WO2024184683A1

WO2024184683A1 - Optimizing data transfer in carrier aggregation

Info

Publication number: WO2024184683A1
Application number: PCT/IB2023/052215
Authority: WO
Inventors: Ho Ting Cheng; Khagendra BELBASE; Geoffrey MCHARDY; Jagadish GHIMIRE
Original assignee: Telefonaktiebolaget LM Ericsson AB
Current assignee: Telefonaktiebolaget LM Ericsson AB
Priority date: 2023-03-08
Filing date: 2023-03-08
Publication date: 2024-09-12
Anticipated expiration: 2025-09-08

Abstract

A method performed by a network node is presented. The method comprises transmitting a set of data segmentation input parameters to a primary cell (Pcell) for each set of data segmentation input parameters of a plurality of sets of data segmentation input parameters. The method further comprises receiving at least one performance metric from an external secondary cell (EScell) and storing the at least one performance metric with its associated set of data segmentation input parameters in a performance metrics collection table. The method further comprises selecting a particular set of data segmentation input parameters from the plurality of sets of data segmentation input parameters that leads to an optimized performance metric at the EScell based on the performance metrics collection table and configuring the Pcell with the selected set of data segmentation input parameters to perform the data segmentation.

Description

OPTIMIZING DATA TRANSFER IN CARRIER AGGREGATION

TECHNICAL FIELD

Embodiments of the present disclosure are directed to wireless communications and, more particularly, to methods of optimizing data transfer from primary cells to external secondary cells in carrier aggregation.

BACKGROUND

Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and/or is implied from the context in which it is used. All references to a/an/the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and/or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features, and advantages of the enclosed embodiments will be apparent from the following description.

Carrier aggregation (CA) is one of the promising fifth generation (5G) technology and other wireless communication technologies. By combining different parts of the radio spectrum, multiple carriers in a single or multiple base stations enable dynamic air interface load distribution among cells, enhance peak throughput performance, and improve coverage performance.

In inter-gNB CA where carriers are in separate gNBs, traffic data destined for a user needs to split between cells, where the Pcell (Primary Cell) needs to forward the data to one or more external Scells (or EScells). A better spectrum usage is one of the benefits of employing CA.

If a Pcell transfers more data to an EScell than it can handle (i.e., a traffic incoming rate to an EScell exceeds a traffic draining rate), the EScell is considered congested and the user data at the EScell will experience a long delay. A transport block (TB) segmentation approach proposed in W02022090783A1 divides the data to be transferred from a Pcell to an EScell into data units referred to as transport block (TB) segments. In this data segmentation, a Pcell estimates the amount of data an EScell can drain such that the EScell drain rate matches the incoming rate of data sent from the Pcell to avoid congestion and maintain peak throughput performance.

A hallmark of this design is that the external SCell node user plane processing is lightweight because it does not involve further segmentation of the incoming TB segments. The SCell node user plane medium access control (MAC) processing is limited to concatenation of a discrete number of received TB- segments into the outgoing MAC protocol data unit (PDU), also referred to as “TB-segment fitting.”

The existing approach, however, is static and thus suboptimal. Optimized data transfer from Pcells and EScells is desired to improve peak throughput performance. The existing solutions result in suboptimal system performance.

TB segmentation is considered as the method of data segmentation for inter-gNB CA in this example. In inter-gNB CA with two component carriers (CCs) using dynamic spectrum sharing (DSS), NR physical downlink shared channel (PDSCH) is assumed to have 92 resource elements (RE) per physical resource block (PRB) in TB segmentation calculation in the baseline. Notice that the more REs considered in TB segmentation, the more the amount of user data sent from a Pcell to an EScell. Thus, EScell throughput increases as the number of REs considered increases, unless congestion occurs.

FIGURE 1 illustrates example system-level simulations for determining EScell throughput with different numbers of available REs. In FIGURE 1, the top table shows the EScell throughput with different number of available REs when the channel bandwidth is 20 MHz, and the bottom table shows the EScell throughput with different number of available REs when the channel bandwidth is 10MHz. The example system-level simulations in FIGURE 1 show that the static value (of 92) does not optimize the EScell throughput. Thus, as illustrated in FIGURE 1, different numbers of available REs considered in data segmentation may result in better or worse EScell throughput.

EScell throughput may be optimized when the number of available REs considered in the TB segmentation calculation is optimized, and the optimal number of available REs considered may be different from one system configuration to another. This is because of the improved opportunities of TB-segment fitting in the SCell node. The current technology lacks technical solutions to determine an optimized set of parameters to optimize TB segmentation for communicating data from a Pcell to an EScell, and to optimize EScell performance metrics, including throughput, latency, and transport block size.

SUMMARY

As described above, certain challenges currently exist in TB segmentation at a Pcell, data transfer from the Pcell to an EScell, and optimizing the EScell performance metrics. Certain aspects of the present disclosure and their embodiments may provide solutions to these or other challenges. For example, some embodiments include a computing module that trains data segmentation at the Pcell with new parameters, collects key performance metrics from the EScell, and configures the optimized data segmentation parameters for the system. Particular embodiments adaptively adjust the input parameters of a data segmentation algorithm to determine an optimal set of input parameters for data segmentation, thereby optimizing EScell system performance.

Particular embodiments improve peak throughput performance via a self-optimized network (SON) algorithm regardless of inter-gNB CA system configurations.

Particular embodiments eliminate or reduce manual parameter tuning for cell site optimization and facilitates time-to-market deployment. In the manual parameter tuning for cell site optimization, the tuned parameters are not globally optimized. Rather, the manually- tuned parameters may be locally optimized among a limited neighborhood of parameters at best. It is challenging to determine a globally optimized set of parameters for cell site optimization. Particular embodiments determine a globally-optimized set of parameters that leads to optimizing each and every performance metric of a EScell node.

According to some embodiments, a method is performed by a network node for determining input parameters for data segmentation at a primary cell (Pcell) to optimize performance metrics of an external secondary cell (EScell). The method comprises transmitting a set of data segmentation input parameters to the Pcell for each set of data segmentation input parameters of a plurality of sets of data segmentation input parameters. Each parameter of the at least one set of input parameters may be used to perform a data segmentation in which a size of each data segment to be communicated to the EScell may be determined according to the respective set of data segmentation input parameters. The method further comprises receiving at least one performance metric from the EScell for each set of data segmentation input parameters of a plurality of sets of data segmentation input parameters. Each of the at least one performance metric may be determined according to data transmission from the Pcell to the EScell over a transmission window in which the size of each data segment communicated to the EScell may be determined according to the respective set of input parameters. The method further comprises storing the at least one performance metric with its associated set of data segmentation input parameters in a performance metrics collection table. The method further comprises selecting a particular set of data segmentation input parameters from the plurality of sets of data segmentation input parameters that leads to an optimized performance metric at the EScell based on the performance metrics collection table. The method further comprises configuring the Pcell with the selected set of data segmentation input parameters to perform the data segmentation.

In particular embodiments, each set of data segmentation input parameters may comprise one or more of a number of nominal symbols in a physical downlink shared channel (PDSCH), an amount of resource elements (RE) overhead, a number of available physical resource blocks (PRBs), a spectral efficiency, and at least one pattern of data segments.

In particular embodiments, the at least one pattern of data segments may comprise alternating between one resource block group (RBG) and two RBGs in alternating data segments.

In particular embodiments, the at least one performance metric may comprise one or more of a sum throughput, a max-min throughput, a latency, transport block size, a utility function comprising one or more of the sum throughput, the max-min throughput, the latency, and the transport block size.

In particular embodiments, selecting the particular set of data segmentation input parameters from the plurality of sets of data segmentation input parameters that leads to an optimized performance metric at the EScell based on the performance metrics collection table may be further based on a cell loading factor at the EScell,. The cell loading factor indicates an amount of resource consumption at the EScell.

In particular embodiments, the particular set of input parameters may be further determined based on a cell loading factor at the EScell, where the cell loading factor indicates an amount of resource consumption at the EScell.

In particular embodiments, the method may further comprise monitoring at least one performance metric associated with data transmission from the Pcell to the EScell, determining the at least one performance metric is outside of an expected range, and in response to determining that the at least one performance metric is outside of the expected range, repeating the steps for each set of data segmentation input parameters of the plurality of sets of data segmentation input parameters to update the performance metrics collection table, where the steps may include transmitting the set of data segmentation input parameters to the Pcell, receiving at least one performance metric from the EScell, and storing the at least one performance metric with its associated set of data segmentation input parameters in a performance metrics collection table.

In particular embodiments, the method may further comprise determining a network configuration parameter has changed and in response to determining that the at least one performance metric is outside of the expected range, repeating the steps for each set of data segmentation input parameters of the plurality of sets of data segmentation input parameters to update the performance metrics collection table, where the steps include transmitting the set of data segmentation input parameters to the Pcell, receiving at least one performance metric from the EScell, and storing the at least one performance metric with its associated set of data segmentation input parameters in a performance metrics collection table.

In particular embodiments, a duration of the transmission window may be based on whether adaptive PDCCH is configured.

In particular embodiments, the network node may comprise one of a base station and a core network node.

According to some embodiments, a network node comprises processing circuitry operable to perform any of the network node methods described above.

Also disclosed is a computer program product comprising a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by network node described above.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the disclosed embodiments and their features and advantages, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which:

FIGURE 1 illustrates example system-level simulations for determining EScell throughput using a different number of available resource elements (Res);

FIGURE 2 illustrates an embodiment of the proposed system architecture for optimizing data segmentation at a Pcell node and optimizing performance metrics at an EScell node, according to certain embodiments;

FIGURE 3 illustrates an example flowchart of optimized data segmentation, according to certain embodiments;

FIGURE 4 illustrates an example time diagram for transport block (TB) segmentation optimization, according to certain embodiments;

FIGURE 5 illustrates example system-level simulations conducted to illustrate the technical advantages of particular embodiments;

FIGURE 6 illustrates an example of a cloud infrastructure for implementing data segmentation, according to certain embodiments;

FIGURE 7 illustrates an example O-RAN configuration with existing data segmentation, and an example O-RAN configuration with data segmentation according to certain embodiments;

FIGURE 8 illustrates an example wireless network, according to certain embodiments;

FIGURE 9 illustrates an example user equipment, according to certain embodiments;

FIGURE 10 illustrates an example flow diagram for a method for optimizing data segmentation, according to certain embodiments; and

FIGURE 11 illustrates a schematic block diagram of an apparatus in a wireless network, according to certain embodiments.

DETAILED DESCRIPTION

Particular embodiments described herein extend inter-gNB carrier aggregation (CA) architecture in which the function of data segmentation resides at a Pcell-node, where the size of each data segment to be sent from a Pcell-node to an external Scell-node is calculated based on the channel configurations, UE channel conditions at an EScell, loading at an EScell, etc.

There are differences between the existing system architecture and particular embodiments described herein. For example, particular embodiments improve or optimize the EScell system performance by determining the optimized set of data segmentation parameters to use for data segmentation at a Pcell, where the optimized set of data segmentation parameters leads to the optimized EScell performance metrics, including throughput, latency, and transport block size. Additional differences are described in conjunction with the description of FIGURES 2-11. In this manner, particular embodiments provide practical applications for improving the wireless communication, in general, and more specifically, for improving the EScell system performance and network traffic between the Pcell and the EScell.

FIGURE 2 illustrates an embodiment of the proposed system architecture for optimizing data segmentation at a Pcell node and optimizing performance metrics at an EScell node, according to certain embodiments. As seen in the illustrated example in FIGURE 2, a computing module resides in the network controller. In some embodiments, the network controller may be a network node 160 described in FIGURE 8, and the computing module may be a component in network node 160. In some embodiments, the computing module may include a processor (e.g., a processing circuitry among processing circuitries 170 of FIGURE 8) in signal communication with a memory (e.g., a memory among device readable medium 180 of FIGURE 8). The memory may store instructions that when executed by the processor, cause the processor to perform one or more operations related to transport block (TB) segmentation, according to certain embodiments. Input parameters of data segmentation that are adaptively adjusted are communicated from the computing module to the Pcell-node. Performance metrics (e.g., EScell throughput) obtained during training are fed back to the computing module, which populates a performance metric collection table. After training is complete, the computing module determines and communicates the optimal set of data segmentation parameters to be configured at the Pcell-node. In some embodiments, an alternate system architecture variant includes the computing module in the Pcell-node and the performance metrics are fed back from an EScell-node to a Pcell-node during the training phase. Data segments may be in bytes, physical resource blocks (PRBs), resource block groups (RBGs), TB segments, etc.

In some embodiments, the parameters to the data segmentation algorithm include, but not limited to, the following to determine the size of each data segment to be transferred from a Pcell-node to an EScell-node: number of nominal symbols in PDSCH; amount of resource element (RE) overhead; number of available PRBs; spectral efficiency; and patterns of data segments (e.g., alternating between 1 RBG and 2 RBGs).

In some embodiments, the metrics to be collected at an EScell in the training phase include, but not limited to, the following: sum throughput, max-min throughput, latency, transport block size, and a utility function that consists of multiple performance metrics. In some embodiments, one data segmentation optimization method employs parameter sweeping to find an optimal set input parameters in which the EScell metric is optimized (e.g., EScell throughput is maximized).

If TB segments are considered as data segments to be sent from Pcell to EScell in inter- gNB CA, the “Data Segmentation” block in the aforementioned system diagram shown in FIGURE 2 may be replaced by “TB Segmentation”.

In some embodiments, the proposed solution for TB segmentation optimization may be implemented as follows:

1. Overestimate slightly the number of nominal symbols used in TB segmentation,

2. Determine the total number of available REs in TB segmentation, and

3. Iterate the number of RE overhead that should be removed from the total number of available REs, a. A new training event begins for a fixed number of RE overhead. b. At the end of each training that is normally dictated by the size of an observation window, performance metrics are logged in a key performance indicator (KPI) table along with the corresponding set of TB segmentation parameters.

In some embodiments, to optimize the number of RE overhead used in TB segmentation, a parameter sweep may be performed by iterating all possible values of the number of RE overhead and store the system performance in a KPI table. This approach gives close to globally optimal solutions by avoiding being trapped at local optimal solutions at the expense of time complexity.

Another approach of lower complexity uses a gradient decent method to determine a locally optimal set of input parameters. If an objective function (e.g., throughput maximization) is convex, the locally optimal input parameters are also the globally optimal input parameters. However, in practice, the objective function is likely non-convex, and the solution determined by the gradient decent is only a locally optimal solution, which can be far from the globally optimal solution.

At the end of a training phase, the input parameters of TB segmentation that optimize the objective function (e.g., maximize EScell throughput) are fetched from the KPI table and are communicated from the computing module to the Pcell-node so that the Pcell-node will perform TB segmentation optimally during an application phase. FIGURE 3 illustrates an example flowchart of optimized data segmentation, according to certain embodiments. In some embodiments, the operations described in FIGURE 3 may be performed by the computing module illustrated in FIGURE 2, which may be an embodiment of network node 160 illustrated in FIGURE 8.

At 301, the size of an observation window to be used in a training phase is selected. The observation window may indicate a time period during which EScell performance parameters (e.g., KPI(s)) are observed and collected at 304, according to certain embodiments. At 302, a set of input parameters for data segmentation may be selected. For example, the computing module may select a set of input parameters from among a sweeping range of parameters that are determined for the training phase of the computing module, according to certain embodiments. At 303, the computing module inputs the selected input parameters to the data segmentation module in Pcell. At 304, the computing module collects the KPI(s) at an EScell after an observation window expires and adds a new entry to the KPI table. The collected KPI(s) are added to entry in the KPI table. At 305, the computing module determines whether the training phase is finished. The computing module may determine that the training phase is finished if all the variations and combinations of the input parameters are tested (e.g., selected and inputted to the data segmentation module at the Pcell) and respective EScell performance metrics (e.g., KPI(s)) are collected from the EScell. When the training phase is finished, the computing module may proceed to 306. Otherwise, the computing module may return to 302. At 306, the computing module determines the optimized input parameters for data segmentation from the KPI table that maximizes an objective function. For example, the computing module may select a particular set of input parameters from the KPI table that leads to maximizing the objective function. The objective function may be finding a maximum of one or more EScell performance metrics, including the sum throughput, the max-min throughput, the latency, the transport block size, and the utility function that consists of multiple performance metrics. At 307, the computing module configures the data segmentation module at the Pcell with the optimized set of input parameters. At 308, the computing module determines whether retraining is needed. The computing module may determine that retraining is needed if at least one EScell KPI is no longer optimized. If it is determined that retraining is needed, the computing module may return to 301. Otherwise, the flowchart may end.

FIGURE 4 illustrates an example time diagram for TB segmentation optimization, according to certain embodiments. As illustrated in FIGURE 4, the TB segmentation optimization process may begin when the training phase starts. During the training phase, a plurality of sets of input parameters are generated by the computing module (see FIGURE 2). Each set of input parameters from among the plurality of sets of input parameters is transmitted to Pcell to perform a respective TB segmentation. The Pcell communicates the respective TB segments to the EScell at each iteration of the input parameters. The respective EScell KPIs are reported to the network controller for each iteration of the input parameters. The reported EScell KPIs are stored in KPI tables.

For example, regarding the first set of input parameters, the network controller transmits a first set of input parameters to the Pcell. The Pcell is configured to perform the TB segmentation using the first set of input parameters. The Pcell transmits the TB segments to the EScell for scheduling. The scheduling may refer to scheduling any transmission, such as downlink data, DCI, etc. The EScell reports the EScell KPIs (e.g., throughput and other KPIs described herein) to the network controller. The reported EScell KPIs are stored in the KPI tables. A similar sequence of operations may be performed for other iterations of the input parameters and respective EScell KPIs until the last set of input parameters. The training phase may end when the last EScell KPIs are stored in the KPI tables.

The application phase may begin when the training phase ends. During the application phase, the optimized set of input parameters is determined by the computing module (see FIGURE 2). The optimized set of input parameters is determined based on determining that the optimized set of input parameters leads to the optimized EScell KPIs stored in the KPI tables during the training phase. The optimized set of input parameters is transmitted to the Pcell. The Pcell is configured to perform the TB segmentation using the optimized set of input parameters. The Pcell transmits the TB segments to the EScell for scheduling. The EScell transmits the EScell KPIs to the network controller. The computing module (see FIGURE 2) may determine whether retraining is needed. Retraining may be triggered if reported KPIs are not within an expected range.

In practice, the number of nominal symbols for data channels may vary over time due to advanced radio resource management (RRM) solutions in wireless nodes, e.g., adaptive PDCCH. There are two options to address this situation. As one option, some embodiments may provide 3 KPI tables if an adaptive 3-symbol NR PDCCH feature is enabled and adaptively select a set of optimized input parameters for TB segmentation. As another option, some embodiments may provide a larger observation window so that the effect of advanced RRM algorithm can be averaged out. In an adaptive PDSCH, the number of REs may vary based on the size of TBs, for example, in DO or other data communications.

In some cases, the loading at an EScell may vary from time to time, and the proposed algorithm can be extended to incorporate a cell loading factor when a KPI table is constructed. Unlike advanced RRM algorithms, the loading statistics usually change relatively slowly. Therefore, given the cell loading information, the network controller will then perform a table lookup to determine the optimized set of TB segmentation input parameters.

Retraining may be needed when some of the system configurations have been updated. For example, the maximum MCS may be capped at MCS 20 (as opposed to 27) so the previously selected set of input parameters are no longer optimal. A new KPI table or a new entry to the existing KPI table would be needed.

FIGURE 5 illustrates example system-level simulations conducted to illustrate the technical advantages of particular embodiments. In FIGURE 5, plots illustrating a number of RE overhead (in x-axis) and EScell throughput (in y-axis) for 20 MHz and 10 MHz channel bandwidths are shown on the left and right side, respectively. The optimized setting for the number of RE overhead is marked in both plots. In the illustrated example (on the left plot), the optimized setting for the number of RE overhead is 10 when the channel bandwidth is 20 MHz. Similarly, as illustrated on the right plot, the optimized setting for the number of RE overhead is 20 when the channel bandwidth is 10 MHz

FIGURE 6 illustrates an embodiment of a cloud infrastructure for implementing the data segmentation, according to certain embodiments. In some embodiments, data segmentation may be performed at a Pcell-node with configurations set at the time of deployment. With the cloud infrastructure example shown in FIGURE 6, input parameters for data segmentation and the proposed algorithm may be stored in a computational module residing in the cloud.

The KPIs needed to drive the algorithm are sent from an EScell-node to the cloud, while the input parameters for data segmentation are sent from the cloud to a Pcell-node. The delay requirement required for the communication between a Pcell-node or EScell-node and the cloud is typically not strict because the algorithm operates on an observation window that is in the tens (if not hundreds or thousands) of seconds for the desired KPIs to be meaningful. One advantage of incorporating particular embodiments in the cloud is that, after a KPI table is constructed, the KPI table may be shared and reused in other carrier aggregation sites without the need of an initial training phase.

FIGURE 7 illustrates an example O-RAN configuration with existing data segmentation, and an example O-RAN configuration with data segmentation according to particular embodiments.

The O-RAN configuration with existing data segmentation may generally include a radio unit, a distributed unit, a central unit, a platform, and an application. In O-RAN configuration with existing data segmentation, data segmentation is normally performed in distributed units (DUs) because this belongs to MAC-layer resource allocation.

The O-RAN configuration with the proposed data segmentation may generally include a radio unit, a distributed unit, a central unit, a platform, and an application. Particular embodiments may be incorporated in a O-DU as part of the O-RAN implementation without altering the existing radio unit, central unit, platform, application, etc.

FIGURE 8 illustrates an example wireless network, according to certain embodiments. The wireless network may comprise and/or interface with any type of communication, telecommunication, data, cellular, and/or radio network or other similar type of system. In some embodiments, the wireless network may be configured to operate according to specific standards or other types of predefined rules or procedures. Thus, particular embodiments of the wireless network may implement communication standards, such as Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Long Term Evolution (LTE), and/or other suitable 2G, 3G, 4G, or 5G standards; wireless local area network (WLAN) standards, such as the IEEE 802.11 standards; and/or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave and/or ZigBee standards.

Network 106 may comprise one or more backhaul networks, core networks, IP networks, public switched telephone networks (PSTNs), packet data networks, optical networks, wide-area networks (WANs), local area networks (LANs), wireless local area networks (WLANs), wired networks, wireless networks, metropolitan area networks, and other networks to enable communication between devices.

Network node 160 and WD 110 comprise various components described in more detail below. These components work together to provide network node and/or wireless device functionality, such as providing wireless connections in a wireless network. In different embodiments, the wireless network may comprise any number of wired or wireless networks, network nodes, base stations, controllers, wireless devices, relay stations, 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.

As used herein, network node refers to equipment capable, configured, arranged and/or operable to communicate directly or indirectly with a wireless device and/or with other network nodes or equipment in the wireless network to enable and/or provide wireless access to the wireless device and/or to perform other functions (e.g., administration) in the wireless 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 may then also 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). Yet further examples of network nodes include 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), core network nodes (e.g., MSCs, MMEs), O&M nodes, OSS nodes, SON nodes, positioning nodes (e.g., E-SMLCs), and/or MDTs.

As another example, a network node may be a virtual network node as described in more detail below. More generally, however, network nodes may represent any suitable device (or group of devices) capable, configured, arranged, and/or operable to enable and/or provide a wireless device with access to the wireless network or to provide some service to a wireless device that has accessed the wireless network. In FIGURE 8, network node 160 includes processing circuitry 170, device readable medium 180, interface 190, auxiliary equipment 184, power source 186, power circuitry 187, and antenna 162. Although network node 160 illustrated in the example wireless network of FIGURE 8 may represent a device that includes the illustrated combination of hardware components, other embodiments may comprise network nodes with different combinations of components.

It is to be understood that a network node comprises any suitable combination of hardware and/or software needed to perform the tasks, features, functions and methods disclosed herein. Moreover, while the components of network node 160 are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, a network node may comprise multiple different physical components that make up a single illustrated component (e.g., device readable medium 180 may comprise multiple separate hard drives as well as multiple RAM modules).

Similarly, network node 160 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 network node 160 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 NodeB ’s. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node.

In some embodiments, network node 160 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate device readable medium 180 for the different RATs) and some components may be reused (e.g., the same antenna 162 may be shared by the RATs). Network node 160 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 160, such as, for example, GSM, WCDMA, LTE, NR, WiFi, 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 160.

Processing circuitry 170 is configured to perform any determining, calculating, or similar operations (e.g., certain obtaining operations) described herein as being provided by a network node. These operations performed by processing circuitry 170 may include processing information obtained by processing circuitry 170 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.

Processing circuitry 170 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 160 components, such as device readable medium 180, network node 160 functionality.

For example, processing circuitry 170 may execute instructions stored in device readable medium 180 or in memory within processing circuitry 170. Such functionality may include providing any of the various wireless features, functions, or benefits discussed herein. In some embodiments, processing circuitry 170 may include a system on a chip (SOC).

In some embodiments, processing circuitry 170 may include one or more of radio frequency (RF) transceiver circuitry 172 and baseband processing circuitry 174. In some embodiments, radio frequency (RF) transceiver circuitry 172 and baseband processing circuitry 174 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 172 and baseband processing circuitry 174 may be on the same chip or set of chips, boards, or units

In certain embodiments, some or all of the functionality described herein as being provided by a network node, base station, eNB or other such network device may be performed by processing circuitry 170 executing instructions stored on device readable medium 180 or memory within processing circuitry 170. In alternative embodiments, some or all of the functionality may be provided by processing circuitry 170 without executing instructions stored on a separate or discrete device readable medium, such as in a hard-wired manner. In any of those embodiments, whether executing instructions stored on a device readable storage medium or not, processing circuitry 170 can be configured to perform the described functionality. The benefits provided by such functionality are not limited to processing circuitry 170 alone or to other components of network node 160 but are enjoyed by network node 160 as a whole, and/or by end users and the wireless network generally. Device readable medium 180 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 processing circuitry 170. Device readable medium 180 may store any suitable instructions, data or information, including a computer program, software, an application including one or more of logic, rules, code, tables, etc. and/or other instructions capable of being executed by processing circuitry 170 and, utilized by network node 160. Device readable medium 180 may be used to store any calculations made by processing circuitry 170 and/or any data received via interface 190. In some embodiments, processing circuitry 170 and device readable medium 180 may be considered to be integrated.

Interface 190 is used in the wired or wireless communication of signaling and/or data between network node 160, network 106, and/or WDs 110. As illustrated, interface 190 comprises port(s)/terminal(s) 194 to send and receive data, for example to and from network 106 over a wired connection. Interface 190 also includes radio front end circuitry 192 that may be coupled to, or in certain embodiments a part of, antenna 162.

Radio front end circuitry 192 comprises filters 198 and amplifiers 196. Radio front end circuitry 192 may be connected to antenna 162 and processing circuitry 170. Radio front end circuitry may be configured to condition signals communicated between antenna 162 and processing circuitry 170. Radio front end circuitry 192 may receive digital data that is to be sent out to other network nodes or WDs via a wireless connection. Radio front end circuitry 192 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 198 and/or amplifiers 196. The radio signal may then be transmitted via antenna 162. Similarly, when receiving data, antenna 162 may collect radio signals which are then converted into digital data by radio front end circuitry 192. The digital data may be passed to processing circuitry 170. In other embodiments, the interface may comprise different components and/or different combinations of components.

In certain alternative embodiments, network node 160 may not include separate radio front end circuitry 192, instead, processing circuitry 170 may comprise radio front end circuitry and may be connected to antenna 162 without separate radio front end circuitry 192. Similarly, in some embodiments, all or some of RF transceiver circuitry 172 may be considered a part of interface 190. In still other embodiments, interface 190 may include one or more ports or terminals 194, radio front end circuitry 192, and RF transceiver circuitry 172, as part of a radio unit (not shown), and interface 190 may communicate with baseband processing circuitry 174, which is part of a digital unit (not shown).

Antenna 162 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. Antenna 162 may be coupled to radio front end circuitry 192 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In some embodiments, antenna 162 may comprise one or more omni-directional, sector or panel antennas operable to transmit/receive radio signals between, for example, 2 GHz and 66 GHz. An omni-directional antenna may be used to transmit/receive radio signals in any direction, a sector antenna may be used to transmit/receive radio signals from devices within a particular area, and a panel antenna may be a line of sight antenna used to transmit/receive radio signals in a relatively straight line. In some instances, the use of more than one antenna may be referred to as MIMO. In certain embodiments, antenna 162 may be separate from network node 160 and may be connectable to network node 160 through an interface or port.

Antenna 162, interface 190, and/or processing circuitry 170 may be configured to perform any receiving operations and/or certain obtaining operations described herein as being performed by a network node. Any information, data and/or signals may be received from a wireless device, another network node and/or any other network equipment. Similarly, antenna 162, interface 190, and/or processing circuitry 170 may be configured to perform any transmitting operations described herein as being performed by a network node. Any information, data and/or signals may be transmitted to a wireless device, another network node and/or any other network equipment.

Power circuitry 187 may comprise, or be coupled to, power management circuitry and is configured to supply the components of network node 160 with power for performing the functionality described herein. Power circuitry 187 may receive power from power source 186. Power source 186 and/or power circuitry 187 may be configured to provide power to the various components of network node 160 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). Power source 186 may either be included in, or external to, power circuitry 187 and/or network node 160.

For example, network node 160 may be connectable to an external power source (e.g., an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry 187. As a further example, power source 186 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry 187. The battery may provide backup power should the external power source fail. Other types of power sources, such as photovoltaic devices, may also be used.

Alternative embodiments of network node 160 may include additional components beyond those shown in FIGURE 8 that may be responsible 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, network node 160 may include user interface equipment to allow input of information into network node 160 and to allow output of information from network node 160. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for network node 160.

As used herein, wireless device (WD) refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other wireless devices. Unless otherwise noted, the term WD may be used interchangeably herein with user equipment (UE). Communicating wirelessly may involve transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information through air.

In some embodiments, a WD may be configured to transmit and/or receive information without direct human interaction. For instance, a WD may be designed to transmit information to a network on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the network.

Examples of a WD include, but are not limited to, a smart phone, a mobile phone, a cell phone, a voice over IP (VoIP) phone, a wireless local loop phone, a desktop computer, a personal digital assistant (PDA), a wireless cameras, a gaming console or device, a music storage device, a playback appliance, a wearable terminal device, a wireless endpoint, a mobile station, a tablet, a laptop, a laptop-embedded equipment (LEE), a laptop-mounted equipment (LME), a smart device, a wireless customer-premise equipment (CPE), a vehicle-mounted wireless terminal device, etc. A WD may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-everything (V2X) and may in this case be referred to as a D2D communication device.

As yet another specific example, in an Internet of Things (loT) scenario, a WD 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 WD and/or a network node. The WD may in this case be a machine-to-machine (M2M) device, which may in a 3GPP context be referred to as an MTC device. As one example, the WD may be a UE implementing the 3GPP narrow band internet of things (NB-IoT) standard. Examples of such machines or devices are sensors, metering devices such as power meters, industrial machinery, or home or personal appliances (e.g. refrigerators, televisions, etc.) personal wearables (e.g., watches, fitness trackers, etc.).

In other scenarios, a WD may represent a vehicle or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation. A WD as described above may represent the endpoint of a wireless connection, in which case the device may be referred to as a wireless terminal. Furthermore, a WD as described above may be mobile, in which case it may also be referred to as a mobile device or a mobile terminal.

As illustrated, wireless device 110 includes antenna 111, interface 114, processing circuitry 120, device readable medium 130, user interface equipment 132, auxiliary equipment 134, power source 136 and power circuitry 137. WD 110 may include multiple sets of one or more of the illustrated components for different wireless technologies supported by WD 110, such as, for example, GSM, WCDMA, LTE, NR, WiFi, WiMAX, or Bluetooth wireless technologies, just to mention a few. These wireless technologies may be integrated into the same or different chips or set of chips as other components within WD 110.

Antenna 111 may include one or more antennas or antenna arrays, configured to send and/or receive wireless signals, and is connected to interface 114. In certain alternative embodiments, antenna 111 may be separate from WD 110 and be connectable to WD 110 through an interface or port. Antenna 111, interface 114, and/or processing circuitry 120 may be configured to perform any receiving or transmitting operations described herein as being performed by a WD. Any information, data and/or signals may be received from a network node and/or another WD. In some embodiments, radio front end circuitry and/or antenna 111 may be considered an interface.

As illustrated, interface 114 comprises radio front end circuitry 112 and antenna 111. Radio front end circuitry 112 comprise one or more filters 118 and amplifiers 116. Radio front end circuitry 112 is connected to antenna 111 and processing circuitry 120 and is configured to condition signals communicated between antenna 111 and processing circuitry 120. Radio front end circuitry 112 may be coupled to or a part of antenna 111. In some embodiments, WD 110 may not include separate radio front end circuitry 112; rather, processing circuitry 120 may comprise radio front end circuitry and may be connected to antenna 111. Similarly, in some embodiments, some or all of RF transceiver circuitry 122 may be considered a part of interface 114.

Radio front end circuitry 112 may receive digital data that is to be sent out to other network nodes or WDs via a wireless connection. Radio front end circuitry 112 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 118 and/or amplifiers 116. The radio signal may then be transmitted via antenna 111. Similarly, when receiving data, antenna 111 may collect radio signals which are then converted into digital data by radio front end circuitry 112. The digital data may be passed to processing circuitry 120. In other embodiments, the interface may comprise different components and/or different combinations of components.

Processing circuitry 120 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 WD 110 components, such as device readable medium 130, WD 110 functionality. Such functionality may include providing any of the various wireless features or benefits discussed herein. For example, processing circuitry 120 may execute instructions stored in device readable medium 130 or in memory within processing circuitry 120 to provide the functionality disclosed herein.

As illustrated, processing circuitry 120 includes one or more of RF transceiver circuitry 122, baseband processing circuitry 124, and application processing circuitry 126. In other embodiments, the processing circuitry may comprise different components and/or different combinations of components. In certain embodiments processing circuitry 120 of WD 110 may comprise a SOC. In some embodiments, RF transceiver circuitry 122, baseband processing circuitry 124, and application processing circuitry 126 may be on separate chips or sets of chips.

In alternative embodiments, part or all of baseband processing circuitry 124 and application processing circuitry 126 may be combined into one chip or set of chips, and RF transceiver circuitry 122 may be on a separate chip or set of chips. In still alternative embodiments, part or all of RF transceiver circuitry 122 and baseband processing circuitry 124 may be on the same chip or set of chips, and application processing circuitry 126 may be on a separate chip or set of chips. In yet other alternative embodiments, part or all of RF transceiver circuitry 122, baseband processing circuitry 124, and application processing circuitry 126 may be combined in the same chip or set of chips. In some embodiments, RF transceiver circuitry 122 may be a part of interface 114. RF transceiver circuitry 122 may condition RF signals for processing circuitry 120.

In certain embodiments, some or all of the functionality described herein as being performed by a WD may be provided by processing circuitry 120 executing instructions stored on device readable medium 130, which in certain embodiments may be a computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by processing circuitry 120 without executing instructions stored on a separate or discrete device readable storage medium, such as in a hard-wired manner.

In any of those embodiments, whether executing instructions stored on a device readable storage medium or not, processing circuitry 120 can be configured to perform the described functionality. The benefits provided by such functionality are not limited to processing circuitry 120 alone or to other components of WD 110, but are enjoyed by WD 110, and/or by end users and the wireless network generally.

Processing circuitry 120 may be configured to perform any determining, calculating, or similar operations (e.g., certain obtaining operations) described herein as being performed by a WD. These operations, as performed by processing circuitry 120, may include processing information obtained by processing circuitry 120 by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored by WD 110, 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. Device readable medium 130 may be operable to store a computer program, software, an application including one or more of logic, rules, code, tables, etc. and/or other instructions capable of being executed by processing circuitry 120. Device readable medium 130 may include computer memory (e.g., Random Access Memory (RAM) or Read Only Memory (ROM)), mass storage media (e.g., a hard disk), removable storage media (e.g., 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 processing circuitry 120. In some embodiments, processing circuitry 120 and device readable medium 130 may be integrated.

User interface equipment 132 may provide components that allow for a human user to interact with WD 110. Such interaction may be of many forms, such as visual, audial, tactile, etc. User interface equipment 132 may be operable to produce output to the user and to allow the user to provide input to WD 110. The type of interaction may vary depending on the type of user interface equipment 132 installed in WD 110. For example, if WD 110 is a smart phone, the interaction may be via a touch screen; if WD 110 is a smart meter, the interaction may be through a screen that provides usage (e.g., the number of gallons used) or a speaker that provides an audible alert (e.g., if smoke is detected).

User interface equipment 132 may include input interfaces, devices and circuits, and output interfaces, devices and circuits. User interface equipment 132 is configured to allow input of information into WD 110 and is connected to processing circuitry 120 to allow processing circuitry 120 to process the input information. User interface equipment 132 may include, for example, a microphone, a proximity or other sensor, keys/buttons, a touch display, one or more cameras, a USB port, or other input circuitry. User interface equipment 132 is also configured to allow output of information from WD 110, and to allow processing circuitry 120 to output information from WD 110. User interface equipment 132 may include, for example, a speaker, a display, vibrating circuitry, a USB port, a headphone interface, or other output circuitry. Using one or more input and output interfaces, devices, and circuits, of user interface equipment 132, WD 110 may communicate with end users and/or the wireless network and allow them to benefit from the functionality described herein.

Auxiliary equipment 134 is operable to provide more specific functionality which may not be generally performed by WDs. This may comprise specialized sensors for doing measurements for various purposes, interfaces for additional types of communication such as wired communications etc. The inclusion and type of components of auxiliary equipment 134 may vary depending on the embodiment and/or scenario.

Power source 136 may, in some embodiments, be in the form of a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic devices or power cells, may also be used. WD 110 may further comprise power circuitry 137 for delivering power from power source 136 to the various parts of WD 110 which need power from power source 136 to carry out any functionality described or indicated herein. Power circuitry 137 may in certain embodiments comprise power management circuitry.

Power circuitry 137 may additionally or alternatively be operable to receive power from an external power source; in which case WD 110 may be connectable to the external power source (such as an electricity outlet) via input circuitry or an interface such as an electrical power cable. Power circuitry 137 may also in certain embodiments be operable to deliver power from an external power source to power source 136. This may be, for example, for the charging of power source 136. Power circuitry 137 may perform any formatting, converting, or other modification to the power from power source 136 to make the power suitable for the respective components of WD 110 to which power is supplied.

Although the subject matter described herein may be implemented in any appropriate type of system using any suitable components, the embodiments disclosed herein are described in relation to a wireless network, such as the example wireless network illustrated in FIGURE 8. For simplicity, the wireless network of FIGURE 8 only depicts network 106, network nodes 160 and 160b, and WDs 110, 110b, and 110c. In practice, a wireless network may further include any additional elements suitable to support communication between wireless devices or between a wireless device and another communication device, such as a landline telephone, a service provider, or any other network node or end device. Of the illustrated components, network node 160 and wireless device (WD) 110 are depicted with additional detail. The wireless network may provide communication and other types of services to one or more wireless devices to facilitate the wireless devices’ access to and/or use of the services provided by, or via, the wireless network.

FIGURE 9 illustrates an example user equipment, according to certain embodiments. As used herein, a user equipment or 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). UE 200 may be any UE identified by the 3^rd Generation Partnership Project (3GPP), including a NB-IoT UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE. UE 200, as illustrated in FIGURE 9, is one example of a WD configured for communication in accordance with one or more communication standards promulgated by the 3^rd Generation Partnership Project (3GPP), such as 3GPP’s GSM, UMTS, LTE, and/or 5G standards. As mentioned previously, the term WD and UE may be used interchangeable. Accordingly, although FIGURE 9 is a UE, the components discussed herein are equally applicable to a WD, and vice-versa.

In FIGURE 9, UE 200 includes processing circuitry 201 that is operatively coupled to input/output interface 205, radio frequency (RF) interface 209, network connection interface 211, memory 215 including random access memory (RAM) 217, read-only memory (ROM) 219, and storage medium 221 or the like, communication subsystem 231, power source 213, and/or any other component, or any combination thereof. Storage medium 221 includes operating system 223, application program 225, and data 227. In other embodiments, storage medium 221 may include other similar types of information. Certain UEs may use all the components shown in FIGURE 9, or only a subset of the components. 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.

In FIGURE 9, processing circuitry 201 may be configured to process computer instructions and data. Processing circuitry 201 may be configured to implement any sequential state machine operative to execute machine instructions stored as machine-readable computer programs in the memory, such as one or more hardware-implemented state machines (e.g., in discrete logic, FPGA, ASIC, etc.); programmable logic together with appropriate firmware; one or more stored program, 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 201 may include two central processing units (CPUs). Data may be information in a form suitable for use by a computer. In the depicted embodiment, input/output interface 205 may be configured to provide a communication interface to an input device, output device, or input and output device. UE 200 may be configured to use an output device via input/output interface 205.

An output device may use the same type of interface port as an input device. For example, a USB port may be used to provide input to and output from UE 200. The output device may be 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.

UE 200 may be configured to use an input device via input/output interface 205 to allow a user to capture information into UE 200. The input device may 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, another like sensor, or any combination thereof. For example, the input device may be an accelerometer, a magnetometer, a digital camera, a microphone, and an optical sensor.

In FIGURE 9, RF interface 209 may be configured to provide a communication interface to RF components such as a transmitter, a receiver, and an antenna. Network connection interface 211 may be configured to provide a communication interface to network 243a. Network 243a may encompass wired and/or wireless networks such as a local-area network (LAN), a wide-area network (WAN), a computer network, a wireless network, a telecommunications network, another like network or any combination thereof. For example, network 243 a may comprise a Wi-Fi network. Network connection interface 211 may be configured to include a receiver and a transmitter interface used to communicate with one or more other devices over a communication network according to one or more communication protocols, such as Ethernet, TCP/IP, SONET, ATM, or the like. Network connection interface 211 may implement receiver and transmitter functionality appropriate to the communication network links (e.g., optical, electrical, and the like). The transmitter and receiver functions may share circuit components, software or firmware, or alternatively may be implemented separately. RAM 217 may be configured to interface via bus 202 to processing circuitry 201 to provide storage or caching of data or computer instructions during the execution of software programs such as the operating system, application programs, and device drivers. ROM 219 may be configured to provide computer instructions or data to processing circuitry 201. For example, ROM 219 may be configured to store invariant low-level system code or data for basic system functions such as basic input and output (I/O), startup, or reception of keystrokes from a keyboard that are stored in a non-volatile memory.

Storage medium 221 may be configured to include memory such as RAM, ROM, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, floppy disks, hard disks, removable cartridges, or flash drives. In one example, storage medium 221 may be configured to include operating system 223, application program 225 such as a web browser application, a widget or gadget engine or another application, and data file 227. Storage medium 221 may store, for use by UE 200, any of a variety of various operating systems or combinations of operating systems.

Storage medium 221 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), floppy disk drive, 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 microDIMM SDRAM, smartcard memory such as a subscriber identity module or a removable user identity (SIM/RUIM) module, other memory, or any combination thereof. Storage medium 221 may allow UE 200 to access computer-executable instructions, application programs or 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 in storage medium 221, which may comprise a device readable medium.

In FIGURE 9, processing circuitry 201 may be configured to communicate with network 243b using communication subsystem 231. Network 243a and network 243b may be the same network or networks or different network or networks. Communication subsystem 231 may be configured to include one or more transceivers used to communicate with network 243b. For example, communication subsystem 231 may be configured to include one or more transceivers used to communicate with one or more remote transceivers of another device capable of wireless communication such as another WD, UE, or base station of a radio access network (RAN) according to one or more communication protocols, such as IEEE 802.2, CDMA, WCDMA, GSM, LTE, UTRAN, WiMax, or the like. Each transceiver may include transmitter 233 and/or receiver 235 to implement transmitter or receiver functionality, respectively, appropriate to the RAN links (e.g., frequency allocations and the like). Further, transmitter 233 and receiver 235 of each transceiver may share circuit components, software or firmware, or alternatively may be implemented separately.

In the illustrated embodiment, the communication functions of communication subsystem 231 may include 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. For example, communication subsystem 231 may include cellular communication, Wi-Fi communication, Bluetooth communication, and GPS communication. Network 243b may encompass wired and/or wireless networks such as a local-area network (LAN), a wide-area network (WAN), a computer network, a wireless network, a telecommunications network, another like network or any combination thereof. For example, network 243b may be a cellular network, a Wi-Fi network, and/or a near-field network. Power source 213 may be configured to provide alternating current (AC) or direct current (DC) power to components of UE 200.

The features, benefits and/or functions described herein may be implemented in one of the components of UE 200 or partitioned across multiple components of UE 200. Further, the features, benefits, and/or functions described herein may be implemented in any combination of hardware, software or firmware. In one example, communication subsystem 231 may be configured to include any of the components described herein. Further, processing circuitry 201 may be configured to communicate with any of such components over bus 202. In another example, any of such components may be represented by program instructions stored in memory that when executed by processing circuitry 201 perform the corresponding functions described herein. In another example, the functionality of any of such components may be partitioned between processing circuitry 201 and communication subsystem 231. In another example, the non-computationally intensive functions of any of such components may be implemented in software or firmware and the computationally intensive functions may be implemented in hardware.

FIGURE 10 illustrates an example flow diagram for a method 1000 for determining data segmentation parameters for a Pcell, according to one or more embodiments of the present disclosure. In particular embodiments, one or more steps of method 1000 may be performed by network node 160 described with respect to FIGURE 8, a core network node, or a cloud network node.

The method 1000 begins at step 1012, where the network node (e.g., network node 160, core network node, cloud network node, etc.) transmits a set of data segmentation input parameters to a Pcell for each set of data segmentation input parameters of a plurality of sets of data segmentation input parameters. Each parameter of the at least one set of input parameters is used to perform a data segmentation in which a size of each data segment to be communicated to the EScell is determined according to the respective set of data segmentation input parameters.

In particular embodiments, each set of data segmentation input parameters may comprise one or more of a number of nominal symbols in a PDSCH, an amount of RE overhead, a number of available PRBs, a spectral efficiency, and at least one pattern of data segments. The pattern of data segments comprises, for example, alternating between one RBG and two RBGs in alternating data segments. The data segmentation input parameters may comprise any of the data segmentation input parameters described in the embodiments and examples above.

The Pcell then transmits data to the EScell using the set of data segmentation input parameters. The EScell measures the performance of the data transmission.

At step 1014, the network node receives at least one performance metric from the EScell. Each of the at least one performance metrics may be determined according to data transmission from the Pcell to the EScell over a transmission window in which the size of each data segment communicated to the EScell is determined according to the respective set of input parameters.

In particular embodiments, the at least one performance metric may comprise one or more of a sum throughput, a max-min throughput, a latency, transport block size, a utility function comprising one or more of the sum throughput, the max-min throughput, the latency, and the transport block size. The performance metric may comprise any of the performance metrics described in the embodiments and examples above.

In particular embodiments, a duration of the transmission window is based on whether adaptive PDCCH is configured.

At step 1016, the network node stores the at least one performance metric with its associated set of data segmentation input parameters in a performance metrics collection table. In particular embodiments, the network node may perform steps 1012, 1014, and 1016 for each set of data segmentation input parameters of a plurality of sets of data segmentation input parameters.

At step 1018, the network node selects a particular set of data segmentation input parameters from the plurality of sets of data segmentation input parameters that leads to an optimized performance metric at the EScell based on the performance metrics collection table.

In particular embodiments, selecting the particular set of data segmentation input parameters from the plurality of sets of data segmentation input parameters that leads to an optimized performance metric at the EScell based on the performance metrics collection table (at step 1018) may be further based on a cell loading factor at the EScell, where the cell loading factor indicates an amount of resource consumption at the EScell.

In particular embodiments, the particular set of input parameters may further be determined based on a cell loading factor at the EScell, where the cell loading factor indicates an amount of resource consumption at the EScell.

At step 1020, the network node configures the Pcell with the selected set of data segmentation input parameters to perform the data segmentation.

Over time, the selected set of data segmentation input parameters may no longer be optimal. Thus, at step 1022, optionally, the network node may monitor at least one performance metric associated with data transmission from the Pcell to the EScell.

At step 1024, optionally, the network node determines if the at least one performance metric is outside of an expected range.

In particular embodiments, in response to determining that the at least one performance metric is outside of the expected range, the network node may repeat the steps 1012, 1014, 1016 for each set of data segmentation input parameters of the plurality of sets of data segmentation input parameters to update the performance metrics collection table. At step 1026, optionally, the network node determines if a network configuration parameter has changed. According to the embodiments and examples described herein, in response to determining that the at least one performance metric is outside of the expected range, the network node may repeat the steps 1012, 1014, 1016 for each set of data segmentation input parameters of the plurality of sets of data segmentation input parameters to update the performance metrics collection table.

According to the embodiments and examples described herein, the network node comprises one of a base station and a core network node (such as a cloud network node). A backhaul link between the Pcell and the EScell may comprise one of a wired link and a wireless link.

Modifications, additions, or omissions may be made to the method of FIGURE 10. Additionally, one or more steps in the method of FIGURE 10 may be performed in parallel or in any suitable order.

FIGURE 11 illustrates a schematic block diagram of an apparatus in a wireless network (for example, the wireless network illustrated in FIGURE 8). The apparatus includes a network node (e.g., network node 160 illustrated in FIGURE 8 or any other core network node or cloud network node). Apparatus 1300 is operable to carry out the example methods described with reference to FIGURES 1-10 and possibly any other processes or methods disclosed herein. It is also to be understood that the method of FIGURE 10 is not necessarily carried out solely by apparatuses 1300. At least some operations of the method may be performed by one or more other entities.

Virtual apparatus 1300 may comprise processing circuitry, which may include one or more microprocessors or microcontrollers, as well as other digital hardware, which may include digital signal processors (DSPs), special-purpose digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as read-only memory (ROM), random-access memory, cache memory, flash memory devices, optical storage devices, etc. Program code stored in memory includes program instructions for executing one or more telecommunications and/or data communications protocols as well as instructions for carrying out one or more of the techniques described herein, in several embodiments.

In some implementations, the processing circuitry may be used to cause transmitting module 1302, receiving module 1304, storing module 1306, selecting modulel308, configuring module 1310 to perform functions according to one or more embodiments of the present disclosure.

As illustrated in FIGURE 11, apparatus 1300 includes transmitting module 1302 configured to transmit the set of data segmentation input parameters to the Pcell according to any of the embodiments and examples described herein. Receiving module 1304 is configured to receive at least one performance metric from the EScell according to any of the embodiments and examples described herein. Storing module 1306 is configured to store the at least one performance metric with its associated set of data segmentation input parameters in a performance metrics collection table according to any of the embodiments and examples described herein. Selecting module 1308 is configured to select a particular set of data segmentation input parameters from the plurality of sets of data segmentation input parameters that leads to an optimized performance metric at the EScell based on the performance metrics collection table according to any of the embodiments and examples described herein. Configuring module 1310 is configured to configure the Pcell with the selected set of data segmentation input parameters to perform the data segmentation according to any of the embodiments and examples described herein.

The term unit may have conventional meaning in the field of electronics, electrical devices and/or electronic devices and may include, for example, electrical and/or electronic circuitry, devices, modules, processors, memories, logic solid state and/or discrete devices, computer programs or instructions for carrying out respective tasks, procedures, computations, outputs, and/or displaying functions, and so on, as such as those that are described herein.

Modifications, additions, or omissions may be made to the systems and apparatuses disclosed herein without departing from the scope of the invention. The components of the systems and apparatuses may be integrated or separated. Moreover, the operations of the systems and apparatuses may be performed by more, fewer, or other components. Additionally, operations of the systems and apparatuses may be performed using any suitable logic comprising software, hardware, and/or other logic. As used in this document, “each” refers to each member of a set or each member of a subset of a set.

Modifications, additions, or omissions may be made to the methods disclosed herein without departing from the scope of the invention. The methods may include more, fewer, or other steps. Additionally, steps may be performed in any suitable order. The foregoing description sets forth numerous specific details. It is understood, however, that embodiments may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the understanding of this description. Those of ordinary skill in the art, with the included descriptions, will be able to implement appropriate functionality without undue experimentation.

References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments, whether or not explicitly described.

Although this disclosure has been described in terms of certain embodiments, alterations and permutations of the embodiments will be apparent to those skilled in the art. Accordingly, the above description of the embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are possible without departing from the scope of this disclosure, as defined by the claims below.

Claims

CLAIMS:

1. A method performed by a network node for determining input parameters for data segmentation at a primary cell (Pcell) to optimize performance metrics of an external secondary cell (EScell), the method comprising: for each set of data segmentation input parameters of a plurality of sets of data segmentation input parameters: transmitting (1012) the set of data segmentation input parameters to the Pcell, wherein each parameter of the at least one set of input parameters is used to perform a data segmentation in which a size of each data segment to be communicated to the EScell is determined according to the respective set of data segmentation input parameters; receiving (1014) at least one performance metric from the EScell, wherein each of the at least one performance metric is determined according to data transmission from the Pcell to the EScell over a transmission window in which the size of each data segment communicated to the EScell is determined according to the respective set of input parameters; storing (1016) the at least one performance metric with its associated set of data segmentation input parameters in a performance metrics collection table; selecting (1018) a particular set of data segmentation input parameters from the plurality of sets of data segmentation input parameters that leads to an optimized performance metric at the EScell based on the performance metrics collection table; and configuring (1020) the Pcell with the selected set of data segmentation input parameters to perform the data segmentation.

2. The method of claim 1, wherein each set of data segmentation input parameters comprises one or more of the following: a number of nominal symbols in a physical downlink shared channel (PDSCH); an amount of resource elements (RE) overhead; a number of available physical resource blocks (PRBs); a spectral efficiency; and at least one pattern of data segments.

3. The method of claim 2, wherein the at least one pattern of data segments comprises alternating between one resource block group (RBG) and two RBGs in alternating data segments.

4. The method of any one of claims 1-3, wherein the at least one performance metric comprises one or more of: a sum throughput; a max-min throughput; a latency; transport block size; and a utility function comprising one or more of the sum throughput, the max-min throughput, the latency, and the transport block size.

5. The method of any one of claims 1-4, wherein selecting the particular set of data segmentation input parameters from the plurality of sets of data segmentation input parameters that leads to an optimized performance metric at the EScell based on the performance metrics collection table is further based on a cell loading factor at the EScell, wherein the cell loading factor indicates an amount of resource consumption at the EScell.

6. The method of any one of claims 1-5, wherein the particular set of input parameters is further determined based on a cell loading factor at the EScell, wherein the cell loading factor indicates an amount of resource consumption at the EScell.

7. The method of any one of claims 1-6, further comprising: monitoring (1022) at least one performance metric associated with data transmission from the Pcell to the EScell; determining (1024) the at least one performance metric is outside of an expected range; and in response to determining that the at least one performance metric is outside of the expected range, repeating the steps (1012, 1014, 1016) for each set of data segmentation input parameters of the plurality of sets of data segmentation input parameters to update the performance metrics collection table.

8. The method of any one of claims 1-7, further comprising: determining (1026) a network configuration parameter has changed; and in response to determining that the at least one performance metric is outside of the expected range, repeating the steps (1012, 1014, 1016) for each set of data segmentation input parameters of the plurality of sets of data segmentation input parameters to update the performance metrics collection table.

9. The method of any one of claims 1-8, wherein a duration of the transmission window is based on whether adaptive physical downlink control channel (PDCCH) is configured.

10. The method of any one of claims 1-9, wherein the network node comprises one of a base station and a core network node.

11. A network node (160) configured to determine input parameters for data segmentation at a primary cell (Pcell) to optimize performance metrics of an external secondary cell (EScell), the network node comprising processing circuitry (170) operable to: for each set of data segmentation input parameters of a plurality of sets of data segmentation input parameters: transmit the set of data segmentation input parameters to the Pcell, wherein each parameter of the at least one set of input parameters is used to perform a data segmentation in which a size of each data segment to be communicated to the EScell is determined according to the respective set of data segmentation input parameters; receive at least one performance metric from the EScell, wherein each of the at least one performance metric is determined according to data transmission from the Pcell to the EScell over a transmission window in which the size of each data segment communicated to the EScell is determined according to the respective set of input parameters; store the at least one performance metric with its associated set of data segmentation input parameters in a performance metrics collection table; select a particular set of data segmentation input parameters from the plurality of sets of data segmentation input parameters that leads to an optimized performance metric at the EScell based on the performance metrics collection table; and configure the Pcell with the selected set of data segmentation input parameters to perform the data segmentation.

11. The network node of claim 11, wherein each set of data segmentation input parameters comprises one or more of the following: a number of nominal symbols in a physical downlink shared channel (PDSCH); an amount of resource elements (RE) overhead; a number of available physical resource blocks (PRBs); a spectral efficiency; and at least one pattern of data segments.

12. The network node of claim 2, wherein the at least one pattern of data segments comprises alternating between one resource block group (RBG) and two RBGs in alternating data segments.

13. The network node of any one of claims 10-12, wherein the at least one performance metric comprises one or more of: a sum throughput; a max-min throughput; a latency; transport block size; and a utility function comprising one or more of the sum throughput, the max-min throughput, the latency, and the transport block size.

14. The network node of any one of claims 10-13, wherein selecting the particular set of data segmentation input parameters from the plurality of sets of data segmentation input parameters that leads to an optimized performance metric at the EScell based on the performance metrics collection table is further based on a cell loading factor at the EScell, wherein the cell loading factor indicates an amount of resource consumption at the EScell.

15. The network node of any one of claims 10-14, wherein the particular set of input parameters is further determined based on a cell loading factor at the EScell, wherein the cell loading factor indicates an amount of resource consumption at the EScell.

16. The network node of any one of claims 10-15, wherein the processing circuitry is further operable to: monitor at least one performance metric associated with data transmission from the Pcell to the EScell; determine the at least one performance metric is outside of an expected range; and in response to determining that the at least one performance metric is outside of the expected range, repeat a set of steps for each set of data segmentation input parameters of the plurality of sets of data segmentation input parameters to update the performance metrics collection table, the set of steps comprising: for each set of data segmentation input parameters of a plurality of sets of data segmentation input parameters: transmit the set of data segmentation input parameters to the Pcell, wherein each parameter of the at least one set of input parameters is used to perform a data segmentation in which a size of each data segment to be communicated to the EScell is determined according to the respective set of data segmentation input parameters; receive at least one performance metric from the EScell, wherein each of the at least one performance metric is determined according to data transmission from the Pcell to the EScell over a transmission window in which the size of each data segment communicated to the EScell is determined according to the respective set of input parameters; store the at least one performance metric with its associated set of data segmentation input parameters in a performance metrics collection table.

17. The network node of any one of claims 10-16, wherein the processing circuitry is further operable to: determine a network configuration parameter has changed; and in response to determining that the at least one performance metric is outside of the expected range, repeat a set of steps for each set of data segmentation input parameters of the plurality of sets of data segmentation input parameters to update the performance metrics collection table, the set of steps comprising: for each set of data segmentation input parameters of a plurality of sets of data segmentation input parameters: transmit the set of data segmentation input parameters to the Pcell, wherein each parameter of the at least one set of input parameters is used to perform a data segmentation in which a size of each data segment to be communicated to the EScell is determined according to the respective set of data segmentation input parameters; receive at least one performance metric from the EScell, wherein each of the at least one performance metric is determined according to data transmission from the Pcell to the EScell over a transmission window in which the size of each data segment communicated to the EScell is determined according to the respective set of input parameters; store the at least one performance metric with its associated set of data segmentation input parameters in a performance metrics collection table.

18. The network node of any one of claims 10-17, wherein a duration of the transmission window is based on whether adaptive physical downlink control channel (PDCCH) is configured.

19. The network node of any one of claims 10-18, wherein the network node comprises one of a base station and a core network node.

20. the network node of any one of claims 10-19, wherein a backhaul link between the Pcell and the EScell comprises one of a wired link and a wireless link.