CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority to U.S. Patent Application No. 17/675,980, filed on February 18, 2022, and titled “SPATIAL INTER-CELL INTERFERENCE AWARE DOWNLINK COORDINATION,” which claims the benefit of U.S. Provisional Patent Application No. 63/155,635, filed on March 2, 2021, and titled “SPATIAL INTER-CELL INTERFERENCE AWARE DOWNLINK COORDINATION,” the disclosures of which are expressly incorporated by reference in their entireties.

FIELD OF THE DISCLOSURE

[0002] Aspects of the present disclosure generally relate to wireless communications, and more particularly to techniques and apparatuses for an enhancement to spatial inter-cell interference aware downlink coordination.

BACKGROUND

[0003] Wireless communications systems are widely deployed to provide various telecommunications services such as telephony, video, data, messaging, and broadcasts. Typical wireless communications systems may employ multiple-access technologies capable of supporting communications with multiple users by sharing available system resources (e.g., bandwidth, transmit power, and/or the like). 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). [0004] A wireless communications network may include a number of base stations (BSs) that can support communications for a number of user equipment (UEs). A user equipment (UE) may communicate with a base station (BS) via the downlink and uplink. The downlink (or forward link) refers to the communications link from the BS to the UE, and the uplink (or reverse link) refers to the communications link from the UE to the BS. As will be described in more detail, a BS may be referred to as a Node B, a gNB, an access point (AP), a radio head, a transmit and receive point (TRP), a new radio (NR) BS, a 5GNode B, and/or the like.

[0005] The above multiple access technologies have been adopted in various telecommunications standards to provide a common protocol that enables different user equipment to communicate on a municipal, national, regional, and even global level. New Radio (NR), which may also be referred to as 5G, is a set of enhancements to the LTE mobile standard promulgated by the Third Generation Partnership Project (3GPP). NR is designed to better support mobile broadband Internet access by improving spectral efficiency, lowering costs, improving services, making use of new spectrum, and better integrating with other open standards using orthogonal frequency division multiplexing (OFDM) with a cyclic prefix (CP) (CP-OFDM) on the downlink (DL), using CP-OFDM and/or SC-FDM (e.g., also known as discrete Fourier transform spread OFDM (DFT-s-OFDM)) on the uplink (UL), as well as supporting beamforming, multiple-input multiple-output (MIMO) antenna technology, and carrier aggregation.

[0006] Artificial neural networks may comprise interconnected groups of artificial neurons (e.g., neuron models). The artificial neural network may be a computational device or represented as a method to be performed by a computational device. Convolutional neural networks, such as deep convolutional neural networks, are a type of feed-forward artificial neural network. Convolutional neural networks may include layers of neurons that may be configured in a tiled receptive field. It would be desirable to apply neural network processing to wireless communications to achieve greater efficiencies.

SUMMARY

[0007] A method of wireless communication by a first network device includes predicting spatial inter-cell downlink interference experienced by a UE. The method also includes communicating with a second network device to reduce the spatial inter cell downlink interference in a direction of the UE by protecting resources across selected resource sets.

[0008] An apparatus for wireless communication by a first network device is described. The apparatus includes means for predicting spatial inter-cell downlink interference experienced by a UE. The apparatus also includes means for communicating with a second network device to reduce the spatial inter-cell downlink interference in a direction of the UE by protecting resources across selected resource sets.

[0009] A first network device includes a processor and a memory coupled with the processor. The first network device also includes instructions stored in the memory. When the instructions are executed by the processor, the first network device is operable to predict spatial inter-cell downlink interference experienced by a UE. The first network devices is also operable to communicate with a second network device to reduce the spatial inter-cell downlink interference in a direction of the UE by protecting resources across selected resource sets.

[0010] A non-transitory computer-readable medium having program code recorded thereon is executed by a processor of a first network device. The non-transitory computer-readable medium includes program code to predict spatial inter-cell downlink interference experienced by a UE. The non-transitory computer-readable medium also includes program code to communicate with a second network device to reduce the spatial inter-cell downlink interference in a direction of the UE by protecting resources across selected resource sets.

[0011] Aspects generally include a method, apparatus, system, computer program product, non-transitory computer-readable medium, user equipment, base station, wireless communication device, and processing system as substantially described with reference to and as illustrated by the accompanying drawings and specification.

[0012] The foregoing has outlined rather broadly the features and technical advantages of examples according to the disclosure in order that the detailed description that follows may be better understood. Additional features and advantages will be described. The conception and specific examples disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. Characteristics of the concepts disclosed, both their organization and method of operation, together with associated advantages will be better understood from the following description when considered in connection with the accompanying figures. Each of the figures is provided for the purposes of illustration and description, and not as a definition of the limits of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0013] So that features of the present disclosure can be understood in detail, a particular description may be had by reference to aspects, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only certain aspects of this disclosure and are therefore not to be considered limiting of its scope, for the description may admit to other equally effective aspects. The same reference numbers in different drawings may identify the same or similar elements.

[0014] FIGURE l is a block diagram conceptually illustrating an example of a wireless communications network, in accordance with various aspects of the present disclosure.

[0015] FIGURE 2 is a block diagram conceptually illustrating an example of a base station in communication with a user equipment (UE) in a wireless communications network, in accordance with various aspects of the present disclosure.

[0016] FIGURE 3 illustrates an example implementation of designing a neural network using a system-on-a-chip (SOC), including a general-purpose processor, in accordance with certain aspects of the present disclosure.

[0017] FIGURES 4A, 4B, and 4C are diagrams illustrating a neural network, in accordance with aspects of the present disclosure.

[0018] FIGURE 4D is a diagram illustrating an exemplary deep convolutional network (DCN), in accordance with aspects of the present disclosure. [0019] FIGURE 5 is a block diagram illustrating an exemplary deep convolutional network (DCN), in accordance with aspects of the present disclosure.

[0020] FIGURES 6A and 6B illustrate a communications network in which spatial interference experienced by a user equipment is based on a downlink transmit beam from a neighbor base station to a neighbor user equipment (UE), according to aspects of the present disclosure.

[0021] FIGURE 7 is a diagram of a communications network illustrating signal strength measurement of downlink transmit beams from a neighbor base station to enable spatial inter-cell interference aware downlink coordination, according to aspects of the present disclosure.

[0022] FIGURE 8 is a block diagram of a network including a neural processing engine configured to enable spatial inter-cell interference aware downlink coordination, according to aspects of the present disclosure.

[0023] FIGURE 9 is a timing diagram illustrating an example process performed, for example, by a network, for spatial inter-cell interference aware downlink coordination at a serving cell, in accordance with various aspects of the present disclosure.

[0024] FIGURE 10 is a timing diagram illustrating an example process performed, for example, by a network, for spatial inter-cell interference aware downlink coordination at a neighbor cell, in accordance with various aspects of the present disclosure.

[0025] FIGURE 11 is a timing diagram illustrating an example process performed, for example, by a device, for spatial inter-cell interference aware downlink coordination at a central node, in accordance with various aspects of the present disclosure.

[0026] FIGURE 12 is a flow diagram illustrating an example process performed, for example, by a network device, for spatial inter-cell interference aware downlink coordination, in accordance with various aspects of the present disclosure. DETAILED DESCRIPTION

[0027] Various aspects of the disclosure are described more fully below with reference to the accompanying drawings. This disclosure may, however, be embodied in many different forms and should not be construed as limited to any specific structure or function presented throughout this disclosure. Rather, these aspects are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. Based on the teachings, one skilled in the art should appreciate that the scope of the disclosure is intended to cover any aspect of the disclosure, whether implemented independently of or combined with any other aspect of the disclosure. For example, an apparatus may be implemented or a method may be practiced using any number of the aspects set forth. In addition, the scope of the disclosure is intended to cover such an apparatus or method, which is practiced using other structure, functionality, or structure and functionality in addition to or other than the various aspects of the disclosure set forth. It should be understood that any aspect of the disclosure disclosed may be embodied by one or more elements of a claim.

[0028] Several aspects of telecommunications systems will now be presented with reference to various apparatuses and techniques. These apparatuses 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, and/or the like (collectively referred to as “elements”). These elements may be implemented using hardware, software, or combinations thereof. Whether such elements are implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system.

[0029] It should be noted that while aspects may be described using terminology commonly associated with 5G and later wireless technologies, aspects of the present disclosure can be applied in other generation-based communications systems, such as and including 3G and/or 4G technologies.

[0030] Inter-cell interference may result in signal (e.g., signal to interference plus noise ratio (SINR)) degradation for users. The signal degradation for users may be especially significant when a user is at a cell edge. In addition, due to the introduction of massive multiple-input multiple-output (MIMO) antennas in next generation node Bs (gNBs), this inter-cell interference may lead to a significant SINR degradation for users. For example, narrow downlink transmit beams may be highly directional and highly variant with time, resulting in the SINR degradation. In particular, highly directional interference at the cell edge may reduce the data rate and negatively impact a user experience. Furthermore, high variance in the interference makes it more difficult to perform link adaptation, such as predicting a supportable modulation and coding scheme (MCS). This may be challenging for latency sensitive applications, which have a limited delay budget.

[0031] In some aspects of the present disclosure, a neural network is trained to infer the impact of any potential transmit beams of a neighbor base station (e.g., gNB) on any potential victim user equipment (UE). In these aspects of the present disclosure, a neural network is trained over time using UE channel state information (CSI) reference signal (CSI-RS) measurement reports, beam information, and UE locations. In other aspects of the present disclosure, a database stores information regarding the impact of any potential transmit beams of a neighbor base station (e.g., gNB) on any potential victim UE. In these aspects of the present disclosure, the database stores UE channel state information (CSI) reference signal (CSI-RS) measurement reports, beam information, and UE locations to enable a database look-up to determine the potential impact from a neighbor base station on any potential victim UEs.

[0032] Once the neural network is trained to infer the impact of a transmit beam of a neighbor base station on a victim UE, a network device (e.g., gNB) coordinates with the neighbor base station. In these aspects of the present disclosure, network device coordination may prohibit communication of a downlink transmit beam by the neighbor base station during the time/frequency resources used to serve the victim UE. For example, a first user equipment (UE1) may experience significant interference from a downlink transmit beam k of a neighbor base station (gNB2). In this example, spatial inter-cell interference is mitigated when the neighbor base station gNB2 avoids transmitting energy in the direction of the downlink transmit beam k over the resources serving the first user equipment.

[0033] According to aspects of the present disclosure, a serving cell predicts for served UEs the downlink interference experienced from the different potential downlink transmit beams of neighbor cells. For example, the serving cell may identify a subset of the served UEs in which a negative impact potentially caused by the inter-cell downlink interference exceeds a predetermined UE interference threshold. This identification of a potential victim subset of UEs may also include additional criteria, such as a type of traffic a UE is receiving. For example, UEs receiving delay sensitive traffic and/or high reliability traffic may be selected as part of the potential victim subset of UEs.

[0034] In these aspects of the present disclosure, the serving cell sends a request message to the potentially interfering neighbor cells for each of the potential victim subset of UEs. The request message may include a prohibited list of beam indices that the potentially interfering neighbor cells are requested to avoid. In addition, the request message may indicate requested time/frequency resources (e.g., time slots/resource blocks (RBs)) for which protection is requested. In some implementations, a predefined set of time/frequency resources is configured for every cell, such that signaling may simply refer to an index of the proposed set of resources. Alternatively, the time/frequency resources are left for the neighbor cell to decide.

[0035] In some aspects of the present disclosure, learning spatial inter-cell downlink interference and predicting of the victim subset of UEs are performed at the interfering cells, which may be referred to as aggressor neighbor cells. In these aspects of the present disclosure, the serving cell identifies potentially vulnerable UEs based on their location, traffic type, or other selection criteria. Once identified, the serving cell sends a request message to the aggressor neighbor cell(s). The request message may indicate a subset of: (1) a location of the vulnerable UE(s); (2) a UE interference tolerance threshold; and/or (3) a protected resource demand. In other aspects of the present disclosure, an indication of the traffic load of the victim UE is provided to represent an amount of protected resources desired.

[0036] The request message sent by the serving cell may omit the resource demand, which is optional. When the request message omits the resource demand, the protected resources are decided by the neighbor cell. Furthermore, there may be a predefined set of time/frequency resources configured for each cell. In this configuration, signaling of the request message may simply refer to the index of the proposed set of resources that meet the UE resource demand.

[0037] In other implementations, the learning and prediction are performed at a centralized coordination node, such as a network controller. For example, the serving cell identifies potentially vulnerable UEs based on their location, traffic type, or other selection criteria. Once identified, the serving cell transmits information about the identified vulnerable UEs to the centralized coordination node. This information may indicate all or subset of: (1) a location of the vulnerable UE(s); (2) an interference threshold of the vulnerable UEs; and (3) a traffic load (e.g., an updated traffic demand). Some implementations including the central node coordination may be beneficial when multiple aggressor neighbor cells are potentially causing interference to victim UEs.

[0038] FIGURE 1 is a diagram illustrating a network 100 in which aspects of the present disclosure may be practiced. The network 100 may be a 5G or NR network or some other wireless network, such as an LTE network. The wireless network 100 may include a number of BSs 110 (shown as BS 110a, BS 110b, BS 110c, and BS 1 lOd) and other network entities. A BS is an entity that communicates with user equipment (UEs) and may also be referred to as a base station, a NR BS, a Node B, a gNB, a 5G node B (NB), an access point, a transmit and receive point (TRP), and/or the like. Each BS may provide communications coverage for a particular geographic area. In 3 GPP, the term “cell” can refer to a coverage area of a BS and/or a BS subsystem serving this coverage area, depending on the context in which the term is used.

[0039] A BS may provide communications 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., several kilometers in radius) and may allow unrestricted access by UEs with service subscription. A pico cell may cover a relatively small geographic area and may allow unrestricted access by UEs with service subscription. A femto cell may cover a relatively small geographic area (e.g., a home) and may allow restricted access by UEs having association with the femto cell (e.g., UEs in a closed subscriber group (CSG)). A BS for a macro cell may be referred to as a macro BS. A BS for a pico cell may be referred to as a pico BS. A BS for a femto cell may be referred to as a femto BS or a home BS. In the example shown in FIGURE 1, a BS 110a may be a macro BS for a macro cell 102a, a BS 110b may be a pico BS for a pico cell 102b, and a BS 110c may be a femto BS for a femto cell 102c. A BS may support one or multiple (e.g., three) cells. The terms “eNB,” “base station,” “NR BS,” “gNB,” “TRP,” “AP,” “node B,” “5GNB,” and “cell” may be used interchangeably. [0040] In some aspects, a cell may not necessarily be stationary, and the geographic area of the cell may move according to the location of a mobile BS. In some aspects, the BSs may be interconnected to one another and/or to one or more other BSs or network nodes (not shown) in the wireless network 100 through various types of backhaul interfaces such as a direct physical connection, a virtual network, and/or the like using any suitable transport network.

[0041] The wireless network 100 may also include relay stations. A relay station is an entity that can receive a transmission of data from an upstream station (e.g., a BS or a UE) and send a transmission of the data to a downstream station (e.g., a UE or a BS). A relay station may also be a UE that can relay transmissions for other UEs. In the example shown in FIGURE 1, a relay station 1 lOd may communicate with macro BS 110a and a UE 120d in order to facilitate communications between the BS 110a and UE 120d. A relay station may also be referred to as a relay BS, a relay base station, a relay, and/or the like.

[0042] The wireless network 100 may be a heterogeneous network that includes BSs of different types, e.g., macro BSs, pico BSs, femto BSs, relay BSs, and/or the like. These different types of BSs may have different transmit power levels, different coverage areas, and different impact on interference in the wireless network 100. For example, macro BSs may have a high transmit power level (e.g., 5 to 40 Watts) whereas pico BSs, femto BSs, and relay BSs may have lower transmit power levels (e.g., 0.1 to 2 Watts).

[0043] A network controller 130 may couple to a set of BSs and may provide coordination and control for these BSs. The network controller 130 may communicate with the BSs via a backhaul. The BSs may also communicate with one another, e.g., directly or indirectly via a wireless or wireline backhaul.

[0044] UEs 120 (e.g., 120a, 120b, 120c) may be dispersed throughout the wireless network 100, and each UE may be stationary or mobile. A UE may also be referred to as an access terminal, a terminal, a mobile station, a subscriber unit, a station, and/or the like. A UE may be a cellular phone (e.g., a smart phone), a personal digital assistant (PDA), a wireless modem, a wireless communications device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device or equipment, biometric sensors/devices, wearable devices (smart watches, smart clothing, smart glasses, smart wrist bands, smart jewelry (e.g., smart ring, smart bracelet)), an entertainment device (e.g., a music or video device, or a satellite radio), a vehicular component or sensor, smart meters/sensors, industrial manufacturing equipment, a global positioning system device, or any other suitable device that is configured to communicate via a wireless or wired medium.

[0045] The base station 110 may include a neural processing engine 150. For brevity, only one base station 110a is shown as including the neural processing engine 150, but a neighbor base station may also include the neural processing engine 150. The neural processing engine 150 may predict spatial inter-cell downlink interference experienced by a UE. The neural processing engine 150 may also communicate with a second network device to reduce the spatial inter-cell downlink interference in a direction of the UE by protecting resources across selected resource sets.

[0046] The network controller 130 may include a neural processing engine 160. The neural processing engine 160 may predict spatial inter-cell downlink interference experienced by a UE. The neural processing engine 160 may also communicate with a second network device to reduce the spatial inter-cell downlink interference in a direction of the UE by protecting resources across selected resource sets.

[0047] Some UEs may be considered machine-type communications (MTC) or evolved or enhanced machine-type communications (eMTC) UEs. MTC and eMTC UEs include, for example, robots, drones, remote devices, sensors, meters, monitors, location tags, and/or the like, that may communicate with a base station, another device (e.g., remote device), or some other entity. A wireless node may provide, for example, connectivity for or to a network (e.g., a wide area network such as Internet or a cellular network) via a wired or wireless communications link. Some UEs may be considered Internet-of-Things (IoT) devices, and/or may be implemented as NB-IoT (narrowband internet of things) devices. Some UEs may be considered a customer premises equipment (CPE). UE 120 may be included inside a housing that houses components of UE 120, such as processor components, memory components, and/or the like. [0048] In general, any number of wireless networks may be deployed in a given geographic area. Each wireless network may support a particular RAT and may operate on one or more frequencies. A RAT may also be referred to as a radio technology, an air interface, and/or the like. A frequency may also be referred to as a carrier, a frequency channel, and/or the like. Each frequency may support a single RAT in a given geographic area in order to avoid interference between wireless networks of different RATs. In some cases, NR or 5G RAT networks may be deployed.

[0049] In some aspects, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly using one or more sidelink channels (e.g., without using a base station 110 as an intermediary to communicate with one another). For example, the UEs 120 may communicate using peer-to-peer (P2P) communications, device-to-device (D2D) communications, a vehicle-to-everything (V2X) protocol (e.g., which may include a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure (V2I) protocol, and/or the like), a mesh network, and/or the like. In this case, the UE 120 may perform scheduling operations, resource selection operations, and/or other operations described elsewhere as being performed by the base station 110. For example, the base station 110 may configure a UE 120 via downlink control information (DCI), radio resource control (RRC) signaling, a media access control-control element (MAC-CE), or via system information (e.g., a system information block (SIB)).

[0050] As indicated above, FIGURE 1 is provided merely as an example. Other examples may differ from what is described with regard to FIGURE 1.

[0051] FIGURE 2 shows a block diagram of a design 200 of the base station 110 and UE 120, which may be one of the base stations and one of the UEs in FIGURE 1. The base station 110 may be equipped with T antennas 234a through 234t, and UE 120 may be equipped with R antennas 252a through 252r, where in general T > 1 and R > 1.

[0052] At the base station 110, a transmit processor 220 may receive data from a data source 212 for one or more UEs, select one or more modulation and coding schemes (MCS) for each UE based at least in part on channel quality indicators (CQIs) received from the UE, process (e.g., encode and modulate) the data for each UE based at least in part on the MCS(s) selected for the UE, and provide data symbols for all UEs. Decreasing the MCS lowers throughput but increases reliability of the transmission. The transmit processor 220 may also process system information (e.g., for semi-static resource partitioning information (SRPI) and/or the like) and control information (e.g., CQI requests, grants, upper layer signaling, and/or the like) and provide overhead symbols and control symbols. The transmit processor 220 may also generate reference symbols for reference signals (e.g., the cell-specific reference signal (CRS)) and synchronization signals (e.g., the primary synchronization signal (PSS) and secondary synchronization signal (SSS)). A transmit (TX) multiple-input multiple-output (MTMO) processor 230 may perform spatial processing (e.g., precoding) on the data symbols, the control symbols, the overhead symbols, and/or the reference symbols, if applicable, and may provide T output symbol streams to T modulators (MODs) 232a through 232t.

Each modulator 232 may process a respective output symbol stream (e.g., for orthogonal frequency division multiplexing (OFDM) and/or the like) to obtain an output sample stream. Each modulator 232 may further process (e.g., convert to analog, amplify, filter, and upconvert) the output sample stream to obtain a downlink signal. T downlink signals from modulators 232a through 232t may be transmitted via T antennas 234a through 234t, respectively. According to various aspects described in more detail below, the synchronization signals can be generated with location encoding to convey additional information.

[0053] At the TIE 120, antennas 252a through 252r may receive the downlink signals from the base station 110 and/or other base stations and may provide received signals to demodulators (DEMODs) 254a through 254r, respectively. Each demodulator 254 may condition (e.g., filter, amplify, downconvert, and digitize) a received signal to obtain input samples. Each demodulator 254 may further process the input samples (e.g., for OFDM and/or the like) to obtain received symbols. A MIMO detector 256 may obtain received symbols from all R demodulators 254a through 254r, perform MIMO detection on the received symbols if applicable, and provide detected symbols. A receive processor 258 may process (e.g., demodulate and decode) the detected symbols, provide decoded data for the UE 120 to a data sink 260, and provide decoded control information and system information to a controller/processor 280. A channel processor may determine a reference signal received power (RSRP), received signal strength indicator (RSSI), reference signal received quality (RSRQ), channel quality indicator (CQI), and/or the like. In some aspects, one or more components of the UE 120 may be included in a housing. [0054] On the uplink, at the UE 120, a transmit processor 264 may receive and process data from a data source 262 and control information (e.g., for reports comprising RSRP, RSSI, RSRQ, CQI, and/or the like) from the controller/processor 280. Transmit processor 264 may also generate reference symbols for one or more reference signals. The symbols from the transmit processor 264 may be precoded by a TX MEMO processor 266 if applicable, further processed by modulators 254a through 254r (e.g., for DFT-s-OFDM, CP-OFDM, and/or the like), and transmitted to the base station 110. At the base station 110, the uplink signals from the UE 120 and other UEs may be received by the antennas 234, processed by the demodulators 254, detected by a MEMO detector 236 if applicable, and further processed by a receive processor 238 to obtain decoded data and control information sent by the UE 120. The receive processor 238 may provide the decoded data to a data sink 239 and the decoded control information to a controller/processor 240. The base station 110 may include communications unit 244 and communicate to the network controller 130 via the communications unit 244. The network controller 130 may include a communications unit 294, a controller/processor 290, and a memory 292.

[0055] The controller/processor 240 of the base station 110, and/or the controller/processor 280 of the UE 120 of FIGURE 2 may perform one or more techniques associated with machine learning for predicting location-based downlink interference assistance information for the UE 120, as described in more detail elsewhere. For example, the controller/processor 280 of the UE 120 of FIGURE 2 may perform or direct operations of, for example, the processes of FIGURE 8 and/or other processes as described. In addition, the controller/processor 240 of the base station 110 of FIGURE 2 may perform or direct operations of, for example, the process of FIGURE 9 - 12 and/or other processes as described. Memories 242 and 282 may store data and program codes for the base station 110 and UE 120, respectively. A scheduler 246 may schedule UEs for data transmission on the downlink and/or uplink.

[0056] In some aspects, the base station 110 and the network controller 130 may include means for predicting, means for selecting, means for transmitting, means for receiving, means for updating, and/or means for communicating. Such means may include one or more components of the network controller 130, or base station 110 described in connection with FIGURE 2. [0057] As indicated above, FIGURE 2 is provided merely as an example. Other examples may differ from what is described with regard to FIGURE 2.

[0058] In some cases, different types of devices supporting different types of applications and/or services may coexist in a cell. Examples of different types of devices include UE handsets, customer premises equipment (CPEs), vehicles, Internet of Things (IoT) devices, and/or the like. Examples of different types of applications include ultra-reliable low-latency communications (URLLC) applications, massive machine-type communications (mMTC) applications, enhanced mobile broadband (eMBB) applications, vehicle-to-everything (V2X) applications, and/or the like. Furthermore, in some cases, a single device may support different applications or services simultaneously.

[0059] FIGURE 3 illustrates an example implementation of a system-on-a-chip (SOC) 300, which may include a central processing unit (CPU) 302 or a multi-core CPU configured for generating gradients for neural network training, in accordance with certain aspects of the present disclosure. The SOC 300 may be included in the base station 110 or UE 120. Variables (e.g., neural signals and synaptic weights), system parameters associated with a computational device (e.g., neural network with weights), delays, frequency bin information, and task information may be stored in a memory block associated with a neural processing unit (NPU) 308, in a memory block associated with a CPU 302, in a memory block associated with a graphics processing unit (GPU) 304, in a memory block associated with a digital signal processor (DSP) 306, in a memory block 318, or may be distributed across multiple blocks. Instructions executed at the CPU 302 may be loaded from a program memory associated with the CPU 302 or may be loaded from a memory block 318.

[0060] The SOC 300 may also include additional processing blocks tailored to specific functions, such as a GPU 304, a DSP 306, a connectivity block 310, which may include fifth generation (5G) connectivity, fourth generation long term evolution (4G LTE) connectivity, Wi-Fi connectivity, USB connectivity, Bluetooth connectivity, and the like, and a multimedia processor 312 that may, for example, detect and recognize gestures. In one implementation, the NPU is implemented in the CPU, DSP, and/or GPU. The SOC 300 may also include a sensor processor 314, image signal processors (ISPs) 316, and/or navigation module 320, which may include a global positioning system.

[0061] The SOC 300 may be based on an ARM instruction set. In an aspect of the present disclosure, the instructions loaded into the general-purpose processor 302 may comprise program code to predict spatial inter-cell downlink interference experienced by a UE; and program code to communicate with a second network device to reduce the spatial inter-cell downlink interference in a direction of the UE by protecting resources across selected resource sets.

[0062] Deep learning architectures may perform an object recognition task by learning to represent inputs at successively higher levels of abstraction in each layer, thereby building up a useful feature representation of the input data. In this way, deep learning addresses a major bottleneck of traditional machine learning. Prior to the advent of deep learning, a machine learning approach to an object recognition problem may have relied heavily on human engineered features, perhaps in combination with a shallow classifier. A shallow classifier may be a two-class linear classifier, for example, in which a weighted sum of the feature vector components may be compared with a threshold to predict to which class the input belongs. Human engineered features may be templates or kernels tailored to a specific problem domain by engineers with domain expertise. Deep learning architectures, in contrast, may learn to represent features that are similar to what a human engineer might design, but through training. Furthermore, a deep network may learn to represent and recognize new types of features that a human might not have considered.

[0063] A deep learning architecture may learn a hierarchy of features. If presented with visual data, for example, the first layer may learn to recognize relatively simple features, such as edges, in the input stream. In another example, if presented with auditory data, the first layer may learn to recognize spectral power in specific frequencies. The second layer, taking the output of the first layer as input, may learn to recognize combinations of features, such as simple shapes for visual data or combinations of sounds for auditory data. For instance, higher layers may learn to represent complex shapes in visual data or words in auditory data. Still higher layers may learn to recognize common visual objects or spoken phrases. [0064] Deep learning architectures may perform especially well when applied to problems that have a natural hierarchical structure. For example, the classification of motorized vehicles may benefit from first learning to recognize wheels, windshields, and other features. These features may be combined at higher layers in different ways to recognize cars, trucks, and airplanes.

[0065] Neural networks may be designed with a variety of connectivity patterns. In feed-forward networks, information is passed from lower to higher layers, with each neuron in a given layer communicating to neurons in higher layers. A hierarchical representation may be built up in successive layers of a feed-forward network, as described above. Neural networks may also have recurrent or feedback (also called top- down) connections. In a recurrent connection, the output from a neuron in a given layer may be communicated to another neuron in the same layer. A recurrent architecture may be helpful in recognizing patterns that span more than one of the input data chunks that are delivered to the neural network in a sequence. A connection from a neuron in a given layer to a neuron in a lower layer is called a feedback (or top-down) connection.

A network with many feedback connections may be helpful when the recognition of a high-level concept may aid in discriminating the particular low-level features of an input.

[0066] The connections between layers of a neural network may be fully connected or locally connected. FIGURE 4A illustrates an example of a fully connected neural network 402. In a fully connected neural network 402, a neuron in a first layer may communicate its output to every neuron in a second layer, so that each neuron in the second layer will receive input from every neuron in the first layer. FIGURE 4B illustrates an example of a locally connected neural network 404. In a locally connected neural network 404, a neuron in a first layer may be connected to a limited number of neurons in the second layer. More generally, a locally connected layer of the locally connected neural network 404 may be configured so that each neuron in a layer will have the same or a similar connectivity pattern, but with connections strengths that may have different values (e.g., 410, 412, 414, and 416). The locally connected connectivity pattern may give rise to spatially distinct receptive fields in a higher layer, because the higher layer neurons in a given region may receive inputs that are tuned through training to the properties of a restricted portion of the total input to the network. [0067] One example of a locally connected neural network is a convolutional neural network. FIGURE 4C illustrates an example of a convolutional neural network 406.

The convolutional neural network 406 may be configured such that the connection strengths associated with the inputs for each neuron in the second layer are shared (e.g., 408). Convolutional neural networks may be well suited to problems in which the spatial location of inputs is meaningful.

[0068] One type of convolutional neural network is a deep convolutional network (DCN). FIGURE 4D illustrates a detailed example of a DCN 400 designed to recognize visual features from an image 426 input from an image capturing device 430, such as a car-mounted camera. The DCN 400 of the current example may be trained to identify traffic signs and a number provided on the traffic sign. Of course, the DCN 400 may be trained for other tasks, such as identifying lane markings or identifying traffic lights.

[0069] The DCN 400 may be trained with supervised learning. During training, the DCN 400 may be presented with an image, such as the image 426 of a speed limit sign, and a forward pass may then be computed to produce an output 422. The DCN 400 may include a feature extraction section and a classification section. Upon receiving the image 426, a convolutional layer 432 may apply convolutional kernels (not shown) to the image 426 to generate a first set of feature maps 418. As an example, the convolutional kernel for the convolutional layer 432 may be a 5x5 kernel that generates 28x28 feature maps. In the present example, because four different feature maps are generated in the first set of feature maps 418, four different convolutional kernels were applied to the image 426 at the convolutional layer 432. The convolutional kernels may also be referred to as filters or convolutional filters.

[0070] The first set of feature maps 418 may be subsampled by a max pooling layer (not shown) to generate a second set of feature maps 420. The max pooling layer reduces the size of the first set of feature maps 418. That is, a size of the second set of feature maps 420, such as 14x14, is less than the size of the first set of feature maps 418, such as 28x28. The reduced size provides similar information to a subsequent layer while reducing memory consumption. The second set of feature maps 420 may be further convolved via one or more subsequent convolutional layers (not shown) to generate one or more subsequent sets of feature maps (not shown). [0071] In the example of FIGURE 4D, the second set of feature maps 420 is convolved to generate a first feature vector 424. Furthermore, the first feature vector 424 is further convolved to generate a second feature vector 428. Each feature of the second feature vector 428 may include a number that corresponds to a possible feature of the image 426, such as “sign,” “60,” and “100.” A softmax function (not shown) may convert the numbers in the second feature vector 428 to a probability. As such, an output 422 of the DCN 400 is a probability of the image 426 including one or more features.

[0072] In the present example, the probabilities in the output 422 for “sign” and “60” are higher than the probabilities of the others of the output 422, such as “30,” “40,” “50,” “70,” “80,” “90,” and “100”. Before training, the output 422 produced by the DCN 400 is likely to be incorrect. Thus, an error may be calculated between the output 422 and a target output. The target output is the ground truth of the image 426 (e.g., “sign” and “60”). The weights of the DCN 400 may then be adjusted so the output 422 of the DCN 400 is more closely aligned with the target output.

[0073] To adjust the weights, a learning algorithm may compute a gradient vector for the weights. The gradient may indicate an amount that an error would increase or decrease if the weight were adjusted. At the top layer, the gradient may correspond directly to the value of a weight connecting an activated neuron in the penultimate layer and a neuron in the output layer. In lower layers, the gradient may depend on the value of the weights and on the computed error gradients of the higher layers. The weights may then be adjusted to reduce the error. This manner of adjusting the weights may be referred to as “back propagation” as it involves a “backward pass” through the neural network.

[0074] In practice, the error gradient of weights may be calculated over a small number of examples, so that the calculated gradient approximates the true error gradient. This approximation method may be referred to as stochastic gradient descent. Stochastic gradient descent may be repeated until the achievable error rate of the entire system has stopped decreasing or until the error rate has reached a target level. After learning, the DCN may be presented with new images (e.g., the speed limit sign of the image 426) and a forward pass through the network may yield an output 422 that may be considered an inference or a prediction of the DCN. [0075] Deep belief networks (DBNs) are probabilistic models comprising multiple layers of hidden nodes. DBNs may be used to extract a hierarchical representation of training data sets. A DBN may be obtained by stacking up layers of Restricted Boltzmann Machines (RBMs). An RBM is a type of artificial neural network that can learn a probability distribution over a set of inputs. Because RBMs can learn a probability distribution in the absence of information about the class to which each input should be categorized, RBMs are often used in unsupervised learning. Using a hybrid unsupervised and supervised paradigm, the bottom RBMs of a DBN may be trained in an unsupervised manner and may serve as feature extractors, and the top RBM may be trained in a supervised manner (on a joint distribution of inputs from the previous layer and target classes) and may serve as a classifier.

[0076] Deep convolutional networks (DCNs) are networks of convolutional networks, configured with additional pooling and normalization layers. DCNs have achieved state-of-the-art performance on many tasks. DCNs can be trained using supervised learning in which both the input and output targets are known for many exemplars and are used to modify the weights of the network by use of gradient descent methods.

[0077] DCNs may be feed-forward networks. In addition, as described above, the connections from a neuron in a first layer of a DCN to a group of neurons in the next higher layer are shared across the neurons in the first layer. The feed-forward and shared connections of DCNs may be exploited for fast processing. The computational burden of a DCN may be much less, for example, than that of a similarly sized neural network that comprises recurrent or feedback connections.

[0078] The processing of each layer of a convolutional network may be considered a spatially invariant template or basis projection. If the input is first decomposed into multiple channels, such as the red, green, and blue channels of a color image, then the convolutional network trained on that input may be considered three-dimensional, with two spatial dimensions along the axes of the image and a third dimension capturing color information. The outputs of the convolutional connections may be considered to form a feature map in the subsequent layer, with each element of the feature map (e.g., 220) receiving input from a range of neurons in the previous layer (e.g., feature maps 218) and from each of the multiple channels. The values in the feature map may be further processed with a non-linearity, such as a rectification, max(0, x). Values from adjacent neurons may be further pooled, which corresponds to down sampling, and may provide additional local invariance and dimensionality reduction. Normalization, which corresponds to whitening, may also be applied through lateral inhibition between neurons in the feature map.

[0079] The performance of deep learning architectures may increase as more labeled data points become available or as computational power increases. Modem deep neural networks are routinely trained with computing resources that are thousands of times greater than what was available to a typical researcher just fifteen years ago. New architectures and training paradigms may further boost the performance of deep learning. Rectified linear units may reduce a training issue known as vanishing gradients. New training techniques may reduce over-fitting and thus enable larger models to achieve better generalization. Encapsulation techniques may abstract data in a given receptive field and further boost overall performance.

[0080] FIGURE 5 is a block diagram illustrating a deep convolutional network 550. The deep convolutional network 550 may include multiple different types of layers based on connectivity and weight sharing. As shown in FIGURE 5, the deep convolutional network 550 includes the convolution blocks 554A, 554B. Each of the convolution blocks 554A, 554B may be configured with a convolution layer (CONV) 356, a normalization layer (LNorm) 558, and a max pooling layer (MAX POOL) 560.

[0081] The convolution layers 556 may include one or more convolutional filters, which may be applied to the input data to generate a feature map. Although only two of the convolution blocks 554A, 554B are shown, the present disclosure is not so limiting, and instead, any number of the convolution blocks 554A, 554B may be included in the deep convolutional network 550 according to design preference. The normalization layer 558 may normalize the output of the convolution filters. For example, the normalization layer 558 may provide whitening or lateral inhibition. The max pooling layer 560 may provide down sampling aggregation over space for local invariance and dimensionality reduction.

[0082] The parallel filter banks, for example, of a deep convolutional network may be loaded on a CPU 302 or GPU 304 of an SOC 300 to achieve high performance and low power consumption. In alternative embodiments, the parallel filter banks may be loaded on the DSP 306 or an ISP 316 of an SOC 300. In addition, the deep convolutional network 550 may access other processing blocks that may be present on the SOC 300, such as sensor processor 314 and navigation module 320, dedicated, respectively, to sensors and navigation.

[0083] The deep convolutional network 550 may also include one or more fully connected layers 562 (FC1 and FC2). The deep convolutional network 550 may further include a logistic regression (LR) layer 564. Between each layer 556, 558, 560, 562,

564 of the deep convolutional network 550 are weights (not shown) that are to be updated. The output of each of the layers (e.g., 556, 558, 560, 562, 564) may serve as an input of a succeeding one of the layers (e.g., 556, 558, 560, 562, 564) in the deep convolutional network 550 to learn hierarchical feature representations from input data 552 (e.g., images, audio, video, sensor data and/or other input data) supplied at the first of the convolution blocks 554A. The output of the deep convolutional network 550 is a classification score 566 for the input data 552. The classification score 566 may be a set of probabilities, where each probability is the probability of the input data, including a feature from a set of features.

[0084] As indicated above, FIGURES 3-5 are provided as examples. Other examples may differ from what is described with respect to FIGURES 3-5.

[0085] As described above, inter-cell interference may result in signal (e.g., signal to interference plus noise ratio (SINR)) degradation for users. The signal degradation for users may be especially significant when a user is at a cell edge. In addition, due to the introduction of massive multiple-input multiple-output (MIMO) antennas in next generation base stations (e.g., gNBs), this inter-cell interference may be highly directional and highly variant with time. Unfortunately, highly directional interference at the cell edge may reduce the data rate and negatively impact a user experience. Furthermore, high variance in the interference makes it more difficult to perform link adaptation, such as predicting a supportable modulation and coding scheme (MCS).

This is challenging for latency sensitive applications, which have a limited delay budget. This limited delay budget may be insufficient to recover packets through hybrid automatic repeat request (HARQ) procedures when a selected modulation and coding scheme (MCS) is inaccurate. [0086] FIGURES 6A and 6B illustrate a communications network in which spatial interference experienced by a user equipment (UE) is based on a downlink transmit beam from a neighbor base station to a neighbor UE, according to aspects of the present disclosure. As shown FIGURE 6A, in a first interference scenario 600, a first UE 120-1 communicates with a first base station 110-1 through a downlink transmit beam i. Similarly, a second UE 120-2 communicates with a neighbor base station 110-2 through a downlink transmit beam j . In this example, spatial inter-cell interference from the downlink transmit beam j on the downlink transmit beam i results in minimal signal degradation at the first UE 120-1 (e.g., SINR = 20 dB).

[0087] FIGURE 6B illustrates a second interference scenario 650 in which the first UE 120-1 communicates with the first base station 110-1 through the downlink transmit beam i. By contrast, the second UE 120-2 communicates with the neighbor base station 110-2 through a downlink transmit beam k that interferes with the downlink transmit beam i. In this example, spatial inter-cell interference from the downlink transmit beam k on the downlink transmit beam i results in significant signal degradation at the first UE 120-1 (e.g., SINR = 5 dB), which degrades the user experience at the first UE 120- 1

[0088] FIGURE 7 is a diagram of a communications network 700 illustrating signal strength measurement of downlink transmit beams from a neighbor base station to enable spatial inter-cell interference aware downlink coordination, according to aspects of the present disclosure. According to aspects of the present disclosure, a neural network is trained to infer the impact of any potential transmit beams of a neighbor base station (e.g., gNB) on any potential victim UE. In this example, a victim user equipment (UEi) 120-1 communicates with a serving base station (gNBi) 110-1 over a downlink transmit beam i. Unfortunately, the victim UEi 120-1 experiences significant interference from a downlink transmit beam k of a neighbor base station (gNB 2) 110-2 used to communicate with a second user equipment (UE2) 120-2.

[0089] In this example, spatial inter-cell interference on the victim UEi 120-1 is mitigated by coordination with the neighbor base station gNB2 110-2. For example, the neighbor base station gNB2 110-2 may transmit over the downlink beam j to avoid transmitting energy in the direction of the downlink transmit beam k over the resources serving the victim UEi 120-1. Aspects of the present disclosure coordinate with the neighbor base station gNB2 110-2 to transmit over the downlink beam j over the resources used to serve the victim UEi 120-1 to avoid interference for the victim UEi 110-1.

[0090] According to aspects of the present disclosure, a neural network of the serving base station gNBi 110-1 is trained to infer the impact of downlink transmit beams of the neighbor base station gNB2 110-2 on the victim UEi 120-1. In these aspects of the present disclosure, the serving base station gNBi 110-1 coordinates with the neighbor base station gNB2 110-2 to prohibit communication of the downlink transmit beam k by the neighbor base station gNB2 110-2 over the time/frequency resources used to serve the victim UEi 120-1. In some configurations, the prediction of inter-cell interference is performed using machine learning with a neural processing engine (NPE), for example, as shown in FIGURE 8.

[0091] FIGURE 8 is a block diagram of a network including a neural processing engine configured for neural network-based prediction of a spatial inter-cell interference-based distribution to enable spatial inter-cell interference aware downlink coordination, according to aspects of the present disclosure. In aspects of the present disclosure, a database stores information regarding the impact of any potential transmit beams of a neighbor base station (e.g., gNB) on any potential victim UE. In these aspects of the present disclosure, the database stores UE channel state information (CSI) reference signal (CSI-RS) measurement reports, beam information, and UE locations to enable a database look-up to determine the potential impact from a neighbor base station on any potential victim UEs.

[0092] FIGURE 8 illustrates a network 800 including a UE 120 having a location block 830 and a base station 110 having a neural processing engine 810 to implement a neural network. In this example, the location block 830 indicates a UE location 802 (X) to the neural processing engine 810. A neighbor cell transmit precoder 804 (T) (e.g., a channel state information (CSI) beam index) is also input to the neural processing engine 810 from the neighbor cell. Based on the UE location 802 and the neighbor cell transmit precoder 804, the neural processing engine 810 predicts an interference-based distribution 820 (F) for the current location of the UE 120. For example, an interference (e.g., interference over thermal) distribution may be predicted, or a signal to interference plus noise ratio (SINR) distribution may be predicted. [0093] In aspects of the present disclosure, training of the neural network of the neural processing engine 810 may be based on UE channel state information (CSI) reference signal (CSI-RS) measurement reports, as well as the neighbor cell transmit precoder 804 and the UE location 802. The UE CSI-RS measurement report provides neighbor cell signal strength for each beam of a neighbor base station. For example, the signal strength measurement report may be based on a CSI report, including for example, SINR information or reference signal receive power (RSRP) information. In some aspects of the present disclosure, the UE CSI-RS measurement report may provide the SINR information, in which the signal strength corresponds to the serving cell and the interference corresponds to each beam of the neighbor base station.

[0094] In some aspects of the present disclosure, the base station 110 periodically sends precoded CSI-RS signals to the served UE 120. The served UE 120 may use the received CSI-RS signals to estimate the channel conditions. The served UE 120 may also use the received CSI-RS signals to identify a single beam or a combination of beams that results in the strongest received signal quality (e.g., a strongest beam) to help with data channel precoding. In addition, CSI-RS signals may be measured from other cells. For example, the UEs may estimate interference caused by the other cells using the CSI-RS signals from the other cells. The UEs may perform radio resource management using the measured CSI-RS signals. For example, the UEs use the measured CSI-RS signals of other cells to identify whether a neighbor cell is stronger than a current serving cell, which may trigger a handover. In practice, CSI-RS measurements performed by a UE are reported by the UE 120 to the serving cell 110.

[0095] As shown in FIGURE 8, the neural network of the neural processing engine 810 predicts an interference-based distribution 820 for a given UE location 802 based on a neighbor cell transmit precoder 804 and the UE location 802. The neighbor cell transmit precoder 804 may be a precoder index within a known codebook or may be precoder weights. In aspects of the present disclosure, the UE location 802 may be represented in the form of a combination of (x,y,z) coordinates of the UE 120 from a positioning source. The positioning source may be, for example, a global navigation satellite system (GNSS), 5G NR location server, etc. Alternatively, the UE location 802 may be determined based on a set of metrics that represent the location of the UE 120 within a serving cell. For example, the set of metrics that represent the location of the UE 120 within a serving cell may include a serving cell reference signal received power (RSRP) signal, a serving cell precoder (e.g., precoding matrix indicator (PMI)) indicating a strongest transmit beam direction, a serving cell channel quality indicator (CQI), and/or a path loss estimate for a channel between the serving cell and UE 120.

In addition, the UE location 802 may be determined based on other UE sensor information. In some aspects, the UE location 802 may be a geolocation.

[0096] In aspects of the present disclosure, training of the neural network may be performed at various nodes. For example, training of the neural network may be performed at a serving cell. In this example, the interference measurement reports are sent to the base station 110 of the serving cell by the UE 120 along with information about the UE location 802. Position estimation of the UE 120 may be performed at the serving cell base station 110 or communicated to the base station 110 of the serving cell by a separate location server. Alternatively, UE location indicator metrics are reported by the UE 120 to the serving cell to determine the UE location 802.

[0097] In some aspects of the present disclosure, training of the neural network is performed at the neighbor cell(s), such as the neighbor base station gNfE 110-2, as shown in FIGURE 7. In these aspects of the present disclosure, the base station gNBi 110-1 of the serving cell sends the UE location 802 and the interference measurement reports to the neighbor base station gNFE 110-2 to perform training of the neural network at the neighbor base station gNFE 110-2. In other aspects of the present disclosure, training of the neural network is performed at a centralized node. In these aspects of the present disclosure, the base station gNBi 110-1 of the serving cell sends the UE location 802, a serving cell identification (ID), a neighbor cell ID, and interference measurement reports to the centralized node to perform training of the neural network at the centralized node.

[0098] Once the neural network is trained to infer the impact of a transmit beam of a neighbor base station on a victim UE, a network device (e.g., gNB) coordinates with the neighbor base station. In these aspects of the present disclosure, network device coordination may prevent communication of a downlink transmit beam by the neighbor base station over the time/frequency resources used to serve the victim UE. For example, as shown in FIGURE 7, UEi 120-1 experiences significant interference from a downlink transmit beam k of a neighbor base station (gNBi) 110-2. In this example, spatial inter-cell interference is mitigated when the neighbor base station gNB2 110-2 avoids transmitting energy in the direction of the downlink transmit beam k over the resources used to serve the UEi 120-1.

[0099] According to aspects of the present disclosure, undesired neighbor cell beams are identified based on certain criteria according to the predicted interference- based distribution. For example, a mean or percentile exceeding a predetermined threshold may be the criteria. In addition, this spatial interference characterization may be used to coordinate scheduling between nearby cells, such as the neighbor base station gNB2 110-2 to prevent high interference events, for example, as shown in FIGURES 9- 11

[00100] FIGURE 9 is a timing diagram illustrating an example process performed, for example, by UEs 120 (120-1, ..., 120-N), a base station 110-1 of a serving cell 900, and base stations 110-2,..., 110-N of neighbor cells 950 (950-1, ..., 950-N), for spatial inter-cell interference aware downlink coordination at the serving cell 900, in accordance with various aspects of the present disclosure.

[00101] According to aspects of the present disclosure, a base station 110-1 of a serving cell 900 predicts potential downlink interference experienced by UEs 120 (120- 1, ..., 120-N) in the serving cell 900 from the different potential downlink transmit beams of neighbor cells 950 (950-1, ..., 950-N). For example, the base station 110-1 of the serving cell 900 identifies a subset of the served UEs 120 in which a potential negative impact caused by the inter-cell downlink interference from the neighbor cells 950 exceeds a predetermined UE interference threshold. That is the neighbor cells 950 may include a potentially interfering neighbor cell. This identification of a potential victim subset of UEs may also include additional criteria, such as a type of traffic a UE is receiving. For example, UEs receiving delay sensitive traffic and/or high reliability traffic may be selected as part of the potential victim subset of UEs.

[00102] At time tO, the base station 110-1 of the serving cell 900 sends a request message to the potentially interfering neighbor cells 950 for each victim subset of UEs 120. The request message may include a proposal indicating a requested list of beam indices that the potentially interfering neighbor cells 950 are requested to avoid. In addition, the request message may indicate requested time/frequency resources (e.g., time slots/resource blocks (RBs)) for which protection is requested. For example, the request message may indicate an amount of resources to protect. The base station 110-1 may determine how much bandwidth the vulnerable UEs need, for example, a quarter of the allocated resources. In some implementations, a predefined set of time/frequency resources are configured for each cell, such that signaling of the request message at time tO may simply refer to the index of the proposed set of resources. Alternatively, selection of the time/frequency resources is left as a decision for the neighbor cell 950.

[00103] At time tl, the base stations 110-2-N of the aggressor neighbor cells 950 respond with a response message(s) that is received by the base station 110-1 of the serving cell 900 at time tl. The response message may be an acceptance of a proposal indicated by the request message. For example, the response message may indicate an agreement to limit transmitted energy in identified beam directions for the noted time/frequency resources. Alternatively, the response message may include a potential proposal or an alternative proposal for the time/frequency resources to be protected. For example, the response message received at time tl may include an alternative proposal for a different set of resources when the proposal in the request message from the serving cell 900 (e.g., the serving proposal) is not accepted.

[00104] The serving cell 900 may periodically repeat the spatial inter-cell downlink interference prediction as victim UEs 120 change position, channel conditions change, and/or traffic demands change. For example, a UE 120 may move closer to the serving base station 110-1 or the UE may enter idle mode. At time t2, the serving cell 900 may send an updated request message to the neighbor cells 950 based on an updated prediction for the new conditions. In addition, at time t3, the neighbor cells 950 may similarly respond to the updated request message with an updated response message. Once the interference threat for the victim UEs 120 has expired, at time t4, the serving cell 900 may send a cancel request message to stop the resource protection. For example, the serving cell 900 may send a cancel request message at time t4 when the victim UEs 120 enter an idle mode.

[00105] FIGURE 10 is a timing diagram illustrating an example process performed, for example, by UEs 120 (120-1, ..., 120-N), a base station 110-1 of a serving cell 900, and a base station 110-2 of a neighbor cell 950, for spatial inter-cell interference aware downlink coordination at the neighbor cell 950, in accordance with various aspects of the present disclosure.

[00106] In aspects of the present disclosure, predicting of spatial inter-cell downlink interference occurs at the neighbor cell 950, which may be referred to as an aggressor neighbor cell 950. In these aspects of the present disclosure, the base station 110-1 of the serving cell 900 identifies potentially vulnerable UEs based on their location, a traffic type, or other selection criteria. Once identified, at time tO, the base station 110-1 of the serving cell 900 sends a request message to the aggressor neighbor cell 950. The request message may indicate a subset of: (1) a location of the vulnerable UE(s); (2) a UE interference tolerate threshold; and/or (3) a resource demand.

[00107] The request message sent by the base station 110-1 of the serving cell 900 at time tO may omit the resource demand, which is optional. When the request message omits the resource demand, the protected resources are decided by the base station 110- 2 of the aggressor neighbor cell 950. In addition, in lieu of a geolocation, the location of the UE may be represented by a certain set of metrics that represent the location of the UE. Furthermore, there may be a predefined set of time/frequency resources configured for each cell. In this configuration, signaling of the request message sent at time tO may simply refer to the index of the proposed set of resources that meet a UE resource demand.

[00108] In these aspects of the present disclosure, the base station 110-2 of the aggressor neighbor cell 950, in response to the request message at time tO, predicts whether interference from the transmit beams of the aggressor neighbor cell 950 would cause a detrimental impact on the victim UE in excess of the UE interference threshold. In response to a predicted detrimental impact, at time tl, the base station 110-2 of the aggressor neighbor cell 950 sends a response message to the serving cell 900 indicating an agreement to limit transmitted energy in the prohibited beam directions for the requested time/frequency resources of the victim UE. Potentially, the response message at time tl includes a proposal or an alternative proposal (if the resource demand is not accepted) for the time/frequency resources for the protected UEs, such as a resource set to protect. [00109] In some implementations, the base station 110-1 of the serving cell 900 periodically evaluates the location and vulnerability of the protected UEs 120 and the protected resources as the UEs 120 change positions, the channel conditions change, or the traffic demands change. In response to any of these changed conditions, at time t2, the serving cell 900 may send an updated request message with new conditions. After predicting whether the downlink interference would be detrimental to the updated set of vulnerable UEs 120, at time t3, the base station 110-2 of the aggressor neighbor cell 950 responds to the updated request message by transmitting an updated response message. In this example, once the interference threat to the set of vulnerable UEs 120 has expired, at time t4, the base station 110-1 of the serving cell 900 may send a cancel message to the aggressor neighbor cell 950 for terminating the resource protection.

[00110] FIGURE 11 is a timing diagram illustrating an example process performed, for example, by UEs 120 (120-1, ..., 120-N), a base station 110-1 of a serving cell 900, a central node 960, and a base station 110-2 of an aggressor neighbor cell 950 for spatial inter-cell interference aware downlink coordination at the central node, in accordance with various aspects of the present disclosure.

[00111] In some implementations, the prediction may be performed at the central node 960, such as a network controller 130. For example, the base station 110-1 of the serving cell 900 identifies potentially vulnerable UEs based on their location, traffic type, or other selection criteria. Once identified, at time tl, the base station 110-1 of the serving cell 900 sends a message about the identified, vulnerable UEs to the central node 960. This message may indicate all or subset of: (1) a location of the vulnerable UE(s); (2) an interference threshold of the vulnerable UEs; and (3) a traffic load. As noted above, that UE locations may be represented using a combination of: (1) (x,y,z) coordinates (e.g., a geolocation) of the UE from a positioning source and/or (2) a set of metrics representing the location of the UE within the serving cell 900. The positioning source may be, for example, a global navigation satellite system (GNSS) location server, a 5G NR location server, or other location server. In addition, the set of metrics representing the UE location may include a reference signal received power (RSRP) of the serving cell 900, a serving cell precoder (e.g., a strongest transmit beam direction), a serving cell channel quality indicator (CQI), and/or a path loss estimate between the serving cell 900 and UE 120. Other UE sensor information may also be included in the vulnerable UE message at time tO.

[00112] In operation, the central node 960 predicts whether a degradation caused by the interference from downlink transmit beams of the aggressor neighbor cell 950 on a victim UE 120 exceeds the UE interference threshold. At time tl, the central node 960 transmits a coordination request message to the aggressor neighbor cell 950 when the interference of neighbor cell downlink transmit beams exceeds the UE interference threshold. The request message may include a proposal to protect the UE by limiting transmitted energy in specified beam directions for the requested time/frequency resources noted by the request message. In response, at time t2, the base station 110-2 of the aggressor neighbor cell 950 sends a response message to the central node 960. The response message may indicate the base station 110-2 of the aggressor neighbor cell 950 accepts the proposal. Otherwise, the response message may propose an alternative plan to protect the UE. At time t3, the central node 960 sends a response message to the serving cell 900, which may indicate the set of protected resources for the protected UE.

[00113] In some implementations, the central node 960 periodically evaluates the location and vulnerability of the protected UE(s) and the protected resources. The periodic evaluation may determine whether a change is detected in the location of the UE, channel conditions of the UE, and/or the traffic demand on the UE. In response to the detected change, at time t4, the central node 960 may send an updated request message to the aggressor neighbor cell 950 based on the changed conditions. In response, at time t5, the base station 110-2 of the aggressor neighbor cell 950 may respond to the updated request message with an updated response message indicating updated protected resources. At time t6, the central node 960 sends a response message to the serving cell 900, which may inform the base station 110-1 of the updated set of protected resources.

[00114] Once the interference threat expires, a time t7, the serving cell 900 may send a cancel message to the central node 960 and trigger a terminate message at time t8 to terminate the resource protection. Some implementations including the central node may be beneficial when multiple aggressor neighbor cells are potentially causing interference to victim UEs. [00115] FIGURE 12 is a flow diagram illustrating an example process 1200 performed, for example, by a network device, for neural network-based spatial inter-cell interference learning, in accordance with various aspects of the present disclosure. The example process 1200 is an example of a network enhancement for neural network- based spatial inter-cell interference aware downlink coordination.

[00116] As shown in FIGURE 12, in some aspects, the process 1200 includes predicting spatial inter-cell downlink interference experienced by a UE (block 1202).

For example, the base station (e.g., using the controller/processor 240, and/or the memory 242) can predict spatial downlink inter-cell downlink interference. The predicting may occur at a serving base station, an aggressor base station, or at a central node. In some aspects, the predicting is based on a location of the UE, a UE interference tolerance threshold, and/or a resource demand for the UE.

[00117] In some aspects, the process 1200 further includes communicating with a second network device to reduce the spatial inter-cell downlink interference in a direction of the UE by protecting resources across selected resource sets (block 1204). For example, the base station (e.g., using the antenna 234, the DEMOD/MOD 232, the TX MIMO processor 230, the transmit processor 220, the controller/processor 240, and/or the memory 242) can communicate with the second network device to reduce the spatial inter-cell downlink interference in the direction of the UE. In some aspects, the protected resources include prohibited beam indices, time/frequency resources to protect, and/or an amount of resources to protect.

Example Aspects

[00118] Aspect 1 : A method of wireless communication by a first network device, comprising: predicting spatial inter-cell downlink interference experienced by a UE; and communicating with a second network device to reduce the spatial inter-cell downlink interference in a direction of the UE by protecting resources across selected resource sets.

[00119] Aspect 2: The method of Aspect 1, in which the first network device comprises a serving cell and the second network device comprises a potentially interfering neighbor cell, the method further comprising: selecting, by the first network device, the UE for which the predicted inter-cell downlink interference exceeds a predetermined threshold; and transmitting a request message to the second network device.

[00120] Aspect 3 : The method of Aspect 1 or 2, in which the request message indicates beams that cause excessive interference.

[00121] Aspect 4: The method of Aspect 1 or 2, in which the request message indicates time/frequency resources to protect.

[00122] Aspect 5: The method of Aspect 1 or 2, in which the request message indicates an amount of resources to protect, the amount determined based on a traffic demand of the UE.

[00123] Aspect 6: The method of Aspect 1 or 2, in which the request message indicates an index of the selected resource sets within a predetermined list of resource sets.

[00124] Aspect 7: The method of Aspect 1 or 2, further comprising receiving a response message indicating acceptance of a proposal indicated by the request message.

[00125] Aspect 8: The method of Aspect 1 or 2, further comprising receiving a response message indicating an alternative proposal for a different set of resources.

[00126] Aspect 9: The method of Aspect 1 or 2, further comprising updating the request message in response to an updated prediction based on an updated UE location, updated channel conditions for the UE, and/or an updated traffic demand for the UE.

[00127] Aspect 10: The method of any of the Aspects 1-9, in which the first network device comprises a neighbor cell and the second network device comprises a serving cell, the method further comprising: receiving a request message, from the second network device, indicating a location of the UE, a UE interference tolerance threshold, and/or a resource demand for the UE; predicting based on the request message; and transmitting a response message to the second network device.

[00128] Aspect 11 : The method of any of the Aspects 1-10, further comprising receiving updates from the second network device of the UE location, the UE interference tolerance threshold, and/or the resource demand for the UE. [00129] Aspect 12: The method of Aspect 1, in which the first network device comprises a central node and the second network device comprises a serving cell, the method further comprising: receiving a first request message, from the second network device, indicating a location of the UE, a UE interference tolerance threshold, and/or a resource demand for the UE; predicting based on the first request message; transmitting a second request message to an aggressor neighbor cell requesting resource protection; and transmitting a response message to the second network device indicating protected resources.

[00130] Aspect 13: The method of Aspect 12, further comprising updating the predicting based on an updated UE location, updated channel conditions for the UE, and/or an updated resource demand for the UE.

[00131] Aspect 14: An apparatus for wireless communication by a first network device, comprising: means for predicting spatial downlink inter-cell interference experienced by a UE; and means for communicating with a second network device to reduce the spatial downlink inter-cell interference in a direction of the UE by protecting resources across selected resource sets.

[00132] Aspect 15: The apparatus of Aspect 14, in which the first network device comprises a serving cell and the second network device comprises a potentially interfering neighbor cell, the apparatus further comprising: means for selecting, by the first network device, the UE for which the predicted spatial downlink inter-cell interference exceeds a predetermined threshold; and means for transmitting a request message to the second network device.

[00133] Aspect 16: The apparatus of Aspect 15, further comprising means for receiving a response message indicating acceptance of a proposal indicated by the request message.

[00134] Aspect 17: The apparatus of Aspect 15, further comprising means for receiving a response message indicating an alternative proposal for a different set of resources.

[00135] Aspect 18: The apparatus of Aspect 15, further comprising means for updating the request message in response to an updated prediction based on an updated UE location, updated channel conditions for the UE, and/or an updated traffic demand for the EE.

[00136] Aspect 19: The apparatus of Aspect 14, in which the first network device comprises a neighbor cell and the second network device comprises a serving cell, the apparatus further comprising: means for receiving a request message, from the second network device, indicating a location of the EE, a EE interference tolerance threshold, and/or a resource demand for the EE; means for predicting based on the request message; and means for transmitting a response message to the second network device.

[00137] Aspect 20: The apparatus of Aspect 19, further comprising means for receiving updates from the second network device of the EE location, the EE interference tolerance threshold, and/or the resource demand for the EE.

[00138] Aspect 21 : The apparatus of Aspect 14, in which the first network device comprises a central node and the second network device comprises a serving cell, the apparatus further comprising: means for receiving a first request message, from the second network device, indicating a location of the EE, a EE interference tolerance threshold, and/or a resource demand for the EE; means for predicting based on the first request message; means for transmitting a second request message to an aggressor neighbor cell requesting resource protection; and means for transmitting a response message to the second network device indicating protected resources.

[00139] Aspect 22: The apparatus of Aspect 21, further comprising means for updating the predicting based on an updated UE location, updated channel conditions for the UE, and/or an updated resource demand for the UE.

[00140] Aspect 23: A first network device, comprising: a processor; a memory coupled with the processor; instructions stored in the memory and operable, when executed by the processor, to cause the first network device: to predict spatial inter-cell downlink interference experienced by a UE, and to communicate with a second network device to reduce the spatial inter-cell downlink interference in a direction of the UE by protecting resources across selected resource sets.

[00141] Aspect 24: The first network device of Aspect 23, in which the first network device comprises a serving cell and the second network device comprises a potentially interfering neighbor cell, the instructions further cause the first network device: to select the UE for which the predicted inter-cell downlink interference exceeds a predetermined threshold; and to transmit a request message to the second network device.

[00142] Aspect 25: The first network device of Aspect 23, in which the first network device comprises a neighbor cell and the second network device comprises a serving cell, the instructions further cause the first network device: to receive a request message, from the second network device, indicating a location of the UE, a UE interference tolerance threshold, and/or a resource demand for the UE; to predict based on the request message; and to transmit a response message to the second network device.

[00143] Aspect 26: The first network device of Aspect 23, in which the first network device comprises a central node and the second network device comprises a serving cell, the instructions further cause the first network device: to receive a first request message, from the second network device, indicating a location of the UE, a UE interference tolerance threshold, and/or a resource demand for the UE; to predict based on the first request message; to transmit a second request message to an aggressor neighbor cell requesting resource protection; and to transmit a response message to the second network device indicating protected resources.

[00144] Aspect 27: The first network device of Aspect 26, in which the instructions further cause the first network device to update the predicting based on an updated UE location, updated channel conditions for the UE, and/or an updated resource demand for the UE.

[00145] Aspect 28: A non-transitory computer-readable medium having program code recorded thereon, the program code executed by a processor of a first network device and comprising: program code predict spatial inter-cell downlink interference experienced by a UE; and program code to communicate with a second network device to reduce the spatial inter-cell downlink interference in a direction of the UE by protecting resources across selected resource sets.

[00146] Aspect 29: The non-transitory computer-readable medium of clause 28, in which the first network device comprises a neighbor cell and the second network device comprises a serving cell, the non-transitory computer-readable medium further comprising: program code to receive a request message, from the second network device, indicating a location of the UE, a UE interference tolerance threshold, and/or a resource demand for the EE; program code to predict based on the request message; and program code to transmit a response message to the second network device.

[00147] Aspect 30: The non-transitory computer-readable medium of clause 28, in which the first network device comprises a central node and the second network device comprises a serving cell, the non-transitory computer-readable medium further comprising: program code to receive a first request message, from the second network device, indicating a location of the EE, a EE interference tolerance threshold, and/or a resource demand for the EE; program code to predict based on the first request message; program code to transmit a second request message to an aggressor neighbor cell requesting resource protection; and program code to transmit a response message to the second network device indicating protected resources.

[00148] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the aspects to the precise form disclosed. Modifications and variations may be made in light of the above disclosure or may be acquired from practice of the aspects.

[00149] As used, the term “component” is intended to be broadly construed as hardware, firmware, and/or a combination of hardware and software. As used, a processor is implemented in hardware, firmware, and/or a combination of hardware and software.

[00150] Some aspects are described in connection with thresholds. As used, satisfying a threshold may, depending on the context, refer to a value being greater than the threshold, greater than or equal to the threshold, less than the threshold, less than or equal to the threshold, equal to the threshold, not equal to the threshold, and/or the like.

[00151] It will be apparent that systems and/or methods described may be implemented in different forms of hardware, firmware, and/or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and/or methods is not limiting of the aspects. Thus, the operation and behavior of the systems and/or methods were described without reference to specific software code — it being understood that software and hardware can be designed to implement the systems and/or methods based, at least in part, on the description.

[00152] Even though particular combinations of features are recited in the claims and/or disclosed in the specification, these combinations are not intended to limit the disclosure of various aspects. In fact, many of these features may be combined in ways not specifically recited in the claims and/or disclosed in the specification. Although each dependent claim listed below may directly depend on only one claim, the disclosure of various aspects includes each dependent claim in combination with every other claim in the claim set. A phrase referring to “at least one of’ a list of items refers to any combination of those items, including single members. 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, as well as any combination with multiples of the same element (e.g., 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 ordering of a, b, and c).

[00153] No element, act, or instruction used should be construed as critical or essential unless explicitly described as such. Also, as used, the articles “a” and “an” are intended to include one or more items, and may be used interchangeably with “one or more.” Furthermore, as used, the terms “set” and “group” are intended to include one or more items (e.g., related items, unrelated items, a combination of related and unrelated items, and/or the like), and may be used interchangeably with “one or more.” Where only one item is intended, the phrase “only one” or similar language is used. Also, as used, the terms “has,” “have,” “having,” and/or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise.