
The method (900) of any of claims 20 to 31, further comprising at least one of:

preventing one or more CSI-RSs associated with the UE (100) from being generated and/or transmitted during the third time period or the fourth time period;

preventing one or more reports for CSI associated with the UE (100) from being received and/or processed during the third time period or the fourth time period; and

preventing one or more SRSs associated with the UE (100) from being received and/or processed during the fifth time period or the sixth time period.
The method (900) of claim 32, wherein the one or more reports for CSI are one or more reports for PMI.
The method (900) of any of claims 20 to 33, further comprising at least one of:

transmitting, to the UE (100) , a third message indicating at least one of:

- that the UE (100) is to provide a periodic PMI reporting with a prolonged or relaxed periodicity;

- that the UE (100) is to provide a reduced number of aperiodic PMI reporting; and

- that the UE (100) is to transmit a reduced number of SRSs.
A method (1000) at a UE (100) for communicating with a network node (105) based on at least one or more predicted codebooks, the method (1000) comprising:

receiving (S1010) , from the network node (105) , a second message indicating one or more predicted codebooks for a time period in the future; and

communicating (S1020) , with the network node (105) , one or more messages during the time period based on at least the one or more predicted codebooks.
The method (1000) of claim 35, wherein the one or more predicted codebooks are determined by the network node (105) at least based on one or more predicted PMIs.
The method (1000) of claim 36, wherein the one or more predicted PMIs are predicted by the network node (105) at least based on one or more historical PMI data associated with the UE (100) .
The method (1000) of claim 37, wherein the one or more historical PMI data associated with the UE (100) comprises at least one of:

- PMI data that is determined based on one or more CSI reports that are transmitted from the UE (100) to the network node (105) ; and

- PMI data that is determined based on one or more SRSs that are transmitted from the UE (100) to the network node (105) .
The method (1000) of any of claims 35 to 38, wherein the second message indicates:

- the one or more predicted codebooks; and

- one or more sub time periods in the future, each of which is a time period during which a corresponding predicted codebook will be valid.
The method (1000) of claim 39, wherein a sub time period, TP_j, associated with a predicted codebook is indicated as follows:

where t_j is a time instant indicated by the second message, M is the number of the one or more predicted codebooks indicated by the second message, and t_M+1 is equal to the ending time instant of the time period.
The method (1000) of any of claims 35 to 40, further comprising at least one of:

preventing one or more measurements for CSI-RSs from being performed during the time period;

preventing one or more reports for CSI from being generated and/or transmitted during the time period; and

preventing one or more SRSs associated with the UE (100) from being generated and/or transmitted during the time period.
The method (1000) of claim 41, wherein the one or more reports for CSI are one or more reports for PMI.
The method (1000) of any of claims 35 to 42, further comprising:

receiving, from the network node (105) , a third message indicating at least one of:

- that the UE (100) is to provide a periodic PMI reporting with a prolonged or relaxed periodicity;

- that the UE (100) is to provide a reduced number of aperiodic PMI reporting; and

- that the UE (100) is to transmit a reduced number of SRSs.
A UE (100, 1100, 1200, 1500) comprising:

a processor (1106) ;

a memory (1108) storing instructions which, when executed by the processor (1106) , cause the processor (1106) to perform the method (700, 1000) of any of claims 1 to 12 and 35 to 43.
A network node (105, 1100, 1300, 1400) comprising:

a processor (1106) ;

a memory (1108) storing instructions which, when executed by the processor (1106) , cause the processor (1106) to perform the method (800, 900) of any of claims 13 to 34.
A computer program (1110) comprising instructions which, when executed by at least one processor (1106) , cause the at least one processor (1106) to carry out the method (700, 800, 900, 1000) of any of claims 1 to 43.
A carrier (1108) containing the computer program (1110) of claim 46, wherein the carrier (1108) is one of an electronic signal, optical signal, radio signal, or computer readable storage medium.
A telecommunication system (10) comprising:

one or more UEs (100) of claim 44; and

at least one network node (105) of claim 45.