Blockage prediction reporting

Abstract

Aspects of the present disclosure generally relate to wireless communications. In some aspects, a user equipment (UE) may obtain measurement information regarding a set of reference signals. The UE may generate predicted congestion information using a model based at least in part on the measurement information. The UE may send a report indicating the predicted congestion information, wherein the report indicates at least one of a prediction window associated with the predicted blocking information, a time associated with an occurrence of a predicted blocking event, a severity value associated with the predicted blocking event, or a direction value associated with the predicted blocking event. Numerous other aspects are described.

Description

Technical Field

[0001] Aspects of the present disclosure relate generally to wireless communications, and to techniques and apparatus for congestion prediction reporting. Background Art

[0002] Wireless communication systems are widely deployed to provide a variety of telecommunication services, such as telephony, video, data, messaging, and broadcasting. Typical wireless communication systems may employ multiple access technologies that can support communication with multiple users by sharing available system resources (e.g., bandwidth, transmit power, etc.). Examples of such multiple access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single carrier frequency division multiple access (SC-FDMA) systems, time division synchronous code division multiple access (TD-SCDMA) systems, and long term evolution (LTE). LTE / LTE-Advanced is a set of enhancements to the Universal Mobile Telecommunications System (UMTS) mobile standard promulgated by the Third Generation Partnership Project (3GPP).

[0003] A wireless network may include one or more network nodes that support communications for wireless communication devices, such as user equipment (UE) or multiple UEs. A UE may communicate with a network node via downlink communications and uplink communications. A "downlink" (or "DL") refers to a communication link from a network node to a UE, and an "uplink" (or "UL") refers to a communication link from a UE to a network node. Some wireless networks may support device-to-device communications, such as via a local link (e.g., a side link (SL), a wireless local area network (WLAN) link, and / or a wireless personal area network (WPAN) link, etc.).

[0004] The above-mentioned multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different UEs to communicate at a city, country, region and / or global level. New Radio (NR) (which may be referred to as 5G) is a set of enhancements to the LTE mobile standard promulgated by 3GPP. NR is designed to better support mobile broadband Internet access by using orthogonal frequency division multiplexing (OFDM) (CP-OFDM) with a cyclic prefix (CP) on the downlink, using CP-OFDM and / or single carrier frequency division multiplexing (SC-FDM) (also known as discrete Fourier transform spread OFDM (DFT-s-OFDM)) on the uplink, and supporting beamforming, multiple input multiple output (MIMO) antenna technology and carrier aggregation to improve spectrum efficiency, reduce costs, improve services, utilize new spectrum, and better integrate with other open standards. As the demand for mobile broadband access continues to increase, further improvements in LTE, NR and other radio access technologies remain useful. Summary of the invention

[0005] Some aspects described herein relate to a method of wireless communication performed by a user equipment (UE). The method may include obtaining measurement information about a reference signal set. The method may include generating predicted blocking information using a model based at least in part on the measurement information. The method may include sending a report indicating the predicted blocking information, wherein the report indicates at least one of: a prediction window associated with the predicted blocking information, a time associated with an occurrence of a predicted blocking event, a severity value associated with the predicted blocking event, or a direction value associated with the predicted blocking event.

[0006] Some aspects described herein relate to a UE for wireless communication. The UE may include a memory and one or more processors coupled to the memory. The one or more processors may be configured to obtain measurement information about a reference signal set. The one or more processors may be configured to generate predicted blocking information using a model based at least in part on the measurement information. The one or more processors may be configured to send a report indicating the predicted blocking information, wherein the report indicates at least one of: a prediction window associated with the predicted blocking information, a time associated with the occurrence of a predicted blocking event, a severity value associated with the predicted blocking event, or a direction value associated with the predicted blocking event.

[0007] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication by a UE. The instruction set, when executed by one or more processors of the UE, may cause the UE to obtain measurement information about a reference signal set. The instruction set, when executed by one or more processors of the UE, may cause the UE to generate predicted blocking information using a model based at least in part on the measurement information. The instruction set, when executed by one or more processors of the UE, may cause the UE to send a report indicating the predicted blocking information, wherein the report indicates at least one of: a prediction window associated with the predicted blocking information, a time associated with the occurrence of a predicted blocking event, a severity value associated with the predicted blocking event, or a direction value associated with the predicted blocking event.

[0008] Some aspects described herein relate to an apparatus for wireless communication. The apparatus may include means for obtaining measurement information about a reference signal set. The apparatus may include means for generating predicted blocking information using a model based at least in part on the measurement information. The apparatus may include means for sending a report indicating the predicted blocking information, wherein the report indicates at least one of: a prediction window associated with the predicted blocking information, a time associated with an occurrence of a predicted blocking event, a severity value associated with the predicted blocking event, or a direction value associated with the predicted blocking event.

[0009] Aspects collectively include methods, apparatus, systems, computer program products, non-transitory computer-readable media, user equipment, base stations, network entities, network nodes, wireless communication devices and / or processing systems as fully described herein with reference to and as illustrated in the accompanying drawings.

[0010] The features and technical advantages of examples according to the present disclosure have been outlined quite broadly above so that the following specific embodiments may be better understood. Additional features and advantages will be described below. The disclosed concepts and specific examples may be easily used as a basis for modifying or designing other structures for achieving the same purpose of the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. The characteristics of the concepts disclosed herein, both in terms of their organization and method of operation, and the associated advantages will be better understood by considering the following description in conjunction with the accompanying drawings. Each of the figures in the accompanying drawings is provided for the purpose of illustration and description and not as a definition of limitations to the claims.

[0011] Although various aspects are described in the present disclosure by illustrating some examples, it will be understood by those skilled in the art that such aspects can be implemented in many different arrangements and scenarios. The technology described herein can be implemented using different platform types, devices, systems, shapes, sizes and / or packaging arrangements. For example, some aspects can be implemented via integrated chip implementations or other devices based on non-module components (e.g., end-user devices, vehicles, communication equipment, computing equipment, industrial equipment, retail / shopping equipment, medical equipment and / or artificial intelligence devices). Various aspects can be implemented in chip-level components, modular components, non-modular components, non-chip-level components, device-level components and / or system-level components. The equipment incorporating the various aspects and features described may include additional components and features for implementing and implementing the various aspects claimed and described. For example, the transmission and reception of wireless signals may include one or more components (e.g., hardware components, including antennas, radio frequency (RF) chains, power amplifiers, modulators, buffers, processors, interleavers, adders and / or summers) for analog and digital purposes. The various aspects described herein are intended to be implemented in various devices, components, systems, distributed arrangements and / or end-user devices of various sizes, shapes and configurations. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to fully understand the above-mentioned features of the present disclosure, a more specific description of the invention briefly summarized above can be obtained by referring to various aspects (some of which are illustrated in the accompanying drawings). However, it should be noted that the accompanying drawings only illustrate certain typical aspects of the present disclosure and are therefore not to be considered as limiting the scope thereof, as the specification may admit of other equally effective aspects. The same reference numerals in different drawings may identify the same or similar elements.

[0013] Figure 1 is a diagram illustrating an example of a wireless network according to the present disclosure.

[0014] Figure 2 is a diagram illustrating an example of communication between a network node and a user equipment (UE) in a wireless network according to the present disclosure.

[0015] Figure 3 is a diagram illustrating an example decomposed base station architecture according to the present disclosure.

[0016] Figure 4 is a diagram illustrating an example 400 of signaling associated with congestion prediction using wireless sensing.

[0017] Figure 5 is a diagram illustrating an example 500 of training and using a model in conjunction with predicting occlusions according to the present disclosure.

[0018] Figure 6is a diagram illustrating an example process 600 performed, for example, by a UE according to the present disclosure.

[0019] Figure 7 is a diagram of an example apparatus 700 for wireless communications in accordance with the present disclosure. DETAILED DESCRIPTION

[0020] The various aspects of the present disclosure are described more fully below with reference to the accompanying drawings. However, the present disclosure can be embodied in many different forms, and should not be interpreted as being limited to any specific structure or function presented throughout the present disclosure. On the contrary, these aspects are provided so that the present disclosure will be thorough and complete, and the scope of the present disclosure will be fully conveyed to those skilled in the art. It should be understood by those skilled in the art that the scope of the present disclosure is intended to cover any aspect of the disclosure disclosed herein, whether it is implemented independently or in combination with any other aspect of the disclosure. For example, any number of aspects set forth herein may be used to implement a device or implementation method. In addition, the scope of the present disclosure is intended to cover such devices or methods implemented using other structures, functionality, or structures and functionality in addition to or different from the various aspects of the disclosure set forth herein. It should be understood that any aspect of the disclosure disclosed herein may be embodied by one or more elements of the claims.

[0021] Several aspects of telecommunication systems will now be presented with reference to various devices and techniques. These devices and techniques will be described in the following detailed description and illustrated in the accompanying drawings by various blocks, modules, components, circuits, steps, processes, algorithms, etc. (collectively referred to as "elements"). These elements may be implemented using hardware, software, or a combination thereof. Whether these elements are implemented as hardware or software depends on the specific application and design constraints imposed on the overall system.

[0022] Although various aspects may be described herein using terms generally associated with 5G or new radio (NR) radio access technology (RAT), various aspects of the present disclosure may be applicable to other RATs, such as 3G RAT, 4G RAT and / or RATs beyond 5G (e.g., 6G).

[0023] Figure 11 is a diagram illustrating an example of a wireless network 100 according to the present disclosure. The wireless network 100 may be a 5G (e.g., NR) network and / or a 4G (e.g., Long Term Evolution (LTE)) network or may include elements of a 5G (e.g., NR) network and / or elements of a 4G (e.g., Long Term Evolution (LTE)) network, etc. The wireless network 100 may include one or more network nodes 110 (shown as network node 110a, network node 110b, network node 110c, and network node 110d), user equipment (UE) 120 or multiple UEs 120 (shown as UE 120a, UE 120b, UE 120c, UE 120d, and UE 120e), and / or other entities. The network node 110 is a network node that communicates with the UE 120. As shown in the figure, the network node 110 may include one or more network nodes. For example, the network node 110 may be a converged network node, which means that the converged network node is configured to utilize a radio protocol stack that is physically or logically integrated within a single radio access network (RAN) node (e.g., within a single device or unit). As another example, the network node 110 may be a decomposed network node (sometimes referred to as a decomposed base station), which means that the network node 110 is configured to utilize a protocol stack that is physically or logically distributed between two or more nodes (such as one or more central units (CUs), one or more distributed units (DUs), or one or more radio units (RUs)).

[0024] In some examples, the network node 110 is a network node that communicates with the UE 120 via a radio access link, such as an RU, or includes a network node that communicates with the UE 120 via a radio access link, such as an RU. In some examples, the network node 110 is a network node that communicates with other network nodes 110 via a fronthaul link or a midhaul link, such as a DU, or includes a network node that communicates with other network nodes 110 via a fronthaul link or a midhaul link, such as a DU. In some examples, the network node 110 is a network node that communicates with other network nodes 110 via a midhaul link or communicates with a core network via a backhaul link, such as a CU, or includes a network node that communicates with other network nodes 110 via a midhaul link or communicates with a core network via a backhaul link, such as a CU. In some examples, the network node 110 (such as an aggregated network node 110 or a decomposed network node 110) may include multiple network nodes, such as one or more RUs, one or more CUs, and / or one or more DUs. For example, the network nodes 110 may include NR base stations, LTE base stations, Node Bs, eNBs (e.g., in 4G), gNBs (e.g., in 5G), access points, transmit receive points (TRPs), DUs, RUs, CUs, mobility elements of a network, core network nodes, network elements, network equipment, RAN nodes, or combinations thereof. In some examples, the network nodes 110 may be interconnected to each other or to one or more other network nodes 110 in the wireless network 100 via various types of fronthaul interfaces, midhaul interfaces, and / or backhaul interfaces (such as direct physical connections, air interfaces, or virtual networks, using any suitable transport network).

[0025] In some examples, the network node 110 may provide communication coverage for a particular geographic area. In the Third Generation Partnership Project (3GPP), the term "cell" may refer to the coverage area of ​​the network node 110 and / or the network node subsystem serving the coverage area, depending on the context in which the term is used. The network node 110 may provide communication coverage for a macro cell, a pico cell, a femto cell, and / or another type of cell. A macro cell may cover a relatively large geographic area (e.g., a radius of several kilometers) and may allow unrestricted access by a UE 120 with a service subscription. A pico cell may cover a relatively small geographic area and may allow unrestricted access by a UE 120 with a service subscription. A femto cell may cover a relatively small geographic area (e.g., a home) and may allow restricted access by a UE 120 associated with the femto cell (e.g., a UE 120 in a closed subscriber group (CSG)). A network node 110 for a macro cell may be referred to as a macro network node. A network node 110 for a pico cell may be referred to as a pico network node. The network node 110 for a femto cell may be referred to as a femto network node or a home network node. Figure 1 In the example shown in , network node 110a may be a macro network node for macro cell 102a, network node 110b may be a pico network node for pico cell 102b, and network node 110c may be a femto network node for femto cell 102c. A network node may support one or more (e.g., three) cells. In some examples, a cell may not necessarily be stationary, and the geographic area of ​​a cell may move depending on the location of a mobile network node 110 (e.g., a mobile network node).

[0026] In some aspects, the term "base station" or "network node" may refer to an aggregated base station, a decomposed base station, an integrated access and backhaul (IAB) node, a relay node, or one or more components thereof. For example, in some aspects, a "base station" or "network node" may refer to a CU, a DU, a RU, a near real-time (near RT) RAN intelligent controller (RIC) or a non-real-time (non-RT) RIC, or a combination thereof. In some aspects, the term "base station" or "network node" may refer to a device configured to perform one or more functions (such as those described herein in conjunction with network node 110). In some aspects, the term "base station" or "network node" may refer to multiple devices configured to perform one or more functions. For example, in some distributed systems, each of a number of different devices (which may be located in the same geographic location or in different geographic locations) may be configured to perform at least a portion of a function, or to repeat the execution of at least a portion of the function, and the term "base station" or "network node" may refer to any one or more of these different devices. In some aspects, the term "base station" or "network node" may refer to one or more virtual base stations or one or more virtual base station functions. For example, in some aspects, two or more base station functions may be instantiated on a single device. In some aspects, the term "base station" or "network node" may refer to one of the base station functions but not the other. In this way, a single device may include more than one base station.

[0027] The wireless network 100 may include one or more relay stations. A relay station is a network node that can receive transmissions of data from an upstream node (e.g., a network node 110 or a UE 120) and transmit transmissions of data to a downstream node (e.g., a UE 120 or a network node 110). A relay station may be a UE 120 that can relay transmissions for other UEs 120. Figure 1 In the example shown in , a network node 110d (e.g., a relay network node) may communicate with a network node 110a (e.g., a macro network node) and a UE 120d to facilitate communication between the network node 110a and the UE 120d. A network node 110 that relays communications may be referred to as a relay station, a relay base station, a relay network node, a relay node, a relay, etc.

[0028] The wireless network 100 may be a heterogeneous network that includes different types of network nodes 110, such as macro network nodes, pico network nodes, femto network nodes, relay network nodes, etc. These different types of network nodes 110 may have different transmit power levels, different coverage areas, and / or different impacts on interference in the wireless network 100. For example, a macro network node may have a high transmit power level (e.g., 5 watts to 40 watts), while a pico network node, a femto network node, and a relay network node may have a lower transmit power level (e.g., 0.1 watt to 2 watts).

[0029] The network controller 130 may be coupled to or in communication with a set of network nodes 110 and may provide coordination and control for the network nodes 110. The network controller 130 may communicate with the network nodes 110 via a backhaul communication link or a midhaul communication link. The network nodes 110 may also communicate directly with each other or indirectly via a wireless or wired backhaul communication link. In some aspects, the network controller 130 may be or may include a CU or a core network device.

[0030] UE 120 can be distributed throughout the wireless network 100, and each UE 120 can be stationary or mobile. UE 120 can include, for example, an access terminal, a terminal, a mobile station, and / or a subscriber unit. UE 120 can be a cellular phone (e.g., a smart phone), a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet computer, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device, a biometric device, a wearable device (e.g., a smart watch, smart clothing, smart glasses, a smart wristband, smart jewelry (e.g., a smart ring or a smart bracelet)), an entertainment device (e.g., a music device, a video device, and / or a satellite radio), a vehicle component or sensor, a smart meter / sensor, an industrial manufacturing equipment, a global positioning system device, a UE function of a network node, and / or any other suitable device configured to communicate via a wireless or wired medium.

[0031] Some UEs 120 may be considered as machine type communication (MTC) or evolved or enhanced machine type communication (eMTC) UEs. MTC UEs and / or eMTC UEs may include, for example, robots, drones, remote devices, sensors, meters, monitors, and / or location tags, which may communicate with a network node, another device (e.g., a remote device), or some other entity. Some UEs 120 may be considered as Internet of Things (IoT) devices, and / or may be implemented as NB-IoT (narrowband IoT) devices. Some UEs 120 may be considered as customer premises equipment. UE 120 may be included inside a housing that houses components of UE 120, such as a processor component and / or a memory component. In some examples, a processor component and a memory component may be coupled together. For example, a processor component (e.g., one or more processors) and a memory component (e.g., a memory) may be operably coupled, communicatively coupled, electronically coupled, and / or electrically coupled.

[0032] In general, any number of wireless networks 100 may be deployed in a given geographic area. Each wireless network 100 may support a specific RAT and may operate on one or more frequencies. RAT may be referred to as a radio technology, air interface, etc. Frequency may be referred to as a carrier, frequency channel, etc. Each frequency may support a single RAT in a given geographic area to avoid interference between wireless networks of different RATs. In some cases, NR or 5G RAT networks may be deployed.

[0033] In some examples, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly using one or more side link channels (e.g., without using network node 110 as an intermediary to communicate with each other). For example, UE 120 may communicate using peer-to-peer (P2P) communication, device-to-device (D2D) communication, vehicle-to-everything (V2X) protocols (e.g., which may include vehicle-to-vehicle (V2V) protocols, vehicle-to-infrastructure (V2I) protocols, or vehicle-to-pedestrian (V2P) protocols), and / or mesh networks. In such examples, UE 120 may perform scheduling operations, resource selection operations, and / or other operations described elsewhere herein as being performed by network node 110.

[0034] The devices of the wireless network 100 may communicate using an electromagnetic spectrum that may be subdivided into various categories, bands, channels, etc., based on frequency or wavelength. For example, the devices of the wireless network 100 may communicate using one or more operating bands. In 5G NR, two initial operating bands have been identified as frequency ranges designated FR1 (410 MHz to 7.125 GHz) and FR2 (24.25 GHz to 52.6 GHz). It should be understood that, although portions of FR1 are greater than 6 GHz, FR1 is often (interchangeably) referred to as the "below 6 GHz" band in various documents and articles. Similar naming issues sometimes occur with respect to FR2, which is often (interchangeably) referred to as the "millimeter wave" band in documents and articles, although different from the extremely high frequency (EHF) band (30 GHz to 300 GHz) identified as the "millimeter wave" band by the International Telecommunication Union (ITU).

[0035] Frequencies between FR1 and FR2 are generally referred to as mid-band frequencies. Recent 5G NR research has identified the operating bands for these mid-band frequencies as frequency range designation FR3 (7.125GHz to 24.25GHz). The bands falling within FR3 can inherit FR1 characteristics and / or FR2 characteristics, and therefore the features of FR1 and / or FR2 can be effectively extended to mid-band frequencies. In addition, currently higher frequency bands are being explored to extend 5G NR operations to more than 52.6GHz. For example, three higher operating bands have been identified as frequency range designations FR4a or FR4-1 (52.6GHz to 71GHz), FR4 (52.6GHz to 114.25GHz) and FR5 (114.25GHz to 300GHz). Each of these higher frequency bands falls within the EHF band.

[0036] In view of the above examples, unless otherwise specifically stated, it should be understood that where the term "below 6 GHz" or the like is used herein, the term may broadly refer to frequencies that may be lower than 6 GHz, may be within FR1, or may include mid-band frequencies. In addition, unless otherwise specifically stated, it should be understood that where the term "millimeter wave" or the like is used herein, the term may broadly refer to frequencies that may include mid-band frequencies, may be within FR2, FR4, FR4-a or FR4-1, and / or FR5, or may be within the EHF band. It is contemplated that the frequencies included in these operating bands (e.g., FR1, FR2, FR3, FR4, FR4-a, FR4-1, and / or FR5) may be modified, and the techniques described herein are applicable to those modified frequency ranges.

[0037] In some aspects, UE 120 may include a communication manager 140. As described in more detail elsewhere herein, the communication manager 140 may obtain measurement information about a reference signal set; generate predicted blocking information using a model based at least in part on the measurement information; and send a report indicating the predicted blocking information, wherein the report indicates at least one of: a prediction window associated with the predicted blocking information, a time associated with an occurrence of a predicted blocking event, a severity value associated with the predicted blocking event, or a direction value associated with the predicted blocking event. Additionally or alternatively, the communication manager 140 may perform one or more other operations described herein.

[0038] As mentioned above, Figure 1 are provided as examples. Other examples may vary from Figure 1 An example of description.

[0039] Figure 2 2 is a diagram illustrating an example 200 of a network node 110 communicating with a UE 120 in a wireless network 100 according to the present disclosure. The network node 110 may be equipped with a set of antennas 234a to 234t, such as T antennas (T≥1). The UE 120 may be equipped with a set of antennas 252a to 252r, such as R antennas (R≥1). The network node 110 of example 200 includes one or more radio frequency components, such as an antenna 234 and a modem 254. In some examples, the network node 110 may include an interface, a communication component, or another component that facilitates communication with the UE 120 or another network node. Some network nodes 110 may not include a radio frequency component that facilitates direct communication with the UE 120, such as one or more CUs or one or more DUs.

[0040] At the network node 110, a transmit processor 220 may receive data intended for a UE 120 (or a set of UEs 120) from a data source 212. The transmit processor 220 may select one or more modulation and coding schemes (MCS) for the UE 120 based at least in part on one or more channel quality indicators (CQIs) received from the UE 120. The network node 110 may process (e.g., encode and modulate) the data for the UE 120 based at least in part on the MCS selected for the UE 120, and may provide data symbols for the UE 120. The transmit processor 220 may process system information (e.g., for semi-static resource partitioning information (SRPI)) and control information (e.g., CQI requests, grants, and / or upper layer signaling), and provide overhead symbols and control symbols. The transmit processor 220 may generate reference symbols for reference signals (e.g., cell-specific reference signals (CRS) or demodulation reference signals (DMRS)) and synchronization signals (e.g., primary synchronization signals (PSS) or secondary synchronization signals (SSS)). The transmit (TX) multiple-input multiple-output (MIMO) processor 230 may perform spatial processing (e.g., precoding) on ​​data symbols, control symbols, overhead symbols, and / or reference symbols, where applicable, and may provide a set of output symbol streams (e.g., T output symbol streams) to a corresponding set of modems 232 (e.g., T modulators) (shown as modems 232a to 232t). For example, each output symbol stream may be provided to a modulator component (shown as MOD) of the modem 232. Each modem 232 may process a corresponding output symbol stream (e.g., for OFDM) using a corresponding modulator component to obtain an output sample stream. Each modem 232 may also process (e.g., convert to analog, amplify, filter, and / or upconvert) the output sample stream using a corresponding modulator component to obtain a downlink signal. The modems 232a through 232t may transmit a set of downlink signals (eg, T downlink signals) via a corresponding set of antennas 234 (eg, T antennas) (shown as antennas 234a through 234t).

[0041] At the UE 120, a set of antennas 252 (shown as antennas 252a to 252r) may receive downlink signals from the network node 110 and / or other network nodes 110 and may provide a set of received signals (e.g., R received signals) to a set of modems 254 (e.g., R modems) (shown as modems 254a to 254r). For example, each received signal may be provided to a demodulator component (shown as DEMOD) of the modem 254. Each modem 254 may use a corresponding demodulator component to condition (e.g., filter, amplify, downconvert, and / or digitize) the received signal to obtain input samples. Each modem 254 may use a demodulator component to further process the input samples (e.g., for OFDM) to obtain received symbols. A MIMO detector 256 may obtain received symbols from the modem 254, may perform MIMO detection on the received symbols where applicable, and may provide detected symbols. The receive processor 258 may process (e.g., demodulate and decode) the detected symbols, may provide decoded data for the UE 120 to the data sink 260, and may provide decoded control information and system information to the controller / processor 280. The term "controller / processor" may refer to one or more controllers, one or more processors, or a combination thereof. The channel processor may determine a reference signal received power (RSRP) parameter, a received signal strength indicator (RSSI) parameter, a reference signal received quality (RSRQ) parameter, and / or a CQI parameter, among other things. In some examples, one or more components of the UE 120 may be included in the housing 284.

[0042] The network controller 130 may include a communication unit 294, a controller / processor 290, and a memory 292. The network controller 130 may include, for example, one or more devices in a core network. The network controller 130 may communicate with the network node 110 via the communication unit 294.

[0043] One or more antennas (e.g., antennas 234a to 234t and / or antennas 252a to 252r) may include or be included within one or more antenna panels, one or more antenna groups, one or more sets of antenna elements, and / or one or more antenna arrays, etc. Antenna panels, antenna groups, sets of antenna elements, and / or antenna arrays may include one or more antenna elements (in a single housing or multiple housings), sets of coplanar antenna elements, sets of non-coplanar antenna elements, and / or may be coupled to one or more transmit and / or receive components (such as, Figure 2 One or more antenna elements of one or more components in.

[0044] On the uplink, at the UE 120, a transmit processor 264 may receive and process data from a data source 262 and control information from a controller / processor 280 (e.g., for reports including RSRP, RSSI, RSRQ, and / or CQI). The transmit processor 264 may generate reference symbols for one or more reference signals. The symbols from the transmit processor 264 may be pre-decoded by a TX MIMO processor 266, where applicable, further processed by a modem 254 (e.g., for DFT-s-OFDM or CP-OFDM), and transmitted to the network node 110. In some examples, the modem 254 of the UE 120 may include a modulator and a demodulator. In some examples, the UE 120 includes a transceiver. The transceiver may include any combination of an antenna 252, a modem 254, a MIMO detector 256, a receive processor 258, a transmit processor 264, and / or a TX MIMO processor 266. The transceiver may be used by a processor (eg, controller / processor 280) and memory 282 to perform the functions described herein (eg, with reference to Figures 4 to 7 ) or any aspect of any of the methods described herein.

[0045] At the network node 110, uplink signals from the UE 120 and / or other UEs may be received by the antenna 234, processed by the modem 232 (e.g., a demodulator component of the modem 232 shown as DEMOD), detected by the MIMO detector 236 (where applicable), and further processed by the receive processor 238 to obtain decoded data and control information transmitted by the UE 120. The receive processor 238 may provide the decoded data to the data sink 239 and the decoded control information to the controller / processor 240. The network node 110 may include a communication unit 244 and may communicate with the network controller 130 via the communication unit 244. The network node 110 may include a scheduler 246 to schedule one or more UEs 120 for downlink and / or uplink communications. In some examples, the modem 232 of the network node 110 may include a modulator and a demodulator. In some examples, the network node 110 includes a transceiver. The transceiver may include any combination of antenna 234, modem 232, MIMO detector 236, receive processor 238, transmit processor 220, and / or TX MIMO processor 230. The transceiver may be used by a processor (e.g., controller / processor 240) and memory 242 to perform operations described herein (e.g., with reference to Figures 4 to 7 ) or any aspect of any of the methods described herein.

[0046] The controller / processor 240 of the network node 110, the controller / processor 280 of the UE 120, and / or Figure 2 Any other components of the network node 110 may perform one or more techniques associated with blocking prediction reporting, as described in more detail elsewhere herein. For example, the controller / processor 240 of the network node 110, the controller / processor 280 of the UE 120, and / or Figure 2 Any other component that can execute or guide e.g. Figure 6 600 and / or operations of other processes as described herein. Memory 242 and memory 282 may store data and program codes for network node 110 and UE 120, respectively. In some examples, memory 242 and / or memory 282 may include a non-transitory computer-readable medium storing one or more instructions (e.g., code and / or program code) for wireless communication. For example, the one or more instructions, when executed by one or more processors of network node 110 and / or UE 120 (e.g., directly, or after compilation, conversion, and / or interpretation), may cause the one or more processors, UE 120, and / or network node 110 to execute or direct, for example Figure 6 The process 600 and / or operations of other processes as described herein. In some examples, executing instructions may include running instructions, converting instructions, compiling instructions, and / or interpreting instructions, etc.

[0047] In some aspects, the UE 120 includes means for obtaining measurement information about a reference signal set; means for generating predicted blocking information using a model based at least in part on the measurement information; and / or means for sending a report indicating the predicted blocking information, wherein the report indicates at least one of: a prediction window associated with the predicted blocking information, a time associated with an occurrence of a predicted blocking event, a severity value associated with the predicted blocking event, or a direction value associated with the predicted blocking event. Means for UE 120 to perform operations described herein may include, for example, one or more of the communication manager 140, the antenna 252, the modem 254, the MIMO detector 256, the receive processor 258, the transmit processor 264, the TX MIMO processor 266, the controller / processor 280, or the memory 282.

[0048] Although Figure 2 The blocks in the 2000 and 2010 are illustrated as distinct components, but the functionality described above for these blocks may be implemented in a single hardware, software, or combined component or in various combinations of components. For example, the functionality described for the transmit processor 264, the receive processor 258, and / or the TX MIMO processor 266 may be performed by or under the control of the controller / processor 280.

[0049] As mentioned above, Figure 2 are provided as examples. Other examples may vary from Figure 2 An example of description.

[0050] The deployment of a communication system (such as a 5G NR system) can be arranged with various components or components in a variety of ways. In a 5G NR system or network, a network node, a network entity, a mobility element of a network, a RAN node, a core network node, a network element, a base station or network equipment can be implemented in an aggregated or decomposed architecture. For example, a base station (such as a node B (NB), an evolved NB (eNB), a NR BS, a 5GNB, an access point (AP), a TRP or a cell, etc.) or one or more units (or one or more components) performing base station functionality can be implemented as an aggregated base station (also referred to as an independent base station or a monolithic base station) or a decomposed base station. "Network entity" or "network node" may refer to a decomposed base station or one or more units of a decomposed base station (such as one or more CUs, one or more DUs, one or more RUs, or a combination thereof).

[0051] An aggregated base station (e.g., an aggregated network node) may be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node (e.g., within a single device or unit). A decomposed base station (e.g., a decomposed network node) may be configured to utilize a protocol stack that is physically or logically distributed between two or more units (such as one or more CUs, one or more DUs, or one or more RUs). In some examples, a CU may be implemented within a network node, and one or more DUs may be co-located with the CU, or alternatively, may be geographically or virtually distributed across one or more other network nodes. A DU may be implemented to communicate with one or more RUs. Each of a CU, a DU, and a RU may also be implemented as a virtual unit, such as a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU), and the like.

[0052] Base station type operations or network designs may take into account the aggregated nature of base station functionality. For example, a decomposed base station may be utilized in an IAB network, an open radio access network (O-RAN (such as a network configuration initiated by the O-RAN Alliance)), or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN)) to facilitate the scaling of a communication system by separating base station functionality into one or more units that can be deployed separately. A decomposed base station may include functionality implemented across two or more units at various physical locations, as well as functionality implemented virtually for at least one unit, which may enable flexibility in network design. Individual units of a decomposed base station may be configured for wired or wireless communication with at least one other unit of the decomposed base station.

[0053] Figure 33 is a diagram illustrating an example disaggregated base station architecture 300 according to the present disclosure. The disaggregated base station architecture 300 may include a CU 310 that may communicate directly with a core network 320 via a backhaul link, or indirectly with the core network 320 through one or more disaggregated control units, such as a near-RT RIC 325 via an E2 link, or a non-RT RIC 315 associated with a service management and orchestration (SMO) framework 305, or both. The CU 310 may communicate with one or more DUs 330 via respective midhaul links, such as via an F1 interface. Each of the DUs 330 may communicate with one or more RUs 340 via respective fronthaul links. Each of the RUs 340 may communicate with one or more UEs 120 via respective radio frequency (RF) access links. In some implementations, a UE 120 may be served simultaneously by multiple RUs 340.

[0054] Each of the units (including CU 310, DU 330, RU 340) and the near-RT RIC 325, non-RT RIC 315, and SMO framework 305 may include or be coupled to one or more interfaces configured to receive or send signals, data, or information (collectively referred to as signals) via a wired or wireless transmission medium. Each of the units or an associated processor or controller that provides instructions to one or more communication interfaces of the corresponding unit may be configured to communicate with one or more of the other units via a transmission medium. In some examples, each of these units may include a wired interface and a wireless interface, the wired interface being configured to receive signals or send signals to one or more of the other units via a wired transmission medium, the wireless interface being configured to receive signals or send signals to one or more of the other units via a wired transmission medium, or both.

[0055] In some aspects, CU 310 may host one or more higher layer control functions. Such control functions may include radio resource control (RRC) functions, packet data convergence protocol (PDCP) functions, or service data adaptation protocol (SDAP) functions, etc. Each control function may be implemented using an interface that is configured to communicate signals with other control functions hosted by CU 310. CU 310 may be configured to handle user plane functionality (e.g., central unit-user plane (CU-UP) functionality), control plane functionality (e.g., central unit-control plane (CU-CP) functionality), or a combination thereof. In some specific implementations, CU 310 may be logically split into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP unit may communicate bidirectionally with the CU-CP unit via an interface (such as an E1 interface). As needed, CU310 may be implemented to communicate with DU 330 for network control and signaling.

[0056] Each DU 330 may correspond to a logical unit that includes one or more base station functions for controlling the operation of one or more RUs 340. In some aspects, the DU 330 may host one or more of a radio link control (RLC) layer, a MAC layer, and one or more high physical (PHY) layers, at least in part, according to a functional partitioning (such as that defined by 3GPP). In some aspects, one or more high PHY layers may be implemented by one or more modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, etc. In some aspects, the DU 330 may also host one or more low PHY layers, such as one or more modules for fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming or physical random access channel (PRACH) extraction and filtering, etc. Each layer (also referred to as a module) may be implemented using an interface that is configured to communicate signals with other layers (and modules) hosted by the DU 330 or with control functions hosted by the CU 310.

[0057] Each RU 340 may implement lower layer functionality. In some deployments, the RU 340 controlled by the DU 330 may correspond to a logical node that hosts RF processing functions based on functional partitioning (e.g., functional partitioning defined by 3GPP) (such as lower layer functional partitioning), or low PHY layer functions such as performing FFT, performing iFFT, digital beamforming, or PRACH extraction and filtering, etc. In this architecture, each RU 340 may be operated to handle over-the-air (OTA) communications with one or more UEs 120. In some specific implementations, real-time and non-real-time aspects of control and user plane communications with the RU 340 may be controlled by the corresponding DU 330. In some scenarios, this configuration may enable each DU 330 and CU 310 to be implemented in a cloud-based RAN architecture (such as a vRAN architecture).

[0058] The SMO framework 305 may be configured to support RAN deployment and provisioning of non-virtualized network elements and virtualized network elements. For non-virtualized network elements, the SMO framework 305 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which may be managed via an operation and maintenance interface (such as an O1 interface). For virtualized network elements, the SMO framework 305 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) platform 390) to perform network element lifecycle management (such as instantiating virtualized network elements) via a cloud computing platform interface (such as an O2 interface). Such virtualized network elements may include, but are not limited to, CU 310, DU 330, RU 340, non-RT RIC 315, and near-RT RIC 325. In some specific implementations, the SMO framework 305 may communicate with hardware aspects of the 4G RAN (such as an open eNB (O-eNB) 311) via the O1 interface. Additionally, in some implementations, the SMO framework 305 can communicate directly with each of the one or more RUs 340 via a corresponding O1 interface. The SMO framework 305 can also include a non-RT RIC 315 configured to support the functionality of the SMO framework 305.

[0059] The non-RT RIC 315 may be configured to include logic functions that enable non-real-time control and optimization of RAN elements and resources, artificial intelligence / machine learning (AI / ML) workflows including model training and updating, or policy-based guidance of applications / features in the near-RT RIC 325. The non-RT RIC 315 may be coupled to or in communication with the near-RT RIC 325 (such as via an A1 interface). The near-RT RIC 325 may be configured to include logic functions that enable near-real-time control and optimization of RAN elements and resources via data collection and actions through an interface (such as via an E2 interface) that connects one or more CUs 310, one or more DUs 330, or both, and the O-eNB with the near-RT RIC 325.

[0060] In some implementations, in order to generate an AI / ML model to be deployed in the near-RT RIC 325, the non-RT RIC 315 may receive parameters or external enrichment information from an external server. Such information may be utilized by the near-RT RIC 325 and may be received from a non-network data source or from a network function at the SMO framework 305 or the non-RT RIC 315. In some examples, the non-RT RIC 315 or the near-RT RIC 325 may be configured to tune RAN behavior or performance. For example, the non-RT RIC 315 may monitor long-term trends and patterns of performance and employ AI / ML models to perform corrective actions through the SMO framework 305 (such as via reconfiguration of the O1 interface) or via the creation of RAN management policies (such as A1 interface policies).

[0061] As mentioned above, Figure 3 are provided as examples. Other examples may vary from Figure 3 An example of description.

[0062] Wireless communication devices (such as UE 120 and network node 110) can communicate using beamforming. Beamforming involves generating beams by manipulating the electrical characteristics (e.g., phase, amplitude) of antennas relative to each other. Beamforming is useful for higher frequency communications such as in FR2 or FR4 because such communications experience increased attenuation and are more susceptible to blocking relative to lower frequency communications (such as lower frequency communications in FR1).

[0063] Blockage is a channel condition in which a communication sent by a transmitter is blocked from reaching a receiver to which the communication is directed (such as using a beam). For example, blockage may be caused by an object moving into the propagation path between the transmitter and the receiver. Blockage may result in partial or complete failure of receiving communications. Therefore, it may be beneficial to predict blockage before or when it occurs, so that the transmitter and / or receiver can take mitigation actions (such as selecting an unblocked beam or scheduling around the blockage so that the relevant communication is still received by the receiver). However, the procedures for creating (e.g., training) a model to predict blockage are not well defined. In addition, the reporting format (e.g., the content of the report) for reporting predicted blockage events is not well defined. In the absence of a well-constructed procedure for creating a model and reporting predicted blockage events based at least in part on the model, ambiguity may occur in the training of the model and / or the reporting of predicted blockage events, which may increase the interruption of communications caused by blockage and reduce the efficacy of blockage prediction and reporting.

[0064] Some techniques described herein provide reporting of blocking identified using a model. For example, a UE may send a report indicating whether blocking is predicted, a probability associated with a predicted blocking event, a time associated with a predicted blocking event, a severity associated with a predicted blocking event, a direction associated with a predicted blocking event, and the like. In some aspects, the UE may send a report before the blocking occurs, such that the report indicates a predicted blocking event. Thus, a network node may perform mitigation actions, such as actively switching downlink beams to avoid beam failures due to predicted blocking events. Some techniques described herein provide training of models (such as neural networks) to identify predicted blocking events. For example, some techniques described herein identify techniques for labeling variables associated with a training model and / or an unsupervised learning configuration for training a model, such that the accuracy of predicting blocking is improved. In this way, a UE may train and update a model for predicting blocking, which improves the performance of the model according to conditions at the UE, compared to a "one size fits all" model deployed at many different UEs.

[0065] Figure 4 4 is a diagram illustrating an example 400 of signaling associated with prediction of congestion using wireless sensing. As shown, example 400 includes a UE (e.g., UE 120) and a network node (e.g., network node 110). In some aspects, the UE and the network node may have a communication link. For example, the UE and the network node may communicate via the communication link using a beam. The techniques described herein provide predictions of congestion of the communication link and reports associated with the predictions.

[0066] In some aspects, the UE may receive configuration information from a network node. For example, the configuration information may include a configuration for blocking prediction using wireless sensing. In some aspects, the configuration may indicate content to be included in a report indicating predicted blocking information. For example, the configuration may indicate one or more values ​​to be included in the predicted blocking information. In some aspects, the configuration may indicate one or more parameters associated with a model for blocking prediction, such as one or more parameters for training the model, one or more outputs to be generated by the model, etc. In some aspects, the configuration information may be based at least in part on capability information sent by the UE, which may indicate a capability for blocking prediction, one or more requested parameters of the model, etc.

[0067] As indicated by reference numeral 410, the UE may obtain measurement information about a reference signal set. A network node (e.g., a radio unit associated with the network node) may transmit the reference signal set. In some aspects, the UE may obtain the measurement information by performing one or more measurements (such as by performing wireless sensing). For example, the UE may perform measurements on a plurality of different beams at different times in an observation window (e.g., a first measurement on a first beam, then a second measurement on a second beam, etc.), which may be referred to as beam scanning. In some aspects, the network node may transmit the reference signal set using beam scanning. For example, the network node may transmit reference signals on a plurality of different beams at different times (e.g., transmitting a first reference signal at a first time, transmitting a second reference signal at a second time, etc.). In some aspects, both the UE and the network node may perform beam scanning. In some aspects, the measurement information may indicate a measurement value associated with the reference signal set. For example, the measurement value may include an RSRP value. In some aspects, the measurement value may include a measurement value across a plurality of beams within a time period. The time period to which the measurement information relates may be referred to herein as an observation window. In some aspects, the measurement information may include information about a reference signal set across a range of beam directions and across an observation window. For example, the measurement information may include information about a plurality of beams distributed spatially (e.g., associated with a plurality of beam indices). In some aspects, the UE may obtain the measurement information (and / or the network node may send a reference signal set) based on a periodicity. For example, the periodicity may be indicated in the configuration information. In some aspects, the measurement information may be intra-band. For example, the measurement information may relate to measurements performed in the same frequency range or channel bandwidth as the communication between the UE and the network node. In some aspects, these measurements may be mmWave measurements. As described below, the measurement information may be used to predict blocking in a prediction window. The prediction window is a time window in which the UE (e.g., a model) predicts predicted blocking events.

[0068] As shown by reference numeral 420, the UE may use a model to generate predicted blocking information. In some aspects, the model may include a neural network, such as a convolutional neural network (CNN) or a recurrent neural network. The model may be trained using a machine learning algorithm, such as in combination with Figure 5 The model may receive as input the measurement information, information derived from the measurement information, or a portion of the measurement information. The model may output predicted blocking information. Reporting predicted blocking information is shown in association with reference numeral 430 and is described in more detail below.

[0069] The predicted blocking information may be associated with a prediction window. A prediction window is a time window in which the UE predicts a predicted blocking event. For example, the predicted blocking information may indicate one or more predicted blocking events predicted to occur in the time window and / or information associated with the one or more predicted blocking events, as described below.

[0070] In some aspects, the predicted blocking information may indicate a predicted blocking event. For example, the predicted blocking information may indicate whether the blocking is predicted to occur in the prediction window, which may be referred to as a binary output. As another example, the predicted blocking information may indicate a probability associated with the predicted blocking event. For example, the predicted blocking information may be associated with granularity. The predicted blocking information may indicate a probability according to granularity. For example, at granularity 2, a first value may indicate a probability of 0% to 50% of blocking, and a second value may indicate a probability of 50% to 100% of blocking. In some aspects, the granularity may be configurable. In some aspects, a granularity may be selected from a plurality of granularities that provide flexibility between the accuracy of the predicted blocking information and the reduction of overhead. In some aspects, the UE may, for example, select the granularity based at least in part on a confidence level regarding the occurrence of a predicted blocking event.

[0071] In some aspects, the predicted blocking information may indicate a time associated with the predicted occurrence of the blocking event. For example, the time may occur within a prediction window. In some aspects, the predicted blocking information indicates the time based at least in part on at least one of a time value or a time range. For example, the predicted blocking information may indicate a time window that identifies a predicted start of a predicted blocking event, a predicted end of the blocking, a predicted center time of a predicted blocking event, or a combination thereof, wherein the time window represents the predicted occurrence of the blocking event. As another example, the predicted blocking information may indicate a time range (which may indicate a confidence level) for the predicted blocking event, such as a standard deviation (in terms of time) associated with the time value, wherein the time range represents the predicted occurrence of the blocking event. For example, the predicted blocking information may identify a predicted blocking event that occurs at a time of 20 ms plus or minus 5 ms after the start of the prediction window. In this way, the UE can provide visibility to the network node about when the predicted blocking event is predicted to occur and how quickly the active beam change should be triggered.

[0072] Additionally or alternatively, the predicted congestion information may indicate a selected time interval of the prediction window indicating the time. For example, the prediction window may be divided into a plurality of time intervals. The predicted congestion information may include an index corresponding to the time interval for which the predicted occurrence of the congestion event is predicted. As an example, the prediction window may be divided into 8 time intervals, and 3 bits may be used to indicate the time interval including the predicted occurrence of the congestion event, which may reduce overhead relative to explicit signaling of time values ​​and / or time ranges.

[0073] In some aspects, the predicted blocking information may indicate a severity value associated with the predicted blocking event. In some aspects, the severity value may indicate the expected duration of the predicted blocking event. For example, a longer predicted blocking event may be considered more severe than a shorter predicted blocking event. In some aspects, the severity value may include a quantized value. For example, a first value may indicate a first expected duration range (e.g., 0ms to 200ms), a second value may indicate a second expected duration range (e.g., 200ms to 500ms), etc. In some aspects, the severity value may be based at least in part on a signature of the measurement information. For example, different obstructions (e.g., different obstacles that cause predicted blocking events, such as bicycles, motorcycles, trucks, buses, etc.) may be associated with different signatures in the measurement information. The model may output an indication of the severity value. The indication may be based at least in part on a signature in the measurement information.

[0074] In some aspects, the severity value may indicate a degree of blocking. For example, a predicted blocking event may result in degradation of a communication link between a UE and a network node without completely blocking the communication link. In this example, the predicted blocking information may indicate a predicted degree of blocking associated with the predicted blocking.

[0075] In some aspects, the predicted blocking information may indicate a direction value associated with the predicted blocking event. The direction value may indicate a direction associated with an obstruction causing the predicted blocking event. In some aspects, the direction value may be binary. For example, a predicted blocking event originating from a first direction in two directions (such as a first direction on a two-way street) may be associated with a first direction value, and a predicted blocking event originating from a second direction in two directions may be associated with a second direction value. In some aspects, the direction value may be expressed relative to a reference point (which may be a direction associated with a beam, a geographic location, a coordinate set, etc.). For example, the reference point may be known to the UE and the network node. The first direction value (wherein the direction value may be binary or may not be binary) may indicate that the predicted blocking event originates from a first direction relative to the reference point, and the second direction value may indicate that the predicted blocking event originates from a second direction relative to the reference point. Providing a prediction of the direction value may help identify candidate beams for beam failure recovery procedures. For example, in the case where the UE predicts that the direction of the moving obstruction is from left to right, the network node may indicate a candidate beam in the opposite direction of the moving obstruction, which has a lower probability of being blocked.

[0076] In some aspects, the direction value indicates one or more parameters related to the movement associated with the mobile blocker. For example, the blocker associated with the predicted blocking event (e.g., causing the predicted blocking event) can be in motion relative to the UE and / or the network node associated with the UE (e.g., communicating with the UE, serving the UE). The one or more parameters can indicate the direction associated with the blocker (e.g., vector, indication to the left or right, indication to the hemisphere, etc.), the moving speed associated with the blocker (e.g., line speed, line speed, angular speed, angular speed, etc.), the moving direction associated with the blocker (e.g., vector, basic direction, etc.), etc. In some aspects, the one or more parameters can be expressed relative to a reference point, as described above. In some aspects, the one or more parameters can be related to the movement of the blockage associated with the predicted blocking event. For example, the one or more parameters can indicate the direction, moving speed and / or moving direction associated with the blockage (which can be represented as a blocking area on a sphere around the UE). In some aspects, the direction value can be based at least in part on at least one of the movement of the UE or the movement of the network node associated with the UE. For example, the UE may consider the direction, movement rate, orientation and / or movement direction of the UE and / or network nodes associated with the UE (e.g., serving the UE, communicating with the UE) when determining a direction value associated with a predicted congestion event.

[0077] As shown by reference numeral 430, the UE may send a report indicating predicted blocking information. For example, the UE may send the report via uplink control information (UCI), a medium access control (MAC) control element, etc. In some aspects, the UE may send the report periodically (e.g., every X ms, where X is a quantity). In some aspects, the UE may send the report based at least in part on identifying a predicted blocking event. For example, the UE may send the report in response to identifying a predicted blocking event, and the predicted blocking information may indicate the predicted blocking event. In some aspects, the report may include information indicating a duration of the prediction window, which enables the network node to actively switch the downlink beam of the UE to avoid beam failure.

[0078] In some aspects, the report may indicate a time associated with the predicted blocking event. For example, the report may indicate a time value, a time range, and / or a selected interval associated with the predicted blocking event, as described in conjunction with reference numeral 420. In some aspects, the report may indicate a severity value associated with the predicted blocking event. For example, the report may indicate an expected duration, a quantized version of the expected duration, etc., as described in conjunction with reference numeral 420. In some aspects, the report may indicate a direction value associated with the predicted blocking event. For example, the report may indicate a binary or non-binary direction value, which may be defined relative to a reference point, as described with respect to reference numeral 420.

[0079] As indicated by reference numeral 440, the network node may perform one or more actions based at least in part on the report. For example, the network node may switch the UE to a beam that is not associated with the predicted blocking event. As another example, the network node may identify a set of UEs associated with the predicted blocking event based at least in part on the predicted blocking information, and may switch beams associated with the set of UEs so that communications with the set of UEs are not interrupted.

[0080] As shown in reference numeral 450, the UE may train the model. For example, the UE may use a machine learning algorithm (e.g., a machine learning module) to train the model. Figure 5 The training of modules using machine learning algorithms is described in more detail.

[0081] In some aspects, the UE may train a model based at least in part on information collected by the UE. For example, the UE may train a model based at least in part on beam failure indications associated with a beam failure detection (BFD) process. In the case where a predicted blocking event is associated with a beam failure indication, the UE may assign a first label (e.g., 1) to the predicted blocking event. In the case where the predicted blocking event is not associated with the beam failure indication (i.e., in the case where no blocking actually occurs), the UE may assign a second label (e.g., 0) to the predicted blocking event. As another example, in the case where the predicted blocking event occurs at a time within a threshold time length from the time associated with the beam failure indication in the prediction window, the UE may assign a first label, and in the case where the predicted blocking event is not at a threshold time from the time associated with the beam failure indication in the prediction window, the UE may assign a second label. In some aspects, the time associated with the beam failure indication may be measured from the end of the observation window to the time when the beam failure indication (or observed blocking associated with the beam failure indication) occurs. As yet another example, in the event that the duration of the predicted congestion event is within a threshold length of a duration associated with a beam failure indication associated with the serving beam (such as the duration between when a BFD occurs and when a connection is restored using the beam associated with the BFD), the UE may assign a first label to the severity value, and in the event that the duration of the predicted congestion event is not within the threshold length of the duration associated with the beam failure indication, the UE may assign a second label. In some aspects, the serving beam may be a line of sight (LOS) beam.

[0082] In some aspects, the UE may train a model based at least in part on signals from sensors. For example, the signals from the sensors may provide information that can be used to determine whether obstruction is observed and / or to determine information associated with predicted obstruction. In some examples, the sensor is an obstruction sensor. In some aspects, the sensor may include or be associated with a camera. For example, the UE may determine a direction associated with the obstruction based at least in part on a plurality of images or videos captured by the camera (e.g., the images or videos may indicate whether the source of the obstruction is moving in a first direction or in a second direction). The UE may assign a label to a direction value of the predicted obstruction information based at least in part on the signals from the sensors.

[0083] In some aspects, the UE may train the model based at least in part on an indication of a relative direction of a downlink beam. For example, the UE may receive an indication of a relative direction of a downlink beam from a network node. For example, the relative direction may be based at least in part on the downlink beam direction in azimuth and / or elevation (e.g., "relative direction" in azimuth may be expressed as left (beam pointing to the left side of the gNB panel), center, or right). The UE may train the model based at least in part on the indication of the relative direction. For example, the UE may utilize the indication of the relative direction of the downlink beam to perform an unsupervised learning technique, such as training a model to identify a direction value.

[0084] As mentioned above, Figure 4 are provided as examples. Other examples may vary from Figure 4 An example of description.

[0085] Figure 5 5 is a diagram illustrating an example 500 of training and using a model in conjunction with predicting blocking according to the present disclosure. A machine learning system may be used to perform the model training and use described herein. The machine learning system may include, or may be included in, a computing device, a server, a cloud computing environment, etc. (such as UE 120 described in more detail elsewhere herein).

[0086] The model may be trained using an observation set, as indicated by reference numeral 505. The observation set may be obtained from training data (eg, historical data), such as data collected during one or more processes described herein.

[0087] As shown by reference numeral 510, the observation set includes a feature set. The feature set may include a variable set, and the variables may be referred to as features. A particular observation may include a set of variable values ​​(or feature values) corresponding to the variable set. In some implementations, the machine learning system may determine the variables of the observation set and / or the variable values ​​of a particular observation based on input received from a network node. For example, the machine learning system may identify the feature set (e.g., one or more features and / or feature values) by extracting the feature set from structured data and / or by receiving input from an operator.

[0088] As an example, a feature set for an observation set may include a first feature of measurement information, a second feature of a beam failure indication, a third feature of a sensor signal, a fourth feature of an indication of a relative direction associated with a beam, and the like. These feature values ​​are provided as examples and may be different in other examples. For example, a feature set may include an observed time associated with a blockage, an observed severity value associated with a blockage, a blockage, and the like. In some examples, one or more features of a feature set may be used to train a model without being used to use the model. In some other examples, each feature of a feature set may be used to train and use a model. In some examples, beam failure indications and / or sensor signals may be used to train and use a model.

[0089] As indicated by reference numeral 515, a set of observations may be associated with a target variable. A target variable may represent a variable having a numeric value, may represent a variable having a numeric value that falls within a range of values ​​or has some discrete possible values, may represent a variable that can be selected from one of a plurality of options (e.g., one of a plurality of classes, categories, or labels), and / or may represent a variable having a Boolean value. A target variable may be associated with a target variable value, and the target variable value may be specific to an observation.

[0090] The target variable may represent the value that the model is being trained to predict, and the feature set may represent the variables that are input to the trained model to predict the value of the target variable. The observation set may include the target variable values, so that the model can be trained to recognize patterns in the feature set that lead to the target variable values. A model trained to predict the target variable values ​​may be referred to as a supervised learning model.

[0091] In some implementations, the model may be trained on an observation set that does not include a target variable. This may be referred to as an unsupervised learning model. In this case, the model may learn patterns from the observation set without labeling or supervision, and may provide outputs indicative of such patterns, such as by identifying groups of related items within the observation set using clustering and / or association.

[0092] As shown by reference numeral 520, the machine learning system can train a model using the set of observations and using one or more machine learning algorithms, such as a regression algorithm, a decision tree algorithm, a neural network algorithm, a k-nearest neighbor algorithm, a support vector machine algorithm, etc. After training, the machine learning system can store the model as a trained model 525 for use in analyzing new observations.

[0093] As shown by reference numeral 530, the machine learning system may apply the trained model 525 to the new observation, such as by receiving the new observation and inputting the new observation into the trained model 525. As shown, as an example, the new observation may include a first feature of the measurement information. The machine learning system may apply the trained model 525 to the new observation to generate an output (e.g., a result). The type of output may depend on the type of model and / or the type of machine learning task being performed. For example, the output may include a predicted value of a target variable, such as when supervised learning is employed. Additionally or alternatively, the output may include information identifying a cluster to which the new observation belongs and / or information indicating a degree of similarity between the new observation and one or more other observations, such as when unsupervised learning is employed.

[0094] As an example, the trained model 525 can predict a blockage based at least in part on the newly observed measurement information, as shown by reference numeral 535. Based on the prediction, the machine learning system can send a report indicating the predicted blockage event and / or predicted blockage information associated with the predicted blockage event.

[0095] In some embodiments, the recommendation and / or automated action associated with the new observation may be based on a target variable value having a particular label (e.g., a classification or categorization), may be based on whether the target variable value satisfies one or more thresholds (e.g., whether the target variable value is greater than a threshold, less than a threshold, equal to a threshold, falls within a threshold range, etc.), and / or may be based on the cluster into which the new observation is classified.

[0096] In some implementations, the feedback information can be used to retrain the trained model 525, such as in combination with Figure 4 450. For example, feedback can be provided to the model. The feedback can be associated with actions performed based on recommendations provided by the trained model 525 and / or automated actions performed or caused by the trained model 525. In other words, the recommendations and / or actions output by the trained model 525 can be used as input to retrain the model (e.g., a feedback loop can be used to train and / or update the model). For example, the feedback information can include information about predicted blocking events, as described in conjunction with reference numerals 420 and 450.

[0097] As mentioned above, Figure 5 are provided as examples. Other examples can be combined with Figure 5 Described differently.

[0098] Figure 6 is a diagram illustrating an example process 600 performed, for example, by a UE in accordance with the present disclosure. Example process 600 is an example in which a UE (eg, UE 120) performs operations associated with congestion prediction reporting.

[0099] like Figure 6 As shown, in some aspects, process 600 may include obtaining measurement information about a reference signal set (block 610). For example, as described above, a UE (e.g., using Figure 7 The communication manager 140 and / or the measurement component 708 depicted in FIG. 1 can obtain measurement information about the reference signal set.

[0100] like Figure 6 As further shown in FIG. 6 , in some aspects, process 600 may include generating predicted blocking information based at least in part on the measurement information using the model (block 620). For example, as described above, the UE (e.g., using Figure 7 The communication manager 140 and / or prediction component 710 depicted in FIG. 1 may use the model to generate predicted blocking information based at least in part on the measurement information.

[0101] like Figure 6 As further shown in FIG. 6 , in some aspects, process 600 may include sending a report indicating predicted congestion information, wherein the report indicates at least one of: a prediction window associated with the predicted congestion information, a time associated with an occurrence of a predicted congestion event, a severity value associated with the predicted congestion event, or a direction value associated with the predicted congestion event (block 630). For example, a UE (e.g., using Figure 7 The communication manager 140 and / or the sending component 704 depicted in FIG. 1 may send a report indicating predicted congestion information, wherein the report indicates at least one of: a prediction window associated with the predicted congestion information, a time associated with an occurrence of a predicted congestion event, a severity value associated with the predicted congestion event, or a direction value associated with the predicted congestion event, as described above.

[0102] Process 600 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0103] In a first aspect, the predicted congestion information indicates a time based at least in part on at least one of a time value or a time range.

[0104] In a second aspect, alone or in combination with the first aspect, the predicted congestion information indicates a selected time interval of the prediction window indicating time.

[0105] In a third aspect, alone or in combination with one or more of the first and second aspects, the severity value is associated with an expected duration of a predicted occlusive event.

[0106] In a fourth aspect, alone or in combination with one or more of the first to third aspects, the direction value is a binary value.

[0107] In a fifth aspect, alone or in combination with one or more of the first to fourth aspects, the direction value is expressed relative to a reference point.

[0108] In a sixth aspect, either alone or in combination with one or more of the first to fifth aspects, the model is trained based at least in part on a machine learning algorithm.

[0109] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, the model is trained based at least in part on labels assigned to blockages.

[0110] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, the model is trained based at least in part on labels assigned to observed times associated with occlusions.

[0111] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, the model is trained based at least in part on labels assigned to observed severity values ​​associated with obstructions.

[0112] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, the model is trained based at least in part on signals from sensors regarding observed direction values ​​associated with blockages.

[0113] In an eleventh aspect, alone or in combination with one or more of the first to tenth aspects, the model is trained based at least in part on information about downlink beam directions associated with observed directional values ​​associated with blocking.

[0114] although Figure 6 An example block diagram of process 600 is shown, but in some aspects, process 600 may include Figure 6 The blocks in the process 600 may include additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 600. Additionally or alternatively, two or more blocks in the blocks of the process 600 may be performed in parallel.

[0115] Figure 7is a diagram of an example apparatus 700 for wireless communication according to the present disclosure. The apparatus 700 may be a UE, or the UE may include the apparatus 700. In some aspects, the apparatus 700 includes a receiving component 702 and a transmitting component 704, which may communicate with each other (e.g., via one or more buses and / or one or more other components). As shown, the apparatus 700 may communicate with another apparatus 706 (such as a UE, a base station, or another wireless communication device) using the receiving component 702 and the transmitting component 704. As further shown, the apparatus 700 may include a communication manager 140. The communication manager 140 may include one or more of a measurement component 708, a prediction component 710, or a machine learning component 712, etc.

[0116] In some aspects, the apparatus 700 may be configured to perform Figures 4 to 5 Additionally or alternatively, the apparatus 700 may be configured to perform one or more of the processes described herein (such as Figure 6 In some aspects, Figure 7 The device 700 and / or one or more components shown may include a combination of Figure 2 Additionally or alternatively, Figure 7 One or more of the components shown may be combined with Figure 2 Additionally or alternatively, one or more components in the set of components may be implemented at least in part as software stored in a memory. For example, a component (or a portion of a component) may be implemented as instructions or code stored in a non-transitory computer-readable medium and may be executed by a controller or processor to perform the function or operation of the component.

[0117] The receiving component 702 may receive communications from the device 706, such as reference signals, control information, data communications, or combinations thereof. The receiving component 702 may provide the received communications to one or more other components of the device 700. In some aspects, the receiving component 702 may perform signal processing (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, demapping, equalization, interference cancellation, or decoding, etc.) on the received communications and may provide the processed signals to one or more other components of the device 700. In some aspects, the receiving component 702 may include combining Figure 2 One or more antennas, modems, demodulators, MIMO detectors, receive processors, controllers / processors, memories, or combinations thereof of a described UE.

[0118] The transmitting component 704 may transmit communications, such as reference signals, control information, data communications, or combinations thereof, to the device 706. In some aspects, one or more other components of the device 700 may generate communications and may provide the generated communications to the transmitting component 704 for transmission to the device 706. In some aspects, the transmitting component 704 may perform signal processing (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, etc.) on the generated communications and may transmit the processed signals to the device 706. In some aspects, the transmitting component 704 may include combining Figure 2 One or more antennas, modems, modulators, transmit MIMO processors, transmit processors, controllers / processors, memories, or combinations thereof of the described UE. In some aspects, the transmit component 704 can be co-located with the receive component 702 in a transceiver.

[0119] The measuring component 708 can obtain measurement information about the reference signal set. The predicting component 710 can generate predicted blocking information using the model based at least in part on the measurement information. The sending component 704 can send a report indicating the predicted blocking information, wherein the report indicates at least one of: a prediction window associated with the predicted blocking information, a time associated with the occurrence of a predicted blocking event, a severity value associated with the predicted blocking event, or a direction value associated with the predicted blocking event. The machine learning component 712 can train the model using a machine learning algorithm.

[0120] Figure 7 The number and arrangement of components shown are provided as examples. Figure 7 Additional components, fewer components, different components, or components arranged in a different manner than those shown. Figure 7 Two or more components shown may be implemented in a single component, or Figure 7 The single component shown may be implemented as multiple distributed components. Additionally or alternatively, Figure 7 The illustrated set of component(s) may be described as being executable by Figure 7 Another collection of components shown performs one or more functions.

[0121] The following provides an overview of some aspects of the disclosure:

[0122] Aspect 1: A wireless communication method performed by a user equipment (UE), the method comprising: obtaining measurement information about a reference signal set; generating predicted blocking information using a model based at least in part on the measurement information; and sending a report indicating the predicted blocking information, wherein the report indicates at least one of: a prediction window associated with the predicted blocking information, a time associated with an occurrence of a predicted blocking event, a severity value associated with the predicted blocking event, or a direction value associated with the predicted blocking event.

[0123] Aspect 2: The method according to aspect 1, wherein the predicted congestion information indicates the time based at least in part on at least one of a time value or a time range.

[0124] Aspect 3: The method according to any one of aspects 1 to 2, wherein the predicted congestion information indicates a time interval of the prediction window indicating the selection of the time.

[0125] Aspect 4: The method according to any one of aspects 1 to 3, wherein the severity value is associated with an expected duration of the predicted obstructive event.

[0126] Aspect 5: The method according to any one of aspects 1 to 4, wherein the direction value is a binary value.

[0127] Aspect 6: The method according to any one of aspects 1 to 5, wherein the direction value is expressed relative to a reference point.

[0128] Aspect 7: A method according to any one of Aspects 1 to 6, wherein the model is trained at least in part based on a machine learning algorithm.

[0129] Aspect 8: A method according to any one of Aspects 1 to 7, wherein the model is trained at least in part based on labels assigned to blockages.

[0130] Aspect 9: The method according to any one of aspects 1 to 8, wherein the model is trained based at least in part on labels assigned to observed times associated with blockages.

[0131] Aspect 10: The method according to any one of aspects 1 to 9, wherein the model is trained based at least in part on labels assigned to observed severity values ​​associated with obstructions.

[0132] Aspect 11: The method according to any one of aspects 1 to 10, wherein the model is trained based at least in part on signals from sensors regarding observed direction values ​​associated with blockages.

[0133] Aspect 12: A method according to any one of aspects 1 to 11, wherein the model is trained based at least in part on information about downlink beam directions associated with observed direction values ​​associated with blocking.

[0134] Aspect 13: The method according to any one of aspects 1 to 12, wherein the direction value indicates one or more parameters related to movement associated with a mobile obstruction or movement of a blockage associated with the predicted blocking event.

[0135] Aspect 14: The method according to any one of aspects 1 to 13, wherein the direction value is based at least in part on at least one of movement of the UE or movement of a network node associated with the UE.

[0136] Aspect 15: An apparatus for performing wireless communications at a device, the apparatus comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform one or more of the methods described in Aspects 1 to 14.

[0137] Aspect 16: A device for wireless communication, the device comprising a memory and one or more processors coupled to the memory, the one or more processors configured to perform the method according to one or more of aspects 1 to 14.

[0138] Aspect 17: An apparatus for wireless communication, the apparatus comprising at least one means for performing the method according to one or more of aspects 1 to 14.

[0139] Aspect 18: A non-transitory computer-readable medium storing code for wireless communication, the code comprising instructions executable by a processor to perform the method according to one or more of aspects 1 to 14.

[0140] Aspect 19: A non-transitory computer-readable medium storing an instruction set for wireless communication, the instruction set comprising one or more instructions that, when executed by one or more processors of a device, cause the device to perform one or more of the methods described in aspects 1 to 14.

[0141] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the aspects to the precise forms disclosed. Modifications and variations may be made in light of the above disclosure or may be acquired from implementation of the various aspects.

[0142] As used herein, the term "component" is intended to be broadly interpreted as a combination of hardware and / or hardware and software. Whether referred to as software, firmware, middleware, microcode, hardware description language or other names, "software" should be broadly interpreted as meaning instructions, instruction sets, codes, code segments, program codes, programs, subroutines, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures and / or functions, etc. As used herein, a "processor" is implemented in a combination of hardware and / or hardware and software. It will be apparent that the systems and / or methods described herein can be implemented by a combination of hardware and / or hardware and software in different forms. The actual dedicated control hardware or software code used to implement these systems and / or methods does not limit various aspects. Therefore, there is no reference to a specific software code herein to describe the operation and behavior of the system and / or method, because those skilled in the art will understand that software and hardware can be designed to implement the system and / or method based at least in part on the description herein.

[0143] As used herein, "satisfying a threshold" may refer to a value being greater than a threshold, greater than or equal to a threshold, less than a threshold, less than or equal to a threshold, equal to a threshold, not equal to a threshold, etc., depending on the context.

[0144] Although the specific combination of features is set forth in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of various aspects. Many of these features can be combined in a manner that is not specifically described in the claims and / or is not disclosed in the specification. The disclosure of various aspects includes each dependent claim combined with each other claim in the claim set. As used herein, the phrase "at least one of" the item list refers to any combination of these items (it includes a single member). As an example, "at least one of a, b or c" is intended to cover a, b, c, a+b, a+c, b+c and a+b+c, and any combination with multiple identical elements (for example, a+a, a+a+a, a+a+b, a+a+c, a+b+b, a+c+c, b+b, b+b+b, b+b+c, c+c and c+c+c, or any other sorting of a, b and c).

[0145] Any element, action or instruction used herein should not be interpreted as key or necessary unless explicitly stated. In addition, as used herein, the article "one" is intended to include one or more items, and can be used interchangeably with "one or more". In addition, as used herein, the article "said" is intended to include one or more items in conjunction with the article "said", and can be used interchangeably with "one or more". In addition, as used herein, the terms "set" and "group" are intended to include one or more items, and can be used interchangeably with "one or more". If you only want to refer to an item, the phrase "only one" or similar terms will be used. Moreover, as used herein, the terms "have", "have", "have" etc. are intended to be open terms, which do not limit the elements they modify (for example, "having" A elements can also have B). In addition, the phrase "based on" is intended to mean "based at least in part", unless explicitly stated otherwise. Furthermore, as used herein, the term "or" when used in a series is intended to be open-ended and used interchangeably with "and / or" unless explicitly stated otherwise (e.g., when used in conjunction with "either" or "only one of").

Claims

1. A method of wireless communication performed by a user equipment (UE), the method comprising: obtaining measurement information about a set of reference signals; generating predicted blocking information using a model based at least in part on the measurement information; as well as sending a report indicating the predicted blocking information, wherein the report indicates at least one of: a prediction window associated with the predicted blocking information, The time associated with the predicted occurrence of a blocking event, a severity value associated with the predicted blocking event, or A direction value associated with the predicted blocking event. 2 . The method of claim 1 , wherein the predicted congestion information indicates the time based at least in part on at least one of a time value or a time range. The method according to claim 1 , wherein the predicted blocking information indicates a time interval of the prediction window indicating the selection of the time. The method of claim 1 , wherein the severity value is associated with an expected duration of the predicted occlusive event. The method of claim 1 , wherein the direction value is a binary value. The method of claim 1 , wherein the direction value is expressed relative to a reference point.

7. The method of claim 1, wherein the direction value indicates one or more parameters related to movement associated with a mobile obstruction or movement of an obstruction associated with the predicted obstruction event.

8. The method of claim 1, wherein the direction value is based at least in part on at least one of movement of the UE or movement of a network node associated with the UE.

9. The method of claim 1, wherein the model is trained based at least in part on a machine learning algorithm.

10. The method of claim 1, wherein the model is trained based at least in part on labels assigned to obstructions.

11. The method of claim 1 , wherein the model is trained based at least in part on labels assigned to observed times associated with blockages.

12. The method of claim 1, wherein the model is trained based at least in part on labels assigned to observed severity values ​​associated with obstructions.

13. The method of claim 1, wherein the model is trained based at least in part on signals from sensors regarding observed directional values ​​associated with blockages.

14. The method of claim 1, wherein the model is trained based at least in part on information regarding downlink beam directions associated with observed directional values ​​associated with blockage.

15. A user equipment (UE) for wireless communication, the user equipment (UE) comprising: Memory; and one or more processors coupled to the memory, the one or more processors configured to: obtaining measurement information about a set of reference signals; generating predicted blocking information using a model based at least in part on the measurement information; and sending a report indicating the predicted blocking information, wherein the report indicates at least one of: a prediction window associated with the predicted blocking information, The time associated with the predicted occurrence of a blocking event, a severity value associated with the predicted blocking event, or A direction value associated with the predicted blocking event.

16. The UE of claim 15, wherein the predicted congestion information indicates the time based at least in part on at least one of a time value or a time range. 17 . The UE according to claim 15 , wherein the predicted congestion information indicates a time interval of the prediction window indicating the selection of the time.

18. The UE of claim 15, wherein the severity value is associated with an expected duration of the predicted congestion event. The UE of claim 15 , wherein the direction value is a binary value.

20. The UE of claim 15, wherein the direction value is expressed relative to a reference point.

21. The UE of claim 15, wherein the model is trained based at least in part on a machine learning algorithm.

22. The UE of claim 15, wherein the model is trained based at least in part on labels assigned to obstructions.

23. The UE of claim 15, wherein the model is trained based at least in part on labels assigned to observed times associated with congestion.

24. The UE of claim 15, wherein the model is trained based at least in part on labels assigned to observed severity values ​​associated with obstructions.

25. The UE of claim 15, wherein the model is trained based at least in part on signals from sensors regarding observed direction values ​​associated with blockages.

26. The UE of claim 15, wherein the model is trained based at least in part on information regarding downlink beam directions associated with observed directional values ​​associated with blocking.

27. A non-transitory computer readable medium storing an instruction set for wireless communication, the instruction set comprising: One or more instructions that, when executed by one or more processors of a user equipment (UE), cause the UE to: obtaining measurement information about a set of reference signals; generating predicted blocking information using a model based at least in part on the measurement information; and sending a report indicating the predicted blocking information, wherein the report indicates at least one of: a prediction window associated with the predicted blocking information, The time associated with the predicted occurrence of a blocking event, a severity value associated with the predicted blocking event, or A direction value associated with the predicted blocking event.

28. The non-transitory computer readable medium of claim 27, wherein the predicted congestion information indicates the time based at least in part on at least one of a time value or a time range.

29. A device for wireless communication, the device comprising: means for obtaining measurement information about a set of reference signals; means for generating predicted blocking information using a model based at least in part on said measurement information; and means for sending a report indicating the predicted blocking information, wherein the report indicates at least one of: a prediction window associated with the predicted blocking information, The time associated with the predicted occurrence of a blocking event, a severity value associated with the predicted blocking event, or A direction value associated with the predicted blocking event.

30. The apparatus of claim 29, wherein the predicted congestion information indicates the time based at least in part on at least one of a time value or a time range.

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