WO2025088364A1 - Methods and systems for using a neural network to detect scheduling requests - Google Patents

Abstract

A method (1500) by a network node (910) for detecting a schedule request, SR, includes using a neural network to detect at least one SR transmitted from a user equipment, UE (912), to the network node. For example, the network node may receive at least one signal from the UE and, based on at least one feature associated with the at least one signal, detect via the neural 5 network that the at least one signal includes a SR.

Description

METHODS AND SYSTEMS FOR USING A NEURAL NETWORK

TO DETECT SCHEDULING REQUESTS

TECHNICAL FIELD

The present disclosure relates, in general, to wireless communications and, more particularly, systems and methods for using a neural network to detect scheduling requests (SRs).

BACKGROUND

When a 5^th Generation (5G) User Equipment (UE) needs to transmit uplink (UL) data but does not have any available resources, it can request scheduling from the network by sending a Scheduling Request (SR) on the Physical Uplink Control Channel (PUCCH). This SR signals the intention of the UE to transmit data, but it does not specify the amount of data that the UE has to transmit. To allocate the necessary resources, the network requires information about the amount of data in the UE's buffer, which is subsequently provided separately from the SR in a Buffer Status Report (BSR). The network then allocates resources, including enough resources for the UE to send BSRs, based on its implementation.

Because the network does not know when the UE will require uplink resources, the network needs to be able to detect SR reports on the allocated SR resources. Only one SR is necessary, regardless of the number of UL carrier units in use. The SR can only be sent when the UE is in the RRC CONNECTED state and maintaining UL synchronization and is used exclusively for new data, not retransmitted data.

The SR is sent on the PUCCH because the UE does not have any available Physical Uplink Shared Channel (PUSCH) resources. The network can allocate a dedicated SR resource for each UE, which appears once every n subframes. The cycle of SR is configured through the sr Configindex field in the ScheduleRequestConfig Information Element (IE). The network is aware of the specific correspondence between SR resources and UEs. More specifically, since SR resources are dedicated to UEs and allocated by the gNodeB (gNB), each SR resource corresponds to a specific UE, and the gNB is aware of this correspondence. As a result, the UE does not need to specify its own cell-Radio Network Temporary Identifier (C-RNTI) when sending SR information. The gNB is able to determine which UE is requesting UL resources by knowing the location of the SR resource. For example, the location of the SR resources, configured through the sr-PUCCH-Resourcelndex field in the ScheduleRequestConfig IE, is used by the network to determine which UE is requesting UL resources.

There currently exist certain challenge(s), however. For example, in current 5G New Radio (NR) products, the detection of SR signals on the PUCCH is based on a fixed threshold, which was established through the physical layer simulations. However, this approach has resulted in a high rate of false SR detections in some customer networks, leading to the issuance of high-priority grants by the gNB and a waste of valuable resources. This problem becomes more pronounced as the number of active users increases, negatively impacting the user experience and leading to unnecessary consumption of precious Physical Downlink Control Channel (PDCCH) and PUSCH resources.

SUMMARY

Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, methods and systems are provided for using a neural network to detect SRs.

According to certain embodiments, a method by a network node for detecting a SR includes using a neural network to detect at least one SR transmitted from a UE to the network node.

According to certain embodiments, a network node for detecting a SR is adapted to use a neural network to detect at least one SR transmitted from a UE to the network node.

Certain embodiments may provide one or more of the following technical advantage (s). For example, certain embodiments may provide a technical advantage of utilizing neural networkbased algorithms to significantly enhance SR detection performance, surpassing that of current techniques and solutions.

As another example, certain embodiments may provide a technical advantage of using Signal Interference to Noise Ratio (SINR) and Similarity Thresholds to further reduce false alarm error rates (FAER), resulting in even more robust SR detection.

As yet another example, certain embodiments may provide a technical advantage of providing dynamic adjustment of detection thresholds. As a result certain embodiments may ensure that FAER remain at an acceptable level, leading to more reliable SR detection. As still another example, certain embodiments may provide a technical advantage of employing an on-the-fly online training method, which may allow for continuous refinement of the model on a per-gNB or per-cell basis and may result in the continued improvement of SR detection performance.

Other advantages may be readily apparent to one having skill in the art. Certain embodiments may have none, some, or all of the recited advantages.

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 an example flowchart and signaling diagram for UL data transmission in 5G NR, according to certain embodiments;

FIGURE 2 illustrates SR and Acknowledgment (ACK)ZNon-Acknowledgement (NACK) bits being transmitted using Quadrature phase shift keying (QPSK) modulation symbols, according to certain embodiments;

FIGURES 3A and 3B illustrate tables, which together provide example sample data of the selected features and targets used for training neural network models for SR detection, according to certain embodiments;

FIGURE 4 illustrates an example neural network structure used for SR detection, according to certain embodiments;

FIGURE 5 illustrates example activation functions used for the outputs of the last layer, according to certain embodiments;

FIGURE 6 illustrates example SR prediction error rates for different neural network models, according to certain embodiments;

FIGURE 7 illustrates an example communication system, according to certain embodiments;

FIGURE 8 illustrates an example UE, according to certain embodiments;

FIGURE 9 illustrates an example network node, according to certain embodiments;

FIGURE 10 illustrates a block diagram of a host, according to certain embodiments;

FIGURE 11 illustrates a virtualization environment in which functions implemented by some embodiments may be virtualized, according to certain embodiments; FIGURE 12 illustrates a host communicating via a network node with a UE over a partially wireless connection, according to certain embodiments; and

FIGURE 13 illustrates a method by a network node for detecting a SR, according to certain embodiments.

DETAILED DESCRIPTION

Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

As used herein, ‘node’ can be a network node or a UE. Examples of network nodes are NodeB, base station (BS), multi-standard radio (MSR) radio node such as MSR BS, eNodeB (eNB), gNodeB (gNB), Master eNB (MeNB), Secondary eNB (SeNB), integrated access backhaul (IAB) node, network controller, radio network controller (RNC), base station controller (BSC), relay, donor node controlling relay, base transceiver station (BTS), Central Unit (e.g. in a gNB), Distributed Unit (e.g. in a gNB), Baseband Unit, Centralized Baseband, C-RAN, access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU), Remote Radio Head (RRH), nodes in distributed antenna system (DAS), core network node (e.g. Mobile Switching Center (MSC), Mobility Management Entity (MME), etc.), Operations & Maintenance (O&M), Operations Support System (OSS), Self Organizing Network (SON), positioning node (e.g. E- SMLC), etc.

Another example of a node is user equipment (UE), which is a non-limiting term and refers to any type of wireless device communicating with a network node and/or with another UE in a cellular or mobile communication system. Examples of UE are target device, device to device (D2D) UE, vehicular to vehicular (V2V), machine type UE, MTC UE or UE capable of machine to machine (M2M) communication, Personal Digital Assistant (PDA), Tablet, mobile terminals, smart phone, laptop embedded equipment (LEE), laptop mounted equipment (LME), Unified Serial Bus (USB) dongles, etc.

In some embodiments, generic terminology, “radio network node” or simply “network node (NW node)”, is used. It can be any kind of network node which may comprise base station, radio base station, base transceiver station, base station controller, network controller, evolved Node B (eNB), Node B, gNodeB (gNB), relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH), Central Unit (e.g. in a gNB), Distributed Unit (e.g. in a gNB), Baseband Unit, Centralized Baseband, C-RAN, access point (AP), etc.

The term radio access technology (RAT), may refer to any RAT such as, for example, Universal Terrestrial Radio Access Network (UTRA), Evolved Universal Terrestrial Radio Access Network (E-UTRA), narrow band internet of things (NB-IoT), WiFi, Bluetooth, next generation RAT, NR, 4G, 5G, etc. Any of the equipment denoted by the terms node, network node or radio network node may be capable of supporting a single or multiple RATs.

The term signal or radio signal used herein can be any physical signal or physical channel. Examples of downlink (DL) physical signals are reference signal (RS) such as Primary Synchronization Signal (PSS), Secondary Synchronization Signal (SSS), Channel State Information-Reference Signal (CSI-RS), and Demodulation Reference Signal (DMRS) in Synchronization Signal (SS)ZPhysical Broadcast Channel (PBCH) block, which is referred to more simply as a SSB. Other examples of DL RS include discovery reference signal (DRS), Cell- Specific Reference Signal (CRS), Positioning Reference Signal (PRS), etc. RS may be periodic such as, for example, a RS occasion carrying one or more RSs may occur with certain periodicity (e.g., 20 ms, 40 ms, etc.). The RS may also be aperiodic.

Each SSB carries New Radio-Primary Synchronization Signal (NR-PSS), New RadioSecondary Synchronization Signal (NR-SSS), and New Radio-Physical Broadcast Channel (NR- PBCH) in 4 successive symbols. One or multiple SSBs are transmit in one SSB burst which is repeated with certain periodicity (e.g., 5 ms, 10 ms, 20 ms, 40 ms, 80 ms, and 160 ms). The UE is configured with information about SSB on cells of certain carrier frequency by one or more SS/PBCH block measurement timing configuration (SMTC) configurations. The SMTC configuration comprising parameters such as SMTC periodicity, SMTC occasion length in time or duration, SMTC time offset with regard to reference time (e.g., serving cell’s System Frame Number (SFN)). Therefore, SMTC occasion may also occur with certain periodicity (e.g., 5 ms, 10 ms, 20 ms, 40 ms, 80 ms, and 160 ms). Examples of UL physical signals are reference signal such as Sounding Reference Signal (SRS), DMRS, etc. The term physical channel refers to any channel carrying higher layer information such as, for example, data, control, etc. Examples of physical channels are PBCH, Narrowband-PBCH (NPBCH), PDCCH, Physical Downlink Shared Channel (PDSCH), shortened-PUCCH (sPUCCH), shortened-PDSCH (sPDSCH), shortened- PUCCH (sPUCCH), shortened-PUSCH (sPUSCH), MTC-PDCCH (MPDCCH), Narrowband- PDCCH (NPDCCH), Narrowband-PDSCH (NPDSCH), Enhanced-PDCCH (E-PDCCH), PUSCH, PUCCH, Narrowband-PUSCH (NPUSCH), etc.

To improve the accuracy of the SR detection, systems and methods are proposed that use a Neural Network to analyze the features of a received signal and detect the SRs. A crucial step for effective Machine Learning (ML) data-driven-based algorithms is to choose informative, discriminating, and independent features and adequately define the targets. According to certain embodiments, certain pairs of activation and cost functions are used to train the neural network models for SR detection. For example, in a particular embodiment, six pairs of activation and cost functions are used to train the neural network models. Among these, the Sigmoid function and the Cross-Entropy Loss are included and discussed in more detail below.

In certain example embodiments, one or two targets are used to train the neural network model. Thus, correspondingly, one or two output values are used for SR detection. In particular embodiments, the SINR and similarity thresholds are used to reduce the FA error rate. Instead of using a fixed threshold, the threshold may be dynamically adjusted based on the current network conditions, such as the FAER.

In a particular embodiment, the On-The-Fly online training method can be used to refine the model on a per-gNB or per-cell basis.

Although certain embodiments are described in the context of 5G NR, the solutions, methods, and techniques disclosed herein are also applicable to other wireless systems, such as Long Term Evolution (LTE). The On-The-Fly online training can be implemented in the cloud as well.

The Process of Uplink Data Transmission in 5GNR

FIGURE 1 illustrates an example an example flowchart and signaling diagram 100 for UL data transmission in 5G NR, according to certain embodiments. Specifically, FIGURE 1 illustrates example signaling between a UE 102 and a gNB 104.

As illustrated, the UE 102 signals, to the gNB 104, the UE’s intention to transmit data by sending a SR on the PUCCH Format 1 channel to request resources, at step 106. Upon successful detection of the SR, the gNB sends a Downlink Control Information (DCI) via the PDCCH, at step 108. The DCI includes an UL grant to allocate the necessary resources.

At step 110, the UE sends a BSR on the PUSCH in the allocated resources. The BSR indicates to the gNB 104 the amount of data in the UE’s buffer, which needs to be transmitted. Based on this information, the gNB 104 assigns the appropriate resources and Modulation and Coding Scheme (MCS) for the UE, enabling the UE 102 to transmit UL data on the PUSCH, at step 112. The UE 102 then sends the UL data on the PUSCH, at step 114. PUCCH Resource Configuration and Scheduling Request and ACK/NACK Transmission

According to certain embodiments, each UE is configured with nine Physical Format 1 (PF1) resources. Of these, eight are utilized for Hybrid Automatic Repeat Request (HARQ) acknowledgments (ACKs), which are shared among multiple UEs. The remaining resource is reserved for a SR, which is unique to each UE. Each PF1 resource for ACK/NACK can transmit up to two HARQ feedback bits.

Each UE is also assigned two Physical Format 3 (PF3) resources, which are shared among multiple UEs. Each PF3 resource can transmit between three to twelve HARQ feedback bits. When the gNB schedules more than two HARQ feedback bits, PF3 will be used. Otherwise, PF1 will be utilized.

The SR resource is established periodically, appearing in every "n" subframes, and its offset is associated with the selected entry in the SR mapping matrix. The SR is transmitted using the dedicated SR resource. The ACK/NACK is sent using the resource designated in the DCI. In cases where one or two ACK/NACK bits and SR need to be transmitted simultaneously in the same UL slot, the ACK/NACK bits will be transmitted using the SR resource if the SR is positive. Otherwise, the ACK/NACK bits will be sent using the ACK/NACK resource. If the PUSCH is scheduled in the same slot, the ACK/NACK bits will be transmitted on PUSCH.

QPSK Modulation

In QPSK modulation, the carrier signal's phase is shifted to represent two bits of data simultaneously, allowing for higher data rates while maintaining a relatively compact bandwidth. Since the phase shifts correspond to equal energy transitions, QPSK is less prone to errors caused by noise and interference in the transmission channel. QPSK modulation is crucial for 5G NR systems as it facilitates reliable and fast communication for critical control messages such as scheduling requests and HARQ (Hybrid Automatic Repeat Request) feedback. Its ability to represent two bits of data in a single symbol, while maintaining a compact bandwidth, directly translates to improved data rate and spectral efficiency.

FIGURE 2 illustrates a table 200 providing example QSPK modulation symbols 202, which are used to transmit SR bit 204 and ACK/NACK bits 206, according to certain embodiments. If SR is positive, the ACK/NACK bits are transmitted using the SR resource. If SR is negative, the ACK/NACK bits are transmitted using the ACK/NACK resource. If there is no ACK/NACK transmission, a positive SR is transmitted using the dedicated SR resource. Features and Targets ’ Data Selection for SR Detection

According to previous solutions, a pre-defined threshold is used to decode the SR bits, which may only be optimum in some channel conditions and configurations and may introduce many decoding errors. ML is a powerful tool that can help improve our product performance by increasing the SR detection accuracy and reducing the FAER.

A crucial step for effective ML data-driven-based algorithms is to choose informative, discriminating, and independent features and adequately define the targets. Feature data is the measurable observations and characteristics that can be used to train the model and to infer (predict) the expected or desired results using the model. Target data is the desired and wanted result, which may be referred to as the “golden standard”. Target data can be used to train the model and measure its performance, such as SR detection accuracy, FAER, etc.

According to certain embodiments disclosed herein, the feature data selected for SR detection may include:

• hasHarqBit If one or two HARQ bits are scheduled to be transmitted on the configured pfl resource, its value is set to 1. Otherwise, its value is set to 0.

• pucchHqSymbol Re: The real part of the demodulated PUCCH HARQ feedback symbol.

• pucchHqSymbol Im: The imaginary part of the demodulated PUCCH HARQ feedback symbol.

• pucchSrSymbol Re: The real part of the demodulated PUCCH SR symbol.

• pucchSr Symbol Im: The imaginary part of the demodulated PUCCH SR symbol.

• sinrHqSymbol The signal-to-interference plus noise ratio measured on the HARQ feedback symbol.

• sinrSrSymbol The signal-to-interference plus noise ratio measured on the SR symbol.

The target data may include:

• srPositive: If the UE sends the scheduling request on the SR opportunity slot, its value is set to 1; otherwise, it is set to 0.

• SrNegative: If the UE sends the scheduling request on the SR opportunity slot, its value is set to 0; otherwise, it is set to 1. FIGURES 3A and 3B illustrate tables 3OOA and 3OOB, which together provide example sample data of the selected features 302 and targets 304 used for training neural network models for SR detection, according to certain embodiments. Note that when no HARQ feedback bits are sent on the SR opportunity slot, pucchHqSymbol Re and pucchHqSymbol Im are set to zero and sinrHqSymbol is set to -30dB.

Neural Network Model Training and Inference for SR Detection

FIGURE 4 illustrates an example structure of a neural network 400 used for SR detection, according to certain embodiments. In the depicted example, the neural network 400 has a number of inputs 402, a number of outputs 404, and two hidden layers 406 and 408, each having a number of neurons. The first output is srPositive, and the second output is SrNegative.

The outputs of each hidden layer use the ReEu activation function, and the outputs of the last layer use the selected activation functions, as shown in table 500 illustrated in FIGURE 5. Note that if the Identity (Linear) and TanH are used for the last layer, the target value of 0 should be changed to -1.

During the training phase, two cost functions are used. One is Mean Squared Error (L2 loss), which is defined as

The other is Cross-Entropy, which is defined as

E =i E" i (ij')log (y(i,j)) where N is the total number of input data samples, and Mis the number of features. x(i,j) and y(i,j) are the target and predicted values, respectively. Note that the Cross-Entropy cost function can only be applied to Sigmoid and SoftMax activation functions. Table 1 shows six pairs of the activation and cost functions used for training the neural network models for SR detection.

Table 1

The standard normalization method is applied to the following features before the model training and inferencing are performed:

• pucchHqSymbol Re

• pucchHqSymbol Im

• pucchSrSymbol Re

• pucchSrSymbol Im

• sinrHqSymbol

• sinrSrSymbol

It is a scaling technique where the values are centered around the mean with a unit standard deviation.

During the inference phase, after applying the input data (features) to the neural network model for each SR opportunity slot, we get two outputs, srPositivePred and srNegtivePred.

If srPositivePred > srNegtivePred, the result predicted by the neural network is classified as SR positive. Otherwise, it is classified as SR negative.

Neural Network Inference Model Performance Evaluation for SR Detection

According to certain embodiments, the SR detection error rate, the FAER, and the total error rate may be used to evaluate the neural network inference model performance.

When the UE sends an SR bit on the SR opportunity slot, but the neural network predicts no SR bit, it is an SR Detection Error: • If ((srPositive == True) AND (srPositivePred <= srNegtivePred)), it is an SR detection error

The SR detection error rate (%) is calculated as 100

When the UE doesn't send the SR bit on the SR opportunity slot, but the neural network predicts there is an SR bit, it is a FA error:

• If((srPositive == False) AND (srPositivePred > srNegtivePredf), it is a false alarm error

The FAER (%) is calculated as 100

The total error rate is calculated as 100

FIGURE 6 includes a table 600 illustrating example SR detection error rate, the FAER, and the total error rate for different neural network models, according to certain embodiments. From the simulation results, the following conclusions may be made:

• The prediction error rates from all the models are pretty close.

• The SoftMax activation function with the Cross-Entropy cost function has the lowest SR detection error rate, which is 8.74%

• The TanH activation function with the Mean-Square Error cost function has the lowest SR FA rate, which is 4.32%.

• As a comparison, our current product's SR detection and FAERs are 13.36% and 10.51%, respectively.

The SINR Threshold

As discussed herein, a SR is sent using a dedicated SR resource. ACK/NACK is sent using the resource indicated by the DL DCI. If one or two ACK/NACK bits and SR simultaneously transmit on the same UL slot, the ACK/NACK bits are transmitted using SR resource if SR is positive. Generally speaking, if the UE sends an SR on a UL slot, the estimated SINR of the SR symbol should also be higher regardless of whether the ACK/NACK bits are transmitted on the same UL slot. If it is below a threshold, the UE likely does not send an SR on this slot. The following formula may be used to predict the SR detection result:

• If ((srPositivePred > srNegtivePred) AND (sinrSrSymbol > sinrThr)), the result predicted by the neural network is classified as SR positive. Otherwise, it is classified as SR negative.

The above formula will make an SR detection error when the UE sends the SR with a low SINR. However, it can effectively reduce the FAER when the SR signal is not present.

The Similarity Threshold

The mistakes made by the neural network in SR detection usually happen when the difference between srPositivePred and srNegtivePred is relatively small. In other words, when the values of srPositivePred and srNegtivePred are very close, it's tough for the neural network to make the right decision. To reduce the FAER, the prediction result is classified as SR negative when the difference between srPositivePred and srNegtivePred is less than a threshold. Herein, this is referred to as the similarity threshold.

According to certain embodiments, the following formula may be used to predict the SR detection result:

• If ((srPositivePred - srNegtivePred) > SimilarityThr), the result predicted by the neural network is classified as SR positive. Otherwise, it is classified as SR negative.

When the linear activation function and mean square error cost function are used to train the neural network model, SR detection errors have been shown to proportionally increase with the increase of the similarity threshold. In contrast, the SR false alarm errors inversely decrease with the increase of the similarity threshold. The SR detection and FAERs can be adjusted to acceptable levels by adequately setting the similarity threshold.

The Combined Thresholds

In the third scenario, both the SINR and the Similarity Thresholds may be used, in particular embodiments, to reduce the FAER: • If (((srPositivePred - srNegtivePred) > SimilarityThr) AND (sinrSrSymbol > sinrThr)), the result predicted by the neural network is classified as SR positive.

Otherwise, it is classified as SR negative.

Use One Target Value to Train the Model and Use One Output Value for SR Detection

Certain embodiments described above assume that the neural network model had two target data: srPositive and srNegative. However, according to certain other embodiments, only one target data may be used, which may be srPositive. If the UE sends the SR on the SR opportunity slot, which is a designated time slot for SRs, its value is set to 1; otherwise, it is set to 0.

Table 2 below shows a particular example embodiment where four pairs of activation and cost functions were used to train the neural network models with one target value. These functions were selected based on their performance in detecting SRs sent during the SR opportunity slot.

Table 2

Note that if the Identity (Linear) and TanH are used for the last layer, the target value of 0 should be changed to -1.

If either Relu or Sigmoid activation functions is used for the last layer, the prediction result is: o Scenario 1, no thresholds are used:

■ If srPositivePred > 0.5, the result predicted by the neural network is classified as SR positive. Otherwise, it is classified as SR negative. o Scenario 2, use the SINR threshold:

■ If ((srPositivePred > 0.5) AND (sinrSrSymbol > sinrThr)')' . the result predicted by the neural network is classified as SR positive. Otherwise, it is classified as SR negative. o Scenario 3, use the SINR threshold:

■ If ((srPositivePred > 0.5 + SimilarityThr) AND sinrSrSymbol > sinrThr)')'. the result predicted by the neural network is classified as SR positive. Otherwise, it is classified as SR negative.

If either Linear or TanH activation functions is used for the last layer, the prediction result would be: o Scenario 1, no thresholds are used:

■ If srPositivePred > 0, the result predicted by the neural network is classified as SR positive. Otherwise, it is classified as SR negative. o Scenario 2, use the SINR threshold:

■ If ((srPositivePred > 0) AND (sinrSrSymbol > sinrThr)), the result predicted by the neural network is classified as SR positive. Otherwise, it is classified as SR negative. o Scenario 3, use the SINR threshold:

■ If ((srPositivePred > SimilarityThr) AND (sinrSrSymbol > sinrThr)), the result predicted by the neural network is classified as SR positive. Otherwise, it is classified as SR negative.

Dynamic Threshold

According to certain embodiments disclosed herein, SINR and Similarity Thresholds are used to reduce the FAER. Instead of using a fixed threshold, the threshold may be dynamically adjusted based on the current network conditions, such as the FAER.

For example, in a particular embodiment, if the gNB detects an SR positive, it sends an UL grant to the UE for it to send a BSR on the allocated PUSCH resources. If the PUSCH is decoded successfully, the gNB checks the BSR to determine if the UE has UL data to send. If the UE has no UL data to send, the SR positive is considered false. If the PUSCH fails to decode after retransmissions, and the estimated SINR is below the dtxThreshold, the UE is considered in DTX mode, and the SR positive is also considered false. If the estimated SINR is above the dtxThreshold, the SR detection result is classified as “unknown”.

The SR detection result can be used to dynamically adjust the Similarity Threshold, which is used to reduce the FAER. If the SR positive is TRUE, the Similarity Threshold is decreased by an amount defined as DOWN STEP, as follows: • SimilarityThr- = DOWN_STEP

If the SR positive is FALSE, the Similarity Threshold is increased by UP STEP, as follows:

• SimilarityThr+ = UP_STEP

If the SR detection result is classified as “unknown”, no action is taken.

In a particular embodiment, the ratio of the UP STEP and DOWN STEP parameters, which determine the rate of increase or decrease of the Similarity Threshold, is determined based on the desired FAER target (the FAER):

• UP STEP/DOWN STEP = l/FAER TARGET - 1.

The DOWN_STEP value can be adjusted to achieve the desired level of convergence speed. The FAER target can be pre-determined, e.g., 3%, in a particular embodiment. The FAER is now modified as

Number of times when the SR positive is FALSE Number of times when the SR positive is detected

To present an over-adjustment of the Similarity Threshold, we define two thresholds: THR HIGH and THR LOW, and limit the Similarity Threshold between them. The modified adjustment algorithm would be: o If the SR positive is TRUE, the Similarity Threshold is decreased by DOWN STEP, as follows:

■ SimilarityThr- = DOWN STEP

■ If (SimilarityThr < THR LOW) SimilarityThr = THR LOW o If the SR positive is FALSE, the Similarity Threshold is increased by UP STEP, as follows:

■ SimilarityThr+ = UP STEP

■ If (SimilarityThr > THR HIGH) SimilarityThr = THR HIGH

The two thresholds, THR LOW and THR HIGH, can be determined through the simulation and field test results.

On-The-Fly Online Training

The Neural Network model is often trained using the simulated data or the data collected from the field. It may not be optimal for all the gNBs or cells. The On-The-Fly online training method can be used to refine the model on a per gNB or per cell basis. During the daytime when the traffic volume is high, training data are collected. Assuming one output value and the linear activation function are used, if an SR positive is detected and classified as TRUE, the target data is set to 1. If it is classified as FALSE, the target data is set to -1. All the target data and their corresponding features are collected and stored. During the night when the traffic volume is low, the Neural Network model is retrained using the collected data, which can be used to replace the old model.

FIGURE 7 shows an example of a communication system 900 in accordance with some embodiments. In the example, the communication system 900 includes a telecommunication network 902 that includes an access network 904, such as a radio access network (RAN), and a core network 906, which includes one or more core network nodes 908. The access network 904 includes one or more access network nodes, such as network nodes 910a and 910b (one or more of which may be generally referred to as network nodes 910), or any other similar 3^rd Generation Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 910 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 912a, 912b, 912c, and 912d (one or more of which may be generally referred to as UEs 912) to the core network 906 over one or more wireless connections.

Example wireless communications over a wireless connection include transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 900 may include any number of wired or wireless networks, network nodes, UEs, and/or any other components or systems that may facilitate or participate in the communication of data and/or signals whether via wired or wireless connections. The communication system 900 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system.

The UEs 912 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and/or operable to communicate wirelessly with the network nodes 910 and other communication devices. Similarly, the network nodes 910 are arranged, capable, configured, and/or operable to communicate directly or indirectly with the UEs 912 and/or with other network nodes or equipment in the telecommunication network 902 to enable and/or provide network access, such as wireless network access, and/or to perform other functions, such as administration in the telecommunication network 902. In the depicted example, the core network 906 connects the network nodes 910 to one or more hosts, such as host 916. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 906 includes one more core network nodes (e.g., core network node 908) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and/or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 908. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and/or a User Plane Function (UPF).

The host 916 may be under the ownership or control of a service provider other than an operator or provider of the access network 904 and/or the telecommunication network 902, and may be operated by the service provider or on behalf of the service provider. The host 916 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio/video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

As a whole, the communication system 900 of FIGURE 7 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and/or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and/or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and/or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox. In some examples, the telecommunication network 902 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 902 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 902. For example, the telecommunications network 902 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and/or Massive Machine Type Communication (mMTC)ZMassive loT services to yet further UEs.

In some examples, the UEs 912 are configured to transmit and/or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 904 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 904. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi -radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

In the example, the hub 914 communicates with the access network 904 to facilitate indirect communication between one or more UEs (e.g., UE 912c and/or 912d) and network nodes (e.g., network node 910b). In some examples, the hub 914 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 914 may be a broadband router enabling access to the core network 906 for the UEs. As another example, the hub 914 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 910, or by executable code, script, process, or other instructions in the hub 914. As another example, the hub 914 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 914 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 914 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 914 then provides to the UE either directly, after performing local processing, and/or after adding additional local content. In still another example, the hub 914 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices. The hub 914 may have a constant/persistent or intermitent connection to the network node 910b. The hub 914 may also allow for a different communication scheme and/or schedule between the hub 914 and UEs (e.g., UE 912c and/or 912d), and between the hub 914 and the core network 906. In other examples, the hub 914 is connected to the core network 906 and/or one or more UEs via a wired connection. Moreover, the hub 914 may be configured to connect to an M2M service provider over the access network 904 and/or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 910 while still connected via the hub 914 via a wired or wireless connection. In some embodiments, the hub 914 may be a dedicated hub - that is, a hub whose primary function is to route communications to/from the UEs from/to the network node 910b. In other embodiments, the hub 914 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 910b, but which is additionally capable of operating as a communication start and/or end point for certain data channels.

FIGURE 8 shows a UE 1000 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded/integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE.

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

The UE 1000 includes processing circuitry 1002 that is operatively coupled via a bus 1004 to an input/output interface 1006, a power source 1008, a memory 1010, a communication interface 1012, and/or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in FIGURE 8. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

The processing circuitry 1002 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 1010. The processing circuitry 1002 may be implemented as one or more hardware -implemented state machines (e.g., in discrete logic, field- programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 1002 may include multiple central processing units (CPUs).

In the example, the input/output interface 1006 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and/or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 1000. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

In some embodiments, the power source 1008 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 1008 may further include power circuitry for delivering power from the power source 1008 itself, and/or an external power source, to the various parts of the UE 1000 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 1008. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 1008 to make the power suitable for the respective components of the UE 1000 to which power is supplied.

The memory 1010 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 1010 includes one or more application programs 1014, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1016. The memory 1010 may store, for use by the UE 1000, any of a variety of various operating systems or combinations of operating systems.

The memory 1010 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and/or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 1010 may allow the UE 1000 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 1010, which may be or comprise a device-readable storage medium.

The processing circuitry 1002 may be configured to communicate with an access network or other network using the communication interface 1012. The communication interface 1012 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1022. The communication interface 1012 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 1018 and/or a receiver 1020 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1018 and receiver 1020 may be coupled to one or more antennas (e.g., antenna 1022) and may share circuit components, software or firmware, or alternatively be implemented separately.

In the illustrated embodiment, communication functions of the communication interface 1012 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and/or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol/intemet protocol (TCP/IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1012, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input. A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door/window sensor, a flood/moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or itemtracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and/or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 1000 shown in FIGURE 8.

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

In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and/or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators. FIGURE 9 shows a network node 1100 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and/or operable to communicate directly or indirectly with a UE and/or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NRNodeBs (gNBs)).

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

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

The network node 1100 includes a processing circuitry 1102, a memory 1104, a communication interface 1106, and a power source 1108. The network node 1100 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 1100 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 1100 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 1104 for different RATs) and some components may be reused (e.g., a same antenna 1110 may be shared by different RATs). The network node 1100 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1100, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 1100.

The processing circuitry 1102 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 1100 components, such as the memory 1104, to provide network node 1100 functionality.

In some embodiments, the processing circuitry 1102 includes a system on a chip (SOC). In some embodiments, the processing circuitry 1102 includes one or more of radio frequency (RF) transceiver circuitry 1112 and baseband processing circuitry 1114. In some embodiments, the radio frequency (RF) transceiver circuitry 1112 and the baseband processing circuitry 1114 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 1112 and baseband processing circuitry 1114 may be on the same chip or set of chips, boards, or units.

The memory 1104 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile or non-volatile, non-transitory device-readable and/or computer-executable memory devices that store information, data, and/or instructions that may be used by the processing circuitry 1102. The memory 1104 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and/or other instructions capable of being executed by the processing circuitry 1102 and utilized by the network node 1100. The memory 1104 may be used to store any calculations made by the processing circuitry 1102 and/or any data received via the communication interface 1106. In some embodiments, the processing circuitry 1102 and memory 1104 is integrated.

The communication interface 1106 is used in wired or wireless communication of signaling and/or data between a network node, access network, and/or UE. As illustrated, the communication interface 1106 comprises port(s)/terminal(s) 1116 to send and receive data, for example to and from a network over a wired connection. The communication interface 1106 also includes radio front-end circuitry 1118 that may be coupled to, or in certain embodiments a part of, the antenna 1110. Radio front-end circuitry 1118 comprises filters 1120 and amplifiers 1122. The radio frontend circuitry 1118 may be connected to an antenna 1110 and processing circuitry 1102. The radio front-end circuitry may be configured to condition signals communicated between antenna 1110 and processing circuitry 1102. The radio front-end circuitry 1118 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 1118 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1120 and/or amplifiers 1122. The radio signal may then be transmitted via the antenna 1110. Similarly, when receiving data, the antenna 1110 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1118. The digital data may be passed to the processing circuitry 1102. In other embodiments, the communication interface may comprise different components and/or different combinations of components.

In certain alternative embodiments, the network node 1100 does not include separate radio front-end circuitry 1118, instead, the processing circuitry 1102 includes radio front-end circuitry and is connected to the antenna 1110. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1112 is part of the communication interface 1106. In still other embodiments, the communication interface 1106 includes one or more ports or terminals 1116, the radio frontend circuitry 1118, and the RF transceiver circuitry 1112, as part of a radio unit (not shown), and the communication interface 1106 communicates with the baseband processing circuitry 1114, which is part of a digital unit (not shown).

The antenna 1110 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. The antenna 1110 may be coupled to the radio front-end circuitry 1118 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In certain embodiments, the antenna 1110 is separate from the network node 1100 and connectable to the network node 1100 through an interface or port. The antenna 1110, communication interface 1106, and/or the processing circuitry 1102 may be configured to perform any receiving operations and/or certain obtaining operations described herein as being performed by the network node. Any information, data and/or signals may be received from a UE, another network node and/or any other network equipment. Similarly, the antenna 1110, the communication interface 1106, and/or the processing circuitry 1102 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and/or signals may be transmitted to a UE, another network node and/or any other network equipment.

The power source 1108 provides power to the various components of network node 1100 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1108 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1100 with power for performing the functionality described herein. For example, the network node 1100 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 1108. As a further example, the power source 1108 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

Embodiments of the network node 1100 may include additional components beyond those shown in FIGURE 9 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and/or any functionality necessary to support the subject matter described herein. For example, the network node 1100 may include user interface equipment to allow input of information into the network node 1100 and to allow output of information from the network node 1100. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1100.

FIGURE 10 is a block diagram of a host 1200, which may be an embodiment of the host 916 of FIGURE 7, in accordance with various aspects described herein.

As used herein, the host 1200 may be or comprise various combinations hardware and/or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 1200 may provide one or more services to one or more UEs. The host 1200 includes processing circuitry 1202 that is operatively coupled via a bus 1204 to an input/output interface 1206, a network interface 1208, a power source 1210, and a memory 1212. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures 10 and 11, such that the descriptions thereof are generally applicable to the corresponding components of host 1200.

The memory 1212 may include one or more computer programs including one or more host application programs 1214 and data 1216, which may include user data, e.g., data generated by a UE for the host 1200 or data generated by the host 1200 for a UE. Embodiments of the host 1200 may utilize only a subset or all of the components shown. The host application programs 1214 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 1214 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 1200 may select and/or indicate a different host for over-the-top services for a UE. The host application programs 1214 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

FIGURE 11 is a block diagram illustrating a virtualization environment 1300 in which functions implemented by some embodiments may be virtualized.

In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 1300 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized.

Applications 1302 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein.

Hardware 1304 includes processing circuitry, memory that stores software and/or instructions executable by hardware processing circuitry, and/or other hardware devices as described herein, such as a network interface, input/output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1306 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 1308a and 1308b (one or more of which may be generally referred to as VMs 1308), and/or perform any of the functions, features and/or benefits described in relation with some embodiments described herein. The virtualization layer 1306 may present a virtual operating platform that appears like networking hardware to the VMs 1308.

The VMs 1308 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 1306. Different embodiments of the instance of a virtual appliance 1302 may be implemented on one or more of VMs 1308, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

In the context of NFV, a VM 1308 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 1308, and that part of hardware 1304 that executes that VM, be it hardware dedicated to that VM and/or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 1308 on top of the hardware 1304 and corresponds to the application 1302.

Hardware 1304 may be implemented in a standalone network node with generic or specific components. Hardware 1304 may implement some functions via virtualization. Alternatively, hardware 1304 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1310, which, among others, oversees lifecycle management of applications 1302. In some embodiments, hardware 1304 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1312 which may alternatively be used for communication between hardware nodes and radio units.

FIGURE 12 shows a communication diagram of a host 1402 communicating via a network node 1404 with a UE 1406 over a partially wireless connection in accordance with some embodiments.

Example implementations, in accordance with various embodiments, of the UE (such as a UE 912a of FIGURE 7 and/or UE 1000 of FIGURE 8), network node (such as network node 910a of FIGURE 7 and/or network node 1100 of FIGURE 9), and host (such as host 916 of FIGURE 7 and/or host 1200 of FIGURE 10) discussed in the preceding paragraphs will now be described with reference to FIGURE 12.

Like host 1200, embodiments of host 1402 include hardware, such as a communication interface, processing circuitry, and memory. The host 1402 also includes software, which is stored in or accessible by the host 1402 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 1406 connecting via an over-the-top (OTT) connection 1450 extending between the UE 1406 and host 1402. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 1450.

The network node 1404 includes hardware enabling it to communicate with the host 1402 and UE 1406. The connection 1460 may be direct or pass through a core network (like core network 906 of FIGURE 7) and/or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

The UE 1406 includes hardware and software, which is stored in or accessible by UE 1406 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE 1406 with the support of the host 1402. In the host 1402, an executing host application may communicate with the executing client application via the OTT connection 1450 terminating at the UE 1406 and host 1402. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection 1450 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 1450.

The OTT connection 1450 may extend via a connection 1460 between the host 1402 and the network node 1404 and via a wireless connection 1470 between the network node 1404 and the UE 1406 to provide the connection between the host 1402 and the UE 1406. The connection 1460 and wireless connection 1470, over which the OTT connection 1450 may be provided, have been drawn abstractly to illustrate the communication between the host 1402 and the UE 1406 via the network node 1404, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

As an example of transmitting data via the OTT connection 1450, in step 1408, the host 1402 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 1406. In other embodiments, the user data is associated with a UE 1406 that shares data with the host 1402 without explicit human interaction. In step 1410, the host 1402 initiates a transmission carrying the user data towards the UE 1406. The host 1402 may initiate the transmission responsive to a request transmitted by the UE 1406. The request may be caused by human interaction with the UE 1406 or by operation of the client application executing on the UE 1406. The transmission may pass via the network node 1404, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 1412, the network node 1404 transmits to the UE 1406 the user data that was carried in the transmission that the host 1402 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 1414, the UE 1406 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 1406 associated with the host application executed by the host 1402.

In some examples, the UE 1406 executes a client application which provides user data to the host 1402. The user data may be provided in reaction or response to the data received from the host 1402. Accordingly, in step 1416, the UE 1406 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input/output interface of the UE 1406. Regardless of the specific manner in which the user data was provided, the UE 1406 initiates, in step 1418, transmission of the user data towards the host 1402 via the network node 1404. In step 1420, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 1404 receives user data from the UE 1406 and initiates transmission of the received user data towards the host 1402. In step 1422, the host 1402 receives the user data carried in the transmission initiated by the UE 1406.

One or more of the various embodiments improve the performance of OTT services provided to the UE 1406 using the OTT connection 1450, in which the wireless connection 1470 forms the last segment. More precisely, the teachings of these embodiments may improve one or more of, for example, data rate, latency, and/or power consumption and, thereby, provide benefits such as, for example, reduced user waiting time, relaxed restriction on file size, improved content resolution, better responsiveness, and/or extended battery lifetime.

In an example scenario, factory status information may be collected and analyzed by the host 1402. As another example, the host 1402 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 1402 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 1402 may store surveillance video uploaded by a UE. As another example, the host 1402 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host 1402 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and/or transmitting data.

In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 1450 between the host 1402 and UE 1406, in response to variations in the measurement results. The measurement procedure and/or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host 1402 and/or UE 1406. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 1450 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 1450 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 1404. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host 1402. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 1450 while monitoring propagation times, errors, etc.

FIGURE 13 illustrates an example method 1500 by a network node 910 for detecting a SR, according to certain embodiments. In the illustrated embodiment, the method includes using a neural network to detect at least one SR transmitted from a UE 912.

In a particular embodiment, when using the neural network to detect the at least one SR, the network node 910 receives at least one signal from the UE 912. Based on at least one feature associated with the at least one signal, the network node 910 detects via the neural network that the at least one signal includes a SR.

In a particular embodiment, prior to using the neural network to detect the at least one SR, the network node 910 uses at least one of feature data and target data to train the neural network.

In a further particular embodiment, the feature data includes at least one of: at least one HARQ bit having a value associated with whether the HARQ bits are transmitted on a configured pfl resource; a real part of a demodulated HARQ feedback symbol; an imaginary part of the demodulated HARQ feedback symbol; a real part of a demodulated SR symbol; an imaginary part of the demodulated SR symbol; a SINR value associated with the demodulated HARQ feedback symbol; and a SINR value associated with the demodulated SR symbol.

In a further particular embodiment, the target data includes at least one of a srPositive value and a srNegative value.

In a further particular embodiment, the network node 910 uses at least one cost function and at least one activation function to train the neural network. In a further particular embodiment, the at least one cost function includes at least one of a Mean Squared Error cost function and a Cross-Entropy cost function.

In a further particular embodiment, the at least one activation function includes at least one of: a Linear activation function; a Relu activation function; a Sigmoid activation function; a TanH activation function; and a SoftMax activation function.

In a particular embodiment, after training the neural network, the network node 910 uses inference to predict at least one output value.

In a further particular embodiment, the at least one output value includes at least a srPositivePred value and a srNegativePred value. The network node 910 determines that a SR is present in at least one signal received from a UE when the srPositivePred value is greater than the srNegativePred value.

In a further particular embodiment, the network node 910 determines that a SR is present in at least one signal received from the UE 912 when a SINR value associated with a signal received from a UE 910 is greater than a SINR threshold.

In a further particular embodiment, the network node 910 determines that a SR is present in at least one signal received from a UE 912 when a difference between the srPositivePred value and the srNegativePred value is less than a similarity threshold.

In a further particular embodiment, the at least one output value includes a srPostivePred value. The at least one activation function includes a Relu or Sigmoid activation function. A SR is determined to be present in at least one signal received from a UE when:

• srPositivePred > 0.5; srPositivePred > 0.5 and a SINR value associated with a signal received from a UE 912 is greater than a SINR threshold; or

• srPositivePred > 0.5 + Similarity Threshold Value and a SINR value associated with a signal received from a UE 912 is greater than a SINR threshold.

In a further particular embodiment, the at least one output value includes a srPostivePred value, the at least one activation function comprises a Linear or TanH activation function, and a SR is determined to be present in at least one signal received from a UE when:

• srPositivePred > 0; srPositivePred > 0 and a SINR value associated with a signal received from a UE is greater than a SINR threshold; or

• srPositivePred > Similarity Threshold Value and a SINR value associated with a signal received from a UE is greater than a SINR threshold. In a further particular embodiment, the network node 910 dynamically adjusts at least one of the SINR threshold and the similarity threshold based on at least one network condition.

In a further particular embodiment, the at least one network condition includes a FAER associated with the UE 912.

In a particular embodiment, based on determining that the SR is present in the at least one signal received from the UE 912, the network node 910 transmits an uplink grant to the UE 912. The uplink grant indicates at least one transmission resource associated with an uplink channel. The network node 910 determines whether the SR was correctly detected based on one of:

• if a signal is received from the UE 912 via the at least one transmission resource associated with the uplink channel and decoded and the signal comprises a BSR indicating that the UE 912 has data to send, detecting that the SR was correctly detected;

• if a signal is received from the UE 912 via the at least one transmission resource associated with the uplink channel and decoded and the signal comprises a BSR indicating that the UE 912 does not have data to send, detecting that the SR was incorrectly detected;

• if a signal from the UE 912 cannot be decoded and an estimated SINR value is below a SINR threshold, detecting that the SR was incorrectly detected; or

• if a signal from the UE 912 cannot be decoded and an estimated SINR value associated with the signal is above a SINR threshold, determining that it is unknown whether the SR was correctly detected.

In a particular embodiment, when dynamically adjusting the at least one of the SINR threshold and the similarity threshold based on the at least one network condition, the network node 910 increases the SINR threshold and/or the similarity threshold when the at least one SR is correctly detected, or the network node 910 decreases the SINR threshold and/or the similarity threshold when the at least one SR is incorrectly detected.

In a particular embodiment, using the at least one of feature data and target data to train the neural network includes obtaining the at least one of the feature data and target data during a first time period when traffic volume is greater than a first threshold. The neural network is trained based on the at least one of the feature data and the target data during a second time period when traffic volume is lower than a second threshold.

In a further particular embodiment, obtaining the target data includes transmitting an uplink grant to the UE 912 based on determining that the SR is present in the at least one signal received from the UE 912. The uplink grant indicates at least one transmission resource associated with an uplink channel. The network node 910 determines whether to use the target data to train the neural network based on one or more factors. For example, if a signal is received from the UE 912 via the at least one transmission resource associated with the uplink channel and decoded and the signal comprises a BSR indicating that the UE 912 has data to send, the network node 910 determines/detects that the SR was correctly detected, and the network node 910 determines to use the target data to train the neural network. Likewise, if a signal is received from the UE 912 via the at least one transmission resource associated with the uplink channel and decoded and the signal comprises a BSR indicating that the UE 912 does not have data to send, the network node 910 determines/detects that the SR was incorrectly detected, and the network node determines to use the target data to train the neural network. As another example, if a signal from the UE 912 cannot be decoded and an estimated SINR value is below a SINR threshold, the network node 910 determines/detects that the SR was incorrectly detected, and the network node 910 determines to use the target data to train the neural network. However, in another example, if a signal from the UE 912 cannot be decoded and an estimated SINR value associated with the signal is above a SINR threshold, the network node 910 determines that it is unknown whether the SR was correctly detected, and the network node 910 determines not to use the target data to train the neural network.

In a particular embodiment, the network node 910 refines the neural network based on additional training data associated with the network node 910 and/or a cell served by the network node 910.

In a further particular embodiment, when refining the neural network based on the additional training data, the network node 910 collects the training data during a first time period when traffic volume is greater than a first threshold and trains the neural network based on the at least one of the feature data and the target data during a second time period when traffic volume is lower than a second threshold.

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

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

Claims

1. A method (1500) by a network node (910) for detecting a schedule request, SR, the method comprising: using (1502) a neural network to detect at least one SR transmitted from a user equipment, UE (912), to the network node.

2. The method of Claim 1, wherein using the neural network to detect the at least one SR comprises: receiving at least one signal from the UE; and based on at least one feature associated with the at least one signal, detecting via the neural network that the at least one signal includes a SR.

3. The method of any one of Claims 1 to 2, comprising: prior to using the neural network to detect the at least one SR, using at least one of feature data and target data to train the neural network.

4. The method of Claim 3, wherein the feature data comprises at least one of: at least one Hybrid Automatic Repeat Request, HARQ, bit having a value associated with whether the HARQ bits are transmitted on a configured pfl resource; a real part of a demodulated HARQ feedback symbol; an imaginary part of the demodulated HARQ feedback symbol; a real part of a demodulated SR symbol; an imaginary part of the demodulated SR symbol; a Signal-to-Interference-plus-Noise-Ratio, SINR, value associated with the demodulated HARQ feedback symbol; and a SINR value associated with the demodulated SR symbol.

5. The method of any one of Claims 3 to 4, wherein the target data comprises at least one of: a srPositive value, and a srNegative value.

6. The method of any one of Claims 3 to 5, using at least one cost function and at least one activation function to train the neural network.

7. The method of Claim 6, wherein the at least one cost function comprises at least one of: a Mean Squared Error cost function; and a Cross-Entropy cost function.

8. The method of any one of Claims 6 to 7, wherein the at least one activation function comprises at least one of: a Linear activation function; a Relu activation function; a Sigmoid activation function; a TanH activation function; and a SoftMax activation function.

9. The method of any one of Claims 3 to 6, comprising: after training the neural network, using inference to predict at least one output value.

10. The method of Claim 9, wherein the at least one output value comprises at least a srPositivePred value and a srNegativePred value, and the method comprises determining that a SR is present in at least one signal received from a UE when: the srPositivePred value is greater than the srNegativePred value.

11. The method of Claim 10, comprising determining that a SR is present in at least one signal received from the UE when: a Signal-to-Noise-Interference-Ratio, SINR, value associated with a signal received from a UE is greater than a SINR threshold.

12. The method any one of Claims 10 to 11, comprising determining that a SR is present in at least one signal received from a UE when: a difference between the srPositivePred value and the srNegativePred value is less than a similarity threshold.

13. The method of Claim 10, wherein: the at least one output value comprises a srPostivePred value,' the at least one activation function comprises a Relu or Sigmoid activation function; and a SR is determined to be present in at least one signal received from a UE when: srPositivePred > 0.5, srPositivePred > 0.5 and a Signal-to-Noise-Interference-Ratio, SINR, value associated with a signal received from a UE is greater than a SINR threshold; or srPositivePred > 0.5 + Similarity Threshold Value and a SINR value associated with a signal received from a UE is greater than a SINR threshold.

14. The method of Claim 10, wherein: the at least one output value comprises a srPostivePred value,' the at least one activation function comprises a Linear or TanH activation function; and a SR is determined to be present in at least one signal received from a UE when: srPositivePred > 0, srPositivePred > 0 and a Signal-to-Noise-Interference-Ratio, SINR, value associated with a signal received from a UE is greater than a SINR threshold; or srPositivePred > Similarity Threshold Value and a SINR value associated with a signal received from a UE is greater than a SINR threshold.

15. The method of any one of Claims 11 to 14, comprising dynamically adjusting at least one of the SINR threshold and the similarity threshold based on at least one network condition.

16. The method of Claim 15, wherein the at least one network condition comprises a false alarm error rate, FAER, associated with the UE.

17. The method of any one of Claims 10 to 14, comprising: based on determining that the SR is present in the at least one signal received from the UE, transmitting an uplink grant to the UE, the uplink grant indicating at least one transmission resource associated with an uplink channel; determining whether the SR was correctly detected based on: if a signal is received from the UE via the at least one transmission resource associated with the uplink channel and decoded and the signal comprises a BSR indicating that the UE has data to send, detecting that the SR was correctly detected; if a signal is received from the UE via the at least one transmission resource associated with the uplink channel and decoded and the signal comprises a BSR indicating that the UE does not have data to send, detecting that the SR was incorrectly detected; if a signal from the UE cannot be decoded and an estimated SINR value is below a SINR threshold, detecting that the SR was incorrectly detected; or if a signal from the UE cannot be decoded and an estimated SINR value associated with the signal is above a SINR threshold, determining that it is unknown whether the SR was correctly detected.

18. The method of any one of Claims 13 to 17, wherein dynamically adjusting the at least one of the SINR threshold and the similarity threshold based on the at least one network condition comprises: increasing the SINR threshold and/or the similarity threshold when the at least one SR is correctly detected; or decreasing the SINR threshold and/or the similarity threshold when the at least one SR is incorrectly detected.

19. The method of any one of Claims 3 to 18, wherein using the at least one of feature data and target data to train the neural network comprises: obtaining the at least one of the feature data and target data during a first time period when traffic volume is greater than a first threshold; and training the neural network based on the at least one of the feature data and the target data during a second time period when traffic volume is lower than a second threshold.

20. The method of Claim 19, wherein obtaining the target data comprises: based on determining that the SR is present in the at least one signal received from the UE, transmitting an uplink grant to the UE, the uplink grant indicating at least one transmission resource associated with an uplink channel; determining whether to use the target data to train the neural network based on: if a signal is received from the UE via the at least one transmission resource associated with the uplink channel and decoded and the signal comprises a BSR indicating that the UE has data to send, detecting that the SR was correctly detected and determining to use the target data to train the neural network; if a signal is received from the UE via the at least one transmission resource associated with the uplink channel and decoded and the signal comprises a BSR indicating that the UE does not have data to send, detecting that the SR was incorrectly detected and determining to use the target data to train the neural network; if a signal from the UE cannot be decoded and an estimated SINR value is below a SINR threshold, detecting that the SR was incorrectly detected and determining to use the target data to train the neural network; or if a signal from the UE cannot be decoded and an estimated SINR value associated with the signal is above a SINR threshold, determining that it is unknown whether the SR was correctly detected and determining not to use the target data to train the neural network.

21. The method of any one of Claims 1 to 20, comprising refining the neural network based on additional training data associated with the network node and/or a cell served by the network node.

22. The method of Claim 21, wherein refining the neural network based on the additional training data comprises: collecting the training data during a first time period when traffic volume is greater than a first threshold; and training the neural network based on the at least one of the feature data and the target data during a second time period when traffic volume is lower than a second threshold.

23. A network node (910) for detecting a schedule request, SR, the network node adapted to: use (1502) a neural network to detect at least one SR transmitted from a user equipment,

UE (910), to the network node.

24. The network node of Claim 23, wherein when using the neural network to detect the at least one SR, the network node is adapted to: receive at least one signal from the UE; and based on at least one feature associated with the at least one signal, detect via the neural network that the at least one signal includes a SR.

25. The network node of any one of Claims 23 to 24, adapted to: prior to using the neural network to detect the at least one SR, use at least one of feature data and target data to train the neural network.

26. The network node of Claim 25, wherein the feature data comprises at least one of: at least one Hybrid Automatic Repeat Request, HARQ, bit having a value associated with whether the HARQ bits are transmitted on a configured pfl resource; a real part of a demodulated HARQ feedback symbol; an imaginary part of the demodulated HARQ feedback symbol; a real part of a demodulated SR symbol; an imaginary part of the demodulated SR symbol; a Signal-to-Interference-plus-Noise-Ratio, SINR, value associated with the demodulated HARQ feedback symbol; and a SINR value associated with the demodulated SR symbol.

27. The network node of any one of Claims 25 to 26, wherein the target data comprises at least one of: a srPositive value, and a srNegative value.

28. The network node of any one of Claims 25 to 27, adapted to use at least one cost function and at least one activation function to train the neural network.

29. The network node of Claim 28, wherein the at least one cost function comprises at least one of: a Mean Squared Error cost function; and a Cross-Entropy cost function.

30. The network node of any one of Claims 28 to 29, wherein the at least one activation function comprises at least one of: a Linear activation function; a Relu activation function; a Sigmoid activation function; a TanH activation function; and a SoftMax activation function.

31. The network node of any one of Claims 27to 30, adapted to: after training the neural network, use inference to predict at least one output value.

32. The method of Claim 31, wherein the at least one output value comprises at least a srPositivePred value and a srNegativePred value, and the network node is adapted to determine that a SR is present in at least one signal received from a UE when: the srPositivePred value is greater than the srNegativePred value.

33. The network node of Claim 32, adapted to determine that a SR is present in at least one signal received from the UE when: a Signal-to-Noise-Interference-Ratio, SINR, value associated with a signal received from a UE is greater than a SINR threshold.

34. The network node of any one of Claims 32 to 33, adapted to determine that a SR is present in at least one signal received from a UE when: a difference between the srPositivePred value and the srNegativePred value is less than a similarity threshold.

35. The network node of Claim 32, wherein: the at least one output value comprises a srPostivePred value,' the at least one activation function comprises a Relu or Sigmoid activation function; and a SR is determined to be present in at least one signal received from a UE when: srPositivePred > 0.5, srPositivePred > 0.5 and a Signal-to-Noise-Interference-Ratio, SINR, value associated with a signal received from a UE is greater than a SINR threshold; or srPositivePred > 0.5 + Similarity Threshold Value and a SINR value associated with a signal received from a UE is greater than a SINR threshold.

36. The network node of Claim 35, wherein: the at least one output value comprises a srPostivePred value,' the at least one activation function comprises a Linear or TanH activation function; and a SR is determined to be present in at least one signal received from a UE when: srPositivePred > 0, srPositivePred > 0 and a Signal-to-Noise-Interference-Ratio, SINR, value associated with a signal received from a UE is greater than a SINR threshold, or srPositivePred > Similarity Threshold Value and a SINR value associated with a signal received from a UE is greater than a SINR threshold.

37. The network node of any one of Claims 33 to 36, adapted to dynamically adjust at least one of the SINR threshold and the similarity threshold based on at least one network condition.

38. The network node of Claim 37, wherein the at least one network condition comprises a false alarm error rate, FAER, associated with the UE.

39. The network node of any one of Claims 32 to 38, adapted to: based on determining that the SR is present in the at least one signal received from the UE, transmit an uplink grant to the UE, the uplink grant indicating at least one transmission resource associated with an uplink channel; and determine whether the SR was correctly detected based on: if a signal is received from the UE via the at least one transmission resource associated with the uplink channel and decoded and the signal comprises a BSR indicating that the UE has data to send, detect that the SR was correctly detected; if a signal is received from the UE via the at least one transmission resource associated with the uplink channel and decoded and the signal comprises a BSR indicating that the UE does not have data to send, detect that the SR was incorrectly detected; if a signal from the UE cannot be decoded and an estimated SINR value is below a SINR threshold, detect that the SR was incorrectly detected; or if a signal from the UE cannot be decoded and an estimated SINR value associated with the signal is above a SINR threshold, determine that it is unknown whether the SR was correctly detected.

40. The network node of any one of Claims 35 to 39, wherein when dynamically adjusting the at least one of the SINR threshold and the similarity threshold based on the at least one network condition the network node is adapted to: increase the SINR threshold and/or the similarity threshold when the at least one SR is correctly detected; or decrease the SINR threshold and/or the similarity threshold when the at least one SR is incorrectly detected.

41. The network node of any one of Claims 23 to 40, wherein when using the at least one of feature data and target data to train the neural network the network node is adapted to: obtain the at least one of the feature data and target data during a first time period when traffic volume is greater than a first threshold; and train the neural network based on the at least one of the feature data and the target data during a second time period when traffic volume is lower than a second threshold.

42. The network node of Claim 41, wherein when obtaining the target data, the network node is adapted to: based on determining that the SR is present in the at least one signal received from the UE, transmit an uplink grant to the UE, the uplink grant indicating at least one transmission resource associated with an uplink channel; determine whether to use the target data to train the neural network based on: if a signal is received from the UE via the at least one transmission resource associated with the uplink channel and decoded and the signal comprises a BSR indicating that the UE has data to send, detect that the SR was correctly detected and determine to use the target data to train the neural network; if a signal is received from the UE via the at least one transmission resource associated with the uplink channel and decoded and the signal comprises a BSR indicating that the UE does not have data to send, detect that the SR was incorrectly detected and determine to use the target data to train the neural network; if a signal from the UE cannot be decoded and an estimated SINR value is below a SINR threshold, detect that the SR was incorrectly detected and determine to use the target data to train the neural network; or if a signal from the UE cannot be decoded and an estimated SINR value associated with the signal is above a SINR threshold, determine that it is unknown whether the SR was correctly detected and determine not to use the target data to train the neural network.

43. The network node of any one of Claims 23 to 42, wherein the network node is adapted to refine the neural network based on additional training data associated with the network node and/or a cell served by the network node.

44. The network node of Claim 43, wherein when refining the neural network based on the additional training data, the network node is adapted to: collect the training data during a first time period when traffic volume is greater than a first threshold; and train the neural network based on the at least one of the feature data and the target data during a second time period when traffic volume is lower than a second threshold.