Sensing aided temporal prediction for radio resource measurement enhancement

Abstract

A base station serving a user equipment (UE) senses environmental parameters around an environment of the UE. The base station then uses an artificial intelligence (AI) model trained on the environmental parameters to predict a future line of sight (LOS) path to non-line of sight (NLOS) path transition based on a predicted future LOS path blockage. The base station may then switch from a first transmit (Tx) beam that is in the LOS path to the UE to a second Tx beam that is in NLOS path to the UE and transmit an indication of the LOS path to NLOS path transition to the UE. Accordingly, the UE may receive the indication and adjust a receive (Rx) beam for NLOS path communication with the base station. Other embodiments for sensing and the AI model trained on environmental parameters instead sited at the UE are also discussed.

Description

SENSING AIDED TEMPORAL PREDICTION FOR RADIO RESOURCEMEASUREMENT ENHANCEMENTTECHNICAL FIELD

[0001] This application relates generally to wireless communication systems, including wireless communication systems implementing artificial intelligence (AI) / machine learning (ML) models.BACKGROUND

[0002] Wireless mobile communication technology uses various standards and protocols to transmit data between a base station and a wireless communication device. Wireless communication system standards and protocols can include, for example, 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) (e.g., 4G), 3GPP New Radio (NR) (e.g., 5G), and Institute of Electrical and Electronics Engineers (IEEE) 802. 11 standard for Wireless Local Area Networks (WLAN) (commonly known to industry groups as Wi-Fi®).

[0003] As contemplated by the 3GPP, different wireless communication systems' standards and protocols can use various radio access networks (RANs) for communicating between a base station of the RAN (which may also sometimes be referred to generally as a RAN node, a network node, or simply a node) and a wireless communication device known as a user equipment (UE). 3GPP RANs can include, for example. Global System for Mobile communications (GSM), Enhanced Data Rates for GSM Evolution (EDGE) RAN (GERAN). Universal Terrestrial Radio Access Network (UTRAN), Evolved Universal Terrestrial Radio Access Network (E-UTRAN), and / or Next-Generation Radio Access Network (NG-RAN).

[0004] Each RAN may use one or more radio access technologies (RATs) to perform communication between the base station and the UE. For example, the GERAN implements GSM and / or EDGE RAT, the UTRAN implements Universal Mobile Telecommunication System (UMTS) RAT or other 3 GPP RAT, the E-UTRAN implements LTE RAT (sometimes simply referred to as LTE). and NG-RAN implements NR RAT (sometimes referred to herein as 5G RAT, 5G NR RAT, or simply NR). In certain deployments, the E-UTRAN may also implement NR RAT. In certain deployments, NG-RAN may also implement LTE RAT.

[0005] A base station used by a RAN may correspond to that RAN. One example of an E-UTRAN base station is an Evolved Universal Terrestrial Radio Access Network (E- UTRAN) Node B (also commonly denoted as evolved Node B, enhanced Node B, eNodeB, or eNB). One example of an NG-RAN base station is a next generation Node B (also sometimes referred to as a g Node B or gNB).

[0006] A RAN provides its communication services with external entities through its connection to a core network (CN). For example, E-UTRAN may utilize an Evolved Packet Core (EPC) while NG-RAN may utilize a 5G Core Network (5GC).

[0007] Frequency bands for 5G NR may be separated into two or more different frequency ranges. For example, Frequency Range 1 (FR1) may include frequency bands operating in sub-6 gigahertz (GHz) frequencies, some of which are bands that may be used by previous standards, and may potentially be extended to cover new spectrum offerings from 510 megahertz (MHz) to 7125 MHz. Frequency Range 2 (FR2) may include frequency bands from 24.25 GHz to 52.6 GHz. Note that in some systems, FR2 may also include frequency bands from 52.6 GHz to 71 GHz (or beyond). Bands in the millimeter wave (mmWave) range of FR2 may have smaller coverage but potentially higher available bandwidth than bands in FR1. Skilled persons will recognize these frequency ranges, which are provided by way of example, may change from time to time or from region to region.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0008] To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.

[0009] FIG. 1 illustrates a flow diagram for the use of an AI / ML model at a UE to identify a best Tx-Rx beam pair between a base station and the UE, along with various corresponding diagrammatic illustrations, according to embodiments provided herein.

[0010] FIG. 2 illustrates an example of an AI / ML model for beam management and tracking.

[0011] FIG. 3 illustrates an example of an AI / ML model used to assist beam management and tracking trained on both the reference signal received power (RSRP) values of probing beams and environmental parameters obtained from sensing an environment, according to embodiments herein.

[0012] FIG. 4 illustrates an example of future line of sight (LOS) path blockage prediction by an AI / ML model trained on environmental parameters obtained from sensing performed at a base station, according to embodiments herein.

[0013] FIG. 5 illustrates an example of future LOS path blockage prediction by an AI / ML model based on the RSRP of probing beams and environmental parameters obtained from sensing performed at the UE, according to embodiments herein.

[0014] FIG. 6 illustrates an example of a predicted LOS path to non-line of sight (NLOS) path transition by an AI / ML model trained on the RSRP values of probing beams and environmental parameters obtained by sensing performed at the UE, due to a blocked LOS path, according to embodiments herein.

[0015] FIG. 7 illustrates a flow diagram for signaling between a UE and a base station of an LOS path to NLOS path transition predicted by an AI / ML model trained on the RSRP values of probing beams and environmental parameters obtained by sensing performed at the UE, along with various corresponding diagrammatic illustrations, according to embodiments discussed herein.

[0016] FIG. 8 illustrates a method of a base station serving a UE, according to embodiments herein.

[0017] FIG. 9 illustrates a method of a UE being served by a base station, according to embodiments herein.

[0018] FIG. 10 illustrates a method of a UE being served by a base station, according to embodiments herein.

[0019] FIG. 11 illustrates a method of a base station serving a UE, according to embodiments herein.

[0020] FIG. 12 illustrates an example architecture of a wireless communication system, according to embodiments disclosed herein.

[0021] FIG. 13 illustrates a system for performing signaling between a wireless device and a network device, according to embodiments disclosed herein.DETAILED DESCRIPTION

[0022] Various embodiments are described with regard to a UE. However, reference to a UE is merely provided for illustrative purposes. The example embodiments may be utilized with any electronic component that may establish a connection to a network and is configured with the hardware, software, and / or firmware to exchange information anddata with the network. Therefore, the UE as described herein is used to represent any appropriate electronic component.

[0023] In various wireless communication systems, overhead penalties corresponding to the transmission of reference signals for radio resource management (RRM) (e.g., synchronization signal blocks (SSBs) and / or channel state information-reference signals (CSI-RSs)) are not insignificant. For example, in cases where an SSB-based RRM measurement timing configuration (SMTC) periodicity7of 20 milliseconds (ms) and an SSB burst of 5 ms are used in FR2 in an NR wireless communication system, a corresponding SMTC-associated overhead is understood as 25%.

[0024] Accordingly, in some cases, it may be beneficial to reduce the number of beams used at the transmit (Tx) side and / or the receive (Rx) side in Layer 3 (L3)-related measurements (e.g.. beam measurements for RRM purposes).

[0025] Embodiments disclosed herein relate enhancements for RRM functionality with AI / ML models. In various embodiments, an L3 measurement delay and / or overhead reduction may be achieved by minimizing a Tx / Rx beam sweeping set through the use of spatial beam prediction by an Al / ML model. For example, an SMTC window duration can be reduced by reducing the beam sweeping factor used by / in the SMTC window (e.g., by reducing the number of Tx and / or Rx beams used by the beam sweeping mechanism of the SMTC window).

[0026] In various embodiments, L3 measurement reduction can be achieved by periodically skipping a Tx / Rx beam sweeping instance and using a spatial beam prediction by an AI / ML model in place of actual measurements. For example, an SMTC periodicity may be increased, representing a conceptual skipping of various instances of SMTC-based measurements that would have otherwise occurred at the lower periodicity. The skipped measurements may be replaced by predictions for those measurements generated at an AI / ML model.

[0027] As a result of such reductions of L3 measurements, throughput can be increased. For example, scheduling restrictions at orthogonal frequency division multiplexing (OFDM) symbols that would have otherwise been used for measurements (e.g., symbols that would have otherwise been used for SSBs to be measured) can be removed.Accordingly, as a result of using an AI / ML model and / or algorithm for RRM functionalities, RRM performance (and therefore overall system performance) can be enhanced.

[0028] Beam and LOS Path Blockage Prediction from Environment Awareness

[0029] In some wireless communication systems (e.g., in mmWave systems), there can be a high overhead for beam training. In some such systems, LOS path, or in other words, LOS link blockages represent a challenge for the reliability and latency of the network as beam alignment and / or selection may depend on. for example, the Tx / Rx locations, the geometry of the surrounding environment as well as the dynamics of the environment (e.g., moving objects in the environment). As a result, acquiring some awareness of the surrounding environment and its dynamics can aid in beam selection as the awareness of the surrounding area may help predict future LOS path blockages. For example, some LOS mmWave links can be blocked by moving objects. As a result, this can cause a sudden performance degradation or cell re-selection. Predicting future blockages enables the serving cell to make proactive decisions such as proactively handing off the user to another cell or proactively switching the user to another beam (i.e., temporal prediction).

[0030] RSRP and Sensing Aided Beam Prediction

[0031] Integrating communication and sensing (ICAS) has been considered for certain wireless communication systems (e.g., sixth generation (6G) systems). For example, one of the applications of ICAS is in the context of sensing-aided communication, where sensory data (e.g., radar sensing) can be leveraged to enhance wireless communication performance (e.g.. beam management and / or L3 measurements). Further, in some wireless communication systems, an AI / ML interface based beam management where an AI / ML model is trained to predict the best beams in a spatial domain and / or a temporal domain based on a reduced set of RSRP measurements (i.e., inputs to the AI / ML model) has been considered.

[0032] Embodiments disclosed herein may enhance the AI / ML model abilities by augmenting the training input dataset with sensing data (e.g., in radar-based scenarios). Thus, AI / ML models can be leveraged to, for example, facilitate sensing-aided beam prediction / tracking and / or enable sensing-aided blockage prediction and proactive handoff.

[0033] Example Subset of Probing Beams and an AI / ML Model at a UE

[0034] FIG. 1 illustrates a flow diagram 100 for the use of an AI / ML model 106 at a UE 104 to identify a best Tx-Rx beam pair between a base station 102 and the UE 104, along with various corresponding diagrammatic illustrations, according to embodimentsprovided herein. As illustrated, the base station 102 may transmit 110 reference signals on one or more probing beams 108 to the UE 104. The one or more probing beams 108 used may be a subset of all Tx beams available / useable at the base station 102 per a selected Tx codebook (may be fewer than all the Tx beams of the selected Tx codebook). The particular subset of the one or more probing beams 108 used from the Tx codebook may be specific to the active serving cell. The transmission of the reference signals on the one or more probing beams 108 may occur one Tx beam at a time, according to a beam sweeping fashion.

[0035] Note that in some embodiments, the base station 102 may also transmit 110 information corresponding to the one or more probing beams 108. For example, the base station 102 may transmit 110 beam directions of the one or more probing beams 108 (e.g., in terms of angle domain information and / or SSB information for each of the one or more probing beams 108). Further, the base station 102 may also transmit 110 an applicable Tx codebook size for the one or more probing beams 108 (e.g., a size of the Tx codebook from which the one or more probing beams 108 are taken / sampled). Beam direction and / or codebook information as provided to the UE may enable the UE to properly measure / interpret the reference signals on the one or more probing beams 108 as received.

[0036] The UE 104 scans 112 through one or more of its own Rx beams with respect to the one or more probing beams 108. In other words, the UE 104 takes reference signal measurements of the reference signals on the one or more probing beams 108 using one or more of its own Rx beams, and stores a measured reference signal strength (e.g., reference signal received power (RSRP)) corresponding to each such measurement.

[0037] The set of one or more Rx beams to be used for these measurements may be selected to correspond to the beam direction and / or codebook information for the one or more probing beams 108 as received from the base station 102. For example, the set of one or more Rx beams may be selected from an Rx codebook that is understood to correspond to a Tx codebook indicated by the base station 102, and / or the selection may be based on indicated beam directions from the base station 102.

[0038] In some cases, the UE 104 uses all available Rx beams at the UE 104 (e.g., all Rx beams of a selected Rx codebook) for this procedure. In other cases, the UE 104 maybe configured to use only a subset of all the available Rx beams at the UE 104.

[0039] As a result of these measurements, the UE 104 obtains measured reference signal strengths 114 for various Tx-Rx beam pairs (with each such measurement corresponding to a unique combination of one of the one or more probing beams 108 and one of the Rx beams used to measure the reference signals on those one or more probing beams 108, as described).

[0040] These measured reference signal strengths 114 for these Tx-Rx beam pairs are then provided to the AI / ML model 106, which is trained to use the measured reference signal strengths 114 to generate predicted receive signal strengths 116 of a larger overall set (e.g., all) of Tx-Rx beam pairs between the Tx beams of the base station and the Rx beams of the UE.

[0041] The predicted receive signal strengths 116 may be understood in terms of a reference signal strength map 118 for the relationship between the base station 102 and the UE 104. FIG. 1 illustrates the reference signal strength map 118 in terms of three dimensions. The X dimension 120 and the Y dimension 122 respectively correspond to horizontal and vertical indexes that together identify applicable Tx beams of the base station 102 to which a predicted reference signal strength applies. The Z dimension 124 then contains multiple X-Y planes of such reference signal strengths, where each individual plane represents reference signal strengths for a different one of the Rx beams of the UE 104 with respect to the Tx beams of the base station 102 (as illustrated). Accordingly, the reference signal strength map 118 is understood to contain predicted reference signal strengths (as predicted by the AI / ML model 106) for the larger overall set (e.g.. all) of Tx-Rx beam pairs between the base station 102 and the UE 104.

[0042] The UE 104 then identifies a number K of predicted best Tx-Rx beam pairs that correspond to the highest reference signal strengths among the Tx-Rx beam pairs represented within the reference signal strength map 118. The value of K may be (e.g., previously) configured to the UE 104 by the base station 102, or may be pre-configured per a specification for the type of wireless communication system of the base station 102 and the UE 104. Example values forX include 2, 4, 8, etc.

[0043] With respect to disclosure herein, such a number K of predicted best Tx-Rx beam pairs may sometimes be more simply referred to as the “top-X beam pairs.'’ Further, when, as in the case of FIG. 1, the UE 104 uses an AI / ML model 106 to generate a reference signal strength map 118, and where the applicable type of referencesignal strength is an RSRP, these predicted reference signal strengths associated with these top-A? beam pairs may be denoted RSRPA1@UEK.

[0044] FIG. 1 illustrates (by way of example) a case where K = 4. Accordingly, as illustrated, four highest reference signal strengths are identified from the reference signal strength map 118, which ultimately makes the UE 104 aware of the Tx beam and the Rx beam for each of the set of top-A? (K = 4) beam pairs (e.g., according to the correspondence of the X dimension 120. the Y dimension 122, and the Z dimension 124 of the reference signal strength map 118 to these Tx-Rx beams, as has been described).

[0045] After identifying the top-Al beam pairs, the UE 104 signals 126 the K predicted best Tx beams of the predicted best Tx-Rx beam pairs (the Tx beams represented in the predicted best Tx-Rx beam pairs) to the base station 102. With respect to disclosure herein, such a number K of predicted best Tx beams may sometimes be more simply referred to as the “top-Al Tx beams.” FIG. 1 accordingly illustrates one example of top-K Tx beams 128 as may be understood at the base station 102 according to the signaling 126 from the UE 104.

[0046] The base station 102 proceeds to transmit 130 reference signals on the top-K Tx beams 128. During these transmissions, the UE 104 performs reference signal measurements of the reference signals using 132 the K predicted best Rx beams of the predicted best Tx-Rx beam pairs (the Rx beams represented in the predicted best Tx-Rx beam pairs). With respect to disclosure herein, such a number A? of predicted best Rx beams may sometimes be more simply referred to as the “top-AT Rx beams.” At this part of the procedure, the UE 104 measures a reference signal on a Tx beam using its correspondingly paired Rx beam (as this pairing is understood per the set of the predicted best Tx-Rx beam pairs).

[0047] The UE 104 then identifies a highest received signal strength from among the received signal strengths from these (actual) reference signal measurements. The corresponding one of the predicted best Tx-Rx beam pairs (the predicted best Tx-Rx beam pair associated with the highest measured received signal strength) is identified by the UE 104 as the Tx-Rx beam pair to use for communications between the base station 102 and the UE 104 going forward. Accordingly, the UE 104 reports 134 the Tx beam of this Tx-Rx beam pair to the base station 102. The base station 102 then indicates 136 back to the UE 104 that it has determined to use this Tx beam for subsequent communication with the UE 104. The UE 104 then correspondingly determines to use138 the Rx beam of this Tx-Rx beam pair for subsequent communication with the base station 102.

[0048] The training of an AI / ML and predicting of the best Tx-Rx beam by the AI / ML model, as discussed in FIG. 1, may be applied to various embodiments with relation to FIG. 2. FIG. 3, FIG. 4, FIG. 5, FIG. 6 and / or FIG. 7.

[0049] RSRP Based Spatial and Temporal AI / ML Beam Prediction

[0050] FIG. 2 illustrates an example of an AI / ML model for beam management and tracking.

[0051] In some wireless communication systems, for spatial and temporal beam prediction and beam tracking, an AI / ML model 206 may be trained on, for example, a subset of RSRP values 202 of one or more probing beams (e.g., Tx beams, Rx beams and / or Tx-Rx beam pairs) sampled in both spatial and temporal domains at a time t-1, and a subset of RSRP values 204 of one or more probing beams (e.g., Tx beams, Rx beams and / or Tx-Rx beam pairs) sampled in both spatial and temporal domains at a time t. The subset of RSRP values 202 values at time t-1 and the subset of RSRP values 204 values at time t correspond to various Tx-Rx beam pairs where Tx beams are represented vertically 218 and Rx beams are represented horizontally 220. In this example, RSRP values for Tx-Rx beam pairs illustrated with stars (e.g., RSRP value 216) are provided as inputs to the AI / ML model 206.

[0052] As shown in FIG. 2, the AI / ML model 206 may predict Tx-Rx beam pairs. For example, the AI / ML mode may predict Tx-Rx beam pairs 208 at time t+M, predict Tx- Rx beam pairs 210, at time t+M+1 and predict Tx-Rx beam pairs 212 at time t+M+2. The AI / ML model 206 may also predict the best k beams 214 of the set of predicted Tx-Rx beam pairs illustrated with circles (e.g., best k beams 214). In some cases, the predicted best K beams change slowly over time as the predicted best K beams change from time t+M to time t+M+1 based on various AI / ML model inputs changing over time, such as the subset of RSRP values 202 of probing beams at time t-1 and the subset of RSRP values 204 of probing beams at time t changing due to environment conditions (e.g., LOS path blockage or other interference). In some examples, the AI / ML model 206 discussed in FIG. 2 may share training details similar to those discussed for the AI / ML model illustrated in FIG. 1.

[0053] Embodiments disclosed herein use an AI / ML model for beam prediction, management and tracking where the AI / ML model uses both environmental parameterimages (e.g.. sensed environmental maps) and radio frequency (RF) received powers (e.g.. RSRP) in order to achieve a higher prediction accuracy as compared to an AI / ML model prediction being trained with RSRP values of probing beams without any further training done to account for environmental factors.

[0054] Further, embodiments disclosed herein discuss an AI / ML model trained with, for example, sensed environmental parameters to predict LOS path to NLOS path transitions and predict possible future blockages of the LOS path between a UE and the base station. For example, a multimodal power prediction of the best K beams may be made by an AI / ML model trained on various inputs such as the RSRP of probing beams and radar images from sensing an environment.

[0055] FIG. 3 illustrates an example of an AI / ML model used to assist beam management and tracking trained on both the RSRP values of probing beams and environmental parameters obtained from sensing an environment, according to embodiments herein.

[0056] In some embodiments, for spatial and temporal beam prediction and beam tracking, an AI / ML model 312 may be trained on, for example, a subset of RSRP values 302 of one or more probing beams sampled in both spatial and temporal domains at a time t-1, a subset of RSRP values 304 of one or more probing beams sampled in both spatial and temporal domains at a time t, and various environmental parameters that are determined through sensing of an environment around a UE. The subset of RSRP values 302 at time t-1 and the subset of RSRP values 304 at time t correspond to various Tx and Rx beams, where Tx beams are represented vertically and Rx beams are represented horizontally. In this example, RSRP values for Tx-Rx beam pairs illustrated with stars are provided as inputs to the AI / ML model 312. The environmental parameters are determined by the sensing of an environment where the UE resides and may include, for example, a range-angle map 306, a range-doppler map 308, and a micro-doppler map 310. This is not an exhaustive list and various other sensed environmental parameters may be used as training inputs in the AI / ML model 312. In addition, the sensing of the environment may be performed by either the base station or the UE and may take the form of various sensing types (e.g., radar-based sensing, camera based sensing, sensor based sensing, light detection and ranging (LIDAR) based sensing, or a combination of the foregoing). In some cases, the AI / ML model 312 may also be referred to as a multi-modal power prediction model as it is trained on both RSRP power of probing beams and sensed environmental parameters.

[0057] The AI / ML model 312 may output a beam prediction of Tx-Rx beams at various points of time. For example, the AI / ML model 312 may predict Tx-Rx beam pairs 314 at time t+M, predict Tx-Rx beam pairs 316 at time t+M+1, and predict Tx-Rx beam pairs 318 at time t+M+2, where t+M refers to various points in time offset from each other by the value of M. The AI / ML model 312 may also predict the best K beams 322 of the predicted Tx-Rx beam pairs illustrated as circles (e.g., best K beams 322).

[0058] In some embodiments, the AI / ML model 312 may predict an LOS path blockage between the network and UE. As a result, a beam switch 320 may occur where the predicted best K beams change from time t+M+1 to time t+M+2 as to not transmit a beam into the predicted LOS path blockage that would fail to reach the UE or, in other words, the UE can be handed off to a different beam that is not in the path of the LOS path blockage. In some such cases, an LOS path blockage may not be predicted but rather is detected and currently occurring thus prompting a similar beam switch 320 as to not transmit a beam into the LOS path blockage.

[0059] Predicting an LOS Path to NLOS Path Transition based on Sensing Performed at a Base Station

[0060] FIG. 4 illustrates an example of a future LOS path 404 blockage prediction by an AI / ML model trained on environmental parameters obtained from sensing performed at a base station, according to embodiments herein.

[0061] Consider an example (illustrated in FIG. 4) where a future or potential LOS path blockage 412 blocking the LOS path 404 between a base station (e g., mmWave base station 416) and a UE 402 may be predicted by an AI / ML model. In such examples, the AI / ML model is at the base station, and is trained on environmental parameters obtained from sensing performed by the base station. In some cases, the sensing may include radar sensing 418 with the environmental parameters being calculated / determined from the corresponding radar reflections 420 off the potential blockage 412.

[0062] In the example illustrated in FIG. 4, a UE 402 is being served by a serving cell (e g., mmWave base station 416), and the potential blockage 412 (e.g., a bus or other vehicle) moving in the direction of the UE 402 or the LOS path 404 is considered as the target for sensing (e.g., radar sensing 418). The base station can sense (e.g., using radar sensing 418) environmental parameters about the potential blockage 412 and furtherabout the environment itself. The sensed environmental parameters can be used as an input to train an AI / ML model with other training inputs (e.g.. Tx probing beams).

[0063] The AI / ML model can then predict the best K beams and predict future or potential LOS path 404 blockages that may impact communication with the UE. In some cases, the AI / ML model may be used to predict an LOS path 404 to NLOS path 406 transition due to a future LOS path 404 blockage. If an LOS path 404 blockage is predicted, beam switching from a first beam 408 to a second Tx beam 410 may occur at the base station, with corresponding Rx beam switching at the UE for a predicted best Tx-Rx beam pair. The base station may switch from transmitting a first beam 408 that is in LOS path 404 to the UE to a second Tx beam 410 that is in NLOS path 406 of the UE after an LOS path 404 to NLOS path 406 transition is predicted by the AI / ML model because the first beam 408 would not be received by the UE due to the LOS path 404 blockage 412.

[0064] In some cases, in NLOS path 406 communication, after the LOS path 404 to NLOS path 406 transition, the second Tx beam 410 may be transmitted to an object in the environment that may help to facilitate NLOS path 406 communication by providing an alternative beam path to the UE 402. The object can, for example, take the form of a reflector 414 that reflects the second Tx beam 410 to the UE 402. The object can, for example, take a form of a repeater that repeats the second Tx beam 410 to the UE. The object can, for example, take the form of a communication cluster wherein the communication cluster can be made up of one or more base stations or objects that help further facilitate NLOS path 406 communication. Additionally, the object that facilitates NLOS path 406 communication may be a combination of the foregoing examples. This is not an exhaustive list but rather used as example and any object that may facilitate NLOS path 406 communication may be used. Such objects for NLOS path 406 communication facilitation are hereby referred to as a “communication cluster’7.

[0065] In the example illustrated in FIG. 4, the base station may already have previous information on a reflector 414 in the environment that may be used for NLOS path 406 communication with the UE, but in some other examples, the base station may use the sensed environmental parameters to determine a reflector 414 that may be used for NLOS path 406 communication with the UE 402.

[0066] Signaling LOS Path to NLOS Path Transition Prediction based on UE Sensing

[0067] FIG. 5 illustrates an example of future LOS path 512 blockage prediction by an AI / ML model based on the RSRP of probing beams and environmental parameters obtained from sensing performed at the UE, according to embodiments herein.

[0068] Consider an example (illustrated in FIG. 5) where a future or potential LOS path 512 blockage (e.g., a sensing target 510) may be predicted by an AI / ML model trained on sensed environmental parameters and probing beams (e.g., the RSRP of the probing beams), at the UE 502, based on sensing performed at the UE 502. In FIG. 5, the UE 502 is being served by a base station (e.g., mmWave base station 508), and a sensing target 510 (e.g., a vehicle) moving towards the UE 502. This sensing target 510 is considered as the target for sensing (e.g., sensing types may include radar sensing, camera sensing, sensor sensing, LIDAR sensing and any other sensing of the sort) where the UE, in some cases, can measure radar reflections 514 that are reflected back to the UE from the sensing target 510 to calculate and / or determine environmental parameters of the environment that the UE resides in, such as the location of the sensing target 510 or the speed and / or velocity 518 of the sensing target 510. In some cases, as illustrated in FIG. 5, the base station’s Tx beam 504 may be transmitted to the UE 502 without issue as there is an active LOS path 512 between the base station and the UE 502. Further, in such cases, the AI / ML model trained on the sensed environmental parameters and probing beams (e.g., the RSRP of the probing beams) does not predict an LOS path 512 blockage in the near future. In such cases, the Tx beam 504 of the base station may be transmitted to the UE 502 through the LOS path 512 link with a UE Rx beam 516 that is in LOS path 512 with the base station. As a result, an intermediary assisting communication cluster 506 is not needed and may not be utilized as the transmission occurs due to the use of the unblocked LOS path 512 link. It may be noted that the sensing target 510 is not blocking the LOS path 512 in the illustrated example, but in a possible future example the sensing target 510 may block the LOS path 512 between the Tx beam 504 of the base station and the UE 502 as the sensing target 510 is headed in the direction of the LOS path 512.

[0069] In some embodiments, the AI / ML model trained on the sensed environmental parameters and probing beams (e.g., RSRP of the probing beams) may predict a near future, future, or potential LOS path 512 blockage, thus predicting a possible LOS path to NLOS path transition. Such cases are further explored in FIG. 6.

[0070] FIG. 6 illustrates an example of a predicted LOS path to NLOS path transition by an AI / ML model trained on the RSRP values of probing beams and environmental parameters obtained by sensing performed at the UE, due to a blocked LOS path, according to embodiments herein.

[0071] In some embodiments, the UE can employ sensing (e.g., sensing types may include radar sensing, camera sensing, sensor sensing, LIDAR sensing and any other sensing of the sort) and can calculate / determine various environmental parameters (e.g., doppler-angle image maps based on radar signal processing). Trained on the sensed environmental parameters and probing beams (e.g.. the RSRP of the probing beams), the AI / ML model can predict if and / or when an LOS path to NLOS path transition will take place. The UE can signal this transition to the base station to adjust its own Tx beam (e.g., transitioning from a first Tx beam that is in LOS path to the UE to a second Tx beam that is in NLOS path to the UE). In some embodiments, the UE can also predict, through the AI / ML model, a best Tx-Rx beam pair (i.e., the most optimal beam for NLOS path communication) when the LOS path to NLOS path transition occurs and signal said best Tx-Rx beam pair to the base station.

[0072] Consider an example (illustrated in FIG. 6) where the LOS path 612 between a UE 610 and the base station (e.g., the mmWave base station 614) is blocked by a sensing target 608. The UE 610 may sense the environment by measuring, in some cases, radar reflections 602 that are reflected back to the UE from the sensing target 608, thus obtaining parameters of the environment, the sensing target 608, and the area around the sensing target 608. Based on the environmental parameters and various other training inputs such as probing beams, the AI / ML model can predict that the LOS path 612 is either blocked or is going to be blocked in the future by a blockage (e.g., a vehicle, person, building), thus predicting an LOS path to NLOS path transition. The UE 610 can then indicate to the base station, that an LOS path 612 to NLOS path 618 transition is taking place. The base station (e.g., mmWave base station 614 may then adjust its Tx beam from a first Tx beam 604 that is in LOS path 612 to the UE 610 to a second Tx beam 606 that is in NLOS path 618 to the UE 610 as the first Tx beam 604 used for LOS path 612 communication may no longer be used as the LOS path 612 is blocked.

[0073] In some cases, the second Tx beam 606 may be transmitted by the base station to a communication cluster 616 that may then repeat the second Tx beam 606 to the UE 610, or in other words, help facilitate an NLOS path 618 communication between thebase station and the UE 610. It may be noted that, in some cases, the base station may transmit the second Tx beam 606 without the use of a communication cluster 616 known to the UE but rather an object in the environment that may also facilitate NLOS path communication determined from the sensed environment. In such cases, the second Tx beam 606 may be transmitted, repeated and / or reflected off objects within the environment based on the environmental parameters obtained through the UE 610 based sensing of the environment.

[0074] Additionally, in some embodiments, the UE 610 may signal to the base station a best beam pair 620 for NLOS path communication (i.e., the most optimal beam for NLOS path communication) predicted by the AI / ML model disclosed herein, when an LOS path to NLOS path transition is predicted and / or occurs. In such cases, the best beam pair may be a best Tx beam, a best Rx beam, and / or a best Tx / Rx beam pair.

[0075] FIG. 7 illustrates a flow diagram 700 for signaling between a UE and a base station of an LOS path to NLOS path transition predicted by an AI / ML model trained on the RSRP values of probing beams and environmental parameters obtained by sensing performed at the UE, along with various corresponding diagrammatic illustrations, according to embodiments discussed herein.

[0076] The flow diagram 700 illustrates an example of signaling between a UE 702 and a base station (e.g., a gNB 704). A UE 702 may first predict 706, using an AI / ML model, an LOS path to NLOS path transition and can indicate the detection of the predicted LOS path to NLOS path transition to the base station (e.g., gNB 704). The UE 702 may then transmit 708 a best next (i.e., future) Tx beam, Rx beam and / or Tx / Rx beam pair to the base station (e.g., gNB 704). The base station (e.g., gNB 704), based on the indication of an LOS path to NLOS path transition, can switch 710 from transmitting a beam that is in the LOS path to the UE 702 to transmitting a beam that is in an NLOS path to the UE 702, or in other words adjust its own Tx beam for NLOS path communication with the UE 702.

[0077] Similarly, to the examples considered in FIG. 6 and FIG. 5, consider an example (illustrated in FIG. 7) where a sensing target 718 blocks an LOS path 714 betw een a UE 702 and a base station (e.g., mmWave base station 712) that is transmitting a Tx beam 722. Additionally, in some cases, the blocking of the LOS path 714 can be predicted by an AI / ML model trained on environmental parameters obtained through sensing (e.g., sensing types may include radar sensing, camera sensing, sensor sensing, LIDAR sensingand any other sensing of the sort) and probing beams (e.g., the RSRP value of the probing beams). As a result, the UE 702 may not receive the Tx beam 722 as the LOS path 714 is blocked by the sensing target 718 and NLOS path 720 communication with the UE 702 may be needed (e.g., through a communication cluster 716 , or object in the environment known to the UE due to the sensed environmental parameters). In such cases, an LOS path 714 to NLOS path 720 transition may be detected and transmitted to the base station (e.g., gNB 704).

[0078] Further, in the same example (illustrated in FIG. 7), once an LOS path 732 to NLOS path 734 transition (due to a sensing target 736 blocking the LOS path 732 to the UE 726) is indicated to the base station (e.g., mmWave base station 730). the base station can switch to a new Tx beam 724 that is in NLOS path 734 of the UE 726. In some cases, NLOS path 734 communication can be done with assistance from a communication cluster 728 or a sensed object in the environment that can repeat the new Tx beam 724 from the base station to the UE 726.

[0079] While some embodiments herein discuss the prediction, by an AI / ML model, of an LOS path to NLOS path transition, similar embodiments may be contemplated for the prediction, by an AI / ML model, of an NLOS path to LOS path transition. In such embodiments, instead of predicting a future LOS path blockage, the AI / ML model may predict when the LOS path will be unblocked thus allowing for LOS path communication. The AI / ML model, similar to embodiments discussed herein, can be trained on sensed environmental parameters and Tx probing beams to predict when the LOS path will be unblocked. In some cases, a base station may. based on the predicted NLOS path to LOS path transition, switch from a beam that is in an NLOS path to the UE to a beam that is in a LOS path to the UE and indicate to the UE the NLOS path to LOS path transition. Accordingly, the UE may adjust an Rx beam for LOS path communication with the base station.

[0080] Sensing Aided Spatial and Temporal Beam Prediction

[0081] In some embodiments, a sequence of radar map samples can be used to predict transmit and / or receive beams for the next (i.e., future) few time samples by using an AI / ML model. Sensing (e.g., radar sensing) can provide situational awareness about the communication environment, including the position, shape, and mobility of the static and / or dynamic objects in the environment that affect the propagation of waves to the UE. Leveraging this environmental awareness, the AI / ML could leam to predict a futureLOS link, or in other words, LOS path blockage before it occurs. The moving objects that will cause the LOS path blockages as well as the time and duration of these LOS path blockages can be predicted using the sensing data maps (e.g., range / velocity / doppler maps) as input to the AI / ML model. In some cases, this prediction allows the network to make proactive decisions on hand-off and beam switching to avoid sudden communication interruption, and thus increasing throughput.

[0082] FIG. 8 illustrates a method 800 of a base station serving a UE, according to embodiments herein. The illustrated method 800 includes sensing 802, at the base station, environmental parameters around an environment of the UE. The method 800 further includes using 804 an Al model trained on the environmental parameters to predict a future LOS path to NLOS path transition, wherein the predicted LOS path to NLOS path transition comprises a predicted future blockage that blocks an LOS path from the base station to the UE. The method 800 further includes switching 806, based on the predicted LOS path to NLOS path transition, from a first Tx beam that is in the LOS path to the UE to a second Tx beam that is in an NLOS path to the UE. The method 800 further includes transmitting 808, to the UE, based on the predicted LOS path to NLOS path transition, an indication of beam switching from the first Tx beam that is in the LOS path to the UE to the second Tx beam that is in the NLOS path to the UE.

[0083] In some embodiments of the method 800, sensing the environmental parameters comprises one or more of radar-based sensing, camera-based sensing, sensor-based sensing, and LIDAR based sensing.

[0084] In some embodiments, the method 800 further comprises transmitting, to the UE, a best Tx-Rx beam pair predicted by the Al model trained on the environmental parameters, for NLOS path communication with the UE.

[0085] In some embodiments, the method 800 further comprises transmitting the second Tx beam in the NLOS path to the UE to an object that helps to facilitate NLOS path communication. In some such embodiments, the object comprises one or more of a communication cluster, a repeater, and a reflector.

[0086] In some embodiments of the method 800, the Al model is further trained on RSRP values of Tx-Rx beam pairs corresponding to Tx probing beams. In some such embodiments, the Tx probing beams are sampled in a temporal domain. In some other such embodiments, the Tx probing beams are sampled in a spatial domain.

[0087] Embodiments contemplated herein include an apparatus comprising means to perform one or more elements of the method 800. This apparatus may be. for example, an apparatus of a base station (such as a network device 1318 that is a base station, as described herein).

[0088] Embodiments contemplated herein include one or more non-transitory computer-readable media comprising instructions to cause an electronic device, upon execution of the instructions by one or more processors of the electronic device, to perform one or more elements of the method 800. This non-transitory computer-readable media may be. for example, a memory of a base station (such as a memory 1322 of a network device 1318 that is a base station, as described herein).

[0089] Embodiments contemplated herein include an apparatus comprising logic, modules, or circuitry to perform one or more elements of the method 800. This apparatus may be, for example, an apparatus of a base station (such as a network device 1318 that is a base station, as described herein).

[0090] Embodiments contemplated herein include an apparatus comprising: one or more processors and one or more computer-readable media comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform one or more elements of the method 800. This apparatus may be, for example, an apparatus of a base station (such as a network device 1318 that is a base station, as described herein).

[0091] Embodiments contemplated herein include a signal as described in or related to one or more elements of the method 800.

[0092] Embodiments contemplated herein include a computer program or computer program product comprising instructions, wherein execution of the program by a processing element is to cause the processing element to carry out one or more elements of the method 800. The processor may be a processor of a base station (such as a processor(s) 1320 of a network device 1318 that is a base station, as described herein). These instructions may be, for example, located in the processor and / or on a memory of the base station (such as a memory 1322 of a network device 1318 that is a base station, as described herein).

[0093] FIG. 9 illustrates a method 900 of a UE being served by a base station, according to embodiments herein. The illustrated method 900 includes receiving 902, from the base station, an indication of an LOS path to NLOS path transition predicted, atthe base station, by an Al model trained on environmental parameters sensed by the base station and Tx probing beams, wherein the LOS path to NLOS path transition comprises a predicted future blockage that blocks an LOS path from the UE to the base station. The method 900 further includes adjusting 904, at the UE, based on the received indication, an Rx beam for NLOS path communication with the base station. The method 900 further includes receiving 906, from the base station, a Tx beam that is in an NLOS path to the UE.

[0094] In some embodiments, the method 900 further comprises receiving, from the base station, a best Tx-Rx beam pair predicted, at the base station, by the Al model trained on the environmental parameters and the Tx probing beams.

[0095] In some embodiments, the method 900 further comprises receiving a Tx beam in the NLOS path to the base station, from an object that helps to facilitate the NLOS path communication. In some such embodiments, the object comprises one or more of a communication cluster, a repeater, and a reflector.

[0096] In some embodiments of the method 900, the Al model is further trained on RSRP values of Tx-Rx beam pairs corresponding to the Tx probing beams.

[0097] In some embodiments of the method 900, the Tx probing beams are sampled in a temporal domain.

[0098] In some embodiments of the method 900, the Tx probing beams are sampled in a spatial domain.

[0099] Embodiments contemplated herein include an apparatus comprising means to perform one or more elements of the method 900. This apparatus may be, for example, an apparatus of a UE (such as a wireless device 1302 that is a UE, as described herein).

[0100] Embodiments contemplated herein include one or more non-transitory computer-readable media comprising instructions to cause an electronic device, upon execution of the instructions by one or more processors of the electronic device, to perform one or more elements of the method 900. This non-transitory computer-readable media may be. for example, a memory of a UE (such as a memory 1306 of a wireless device 1302 that is a UE, as described herein).

[0101] Embodiments contemplated herein include an apparatus comprising logic, modules, or circuitry to perform one or more elements of the method 900. This apparatusmay be, for example, an apparatus of a UE (such as a wireless device 1302 that is a UE, as described herein).

[0102] Embodiments contemplated herein include an apparatus comprising: one or more processors and one or more computer-readable media comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform one or more elements of the method 900. This apparatus may be, for example, an apparatus of a UE (such as a wireless device 1302 that is a UE, as described herein).

[0103] Embodiments contemplated herein include a signal as described in or related to one or more elements of the method 900.

[0104] Embodiments contemplated herein include a computer program or computer program product comprising instructions, wherein execution of the program by a processor is to cause the processor to carry out one or more elements of the method 900 The processor may be a processor of a UE (such as a processor(s) 1304 of a wireless device 1302 that is a UE, as described herein). These instructions may be, for example, located in the processor and / or on a memory’ of the UE (such as a memory 1306 of a wireless device 1302 that is a UE, as described herein).

[0105] FIG. 10 illustrates a method 1000 of a UE being served by a base station, according to embodiments herein. The illustrated method 1000 includes sensing 1002, at the UE, environmental parameters around the UE. The method 1000 further includes using 1004 an Al model trained on the environmental parameters to predict a future LOS path to NLOS path transition, wherein the predicted LOS path to NLOS path transition comprises a predicted future blockage that blocks an LOS path from the UE to the base station. The method 1000 further includes adjusting 1006, based on the predicted LOS path to NLOS path transition, an Rx beam for NLOS path communication with the base station. The method 1000 further includes transmitting 1008, to the base station, an indication of the predicted LOS path to NLOS path transition.

[0106] In some embodiments of the method 1000, sensing the environmental parameters comprises one or more of radar-based sensing, camera-based sensing, sensorbased sensing, and LIDAR based sensing.

[0107] In some embodiments, the method 1000 further comprises transmitting, to the base station, a best Tx-Rx beam pair predicted by the Al model trained on the environmental parameters for the NLOS path communication with the base station.

[0108] In some embodiments, the method 1000 further comprises receiving a Tx beam in the NLOS path to the base station from an object that helps to facilitate the NLOS path communication. In some such embodiments, the object comprises one or more of a communication cluster, a repeater, and a reflector.

[0109] In some embodiments of the method 1000. the Al model is further trained on RSRP values of Tx-Rx beam pairs corresponding to Tx probing beams. In some such embodiments, the Tx probing beams are sampled in a temporal domain. In some other such embodiments, the Tx probing beams are sampled in a spatial domain.

[0110] Embodiments contemplated herein include an apparatus comprising means to perform one or more elements of the method 1000. This apparatus may be, for example, an apparatus of a UE (such as a wireless device 1302 that is a UE, as described herein).

[0111] Embodiments contemplated herein include one or more non-transitory computer-readable media comprising instructions to cause an electronic device, upon execution of the instructions by one or more processors of the electronic device, to perform one or more elements of the method 1000. This non-transitory computer- readable media may be. for example, a memory of a UE (such as a memory 1306 of a wireless device 1302 that is a UE, as described herein).

[0112] Embodiments contemplated herein include an apparatus comprising logic, modules, or circuitry to perform one or more elements of the method 1000. This apparatus may be, for example, an apparatus of a UE (such as a wireless device 1302 that is a UE, as described herein).

[0113] Embodiments contemplated herein include an apparatus comprising: one or more processors and one or more computer-readable media comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform one or more elements of the method 1000. This apparatus may be, for example, an apparatus of a UE (such as a wireless device 1302 that is a UE. as described herein).

[0114] Embodiments contemplated herein include a signal as described in or related to one or more elements of the method 1000.

[0115] Embodiments contemplated herein include a computer program or computer program product comprising instructions, wherein execution of the program by a processor is to cause the processor to carry out one or more elements of the method 1000. The processor may be a processor of a UE (such as a processor(s) 1304 of a wireless device 1302 that is a UE, as described herein). These instructions may be, forexample, located in the processor and / or on a memory' of the UE (such as a memory 1306 of a wireless device 1302 that is a UE. as described herein).

[0116] FIG. 11 illustrates a method 1100 of a base station serving a UE, according to embodiments herein. The illustrated method 1100 includes receiving 1102, from the UE, an indication of an LOS path to NLOS path transition predicted, at the UE, by an Al model trained on environmental parameters sensed by the UE and Tx probing beams, wherein the LOS path to NLOS path transition comprises a predicted future blockage that blocks an LOS path from the base station to the UE. The method 1100 further includes switching 1104, based on the received indication, from a first Tx beam that is in the LOS path to the UE to a second Tx beam that is in an NLOS path to the UE. The method 1100 further includes transmitting 1106 the second Tx beam that is in the NLOS path to the UE.

[0117] In some embodiments, the method 1100 further comprises receiving, from the UE, a best Tx-Rx beam pair predicted by the Al model trained on the environmental parameters.

[0118] In some embodiments, the method 1100 further comprises transmitting the second Tx beam in the NLOS path to the UE to an object that helps to facilitate NLOS path communication. In some such embodiments, the object comprises one or more of a communication cluster, a repeater, and a reflector.

[0119] In some embodiments of the method 1100, the Al model is further trained on RSRP values of Tx-Rx beam pairs corresponding to Tx probing beams.

[0120] In some embodiments of the method 1100. the Tx probing beams are sampled in a temporal domain.

[0121] In some embodiments of the method 1100. the Tx probing beams are sampled in a spatial domain.

[0122] Embodiments contemplated herein include an apparatus comprising means to perform one or more elements of the method 1100. This apparatus may be, for example, an apparatus of a base station (such as a network device 1318 that is a base station, as described herein).

[0123] Embodiments contemplated herein include one or more non -transitory computer-readable media comprising instructions to cause an electronic device, upon execution of the instructions by one or more processors of the electronic device, toperform one or more elements of the method 1100. This non-transitory computer- readable media may be. for example, a memory of a base station (such as a memory 1322 of a network device 1318 that is a base station, as described herein).

[0124] Embodiments contemplated herein include an apparatus comprising logic, modules, or circuitry to perform one or more elements of the method 1100. This apparatus may be, for example, an apparatus of a base station (such as a network device 1318 that is a base station, as described herein).

[0125] Embodiments contemplated herein include an apparatus comprising: one or more processors and one or more computer-readable media comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform one or more elements of the method 1100. This apparatus may be, for example, an apparatus of a base station (such as a network device 1318 that is a base station, as described herein).

[0126] Embodiments contemplated herein include a signal as described in or related to one or more elements of the method 1100.

[0127] Embodiments contemplated herein include a computer program or computer program product comprising instructions, wherein execution of the program by a processing element is to cause the processing element to carry out one or more elements of the method 1100. The processor may be a processor of a base station (such as a processor(s) 1320 of a network device 1318 that is a base station, as described herein). These instructions may be, for example, located in the processor and / or on a memory' of the base station (such as a memory 1322 of a network device 1318 that is a base station, as described herein).

[0128] FIG. 12 illustrates an example architecture of a wireless communication system 1200, according to embodiments disclosed herein. The following description is provided for an example wireless communication system 1200 that operates in conjunction with the LTE system standards and / or 5G or NR system standards as provided by 3 GPP technical specifications.

[0129] As shown by FIG. 12, the wireless communication system 1200 includes UE 1202 and UE 1204 (although any number of UEs may be used). In this example, the UE 1202 and the UE 1204 are illustrated as smartphones (e.g., handheld touchscreen mobile computing devices connectable to one or more cellular networks), but may also comprise any mobile or non-mobile computing device configured for wireless communication.

[0130] The UE 1202 and UE 1204 may be configured to communicatively couple with a RAN 1206. In embodiments, the RAN 1206 may be NG-RAN. E-UTRAN, etc. The UE 1202 and UE 1204 utilize connections (or channels) (shown as connection 1208 and connection 1210, respectively) with the RAN 1206, each of which comprises a physical communications interface. The RAN 1206 can include one or more base stations (such as base station 1212 and base station 1214) that enable the connection 1208 and connection 1210.

[0131] In this example, the connection 1208 and connection 1210 are air interfaces to enable such communicative coupling, and may be consistent with RAT(s) used by the RAN 1206, such as. for example, an LTE and / or NR.

[0132] In some embodiments, the UE 1202 and UE 1204 may also directly exchange communication data via a sidelink interface 1216. The UE 1204 is shown to be configured to access an access point (shown as AP 1218) via connection 1220. By way of example, the connection 1220 can comprise a local wireless connection, such as a connection consistent with any IEEE 802.11 protocol, wherein the AP 1218 may comprise a Wi-Fi® router. In this example, the AP 1218 may be connected to another network (for example, the Internet) without going through a CN 1224.

[0133] In some embodiments, the UE 1202 and UE 1204 can be configured to communicate using orthogonal frequency division multiplexing (OFDM) communication signals with each other or with the base station 1212 and / or the base station 1214 over a multicarrier communication channel in accordance with various communication techniques, such as, but not limited to, an orthogonal frequency division multiple access (OFDMA) communication technique (e.g., for downlink communications) or a single carrier frequency division multiple access (SC-FDMA) communication technique (e.g., for uplink and ProSe or sidelink communications), although the scope of the embodiments is not limited in this respect. The OFDM signals can comprise a plurality of orthogonal subcarriers.

[0134] In some embodiments, all or parts of the base station 1212 or base station 1214 may be implemented as one or more software entities running on server computers as part of a virtual network. In addition, or in other embodiments, the base station 1212 or base station 1214 may be configured to communicate with one another via interface 1222. In embodiments where the wireless communication system 1200 is an LTE system (e.g., when the CN 1224 is an EPC), the interface 1222 may be an X2 interface. The X2interface may be defined between two or more base stations (e.g., two or more eNBs and the like) that connect to an EPC, and / or between two eNBs connecting to the EPC. In embodiments where the wireless communication system 1200 is an NR system (e.g., when CN 1224 is a 5GC), the interface 1222 may be an Xn interface. The Xn interface is defined between two or more base stations (e.g., two or more gNBs and the like) that connect to 5GC, between a base station 1212 (e.g., a gNB) connecting to 5GC and an eNB. and / or between two eNBs connecting to 5GC (e.g.. CN 1224).

[0135] The RAN 1206 is shown to be communicatively coupled to the CN 1224. The CN 1224 may comprise one or more network elements 1226, which are configured to offer various data and telecommunications services to customers / subscribers (e.g.. users of UE 1202 and UE 1204) who are connected to the CN 1224 via the RAN 1206. The components of the CN 1224 may be implemented in one physical device or separate physical devices including components to read and execute instructions from a machine- readable or computer-readable medium (e.g., a non-transitory machine-readable storage medium).

[0136] In embodiments, the CN 1224 may be an EPC, and the RAN 1206 may be connected with the CN 1224 via an SI interface 1228. In embodiments, the SI interface 1228 may be split into two parts, an SI user plane (Sl-U) interface, which carries traffic data between the base station 1212 or base station 1214 and a serving gateway (S-GW), and the SI -MME interface, which is a signaling interface between the base station 1212 or base station 1214 and mobility management entities (MMEs).

[0137] In embodiments, the CN 1224 may be a 5GC, and the RAN 1206 may be connected with the CN 1224 via an NG interface 1228. In embodiments, the NG interface 1228 may be split into two parts, an NG user plane (NG-U) interface, which carries traffic data between the base station 1212 or base station 1214 and a user plane function (UPF), and the SI control plane (NG-C) interface, which is a signaling interface between the base station 1212 or base station 1214 and access and mobility' management functions (AMFs).

[0138] Generally, an application server 1230 may be an element offering applications that use internet protocol (IP) bearer resources with the CN 1224 (e.g., packet switched data services). The application server 1230 can also be configured to support one or more communication services (e.g., VoIP sessions, group communication sessions, etc.)for the UE 1202 and UE 1204 via the CN 1224. The application server 1230 may communicate with the CN 1224 through an IP communications interface 1232.

[0139] FIG. 13 illustrates a system 1300 for performing signaling 1334 between a wireless device 1302 and a network device 1318, according to embodiments disclosed herein. The system 1300 may be a portion of a wireless communications system as herein described. The wireless device 1302 may be, for example, a UE of a wireless communication system. The network device 1318 may be, for example, a base station (e.g., an eNB or a gNB) of a wireless communication system.

[0140] The wireless device 1302 may include one or more processor(s) 1304. The processor(s) 1304 may execute instructions such that various operations of the wireless device 1302 are performed, as described herein. The processor(s) 1304 may include one or more baseband processors implemented using, for example, a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a controller, a field programmable gate array (FPGA) device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.

[0141] The wireless device 1302 may include a memory 1306. The memory 1306 may be a non-transitory computer-readable storage medium that stores instructions 1308 (which may include, for example, the instructions being executed by the processor(s) 1304). The instructions 1308 may also be referred to as program code or a computer program. The memory 1306 may also store data used by, and results computed by, the processor(s) 1304.

[0142] The wireless device 1302 may include one or more transceiver(s) 1310 that may include radio frequency (RF) transmitter circuitry and / or receiver circuitry that use the antenna(s) 1312 of the wireless device 1302 to facilitate signaling (e.g., the signaling 1334) to and / or from the wireless device 1302 with other devices (e.g., the network device 1318) according to corresponding RATs.

[0143] The wireless device 1302 may include one or more antenna(s) 1312 (e.g., one, two, four, or more). For embodiments with multiple antenna(s) 1312, the wireless device 1302 may leverage the spatial diversity of such multiple antenna(s) 1312 to send and / or receive multiple different data streams on the same time and frequency resources. This behavior may be referred to as, for example, multiple input multiple output (MIMO) behavior (referring to the multiple antennas used at each of a transmitting device and areceiving device that enable this aspect). MIMO transmissions by the wireless device 1302 may be accomplished according to precoding (or digital beamforming) that is applied at the wireless device 1302 that multiplexes the data streams across the antenna(s) 1312 according to known or assumed channel characteristics such that each data stream is received with an appropriate signal strength relative to other streams and at a desired location in the spatial domain (e.g., the location of a receiver associated with that data stream). Certain embodiments may use single user MIMO (SU-MIMO) methods (where the data streams are all directed to a single receiver) and / or multi user MIMO (MU-MIMO) methods (where individual data streams may be directed to individual (different) receivers in different locations in the spatial domain).

[0144] In certain embodiments having multiple antennas, the wireless device 1302 may implement analog beamforming techniques, whereby phases of the signals sent by the antenna(s) 1312 are relatively adjusted such that the (joint) transmission of the antenna(s) 1312 can be directed (this is sometimes referred to as beam steering).

[0145] The wireless device 1302 may include one or more interface(s) 1314. The interface(s) 1314 may be used to provide input to or output from the wireless device 1302. For example, a wireless device 1302 that is a UE may include interface(s) 1314 such as microphones, speakers, a touchscreen, buttons, and the like in order to allow for input and / or output to the UE by a user of the UE. Other interfaces of such a UE may be made up of transmitters, receivers, and other circuitry' (e.g., other than the transceiver(s) 1310 / antenna(s) 1312 already described) that allow for communication between the UE and other devices and may operate according to known protocols (e.g., Wi-Fi®, Bluetooth®, and the like).

[0146] The wireless device 1302 may include a sensing aided temporal prediction module 1316. The sensing aided temporal prediction module 1316 may be implemented via hardware, software, or combinations thereof. For example, the sensing aided temporal prediction module 1316 may be implemented as a processor, circuit, and / or instructions 1308 stored in the memory 1306 and executed by the processor(s) 1304. In some examples, the sensing aided temporal prediction module 1316 may be integrated within the processor(s) 1304 and / or the transceiver(s) 1310. For example, the sensing aided temporal prediction module 1316 may be implemented by a combination of software components (e.g., executed by a DSP or a general processor) and hardwarecomponents (e.g., logic gates and circuitry) within the processor(s) 1304 or the transceiver(s) 1310.

[0147] The sensing aided temporal prediction module 1316 may be used for various aspects of the present disclosure, for example, aspects of FIG. 1, FIG. 2, FIG. 3, FIG. 4, FIG. 5. FIG. 6, FIG. 7, FIG. 9 and FIG. 10. The sensing aided temporal prediction module 1316 is configured to, for example, using an AI / ML model trained on sensed environmental parameters predict a future LOS path to NLOS path transition based on a predicted future blockage that blocks an LOS path to the base station. In some cases, the sensing aided temporal prediction module 1316 may be configured to, for example, predict, using the AI / ML model trained on the sensed environmental parameters, the best Tx-Rx beam pair for NLOS path communication and transmit the best Tx-Rx beam pair to the base station.

[0148] The network device 1318 may include one or more processor(s) 1320. The processor(s) 1320 may execute instructions such that various operations of the network device 1318 are performed, as described herein. The processor(s) 1320 may include one or more baseband processors implemented using, for example, a CPU, a DSP, an ASIC, a controller, an FPGA device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.

[0149] The network device 1318 may include a memory' 1322. The memory 1322 may be a non-transitory computer-readable storage medium that stores instructions 1324 (which may include, for example, the instructions being executed by the processor(s) 1320). The instructions 1324 may also be referred to as program code or a computer program. The memory 1322 may also store data used by, and results computed by, the processor(s) 1320.

[0150] The network device 1318 may include one or more transceiver(s) 1326 that may include RF transmitter circuitry and / or receiver circuitry that use the antenna(s) 1328 of the network device 1318 to facilitate signaling (e.g., the signaling 1334) to and / or from the network device 1318 with other devices (e.g., the wireless device 1302) according to corresponding RATs.

[0151] The network device 1318 may include one or more antenna(s) 1328 (e.g., one, two, four, or more). In embodiments having multiple antenna(s) 1328, the network device 1318 may perform MIMO, digital beamforming, analog beamforming, beam steering, etc., as has been described.

[0152] The network device 1318 may include one or more interface(s) 1330. The interface(s) 1330 may be used to provide input to or output from the network device 1318. For example, a network device 1318 that is a base station may include interface(s) 1330 made up of transmitters, receivers, and other circuitry (e.g., other than the transceiver(s) 1326 / antenna(s) 1328 already described) that enables the base station to communicate with other equipment in a core network, and / or that enables the base station to communicate with external networks, computers, databases, and the like for purposes of operations, administration, and maintenance of the base station or other equipment operably connected thereto.

[0153] The network device 1318 may include a sensing aided temporal prediction module 1332. The sensing aided temporal prediction module 1332 may be implemented via hardware, software, or combinations thereof. For example, the sensing aided temporal prediction module 1332 may be implemented as a processor, circuit, and / or instructions 1324 stored in the memory 1322 and executed by the processor(s) 1320. In some examples, the sensing aided temporal prediction module 1332 may be integrated within the processor(s) 1320 and / or the transceiver(s) 1326. For example, the sensing aided temporal prediction module 1332 may be implemented by a combination of software components (e.g., executed by a DSP or a general processor) and hardware components (e.g.. logic gates and circuitry) within the processor(s) 1320 or the transceiver(s) 1326.

[0154] The sensing aided temporal prediction module 1332 may be used for various aspects of the present disclosure, for example, aspects of FIG. 1, FIG. 2, FIG. 3, FIG. 4. FIG. 5, FIG. 6, FIG. 7, FIG. 8 and FIG. 11. The sensing aided temporal prediction module 1332 is configured to, for example, using an AI / ML model trained on sensed environmental parameters, predict a future LOS path to NLOS path transition based on a predicted future blockage that blocks an LOS path to the base station. In some cases, the sensing aided temporal prediction module 1316 may be configured to, for example, predict, using the AI / ML model trained on the sensed environmental parameters, the best Tx-Rx beam pair for NLOS path communication and transmit the best Tx-Rx beam pair to the UE.

[0155] For one or more embodiments, at least one of the components set forth in one or more of the preceding figures may be configured to perform one or more operations, techniques, processes, and / or methods as set forth herein. For example, a basebandprocessor as described herein in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth herein. For another example, circuitry associated with a UE, base station, network element, etc. as described above in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth herein.

[0156] Any of the above-described embodiments may be combined with any other embodiment (or combination of embodiments), unless explicitly stated otherwise. The foregoing description of one or more implementations provides illustration and description, but is not intended to be exhaustive or to limit the scope of embodiments to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of various embodiments.

[0157] Embodiments and implementations of the systems and methods described herein may include various operations, which may be embodied in machine-executable instructions to be executed by a computer system. A computer system may include one or more general-purpose or special-purpose computers (or other electronic devices). The computer system may include hardware components that include specific logic for performing the operations or may include a combination of hardware, software, and / or firmware.

[0158] It should be recognized that the systems described herein include descriptions of specific embodiments. These embodiments can be combined into single systems, partially combined into other systems, split into multiple systems or divided or combined in other ways. In addition, it is contemplated that parameters, attributes, aspects, etc. of one embodiment can be used in another embodiment. The parameters, attributes, aspects, etc. are merely described in one or more embodiments for clarity, and it is recognized that the parameters, attributes, aspects, etc. can be combined with or substituted for parameters, attributes, aspects, etc. of another embodiment unless specifically disclaimed herein.

[0159] It is well understood that the use of personally identifiable information should follow privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining the privacy of users. In particular, personally identifiable information data should be managed and handled so asto minimize risks of unintentional or unauthorized access or use, and the nature of authorized use should be clearly indicated to users.

[0160] Although the foregoing has been described in some detail for purposes of clarity, it will be apparent that certain changes and modifications may be made without departing from the principles thereof. It should be noted that there are many alternative ways of implementing both the processes and apparatuses described herein. Accordingly, the present embodiments are to be considered illustrative and not restrictive, and the description is not to be limited to the details given herein, but may be modified within the scope and equivalents of the appended claims.

Claims

CLAIMS1. A method of a base station serving a user equipment (UE), the method comprising: sensing, at the base station, environmental parameters around an environment of the UE; using an artificial intelligence (Al) model trained on the environmental parameters to predict a future line of sight (LOS) path to non-line of sight (NLOS) path transition, wherein the predicted LOS path to NLOS path transition comprises a predicted future blockage that blocks an LOS path from the base station to the UE; switching, based on the predicted LOS path to NLOS path transition, from a first transmit (Tx) beam that is in the LOS path to the UE to a second Tx beam that is in an NLOS path to the UE; and transmitting, to the UE, based on the predicted LOS path to NLOS path transition, an indication of beam switching from the first Tx beam that is in the LOS path to the UE to the second Tx beam that is in the NLOS path to the UE.

2. The method of claim 1, wherein sensing the environmental parameters comprises one or more of radar-based sensing, camera-based sensing, sensor based sensing, and light detection and ranging (LIDAR) based sensing.

3. The method of claim 1, further comprising transmitting, to the UE, a best Tx-receive (Rx) beam pair predicted by the Al model trained on the environmental parameters, for NLOS path communication with the UE.

4. The method of claim 1, further comprising transmitting the second Tx beam in the NLOS path to the UE to an object that helps to facilitate NLOS path communication.

5. The method of claim 4, wherein the object comprises one or more of a communication cluster, a repeater, and a reflector.

6. The method of claim 1, wherein the Al model is further trained on reference signal received power (RSRP) values of Tx-receive (Rx) beam pairs corresponding to Tx probing beams.

7. The method of claim 6, wherein the Tx probing beams are sampled in a temporal domain.

8. The method of claim 6, wherein the Tx probing beams are sampled in a spatial domain.

9. A method of a user equipment (UE) being served by a base station, the method comprising: receiving, from the base station, an indication of a line of sight (LOS) path to non-line of sight (NLOS) path transition predicted, at the base station, by an artificial intelligence (Al) model trained on environmental parameters sensed by the base station and transmit (Tx) probing beams, wherein the LOS path to NLOS path transition comprises a predicted future blockage that blocks an LOS path from the UE to the base station; adjusting, at the UE, based on the indication, a receive (Rx) beam for NLOS path communication with the base station; and receiving, from the base station, a Tx beam that is in an NLOS path to the UE.

10. The method of claim 9, further comprising receiving, from the base station, a best Tx-Rx beam pair predicted, at the base station, by the Al model trained on the environmental parameters and the Tx probing beams.

11. The method of claim 9, further comprising receiving a Tx beam in the NLOS path to the base station, from an object that helps to facilitate the NLOS path communication.

12. The method of claim 11, wherein the object comprises one or more of a communication cluster, a repeater, and a reflector.

13. The method of claim 9, wherein the Al model is further trained on reference signal received power (RSRP) values of Tx-Rx beam pairs corresponding to the Tx probing beams.

14. The method of claim 9, wherein the Tx probing beams are sampled in a temporal domain.

15. The method of claim 9, wherein the Tx probing beams are sampled in a spatial domain.

16. A method of a user equipment (UE) being served by a base station, the method comprising: sensing, at the UE, environmental parameters around the UE; using an artificial intelligence (Al) model trained on the environmental parameters to predict a future line of sight (LOS) path to non-line of sight (NLOS) path transition, wherein the predicted LOS path to NLOS path transition comprises a predicted future blockage that blocks an LOS path from the UE to the base station; adjusting, based on the predicted LOS path to NLOS path transition, a receive (Rx) beam for NLOS path communication with the base station; and transmitting, to the base station, an indication of the predicted LOS path to NLOS path transition.

17. The method of claim 16, wherein sensing the environmental parameters comprises one or more of radar-based sensing, camera-based sensing, sensor based sensing, and light detection and ranging (LIDAR) based sensing.

18. The method of claim 16, further comprising transmitting, to the base station, a best transmit (Tx)-Rx beam pair predicted by the Al model trained on the environmental parameters for the NLOS path communication with the base station.

19. The method of claim 16, further comprising receiving a transmit (Tx) beam in the NLOS path to the base station from an object that helps to facilitate the NLOS path communication.

20. The method of claim 19, wherein the object comprises one or more of a communication cluster, a repeater, and a reflector.

21. The method of claim 16, wherein the Al model is further trained on reference signal received power (RSRP) values of transmit (Tx)-Rx beam pairs corresponding to Tx probing beams.

22. The method of claim 21, wherein the Tx probing beams are sampled in a temporal domain.

23. The method of claim 21, wherein the Tx probing beams are sampled in a spatial domain.

24. A method of a base station serving a user equipment (UE), the method comprising: receiving, from the UE, an indication of a line of sight (LOS) path to non-line of sight (NLOS) path transition predicted, at the UE, by an artificial intelligence (Al) model trained on environmental parameters sensed by the UE and transmit (Tx) probing beams, wherein the LOS path to NLOS path transition comprises a predicted future blockage that blocks an LOS path from the base station to the UE; switching, based on the indication, from a first Tx beam that is in the LOS path to the UE to a second Tx beam that is in an NLOS path to the UE; and transmitting the second Tx beam that is in the NLOS path to the UE.

25. The method of claim 24, further comprising receiving, from the UE, a best Tx- receive (Rx) beam pair predicted by the Al model trained on the environmental parameters.

26. The method of claim 24, further comprising transmitting the second Tx beam in the NLOS path to the UE to an object that helps to facilitate NLOS path communication.

27. The method of claim 26, wherein the object comprises one or more of a communication cluster, a repeater, and a reflector.

28. The method of claim 24, wherein the Al model is further trained on reference signal received power (RSRP) values of Tx-receive (Rx) beam pairs corresponding to Tx probing beams.

29. The method of claim 24, wherein the Tx probing beams are sampled in a temporal domain.

30. The method of claim 24, wherein the Tx probing beams are sampled in a spatial domain.

31. An apparatus comprising means to perform the method of any of claim 1 to claim 30.

32. A computer-readable media comprising instructions to cause an electronic device, upon execution of the instructions by one or more processors of the electronic device, to perform the method of any of claim 1 to claim 30.

33. An apparatus comprising logic, modules, or circuitry to perform the method of any of claim 1 to claim 30.

34. A baseband processor of a user equipment (UE) that is configured to perform the method of any of claim 9 to claim 23.

Images