Patent Description:
Aspects of the present disclosure relate to wireless communications, and more particularly, to techniques for beam management.

In other examples (e.g., in a next generation, a new radio (NR), or <NUM> network), a wireless multiple access communication system may include a number of distributed units (DUs) (e.g., edge units (EUs), edge nodes (ENs), radio heads (RHs), smart radio heads (SRHs), transmission reception points (TRPs), etc.) in communication with a number of central units (CUs) (e.g., central nodes (CNs), access node controllers (ANCs), etc.), where a set of one or more DUs, in communication with a CU, may define an access node (e.g., which may be referred to as a BS, next generation NodeB (gNB or gNodeB), TRP, etc.). A BS or DU may communicate with a set of UEs on downlink channels (e.g., for transmissions from a BS or DU to a UE) and uplink channels (e.g., for transmissions from a UE to a BS or DU).

To these ends, NR supports beam forming, multiple-input multiple-output (MIMO) antenna technology, and carrier aggregation.

<CIT>discusses supporting or performing beam tracking when beamforming is employed in radio communication between the wireless terminal and the network node. <CIT> discusses methods for antenna beam selection in <NUM> radio access networks (RANs) based on machine learning, in particular using distributed cloud-based machine learning.

In accordance with the present invention, there is provided a method and a node for determining one or more beams to utilize for a beam management procedure using adaptive learning as set out in claims <NUM> and <NUM>.

Other aspects of the invention can be found in the dependent claims. Any embodiment referred to and not falling within the scope of the claims is merely an example useful to the understanding of the invention.

Aspects of the present disclosure provide apparatus, methods, processing systems, and computer readable mediums for beam management using adaptive learning.

Certain systems, such as new radio systems (e.g., <NUM> NR), support millimeter wave (mmW) communication. In mmW communication, signals (referred to as mmW signals) used for communicating between devices may have a high carrier frequency (e.g., <NUM> or beyond, such as within a <NUM> to <NUM> frequency band) and may have a wavelength in a <NUM> to <NUM> range. Based on such characteristics of mmW signals, mmW communication can provide high speed (e.g., gigabit speed) communication between devices. However, compared to signals at lower frequencies, mmW signals may experience atmospheric effects and may not propagate well through materials. Thus, mmW signals may experience relatively higher path loss (e.g., attenuation or reduction of power density of the wave corresponding to the mmW signal) as it propagates as compared to lower frequency signals.

In order to overcome path loss, mmW communication systems utilize directional beam forming. Beam forming may involve the use of transmit (TX) beams and/or receive (RX) beams. TX beams correspond to transmitted mmW signals that are directed to have more power in a particular direction as opposed to other directions, such as toward a receiver. By directing the transmitted mmW signals toward a receiver, more energy of the mmW signal is directed to the receiver, thereby overcoming the higher path loss. RX beams correspond to techniques performed at the receiver to apply gain to signals received in a particular direction, while attenuating signals received in other directions. Use of RX beams also helps in overcoming higher path loss, for example, by increasing a signal to noise ratio (SNR) at which the desired mmW signal is received at the receiver. In some aspects, hybrid beam forming (e.g., signal processing in the analog and digital domains) may be used.

Accordingly, in certain aspects, for a particular transmitter to communicate with a particular receiver, the transmitter needs to select a TX beam to use, and the receiver needs to select a RX beam to use. The TX beam and RX beams used for communication is referred to as a beam pairing. In certain aspects, the RX and TX beams of a beam pairing are selected so as to provide sufficient coverage and/or capacity for communication.

In certain aspects, a beam management procedure may be used for selecting (e.g., initial selection, updated selection, refining to narrower beams within previously selected beams, etc.) a beam pairing. As will be discussed in more detail below with respect to <FIG>, a beam management procedure may involve taking measurements of signals using different RX and/or TX beams for reception/transmission and selecting beams for the beam pairing based on the measurements. For example, beams having the highest measured channel or link quality (e.g., throughput, SNR, etc.) among those measured may be selected.

In some cases, as discussed in more detail below with respect to <FIG>, there are a large number of RX and/or TX beams supported at the transmitter and/or the receiver, which may mean there are a large number of measurements that could be performed for the beam management procedure. In addition, the communication environment between a transmitter and receiver may vary at different times, such as due to blockers (e.g., when the user's hand blocks TX/RX beams at a transmitter/receiver, e.g., user equipment (UE), and/or an object blocks the line-of-sight (LOS) path between the transmitter and the receiver), movement and/or rotation of the transmitter/receiver, etc..

To account for such factors, in some cases, a beam management procedure is based on heuristics. A heuristic based beam management procedure attempts to predict realistic deployment scenarios of the transmitter and receiver and typically updates the beam management procedure used by the transmitter and receiver, such as using downloaded software patches, based on issues that are encountered (or expected) over time while the transmitter and receiver communicate. For example, a heuristic based beam management procedure may measure only certain RX and/or TX beams, instead of all of them of the transmitter and receiver, based on parameters of the transmitter and/or receiver.

To further improve beam management procedures, aspects of the present disclosure provide for using adaptive learning as part of a beam management procedure. For example, a UE (and/or a BS), acting as a transmitter and/or receiver, can use an adaptive learning based beam management algorithm that adapts over time based on learning. In particular, the learning may be based on feedback associated with previous beam selections for the UE and/or BS. The feedback may include an indication of the previous beam selections, as well as parameters associated with the previous beam selections. The algorithm can be initially trained based on feedback in a lab setting and then updated (e.g., continuously) using feedback while the UE and/or BS is in deployment. In some examples, the algorithm is a deep reinforcement learning based beam management algorithm that uses machine learning and an artificial neural network to update and apply a predictive model used for beam selection during the beam management procedure. In this manner, the adaptive learning based beam management algorithm learns from the users behaviors (e.g., frequently traversed paths, how the user holds the UE, etc.) and is, therefore, also personalized to the user.

The following description provides examples of using adaptive learning as part of a beam management procedure, and is not limiting of the scope, applicability, or examples set forth in the claims.

For example, the wireless communication network <NUM> may be a new radio system (e.g., a <NUM> NR network). The wireless communication network <NUM> may support mmW communication with beam forming. A node (e.g., a wireless node) in the wireless communication network <NUM>, such as a UE 120a and/or a base station (BS) 110a, may be configured to perform a beam management procedure in order to select a beam pairing for communication with another node. For example, UE 120a and BS 110a can perform a beam management procedure to determine a receive beam of the UE 120a and a transmit beam of the BS 110a as a beam pairing, also referred to as a beam pair link (BPL) to be used for communications (e.g., downlink communications). As will be described in more detail herein, the UE 120a and/or BS 110a may use an adaptive learning based beam management procedure. The UE 120a and/or BS 110a can determine one or more beams to utilize for a beam management procedure using the adaptive learning. As shown in <FIG>, a UE 120a has a beam selection manager <NUM>. The beam selection manager <NUM> may be configured to use an adaptive learning based algorithm to determine/select the beams to use for the beam management procedure, according to one or more aspects described herein. As shown in <FIG>, additionally or alternatively, a BS 110a can have a beam selection manager <NUM>. The beam selection manager <NUM> may be configured to use an adaptive learning algorithm to determine/select the beams to use for the beam management procedure, according to aspects described herein. The UE 120a and/or BS 110a may then perform the beam management procedure using the determined one or more beams.

It should be noted that though certain aspects are described with respect to a beam management procedure being performed by a wireless node, certain aspects of such a beam management procedure may be performed by other types of nodes, such as a node connected by wired connection to a BS.

As illustrated in <FIG>, the wireless communication network <NUM> may include a number of BSs 110a-z (each also individually referred to herein as BS <NUM> or collectively as BSs <NUM>) and other network entities. A BS <NUM> may communicate with UEs 120a-y (each also individually referred to herein as UE <NUM> or collectively as UEs <NUM>) in the wireless communication network <NUM>. Each BS <NUM> may provide communication coverage for a particular geographic area. In some examples, the BSs <NUM> may be interconnected to one another and/or to one or more other BSs or network nodes (not shown) in wireless communication network <NUM> through various types of backhaul interfaces, such as a direct physical connection, a wireless connection, a virtual network, or the like using any suitable transport network.

A network controller <NUM> may be coupled to a set of BSs and provide coordination and control for these BSs.

In some examples, the wireless communication network <NUM> (e.g., a <NUM> NR network) may support mmW communications. As discussed above, such systems using mmW communication may use beam forming to overcome high path-losses and a beam management procedure can be performed to select the beams used for the beam forming.

A BS-beam (e.g., TX or RX) and a UE-beam (e.g., the other of the TX or RX) form a BPL. Both the BS (e.g., BS 110a) and the UE (e.g., UE 120a) may determine (e.g., find/select) at least one adequate beam to form a communication link. For example, on the downlink, the BS 110a uses a transmit beam to transmit and the UE 120a uses a receive beam to receive downlink transmissions. The combination of the transmit beam and the receive beam forms the BPL. The UE 120a and BS 110a establish at least one BPL for the UE 120a to wireless communication network <NUM>. In some examples, multiple BPLs (e.g., a set of BPLs) may be configured for communication between UE 120a and one or more BSs <NUM>. Different BPLs may be used for different purposes, such as for communicating different channels, for communicating with different BSs, and/or as fallback BPLs in case an existing BPL fails.

In some examples, for initial cell acquisition, a UE (e.g., UE 120a) may search for a strongest signal corresponding to a cell associated with a BS (e.g., BS 110a) and the associated UE receive beam and BS transmit beam corresponding to a BPL used to receive/transmit the reference signal. After initial acquisition, the UE 120a may perform new cell detection and measurement. For example, the UE 120a may measure primary synchronization signal (PSS) and secondary synchronization signal (SSS) to detect new cells. As discussed in more detail below with respect to <FIG>, the PSS/SSS may be transmitted by a BS (e.g., BS 110a) in different synchronization signal blocks (SSBs) across one or more synchronization signal (SS) burst sets. The UE 120a can measure the different SSBs, within a SS burst set, to perform a beam management procedure, as discussed further herein.

In <NUM> NR, the beam management procedure for determining of BPLs may be referred to as a P1 procedure. <FIG> illustrates an example P1 procedure <NUM>. A BS <NUM> (e.g., such as the BS 110a) may send a measurement request to a UE <NUM> (e.g., such as the UE 120a) and may subsequently transmit one or more signals (sometimes referred to as the "P1-signal") to the UE <NUM> for measurement. In the P1 procedure <NUM>, the BS <NUM> transmits the signal with beam forming in a different spatial direction (corresponding to a transmit beam <NUM>, <NUM>,. , <NUM>) in each symbol, such that several (e.g., most or all) relevant spatial locations of the cell of the BS <NUM> are reached. In this manner, the BS <NUM> transmits the signal using different transmit beams over time in different directions. In some examples, a SSB is used as the P1-signal. In some examples, channel state information reference signal (CSI-RS), demodulation reference signal (DMRS), or another downlink signal can be used as the P1-signal.

In the P1 procedure <NUM>, to successfully receive at least a symbol of the P1-signal, the UE <NUM> finds (e.g., determines/selects) an appropriate receive beam (<NUM>, <NUM>,. Signals (e.g., SSBs) from multiple BSs can be measured simultaneously for a given signal index (e.g., SSB index) corresponding to a given time period. The UE <NUM> can apply a different receive beam during each occurrence (e.g., each symbol) of the P1-signal. Once the UE <NUM> succeeds in receiving a symbol of the P1-signal, the UE <NUM> and BS <NUM> have discovered a BPL (i.e., the UE RX beam used to receive the P1-signal in the symbol and the BS TX beam used to transmit the P1-signal in the symbol). In some cases, the UE <NUM> does not search all of its possible UE RX beams until it finds best UE RX beam, since this causes additional delay. Instead, the UE <NUM> may select a RX beam once the RX beam is "good enough", for example, having a quality (e.g., SNR) that satisfies a threshold (e.g., predefined threshold). The UE <NUM> may not know which beam the BS <NUM> used to transmit the P1-signal in a symbol; however, the UE <NUM> may report to the BS <NUM> the time at which it observed the signal. For example, the UE <NUM> may report the symbol index in which the P1-signal was successfully received to the BS <NUM>. The BS <NUM> may receive this report and determine which BS TX beam the BS <NUM> used at the indicated time. In some examples, the UE <NUM> measures signal quality of the P1-signal, such as reference signal receive power (RSRP) or another signal quality parameter (e.g., SNR, channel flatness, etc.). The UE <NUM> may report the measured signal quality (e.g., RSRP) to the BS <NUM> together with the symbol index. In some cases, the UE <NUM> may report multiple symbol indices to the BS <NUM>, corresponding to multiple BS TX beams.

As a part of a beam management procedure, the BPL used between a UE <NUM> and BS <NUM> may be refined/changed. For example, the BPL may be refined periodically to adapt to changing channel conditions, for example, due to movement of the UE <NUM> or other objects, fading due to Doppler spread, etc. The UE <NUM> can monitor the quality of a BPL (e.g., a BPL found/selected during the P1 procedure and/or a previously refined BPL) to refine the BPL when the quality drops (e.g., when the BPL quality drops below a threshold or when another BPL has a higher quality). In <NUM> NR, the beam management procedures for beam refinement of BPLs may be referred to as the P2 and P3 procedures to refine the BS-beam and UE-beam, respectively, of an individual BPL.

<FIG> illustrates an example P2 procedure <NUM> and P3 procedure <NUM>. As shown in <FIG>, for the P2 procedure <NUM>, the BS <NUM> transmits symbols of a signal with different BS-beams (e.g., TX beams <NUM>, <NUM>, <NUM>) that are spatially close to the BS-beam of the current BPL. For example, the BS <NUM> transmits the signal in different symbols using neighboring TX beams (e.g., beam sweeps) around the TX beam of the current BPL. As shown in <FIG>, the TX beams used by the BS <NUM> for the P2 procedure <NUM> may be different from the TX beams used by the BS <NUM> for the P1 procedure <NUM>. For example, the TX beams used by the BS <NUM> for the P2 procedure <NUM> may be spaced closer together and/or may be more focused (e.g., narrower) than the TX beams used by the BS <NUM> for the P1 procedure. During the P2 procedure <NUM>, the UE <NUM> keeps its RX beam (e.g., RX beam <NUM>) constant. The UE <NUM> may measure the signal quality (e.g., RSRP) of the signal in the different symbols and indicate the symbol in which the highest signal quality was measured. Based on the indication, the BS <NUM> can determine the strongest (e.g., best, or associated with the highest signal quality) TX beam (i.e., the TX beam used in the indicated symbol). The BPL can be refined accordingly to use the indicated TX beam.

As shown in <FIG>, for the P3 procedure <NUM>, the BS <NUM> maintains a constant TX beam (e.g., the TX beam of the current BPL) and transmits symbols of a signal using the constant TX beam (e.g., TX beam <NUM>). During the P3 procedure <NUM>, the UE <NUM> scans the signal using different RX beams (e.g., RX beams <NUM>, <NUM>, <NUM>) in different symbols. For example, the UE <NUM> may perform a sweep using neighboring RX beams to the RX beam in the current BPL (i.e., the BPL being refined). The UE <NUM> may measure the signal quality (e.g., RSRP) of the signal for each RX beam and identify the strongest UE RX beam. The UE <NUM> may use the identified RX beam for the BPL. The UE <NUM> may report the signal quality to the BS <NUM>.

As discussed above, in some examples, measurement of SSBs may be used for beam management. <FIG> illustrates example SSB locations within an example NR radio frame format <NUM>. As shown in <FIG>, the example <NUM> NR radio frame format <NUM> can include ten <NUM> subframes (subframes with indices <NUM>, <NUM>,. In NR, the basic transmission time interval (TTI) may be referred to as a slot. In NR, a subframe may contain a variable number of slots (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>,. slots) depending on the subcarrier spacing (SCS). NR may support a base SCS of <NUM> and other SCS may be defined with respect to the base SCS (e.g., <NUM>, <NUM>, <NUM>, <NUM>, etc.). In the example shown in <FIG>, the SCS is <NUM>. As shown in <FIG>, the subframe <NUM> (subframe <NUM>) contains <NUM> slots (slots <NUM>, <NUM>,. , <NUM>) with a <NUM> duration. The symbol and slot lengths scale with the subcarrier spacing. Each slot may include a variable number of symbol (e.g., OFDM symbols) periods (e.g., <NUM> or <NUM> symbols) depending on the SCS. For the <NUM> SCS shown in <FIG>, each of the slot <NUM> (slot <NUM>) and slot <NUM> (slot <NUM>) includes <NUM> symbol periods (slots with indices <NUM>, <NUM>,. , <NUM>) with a <NUM> duration.

In some examples, the SSB can be transmitted up to sixty-four times with up to sixty-four different beam directions. The up to sixty-four transmissions of the SSB are referred to as the SS burst set. SSBs in an SS burst set may be transmitted in the same frequency region, while SSBs in different SS bursts sets can be transmitted in different frequency regions. In the example shown in <FIG>, in the subframe <NUM>, SSB is transmitted in each of the slots (slots <NUM>, <NUM>,. In the example shown in <FIG>, in the slot <NUM> (slot <NUM>), an SSB <NUM> is transmitted in the symbols <NUM>, <NUM>, <NUM>, <NUM> and an SSB <NUM> is transmitted in the symbols <NUM>, <NUM>, <NUM>, <NUM>, and in the slot <NUM> (slot <NUM>), an SSB <NUM> is transmitted in the symbols <NUM>, <NUM>, <NUM>, <NUM> and an SSB <NUM> is transmitted in the symbols <NUM>, <NUM>, <NUM>, <NUM>, and so on. The SSB may include a PSS, a SSS, and a two symbol physical broadcast channel (PBCH). The PSS and SSS may be used by UEs for the cell search and acquisition. For example, the PSS may provide half-frame timing, the SSS may provide the control protocol (CP) length and frame timing, and the PSS and SSS may provide the cell identity. The PBCH carries some basic system information, such as downlink system bandwidth, timing information within radio frame, SS burst set periodicity, system frame number, etc..

As shown in <FIG>, the SSBs can be used for measurements using different transmit and receive beams, for example accordingly to a beam management procedure such as the P1 procedure <NUM> shown in <FIG>. <FIG> illustrates an example for a BS <NUM> (e.g., such as the BS 110a) that uses <NUM> TX beams and a UE <NUM> (e.g., such as the UE 120a) that uses <NUM> RX beams. For each SSB, the BS <NUM> uses a different TX beam BS to transmit the SSB. As shown in <FIG>, the UE <NUM> can scan its RX beam <NUM> while the BS <NUM> transmits SSBs <NUM>, <NUM>, <NUM>, <NUM> sweeping its four TX beams <NUM>, <NUM>, <NUM>, <NUM> respectively. A BPL may be identified and used for data communication over a period as discussed. As shown in <FIG>, the BS <NUM> uses the TX beam <NUM> and the UE <NUM> uses the RX beam <NUM> for data communication for a period. The UE <NUM> may then scan its RX beam <NUM> while the BS <NUM> transmits SSBs <NUM>, <NUM> sweeping its TX beams <NUM>, <NUM>, and so on.

As can be seen, as the number of TX/RX beams increases, the number of scans for the UE to scan each of its RX beams over each TX beam can become large. Power consumption may scale linearly with the number of measured SSBs. Thus, the time and power overhead associated with beam management may become large if all beams are actually scanned.

Thus, aspects of the present disclosure provide techniques to assist a node when performing measurements of other nodes when using beam forming, for example by using adaptive learning, that may reduce the number of measurements used for a beam management procedure, and thus reduce power consumption.

A non-adaptive algorithm is deterministic as a function of its inputs. If the algorithm is faced with exactly the same inputs at different times, then its outputs will be exactly the same. An adaptive algorithm is one that changes its behavior based on its past experience. This means that different devices using the adaptive algorithm may end up with different algorithms as time passes.

According to certain aspects, beam management procedures may be performed using an adaptive learning-based beam management algorithm. Thus, over the time, the beam algorithm changes (e.g., adapts, updates) based on new learning. The beam management procedure may be used for initial acquisition, cell discovery after initial acquisition, and/or determining BPLs for strongest cells detected by a UE. For example, the adaptive learning can be used to build a UE codebook, the UE codebook indicating beams to use (e.g., measure) for the beam management procedure. In some examples, the adaptive-learning may be used to select UE receive beams to use for discovering BPLs. The adaptive learning may be used to intelligently select which UE receive beams to use to measure signals, based on training and experience, such that fewer beams may be measured, while still finding a suitable BPL (e.g., that satisfies a threshold signal quality).

In some examples, the adaptive learning-based beam management involves training a model, such as a predictive model. The model may be used during the beam management procedure to select which UE receive beams to use to measure signals. The model may be trained based on training data (e.g., training information), which may include feedback, such as feedback associated with the beam management procedure. <FIG> illustrates an example networked environment <NUM> in which a predictive model <NUM> is used for beam management, according with certain aspects of the present disclosure.

As shown in <FIG>, networked environment <NUM> includes a node <NUM>, a training system <NUM>, and a training repository <NUM>, communicatively connected via network <NUM>. The node <NUM> may be a UE (e.g., such as the UE 120a in the wireless communication network <NUM>) or a BS (e.g., such as the BS 110a in the wireless communication network <NUM>). The network <NUM> may be a wireless network such as the wireless communication network <NUM>, which may be a <NUM> NR network. While the training system <NUM>, node <NUM>, and training repository <NUM> are illustrated as separate components in <FIG>, it should be recognized by one of ordinary skill in the art that training system <NUM>, node <NUM>, and training repository <NUM> may be implemented on any number of computing systems, either as one or more standalone systems or in a distributed environment.

The training system <NUM> generally includes a predictive model training manager <NUM> that uses training data to generate a predictive model <NUM> for beam management. The predictive model <NUM> may be determined based on the information in the training repository <NUM>.

The training repository <NUM> may include training data obtained before and/or after deployment of the node <NUM>. The node <NUM> may be trained in a simulated communication environment (e.g., in field testing, drive testing) prior to deployment of the node <NUM>. For example, various beam management procedures (e.g., various selections of UE RX beams for measuring signals) can be tested in various scenarios, such as at different UE speeds, with the UE stationary, at various rotations of the UE, with various BS deployments/geometries, etc., to obtain training information related to the beam management procedure. This information can be stored in the training repository <NUM>. After deployment, the training repository <NUM> can be updated to include feedback associated with beam management procedures performed by the node <NUM>. The training repository can also be updated with information from other BSs and/or other UEs, for example, based on learned experience by those BSs and UEs, which may be associated with beam management procedures performed by those BSs and/or UEs.

The predictive model training manager <NUM> may use the information in the training repository <NUM> to determine the predictive model <NUM> (e.g., algorithm) used for beam management, such as to select UE RX beams for measuring signals. As discussed in more detail herein, the predictive model training manager <NUM> may use various different types of adaptive learning to form the predictive model <NUM>, such as machine learning, deep learning, reinforcement learning, etc. The training system <NUM> may adapt (e.g., update/refine) the predictive model <NUM> over time. For example, as the training repository is updated with new training information (e.g., feedback), the model <NUM> is updated based on the new learning/experience.

The training system <NUM> may be located on the node <NUM>, on a BS in the network <NUM>, or on a different entity that determines the predictive model <NUM>. If located on a different entity, then the predictive model <NUM> is provided to the node <NUM>.

The training repository <NUM> may be a storage device, such as a memory. The training repository <NUM> may be located on the node <NUM>, the training system <NUM>, or another entity in the network <NUM>. The training repository <NUM> may be in cloud storage. The training repository <NUM> may receive training information from the node <NUM>, entities in the network <NUM> (e.g., BSs or UEs in the network <NUM>), the cloud, or other sources.

As described above, the node <NUM> is provided with (or generates, e.g., if the training system <NUM> is implemented in the node <NUM>) the predictive model <NUM>. As illustrated, the node <NUM> may include a beam selection manager <NUM> configured to use the predictive model <NUM> for beam management (e.g., such as one of the beam management procedures discussed above with respect to <FIG>). In some examples, the node <NUM> utilizes the predictive model <NUM> to build a UE codebook and/or to determine/select beams from the UE codebook to use for a beam management procedure. The predictive model <NUM> is updated as the training system <NUM> adapts the predictive model <NUM> with new learning.

Thus, the beam management algorithm, using the predictive model <NUM>, of the node <NUM> is adaptive learning-based, as the algorithm used by the node <NUM> changes over time, even after deployment, based on experience/feedback the node <NUM> obtains in deployment scenarios (and/or with training information provided by other entities as well).

According to certain aspects, the adaptive learning may use any appropriate learning algorithm. As mentioned above, the learning algorithm may be used by a training system (e.g., such as the training system <NUM>) to train a predictive model (e.g., such as the predictive model <NUM>) for an adaptive-learning based beam management algorithm used by a device (e.g., such as the node <NUM>) for a beam management procedure. In some examples, the adaptive learning algorithm is an adaptive machine learning algorithm, an adaptive reinforcement learning algorithm, an adaptive deep learning algorithm, an adaptive continuous infinite learning algorithm, or an adaptive policy optimization reinforcement learning algorithm (e.g., a proximal policy optimization (PPO) algorithm, a policy gradient, a trust region policy optimization (TRPO) algorithm, or the like). In some examples, the adaptive learning algorithm is modeled as a partially observable Markov Decision Process (POMDP). In some examples, the adaptive learning algorithm is implemented by an artificial neural network (e.g., a deep Q network (DQN) including one or more deep neural networks (DNNs)).

In some examples, the adaptive learning (e.g., used by the training system <NUM>) is performed using a neural network. Neural networks may be designed with a variety of connectivity patterns. In feed-forward networks, information is passed from lower to higher layers, with each neuron in a given layer communicating to neurons in higher layers. A hierarchical representation may be built up in successive layers of a feed-forward network. Neural networks may also have recurrent or feedback (also called top-down) connections. In a recurrent connection, the output from a neuron in a given layer may be communicated to another neuron in the same layer. A recurrent architecture may be helpful in recognizing patterns that span more than one of the input data chunks that are delivered to the neural network in a sequence. A connection from a neuron in a given layer to a neuron in a lower layer is called a feedback (or top-down) connection. A network with many feedback connections may be helpful when the recognition of a high-level concept may aid in discriminating the particular low-level features of an input.

In some examples, the adaptive learning (e.g., used by the training system <NUM>) is performed using a deep belief network (DBN). DBNs are probabilistic models comprising multiple layers of hidden nodes. DBNs may be used to extract a hierarchical representation of training data sets. A DBN may be obtained by stacking up layers of Restricted Boltzmann Machines (RBMs). An RBM is a type of artificial neural network that can learn a probability distribution over a set of inputs. Because RBMs can learn a probability distribution in the absence of information about the class to which each input could be categorized, RBMs are often used in unsupervised learning. Using a hybrid unsupervised and supervised paradigm, the bottom RBMs of a DBN may be trained in an unsupervised manner and may serve as feature extractors, and the top RBM may be trained in a supervised manner (on a joint distribution of inputs from the previous layer and target classes) and may serve as a classifier.

In some examples, the adaptive learning (e.g., used by the training system <NUM>) is performed using a deep convolutional network (DCN). DCNs are networks of convolutional networks, configured with additional pooling and normalization layers. DCNs have achieved state-of-the-art performance on many tasks. DCNs can be trained using supervised learning in which both the input and output targets are known for many exemplars and are used to modify the weights of the network by use of gradient descent methods. DCNs may be feed-forward networks. In addition, as described above, the connections from a neuron in a first layer of a DCN to a group of neurons in the next higher layer are shared across the neurons in the first layer. The feed-forward and shared connections of DCNs may be exploited for fast processing. The computational burden of a DCN may be much less, for example, than that of a similarly sized neural network that comprises recurrent or feedback connections.

An artificial neural network, which may be composed of an interconnected group of artificial neurons (e.g., neuron models), is a computational device or represents a method performed by a computational device. These neural networks may be used for various applications and/or devices, such as Internet Protocol (IP) cameras, Internet of Things (IoT) devices, autonomous vehicles, and/or service robots. Individual nodes in the artificial neural network may emulate biological neurons by taking input data and performing simple operations on the data. The results of the simple operations performed on the input data are selectively passed on to other neurons. Weight values are associated with each vector and node in the network, and these values constrain how input data is related to output data. For example, the input data of each node may be multiplied by a corresponding weight value, and the products may be summed. The sum of the products may be adjusted by an optional bias, and an activation function may be applied to the result, yielding the node's output signal or "output activation. " The weight values may initially be determined by an iterative flow of training data through the network (e.g., weight values are established during a training phase in which the network learns how to identify particular classes by their typical input data characteristics).

Different types of artificial neural networks can be used to implement adaptive learning (e.g., used by the training system <NUM>), such as recurrent neural networks (RNNs), multilayer perceptron (MLP) neural networks, convolutional neural networks (CNNs), and the like. RNNs work on the principle of saving the output of a layer and feeding this output back to the input to help in predicting an outcome of the layer. In MLP neural networks, data may be fed into an input layer, and one or more hidden layers provide levels of abstraction to the data. Predictions may then be made on an output layer based on the abstracted data. MLPs may be particularly suitable for classification prediction problems where inputs are assigned a class or label. Convolutional neural networks (CNNs) are a type of feed-forward artificial neural network. Convolutional neural networks may include collections of artificial neurons that each has a receptive field (e.g., a spatially localized region of an input space) and that collectively tile an input space. Convolutional neural networks have numerous applications. In particular, CNNs have broadly been used in the area of pattern recognition and classification. In layered neural network architectures, the output of a first layer of artificial neurons becomes an input to a second layer of artificial neurons, the output of a second layer of artificial neurons becomes an input to a third layer of artificial neurons, and so on. Convolutional neural networks may be trained to recognize a hierarchy of features. Computation in convolutional neural network architectures may be distributed over a population of processing nodes, which may be configured in one or more computational chains. These multi-layered architectures may be trained one layer at a time and may be fine-tuned using back propagation.

In some examples, when using an adaptive machine learning algorithm, the training system <NUM> generates vectors from the information in the training repository <NUM>. In some examples, the training repository <NUM> stores vectors. In some examples, the vectors map one or more features to a label. For example, the features may correspond to various deployment scenario patterns discussed herein, such as the UE mobility, speed, rotation, channel conditions, BS deployment/geometry in the network, etc. The label may correspond to the predicted optimal beam selection (e.g., of RX beams) associated with the features for performing a beam management procedure. The predictive model training manager <NUM> may use the vectors to train the predictive model <NUM> for the node <NUM>. As discussed above, the vectors may be associated with weights in the adaptive learning algorithm. As the learning algorithm adapts (e.g., updates), the weights applied to the vectors can also be changed. Thus, when the beam management procedure is performed again, under the same features (e.g., under the same set of conditions), the model may give the node <NUM> a different result (e.g., a different beam selection).

<FIG> conceptually illustrates an example reinforcement learning model. Reinforcement learning may be a semi-supervised learning model in machine learning. Reinforcement learning allows an agent <NUM> (e.g., node <NUM> and/or training system <NUM>) to take actions (e.g., beam selection) based on states (e.g., RSPRs of SSBs using different beams) observed by an interpreter <NUM> (e.g., such as the node <NUM>) and interact with an environment <NUM> (e.g., the current deployment scenario) so as to maximize the total rewards (e.g., physical downlink shared channel (PDSCH) throughput using selected beams) which may be observed by the interpreter <NUM> and fed back to the agent <NUM> as reinforcement. In some examples, the agent <NUM> and interpreter <NUM> may be implemented as the same or separate components device that may perform various functions of the node <NUM>, training system <NUM>, and/or training repository <NUM>.

In some examples, reinforcement learning is modeled as a Markov Decision Process (MDP). A MDP is a discrete, time stochastic, control process. The MDP provides a mathematical framework for modeling decision making in situations where outcomes may be partly random and partly under the control of a decision maker. In MDP, at each time step, the process is in a state, of a set of S finite states, and the decision maker may choose any action, of a finite set of actions A, that is available in that state. The process responds at the next time step by randomly moving into a new state, and giving the decision maker a corresponding reward, where Ra(s,s') is the immediate reward (or expected immediate reward) after transitioning from state s to state s'. The probability that the process moves into its new state is influenced by the chosen action, for example, according to a state transition function. The state transition may be given by Pa(s,s') = Pr (st+<NUM> = s' |st = s, αt = α).

A MDP seeks to find a policy for the decision: a function of π that specifies the action π(s) that the decision maker will choose when in state s. The goal is to choose a policy π that maximizes the rewards. For example, a policy that maximizes a cumulative function of the rewards, such as a discounted summation. The following shows an example function: <MAT> where
αt = π(st), the action given by the policy, and γ is the discount factor and satisfies <NUM> ≤ γ ≤ <NUM>.

The solution for the MDP is a policy which describes the best action for each state in the MDP, for example that maximizes the expected discounted reward.

In some examples, a partially observable MDP is used (POMDP). POMDP may be used when the state may not be known when the action is taken, and, therefore, the probabilities and/or rewards may be unknown. For POMDP, reinforcement learning may be used. The following function may be defined: <MAT>.

Experience during learning may be based on (s,a) pairs together with the outcome s'. For example, if the node was previously in a state s, and made a beam selection a, and achieved a throughput s'. In this example, the node may update the array Q directly based on the learned experience. This may be referred to as Q-learning. In some examples, the learning algorithm may be continuous.

In some examples, for the adaptive learning-based beam management algorithm, the state may correspond to the M strongest beam quality measurements (e.g., reference signal received power (RSRP) of SSBs on different beams) in the environment (e.g., a current deployment scenario of a UE), which include the conditions discussed herein including UE mobility, BS deployment pattern (e.g., geometry), blockers, etc. The action may correspond to the beam selection. The reward may be the throughput achieved using the beam selection, such a PDSCH throughput. The reward could be another parameter, such as spectral efficiency for example. Thus, using such an MDP, at a given time, in a given state, the node can employ the algorithm to find the policy that specifies the beam selection to maximize the throughput. As discussed above, the reward may be discounted. For beam management, the reward may be offset by some penalty as a function of measured SSBs, for example, to optimize for minimum power.

Referring back to the example networked environment <NUM> in <FIG> and reinforcement learning model <NUM> in <FIG>, in some examples, the predictive model training manager <NUM> or agent <NUM> may use reinforcement learning for a predictive model (e.g., the predictive model <NUM>) to determine the policy (e.g., the MDP solution). The node <NUM> or agent <NUM> may take an action, such as a beam selection for a beam management procedure, based on the policy given by the predictive model (e.g., predictive model <NUM>) for a current state (e.g., observed by node <NUM> or interpreter <NUM>), at a given time, in the environment (e.g., environment <NUM>). The reinforcement learning algorithm and predictive model may be updated/adapted based on learned experience (e.g., which may be stored in the training repository <NUM>).

The framework of reinforcement learning provides the tools to optimally solve the POMDP. The learning changes the weights of the multi-level perceptron (e.g., the neural net) that decides on the next action to take. The algorithm in deep ML is encoded in the neural net weights. Thus, changing the weights changes the algorithm.

In some examples, the adaptive learning-based beam management uses an adaptive deep learning algorithm. The adaptive deep learning algorithm may be a deep Q network (DQN) algorithm implemented by a neural network. <FIG> conceptually illustrates an example DQN learning model <NUM>, in accordance with certain aspects of the present disclosure. As shown in <FIG>, an agent <NUM> (e.g., such as the agent <NUM> or node <NUM>) includes an artificial neural network, for example, such as a deep neural network (DNN) <NUM> as shown in the example in <FIG>. For a current environment <NUM> (e.g., such as the environment <NUM>), which may be a real deployment scenario involving a UE (e.g., UE 120a) and a BS (e.g., BS 110a) and various conditions as described herein, the agent <NUM> observes a state <NUM> (s). For example, the observed state may be the M strongest RSRPs corresponding to measured SSBs using different beams for a beam management procedure.

In some examples, the adaptive learning algorithm is modeled as a POMDP with reinforcement learning. A POMDP can be used when the state may not be known when the action is taken, and, therefore, the probabilities and/or rewards may be unknown. For POMDP, reinforcement learning may be used. The Q array may be defined as: <MAT>.

As shown in <FIG>, for a given state <NUM> s (e.g., the RSRPs) and possible actions a, are input to the DNN <NUM>, which can perform the algorithm to output a value (e.g., parameter θ) per possible action a, to determine the policy (e.g., πθ (s,a)) based on a maximal value. The policy and corresponding action is taken and applied to the environment. For example, the agent <NUM> makes a beam selection and then uses the selected beams in the environment <NUM>. As shown in <FIG>, the reward for the action is fed back to the agent <NUM> to update the algorithm. For example, the throughput achieved with the selected beams may be fed back. Based on the feedback, the agent <NUM> updates the DNN <NUM> (e.g., by changing weights associated with vectors).

According to certain aspects, the adaptive learning based-beam management allows for continuous infinite learning. In some examples, the learning may be augmented with federated learning. For example, while some machine learning approaches use a centralized training data on a single machine or in a data center; with federated learning, the learning may be collaborative involving multiple devices to form the predictive model. With federated learning, training of the model can be done on the device, with collaborative learning from multiple devices. For example, referring back to <FIG>, the node <NUM>, agent <NUM>, and agent <NUM>, can receive training information and/or updated trained models, from various different devices.

In an illustrative example, multiple different UEs' beam management algorithm can be trained in multiple different scenarios of operation, for example, using deep reinforcement learning. The output of the training from the different UEs can be combined to train the beam management algorithm for the UEs. Once the beam management algorithm is trained, the algorithm may continue learning based on actual deployment scenarios. As discussed above, the state may be the best M RSRP measurements at the current time; the reward may be the measured PDSCH throughput for the current best beam pair; and the action may be the selection of which beam pair/pairs to measure.

According to certain aspects, the adaptive learning based-beam management allows for personalization to the user and for design robustness. In some examples, the adaptive learning based-beam management may be optimized. For example, as the user (e.g., such as the node <NUM>) visits/traverses a path (e.g., an environment) the adaptive algorithm learns and optimizes to that environment. Also, different BS vendors can have a different beam management implementation, such as how the SSBs are transmitted. For example, some BS vendors transmit many narrow TX beams, which will serve as the data beams as well; and other vendors transmit a few wide beams and use beam refinement (e.g., P2 and/or P3 procedure) to narrow and track data beams. In some examples, the adaptive learning based-beam management may be optimized to the particular beam management implementation for a vendor. In some examples, the adaptive learning based-beam management may be optimized to the user, for example, the way the user holds/uses the UE affects the possible blockage of its beams.

<FIG> is a flow diagram illustrating example operations <NUM> for wireless communication, in accordance with certain aspects of the present disclosure. The operations <NUM> may be performed, for example, by a node (e.g., such as the node <NUM>, which may be a BS 110a or a UE 120a in the wireless communication network <NUM> which may be wireless nodes). Operations <NUM> may be implemented as software components that are executed and run on one or more processors (e.g., controller/processor <NUM>, <NUM> of <FIG>). Further, the transmission and reception of signals by the node in operations <NUM> may be enabled, for example, by one or more antennas (e.g., antennas <NUM>, <NUM> of <FIG>). In certain aspects, the transmission and/or reception of signals by the node may be implemented via a bus interface of one or more processors (e.g., controller/processor <NUM>, <NUM>) obtaining and/or outputting signals.

The operations <NUM> may begin, at <NUM>, by determining one or more beams to utilize for a beam management procedure using adaptive learning.

At <NUM>, the node performs the beam management procedure using the determined one or more beams.

According to certain aspects, the adaptive learning uses an adaptive learning algorithm. The adaptive learning algorithm may be updated (e.g., adapted) based on feedback and/or training information. The node may perform another beam management procedure using the updated adaptive learning algorithm. The feedback may be feedback associated with the beam management procedure. For example, after performing the beam management procedure using the determined one or more beams, the node may receive feedback regarding a throughput achieved, and the beam management algorithm may be updated based on the feedback. In some examples, the feedback may be associated with a beam management performed by a different device, such as a different node.

<FIG> is an example call flow diagram illustrating example signaling <NUM> for beam management using adaptive learning, in accordance with certain aspects of the present disclosure. As shown in <FIG>, a UE <NUM> (e.g., such as the UE 120a) may have an initial learning algorithm (e.g., including a predictive model) at <NUM>. In some examples, the UE <NUM> may train the initial learning algorithm or the learning algorithm may be trained and then provided to the UE <NUM>. At <NUM>, the UE <NUM> performs a beam management procedure (e.g., such as the P1 procedure <NUM>) with one or more BSs <NUM>. For example, the UE <NUM> may determine beams to use and/or measure using the adaptive learning algorithm. At <NUM>, the UE <NUM> receives additional training information and/or feedback. For example, the UE <NUM> may receive feedback from the BS <NUM> (e.g., such as the BS 110a) regarding the beam management procedure performed at <NUM>, such as PDSCH throughput achieved using the selected beams. Additionally or alternatively, the UE <NUM> may receive additional training information from the BS <NUM> and/or another UE <NUM>. At <NUM>, the UE <NUM> determines an updated adaptive learning algorithm based on the additional training information and/or feedback. At <NUM>, the UE <NUM> can perform another beam management with the BS <NUM> (or another BS) with the updated adaptive learning algorithm.

In some examples, the training information (and/or feedback) includes training information obtained from deploying one or more UEs in one or more simulated communication environments prior to network deployment of the one or more UEs; training information obtained by feedback previously received while the one or more UEs were deployed in one or more communication environments (e.g., based on measurements and/or a beam management procedure performed by the UE); training information from the network, one or more UEs, and/or a cloud; and/or training information received while the node was online and/or idle.

In some examples, using the adaptive learning algorithm, at <NUM>, includes the node outputting an action on based on one or more inputs; where the feedback is associated with the action; and updating the adaptive learning algorithm based on the feedback includes adjusting one or more weights applied to the one or more inputs.

In some examples, the adaptive learning algorithm used by the node, at <NUM>, may be an adaptive machine learning algorithm; an adaptive reinforcement learning algorithm; an adaptive deep learning algorithm; an adaptive continuous infinite learning algorithm; and/or an adaptive policy optimization reinforcement learning algorithm. As discussed above with respect to <FIG>, the adaptive learning algorithm may be modeled as a POMDP. The adaptive learning algorithm may be implemented by an artificial neural network. In some examples, the artificial neural network may be a DQN including one or more DNNs. Determining the one or more beams using the adaptive learning may include passing state parameters and action parameters through the one or more DNNs; for each state parameter, outputting a value for each action parameter; and selecting an action associated with a maximum output value. Updating the adaptive learning algorithm may include adjusting one or more weights associated with one or more neuron connections in the artificial neural networks.

In some examples, determining the one or more beams to utilize for the beam management procedure using the adaptive learning, at <NUM>, includes determining one or more beams to include in a codebook based on the adaptive learning; and selecting one or more beams from the codebook to utilize for the beam management procedure.

In some examples, determining the one or more beams to utilize for the beam management procedure using the adaptive learning, at <NUM>, includes using the adaptive learning to select one or more beams from a codebook to utilize for the beam management procedure.

In some examples, the adaptive learning is used to select BPLs.

In some examples, the adaptive learning uses a state parameter associated with a channel measurement, a reward parameter associated with a received signal throughput or spectral efficiency, and an action parameter associated with selection of a beam pair corresponding to the channel measurement. In some examples, the channel measurement includes RSRP; spectral efficiency, channel flatness, and/or signal-to-noise ratio (SNR). In some examples, the received signal is a PDSCH transmission.

In some examples, the reward parameter is offset by a penalty amount. In some examples, the penalty amount is dependent on a number of beams measured for the beam management procedure (e.g., beams used for transmission and/or reception of SSBs). In some examples, the penalty amount is dependent on an amount of power consumption associated with the beam management procedure.

According to the claimed invention, performing the beam management procedure using the determined one or more beams, at <NUM>, includes measuring a channel based on SSB transmissions from a BS using the one or more determined beams, the SSB transmissions associated with a plurality of different transmit beams of the BS; and selecting one or more BPLs associated with channel measurements that are channel measurements above a channel measurement threshold and/or are one or more strongest channel measurements among all channel measurements associated with the measured SSB transmissions. The one or more determined beams are a subset of available receive beams. In some examples, the node receives a PDSCH using one of the one or more selected BPLs; determines a throughput associated with the PDSCH; updates the adaptive learning algorithm based on the determined throughput; and uses the updated adaptive learning algorithm to determine another one or more beams to utilize for performing another beam management procedure to select another one or more BPLs.

<FIG> is an example call flow diagram according to the claimed invention illustrating example signaling <NUM> for a BPL discovery procedure (e.g., such as the P1 procedure <NUM>) using adaptive learning, in accordance with certain aspects of the present disclosure. As shown in <FIG>, at <NUM>, UE <NUM> (e.g., such as the UE 120a) may perform initial training in a simulated environment before deployment at <NUM>. The initial training at <NUM> may train an initial learning algorithm (e.g., including a predictive model) at the UE <NUM>. At <NUM> the UE <NUM> may be deployed in a network with at least one BS <NUM> (e.g., such as the BS 110a). The UE <NUM> may perform a beam management procedure (e.g., such as a P1 procedure <NUM>) in the network with one or more BSs <NUM>. For example, as shown in <FIG>, at <NUM>, the UE <NUM> may select beams, or RX/TX beam pairs, using the adaptive learning algorithm. At <NUM>, the UE <NUM> measures SSB transmission(s) received, at <NUM>, from the BS <NUM>, using the beam(s) selected at <NUM>. At <NUM>, the UE <NUM> reports the measurements and/or BPL selection(s) to the BS <NUM>. Then, at <NUM>, the BS <NUM> transmits PDSCH to the UE <NUM> using the BPL indicated by the UE <NUM> (or selected based on the measurements reported by the UE <NUM>). At <NUM>, the UE <NUM> may determine the PDSCH throughput. The PDSCH throughput may act as feedback, or reinforcement, for the adaptive learning. At <NUM>, the UE <NUM> updates the adaptive learning algorithm based on the feedback. Optionally, the UE <NUM> may receive additional training information and/or feedback from the BS <NUM> and/or another UE <NUM> (e.g., UE <NUM>), that the UE <NUM> may use to update the adaptive learning algorithm at <NUM>. The UE <NUM> may then perform another beam management with the BS <NUM> (or another BS) with the updated adaptive learning algorithm.

The processing system <NUM> includes a processor <NUM> coupled to a computer-readable medium/memory <NUM> via a bus <NUM>. In certain aspects, the computer-readable medium/memory <NUM> is configured to store instructions (e.g., computer-executable code) that when executed by the processor <NUM>, cause the processor <NUM> to perform the operations illustrated in <FIG>, or other operations for performing the various techniques discussed herein for adaptive learning-based beam management. In certain aspects, computer-readable medium/memory <NUM> stores code <NUM> for determining one or more beams to utilize for a beam management procedure using adaptive learning; and code <NUM> performing the beam management procedure using the determined one or more beams. In certain aspects, the processor <NUM> has circuitry configured to implement the code stored in the computer-readable medium/memory <NUM>. The processor <NUM> includes circuitry <NUM> for determining one or more beams to utilize for a beam management procedure using adaptive learning; and circuitry <NUM> for performing the beam management procedure using the determined one or more beams.

In some examples, communications device <NUM> may include a system-on-a-chip (SOC) (not shown), which may include a central processing unit (CPU) or a multicore CPU configured to perform adaptive learning-based beam management, in accordance with certain aspects of the present disclosure. Variables (e.g., neural signals and synaptic weights), system parameters associated with a computational device (e.g., neural network with weights), delays, frequency bin information, and task information may be stored in a memory block associated with a neural processing unit (NPU), in a memory block associated with a CPU, in a memory block associated with a digital signal processor (DSP), in a different memory block, or may be distributed across multiple memory blocks. Instructions executed at the CPU may be loaded from a program memory associated with the CPU or may be loaded from a different memory block.

In some examples, the adaptive learning-based beam management described herein may allow for a P1 procedure to be improved by adaptively updating the beam management algorithm such that the beam selection may be refined to more intelligently select the beams to measure based on the learning. Thus, the UE may find BPLs while measuring fewer beams.

For example, antennas <NUM>, processors <NUM>, <NUM>, <NUM>, and/or controller/processor <NUM> of the UE 120a and/or antennas <NUM>, processors <NUM>, <NUM>, <NUM>, and/or controller/processor <NUM> of the BS 110a may be used to perform the various techniques and methods described herein. As shown in <FIG>, the controller/processor <NUM> of the UE 120a has a beam selection manager <NUM> that may be configured for determining beams using adaptive learning, for example to use for a beam management procedure, according to aspects described herein. As shown in <FIG>, additionally or alternatively, the controller/processor <NUM> of the BS 110a can have a beam selection manager <NUM> that may be configured for determining beams using adaptive learning, according to aspects described herein.

At the BS 110a, a transmit processor <NUM> may receive data from a data source <NUM> and control information from a controller/processor <NUM>. The transmit processor <NUM> may also generate reference symbols, such as for the primary synchronization signal (PSS), secondary synchronization signal (SSS), and cell-specific reference signal (CRS). A transmit (TX) multiple-input multiple-output (MIMO) processor <NUM> may perform spatial processing (e.g., precoding) on the data symbols, the control symbols, and/or the reference symbols, if applicable, and may provide output symbol streams to the modulators (MODs) 1232a-1232t. Downlink signals from modulators 1232a-1232t may be transmitted via the antennas 1234a-1234t, respectively.

At the UE 120a, the antennas 1252a-1252r may receive the downlink signals from the BS 110a and may provide received signals to the demodulators (DEMODs) in transceivers 1254a-1254r, respectively. A MIMO detector <NUM> may obtain received symbols from all the demodulators 1254a-1254r, perform MIMO detection on the received symbols if applicable, and provide detected symbols. A receive processor <NUM> may process (e.g., demodulate, deinterleave, and decode) the detected symbols, provide decoded data for the UE 120a to a data sink <NUM>, and provide decoded control information to a controller/processor <NUM>.

On the uplink, at UE 120a, a transmit processor <NUM> may receive and process data (e.g., for the physical uplink shared channel (PUSCH)) from a data source <NUM> and control information (e.g., for the physical uplink control channel (PUCCH) from the controller/processor <NUM>. The symbols from the transmit processor <NUM> may be precoded by a TX MIMO processor <NUM> if applicable, further processed by the demodulators in transceivers 1254a-1254r (e.g., for SC-FDM, etc.), and transmitted to the base station 110a. At the BS 110a, the uplink signals from the UE 120a may be received by the antennas <NUM>, processed by the modulators <NUM>, detected by a MIMO detector <NUM> if applicable, and further processed by a receive processor <NUM> to obtain decoded data and control information sent by the UE 120a.

The controllers/processors <NUM> and <NUM> may direct the operation at the BS 110a and the UE 120a, respectively. The controller/processor <NUM> and/or other processors and modules at the BS 110a may perform or direct the execution of processes for the techniques described herein.

The techniques described herein may be used for various wireless communication technologies, such as 3GPP Long Term Evolution (LTE), LTE-Advanced (LTE-A), code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal frequency division multiple access (OFDMA), single-carrier frequency division multiple access (SC-FDMA), time division synchronous code division multiple access (TD-SCDMA), and other networks. NR access (e.g., <NUM> NR) may support various wireless communication services, such as mmW. Beam forming may be supported and beam direction may be dynamically configured.

In NR systems, the term "cell", BS, next generation NodeB (gNB or gNodeB), access point (AP), distributed unit (DU), carrier, or transmission reception point (TRP) may be used interchangeably.

A node, such as a wireless node, may provide, for example, connectivity for or to a network (e.g., a wide area network such as Internet or a cellular network) via a wired or wireless communication link.

If implemented in hardware, an example hardware configuration may comprise a processing system in a node. In the case of a UE 120a (see <FIG>), a user interface (e.g., keypad, display, mouse, joystick, etc.) may also be connected to the bus.

By way of example, the machine-readable media may include a transmission line, a carrier wave modulated by data, and/or a computer readable storage medium with instructions stored thereon separate from the node, all of which may be accessed by the processor through the bus interface.

For example, such a computer program product may comprise a computer-readable medium storing instructions (and/or encoded) thereon, the instructions being executable by one or more processors to perform the operations described herein, for example, instructions for performing the operations described herein and illustrated in <FIG>.

Claim 1:
A method for wireless communication by a user equipment, UE, (<NUM>), comprising:
determining one or more beams to utilize for a beam management procedure using adaptive learning;
performing the beam management procedure using the determined one or more beams including: measuring a channel based on synchronization signal block (SSB) transmissions from a base station (BS) using the determined one or more beams, the SSB transmissions associated with one or more transmit beams of the BS; selecting one or more beam pair links (BPLs) associated with one or more channel measurements that are above a channel measurement threshold, or one or more strongest channel measurements among all channel measurements associated with the SSB transmissions;
reporting the selected BPLs to the BS;
receiving a physical downlink shared channel (PDSCH) using one of the one or more BPLs;
determining a throughput associated with the PDSCH;
updating the adaptive learning algorithm based on the determined throughput; and
using the updated adaptive learning algorithm to determine another one or more beams to utilize for performing another beam management procedure to select another one or more BPLs.