Spatial Inter-Cell Interference Aware Downlink Cooperation

Abstract

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

Description

Claiming priority

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. patent application Ser. No. 17 / 675,980, entitled "SPATIAL INTER-CELL INTERFERENCE AWARE DOWNLINK COORDINATION," filed on February 18, 2022, which claims the benefit of U.S. Provisional Patent Application No. 63 / 155,635, entitled "SPATIAL INTER-CELL INTERFERENCE AWARE DOWNLINK COORDINATION," filed on March 2, 2021, the entire disclosures of which are expressly incorporated by reference. [Technical field]

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

[0003]

[0003] Wireless communication systems have been widely deployed to provide various telecommunication services, such as telephone, video, data, messaging, and broadcast. A typical wireless communication system may employ multiple access technologies capable of supporting 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 extensions to the Universal Mobile Telecommunications System (UMTS) mobile standard promulgated by the 3rd Generation Partnership Project (3GPP).

[0004]

[0004] A wireless communication network may include several base stations (BSs) that can support communication for several user equipments (UEs). The user equipments (UEs) may communicate with the base stations (BSs) via downlinks and uplinks. The downlink (or forward link) refers to the communication link from the BS to the UE, and the uplink (or reverse link) refers to the communication link from the UE to the BS. As 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 receive point (TRP), a new radio (NR) BS, a 5G Node B, etc.

[0005]

[0005] The above multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that allows different user equipment to communicate on a city, national, regional, or even global scale. New Radio (NR), sometimes referred to as 5G, is a set of extensions 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, utilizing new spectrum, and better integrating with other open standards using Orthogonal Frequency Division Multiplexing (OFDM) with Cyclic Prefix (CP) (CP-OFDM) on the downlink (DL) and CP-OFDM and / or SC-FDM (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]

[0006] An artificial neural network may comprise an interconnected group of artificial neurons (e.g., neuron models). An artificial neural network may be a computing device or may be represented as a method to be implemented by a computing device. A convolutional neural network, such as a deep convolutional neural network, is a type of feed-forward artificial neural network. A convolutional neural network may include layers of neurons that may be arranged in tiled receptive fields. It would be desirable to apply neural network processing to wireless communications to achieve greater efficiency. Summary of the Invention

[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 including communicating with a second network device to reduce spatial inter-cell downlink interference in a direction of the UE by protecting resources across a selected resource set.

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

[0009]

[0009] The first network device includes a processor and a memory coupled to 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 the UE. The first network device is also operable to communicate with a second network device to reduce spatial inter-cell downlink interference in a direction of the UE by protecting resources across a selected resource set.

[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 for predicting spatial inter-cell downlink interference experienced by a UE. The non-transitory computer-readable medium also includes program code for communicating with a second network device to reduce spatial inter-cell downlink interference in a direction of the UE by protecting resources across a selected resource set.

[0011]

[0011] Aspects generally include methods, apparatus, systems, computer program products, non-transitory computer-readable media, user equipment, base stations, wireless communication devices, and processing systems, as substantially described with reference to and as illustrated by the accompanying drawings and specification.

[0012]

[0012] The foregoing has outlined rather broadly the features and technical advantages of examples according to the present disclosure so that the following detailed description may be better understood. Additional features and advantages are described. The concepts and 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. The nature of the disclosed concepts, both their organization and method of operation, together with associated advantages, will be better understood in consideration of the following description in conjunction with the accompanying figures. Each of the figures is provided for the purpose of illustration and description, and not as a definition of the limits of the claims.

[0013]

[0013] So that the features of the present disclosure may be understood in detail, specific descriptions may be had by reference to the embodiments, some of which are illustrated in the accompanying drawings. It should be noted, however, that the description may lead to other equally valid embodiments, and that the accompanying drawings illustrate only some embodiments of the present disclosure, and therefore should not be considered as limiting the scope of the present disclosure. The same reference numbers in different drawings may identify the same or similar elements. [Brief description of the drawings]

[0014] [Figure 1]

[0014] A block diagram conceptually illustrating an example of a wireless communication network in accordance with various aspects of the present disclosure. [Diagram 2]

[0015] 1 is a block diagram conceptually illustrating an example of a base station in communication with a user equipment (UE) in a wireless communication network, in accordance with various aspects of the present disclosure. [Diagram 3]

[0016] FIG. 1 illustrates an example implementation of designing a neural network using a system-on-chip (SOC) that includes a general-purpose processor, in accordance with some aspects of the present disclosure. [Figure 4A]

[0017] FIG. 1 illustrates a neural network according to an aspect of the present disclosure. [Figure 4B] FIG. 1 illustrates a neural network according to an aspect of the present disclosure. [Figure 4C] FIG. 1 illustrates a neural network according to an aspect of the present disclosure. [Figure 4D]

[0018] FIG. 1 illustrates an example deep convolutional network (DCN), in accordance with aspects of the present disclosure. [Diagram 5]

[0019] FIG. 1 is a block diagram illustrating an example deep convolutional network (DCN), in accordance with aspects of the present disclosure. [Figure 6A]

[0020] FIG. 1 illustrates a communication network in which spatial interference experienced by a user equipment is based on a downlink transmission beam from a neighbor base station to a neighbor user equipment (UE), according to an aspect of the present disclosure. [Figure 6B] FIG. 1 illustrates a communication network in which spatial interference experienced by a user equipment is based on a downlink transmission beam from a neighbor base station to a neighbor user equipment (UE), according to an aspect of the present disclosure. [Figure 7]

[0021] 1 is a diagram of a communication network illustrating signal strength measurements of downlink transmission beams from neighbor base stations to enable spatial inter-cell interference aware downlink cooperation in accordance with an aspect of the present disclosure. [Figure 8]

[0022] FIG. 1 illustrates a block diagram of a network including a neural processing engine configured to enable spatial inter-cell interference aware downlink cooperation, according to an aspect of the present disclosure. [Figure 9]

[0023] 4 is a timing diagram illustrating an example process implemented, eg, by a network, for spatial inter-cell interference aware downlink coordination in a serving cell, in accordance with various aspects of the present disclosure. [Figure 10]

[0024] 4 is a timing diagram illustrating an example process, eg, implemented by a network, for spatial inter-cell interference aware downlink coordination in neighbor cells, in accordance with various aspects of the present disclosure. [Figure 11]

[0025] 1 is a timing diagram illustrating an example process, implemented by, for example, a device, for spatial inter-cell interference aware downlink coordination at a central node, in accordance with various aspects of the present disclosure. [Figure 12]

[0026] 1 is a flow diagram illustrating an example process, eg, implemented by a network device, for spatial inter-cell interference aware downlink coordination in accordance with various aspects of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015]

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

[0016]

[0028] Several aspects of a telecommunications system are now presented with reference to various apparatus and techniques that are described in the detailed description that follows 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 such elements are implemented as hardware or software depends on the particular application and design constraints imposed on the overall system.

[0017]

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

[0018]

[0030] Inter-cell interference may cause signal (e.g., signal-to-interference-plus-noise ratio (SINR)) degradation for users. The signal degradation for users may be significant, especially when the users are at the cell edge. Furthermore, with the introduction of massive multiple-input multiple-output (MIMO) antennas in next-generation Node Bs (gNBs), this inter-cell interference may lead to significant SINR degradation for users. For example, narrow downlink transmit beams may be highly directional and highly variable over time, resulting in SINR degradation. Highly directional interference, especially at cell edges, may reduce data rates and adversely affect user experience. Furthermore, high variability of interference makes it more difficult to implement link adaptation, such as predicting supportable modulation and coding schemes (MCS). This may be challenging for latency-sensitive applications with limited delay budgets.

[0019]

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

[0020]

[0032] Once the neural network is trained to infer the impact of the neighbor base station's transmit beam on the victim UE, the network device (e.g., gNB) cooperates with the neighbor base station. In these aspects of the disclosure, the network device cooperation may prohibit communication of the 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 on the resources serving the first user equipment.

[0021]

[0033] According to aspects of the present disclosure, a serving cell predicts downlink interference experienced by served UEs from different potential downlink transmission beams of neighbor cells. For example, the serving cell may identify a subset of served UEs for which the adverse impact potentially caused by inter-cell downlink interference exceeds a predefined UE interference threshold. This identification of the potential victim subset of UEs may also include additional criteria, such as the type of traffic the UEs are 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.

[0022]

[0034] In these aspects of the disclosure, the serving cell sends a request message to potentially interfering neighbor cells for each of the UE's potential victim subsets. The request message may include a forbidden list of beam indices that the potentially interfering neighbor cells are requested to avoid. Additionally, the request message may indicate the 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, and thus the signaling may simply refer to the index of the proposed set of resources. Alternatively, the time / frequency resources are left to the neighbor cells to decide.

[0023]

[0035] In some aspects of the present disclosure, learning spatial inter-cell downlink interference and predicting a victim subset of UEs is performed in an interfering cell, sometimes referred to as an aggressor neighbor cell. 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 (1) the location(s) of the vulnerable UE(s), (2) a UE interference tolerance threshold, and / or (3) a subset of protected resource demands. In other aspects of the present disclosure, an indication of the traffic load of the victim UE is provided to represent the amount of protected resources desired.

[0024]

[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 determined by the neighbor cell. Furthermore, there may be a predefined set of time / frequency resources configured for each cell. In this configuration, the signaling of the request message may simply refer to an index of the proposed set of resources that meets the UE resource demand.

[0025]

[0037] In other implementations, the learning and prediction are performed in a centralized cooperative node, such as a network controller. For example, a 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 cooperative node. This information may indicate all or a subset of: (1) the location(s) of the vulnerable UE(s), (2) the interference threshold of the vulnerable UE, and (3) the traffic load (e.g., updated traffic demand). Some implementations involving central node cooperation may be beneficial when multiple aggressor neighbor cells are potentially causing interference to a victim UE.

[0026]

[0038] FIG. 1 illustrates a network 100 in which aspects of the disclosure may be implemented. The network 100 may be a 5G network or a NR network, or some other wireless network, such as an LTE network. The wireless network 100 may include several BSs 110 (shown as BS 110a, BS 110b, BS 110c, and BS 110d) and other network entities. A BS is an entity that communicates with user equipment (UE) and may also be referred to as a base station, NR BS, Node B, gNB, 5G Node B (NB), access point, transmit reception point (TRP), etc. Each BS may provide communication coverage for a particular geographic area. In 3GPP, 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.

[0027]

[0039] A BS 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 few kilometers in radius) and may allow unrestricted access by UEs with a service subscription. A pico cell may cover a relatively small geographic area and may allow unrestricted access by UEs with a service subscription. A femto cell may cover a relatively small geographic area (e.g., a home) and may allow restricted access by UEs with an 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 FIG. 1, BS 110a may be a macro BS for macro cell 102a, BS 110b may be a pico BS for pico cell 102b, and BS 110c may be a femto BS for 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,” “5G NB,” and “cell” may be used interchangeably.

[0028]

[0040] In some aspects, the cells may not necessarily be fixed and the geographic area of ​​the cells may move according to the location of the mobile BS. In some aspects, the BSs may be interconnected to each other and / or to one or more other BSs or network nodes (not shown) in wireless network 100 through various types of backhaul interfaces, such as direct physical connections, virtual networks, etc., using any suitable transport network.

[0029]

[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 the 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 FIG. 1, a relay station 110d may communicate with a macro BS 110a and a UE 120d to facilitate communication between the BS 110a and the UE 120d. A relay station may also be referred to as a relay BS, a relay base station, a relay, etc.

[0030]

[0042] The wireless network 100 may be a heterogeneous network including different types of BSs, e.g., macro BSs, pico BSs, femto BSs, relay BSs, etc. These different types of BSs may have different transmit power levels, different coverage areas, and different impacts on interference in the wireless network 100. For example, a macro BS may have a high transmit power level (e.g., 5-40 watts), while a pico BS, femto BS, and relay BS may have a lower transmit power level (e.g., 0.1-2 watts).

[0031]

[0043] A network controller 130 may couple to a set of BSs and 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 each other directly or indirectly, for example, via wireless or wireline backhaul.

[0032]

[0044] The UEs 120 (e.g., 120a, 120b, 120c) may be dispersed throughout the wireless network 100, and each UE may be fixed or mobile. A UE may also be referred to as an access terminal, terminal, mobile station, subscriber unit, station, etc. A UE may be a cellular phone (e.g., a smartphone), 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, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device or equipment, a biometric sensor / device, a wearable device (smart watch, smart clothing, smart glasses, smart wristband, smart jewelry (e.g., smart ring, smart bracelet)), an entertainment device (e.g., a music or video device, or a satellite radio), a vehicle component or sensor, a smart meter / sensor, industrial manufacturing equipment, a global positioning system device, or any other suitable device configured to communicate over a wireless or wired medium.

[0033]

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

[0034]

[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 the UE. The neural processing engine 160 may also communicate with a second network device to reduce spatial inter-cell downlink interference in the direction of the UE by protecting resources across a selected resource set.

[0035]

[0047] Some UEs may be considered as machine type communication (MTC) UEs or evolved or enhanced machine type communication (eMTC) UEs. MTC UEs and eMTC UEs include, for example, a robot, a drone, a remote device, a sensor, a meter, a monitor, a location tag, etc., that may communicate with a base station, another device (e.g., a 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 the Internet or a cellular network) via a wired or wireless communication link. Some UEs may be considered as Internet of Things (IoT) devices and / or implemented as NB-IoT (narrowband Internet of Things) devices. Some UEs may be considered as customer premises equipment (CPE). The UE 120 may be included in a housing that stores components of the UE 120, such as a processor component, a memory component, etc.

[0036]

[0048] Generally, 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. The RAT may also be referred to as a radio technology, an air interface, etc. The frequencies may also be referred to as a carrier, a 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.

[0037]

[0049] In some aspects, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly (e.g., without using a base station 110 as an intermediary to communicate with each other) using one or more sidelink channels. For example, the UEs 120 may communicate using peer-to-peer (P2P) communications, device-to-device (D2D) communications, vehicle-to-anything (V2X) protocols (which may include, e.g., vehicle-to-vehicle (V2V) protocols, vehicle-to-infrastructure (V2I) protocols, etc.), mesh networks, etc. In this case, the UEs 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 the UEs 120 via downlink control information (DCI), radio resource control (RRC) signaling, media access control-control element (MAC-CE), or via system information (e.g., system information block (SIB)).

[0038]

[0050] As noted above, Figure 1 is provided as an example only. Other examples may differ from those described with respect to Figure 1.

[0039]

[0051] 2 shows a block diagram of a design 200 of a base station 110, which may be one of the base stations in FIG. 1, and a UE 120, which may be one of the UEs in FIG. 1. 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.

[0040]

[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 a channel quality indicator (CQI) received from the UE, process (e.g., encode and modulate) the data for each UE based at least in part on the selected MCS(es) for that UE, and provide data symbols for all UEs. Reducing the MCS reduces throughput but increases reliability of transmission. The transmit processor 220 may also process system information (e.g., for semi-static resource partitioning information (SRPI), etc.) and control information (e.g., CQI requests, grants, higher layer signaling, etc.) and provide overhead symbols and control symbols. The transmit processor 220 may also generate reference symbols for reference signals (e.g., cell-specific reference signals (CRS)) and synchronization signals (e.g., primary synchronization signals (PSS) and secondary synchronization signals (SSS)). A transmit (TX) multiple-input multiple-output (MIMO) processor 230 may perform spatial processing (e.g., precoding) on ​​the data symbols, control symbols, overhead symbols, and / or 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), etc.) 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. The T downlink signals from the modulators 232a through 232t may be transmitted via T antennas 234a through 234t, respectively. According to various aspects described in more detail below, a synchronization signal may be generated using location coding to convey additional information.

[0041]

[0053] At the UE 120, antennas 252a-252r may receive downlink signals from the base station 110 and / or other base stations and may provide received signals to demodulators (DEMODs) 254a-254r, respectively. Each demodulator 254 may condition (e.g., filter, amplify, downconvert, and digitize) the received signal to obtain input samples. Each demodulator 254 may further process the input samples (e.g., for OFDM, etc.) to obtain received symbols. A MIMO detector 256 may obtain received symbols from all R demodulators 254a-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 and provide decoded data for the UE 120 to a data sink 260 and provide decoded control and system information to the controller / processor 280. The channel processor may determine a reference signal received power (RSRP), a received signal strength indicator (RSSI), a reference signal received quality (RSRQ), a channel quality indicator (CQI), etc. In some aspects, one or more components of the UE 120 may be included in a housing.

[0042]

[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, etc.) from the controller / processor 280. The 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 MIMO processor 266 if applicable, further processed by modulators 254a-254r (e.g., for DFT-s-OFDM, CP-OFDM, etc.), and transmitted to the base station 110. At the base station 110, uplink signals from the UE 120 and other UEs may be received by antennas 234, processed by demodulator 254, detected by a MIMO 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 may provide the decoded control information to the controller / processor 240. The base station 110 may include a communication unit 244 and communicate to the network controller 130 via the communication unit 244. The network controller 130 may include a communication unit 294, a controller / processor 290, and a memory 292.

[0043]

[0055] The controller / processor 240 of the base station 110 of FIG. 2 and / or the controller / processor 280 of the UE 120 may implement one or more techniques related to 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 FIG. 2 may implement or direct operations of, for example, the process of FIG. 8 and / or other processes as described. Additionally, the controller / processor 240 of the base station 110 of FIG. 2 may implement or direct operations of, for example, the process of FIG. 9-FIG. 12 and / or other processes as described. The memories 242 and 282 may store data and program codes for the base station 110 and the UE 120, respectively. The scheduler 246 may schedule the UE for data transmission on the downlink and / or the uplink.

[0044]

[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 the base station 110 described with respect to FIG.

[0045]

[0057] As noted above, Figure 2 is provided as an example only. Other examples may differ from those described with respect to Figure 2.

[0046]

[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 (CPE), vehicles, Internet of Things (IoT) devices, etc. 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-anything (V2X) applications, etc. Additionally, in some cases, a single device may support different applications or services simultaneously.

[0047]

[0059] 3 illustrates an exemplary implementation of a system-on-chip (SOC) 300 that may include a central processing unit (CPU) 302 or a multi-core CPU configured to generate gradients for neural network training according to some aspects of the disclosure. The SOC 300 may be included in a base station 110 or a UE 120. Variables (e.g., neural signals and synaptic weights), system parameters associated with a computing 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 the 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 distributed across multiple blocks. Instructions executed in the CPU 302 may be loaded from a program memory associated with the CPU 302 or from the memory block 318.

[0048]

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

[0049]

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

[0050]

[0062] Deep learning architectures may perform object recognition tasks by learning to represent inputs at successively higher levels of abstraction at each layer, thereby accumulating useful feature representations of the input data. In this way, deep learning addresses a major bottleneck of traditional machine learning. Prior to the advent of deep learning, machine learning approaches to object recognition problems may have relied heavily on human-designed features, possibly in combination with shallow classifiers. A shallow classifier may be, for example, a two-class linear classifier in which a weighted sum of feature vector components may be compared to a threshold to predict which class an input belongs to. Human-designed features may be templates or kernels adapted to a particular problem domain by an engineer with domain expertise. In contrast, deep learning architectures learn to represent features that are similar to those that a human engineer might design, but may do so through training. Furthermore, deep networks may learn to represent and recognize new types of features that humans may not have considered.

[0051]

[0063] Deep learning architectures may learn a hierarchy of features. For example, when presented with visual data, a first layer may learn to recognize relatively simple features in the input stream, such as edges. In another example, when presented with auditory data, the first layer may learn to recognize spectral power at specific frequencies. A second layer, taking the output of the first layer as input, may learn to recognize combinations of features, such as simple shapes in the case of visual data, or combinations of sounds in the case of auditory data. For example, higher layers may learn to represent complex shapes in visual data, or words in auditory data. Even higher layers may learn to recognize common visual objects or spoken phrases.

[0052]

[0064] Deep learning architectures can work particularly well when applied to problems that have a natural hierarchical structure. For example, motor vehicle classification can benefit from first learning to recognize wheels, windshields, and other features. These features can be combined in different ways in higher layers to recognize cars, trucks, and planes.

[0053]

[0065] Neural networks can be designed with a variety of connectivity patterns. In feedforward networks, information is passed from lower layers to higher layers, with each neuron in a given layer communicating to a neuron in a higher layer. As explained above, in successive layers of a feedforward network, a hierarchical representation can be accumulated. Neural networks can also have recurrent or (also called top-down) feedback connections. In recurrent connections, the output from a neuron in a given layer can be communicated to another neuron in the same layer. Recurrent architectures can be useful for recognizing patterns across two or more of the input data chunks that are delivered sequentially to the neural network. Connections from neurons in a given layer to neurons in a lower layer are called feedback (or top-down) connections. Networks with many feedback connections can be useful when recognition of high-level concepts can help discriminate certain low-level features of the input.

[0054]

[0066] The connections between layers of a neural network can be fully connected or locally connected. FIG. 4A shows an example of a fully connected neural network 402. In the fully connected neural network 402, a neuron in a first layer can communicate its output to every neuron in a second layer, such that each neuron in the second layer receives input from every neuron in the first layer. FIG. 4B shows an example of a locally connected neural network 404. In the locally connected neural network 404, a neuron in a first layer can be connected to a limited number of neurons in the second layer. More generally, the locally connected layer of the locally connected neural network 404 can be configured such that each neuron in the layer has the same or similar connectivity pattern, but with connection strengths that can have different values ​​(e.g., 410, 412, 414, and 416). The connectivity pattern of the locally connected can result in spatially separate receptive fields in the upper layer, since the upper layer neurons in a given region can receive inputs that are conditioned through training to the properties of a limited portion of the total inputs to the network.

[0055]

[0067] One example of a locally connected neural network is a convolutional neural network. Figure 4C shows 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 suitable for problems where the spatial location of the inputs is meaningful.

[0056]

[0068] One type of convolutional neural network is the deep convolutional network (DCN). Figure 4D shows a detailed example of a DCN 400 designed to recognize visual features from an image 426 input from an image capture device 430, such as an on-board camera. The DCN 400 of this example may be trained to identify traffic signs and numbers provided on the traffic signs. Of course, the DCN 400 may be trained for other tasks, such as identifying lane markings or identifying traffic signals.

[0057]

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

[0058]

[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, the size of the second set of feature maps 420, such as 14×14, is smaller than the size of the first set of feature maps 418, such as 28×28. The reduced size provides similar information to subsequent layers 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).

[0059]

[0071] In the example of FIG. 4D , the second set of feature maps 420 are convolved to generate a first feature vector 424. Moreover, the first feature vector 424 is further convolved to generate a second feature vector 428. Each feature in the second feature vector 428 may include a number corresponding 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 into probabilities. Thus, the output 422 of the DCN 400 is the probability that the image 426 contains one or more features.

[0060]

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

[0061]

[0073] To adjust the weights, the learning algorithm may calculate a gradient vector for the weights. The gradient may indicate the amount by which the error increases or decreases if the weights are adjusted. In the top layer, the gradient may correspond directly to the values ​​of the weights connecting the activated neurons in the penultimate layer and the neurons in the output layer. In lower layers, the gradient may depend on the values ​​of the weights and the calculated error gradients of the upper layers. The weights may then be adjusted to reduce the error. This manner of adjusting weights is sometimes called "backpropagation" because it involves a "backward pass" through the neural network.

[0062]

[0074] In practice, the error gradient of the weights may be calculated over a small number of examples such that the calculated gradient approximates the true error gradient. This approximation method is sometimes called stochastic gradient descent. Stochastic gradient descent may be repeated until the achievable error rate of the overall system no longer decreases or until the error rate reaches a target level. After training, the DCN may be presented with a new image (e.g., a speed limit sign in image 426) and a forward pass through the network may result in an output 422, which may be considered the inference or prediction of the DCN.

[0063]

[0075] A deep belief network (DBN) is a probabilistic model with multiple layers of hidden nodes. DBNs can be used to extract hierarchical representations of a training dataset. DBNs can be obtained by stacking layers of Restricted Boltzmann Machines (RBMs). RBMs are a type of artificial neural network that can learn probability distributions over a set of inputs. RBMs are often used in unsupervised learning because they can learn probability distributions in the absence of information about the class into which each input should be categorized. Using a hybrid unsupervised and supervised paradigm, the lower RBM of the DBN can be trained in an unsupervised manner and act as a feature extractor, and the upper RBM can be trained in a supervised manner (on the joint distribution of inputs from the previous layer and the target class) and act as a classifier.

[0064]

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

[0065]

[0077] The DCN may be a feedforward network. Furthermore, as described above, the connections from neurons in the first layer of the DCN to groups of neurons in the next higher layer are shared across the neurons in the first layer. The feedforward and shared connections of the DCN may be exploited for high speed processing. The computational burden of the DCN may be much less than that of a similarly sized neural network with, for example, recurrent or feedback connections.

[0066]

[0078] The processing of each layer of the convolutional network may be considered as a spatially invariant template or basis projection. If the input is initially decomposed into multiple channels, such as the red, green, and blue channels of a color image, the convolutional network trained on that input may be considered to be three-dimensional, with two spatial dimensions along the axes of the image and a third dimension capturing color information. The output of the convolutional combinations may be considered to form a feature map in the subsequent layer, where each element of the feature map (e.g., 220) may receive inputs from various neurons in the previous layer (e.g., feature map 218) and from each of the multiple channels. The values ​​in the feature map may be further processed using nonlinearities, such as rectification, max(0,x), etc. Values ​​from neighboring neurons may be further pooled, which corresponds to downsampling and may provide further local invariance and dimensionality reduction. Normalization, which corresponds to whitening, may also be applied by lateral inhibition between neurons in the feature map.

[0067]

[0079] The performance of deep learning architectures can improve as more labeled data points become available or as computational power increases. Modern deep neural networks are routinely trained with computing resources thousands of times greater than those available to the average researcher only 15 years ago. New architectures and training paradigms can further improve deep learning performance. Rectified linear units can reduce a training problem known as vanishing gradients. New training techniques can reduce over-fitting and thus enable larger models to achieve better generalization. Encapsulation techniques can extract data in a given receptive field, further improving overall performance.

[0068]

[0080] 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 FIG. 5, the deep convolutional network 550 includes convolutional blocks 554A, 554B. Each of the convolutional blocks 554A, 554B may be composed of a convolutional layer (CONV) 356, a normalization layer (LNorm) 558, and a max pooling layer (MAX POOL) 560.

[0069]

[0081] The convolution layer 556 may include one or more convolution filters, which may be applied to the input data to generate feature maps. Although only two of the convolution blocks 554A, 554B are shown, the disclosure is not so limited, and instead, any number of convolution blocks 554A, 554B may be included in the deep convolution network 550 according to design preferences. The normalization layer 558 may normalize the output of the convolution filters. For example, the normalization layer 558 may perform whitening or lateral suppression. The max pooling layer 560 may perform downsampling aggregation across space for local invariance and dimensionality reduction.

[0070]

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

[0071]

[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, there are weights (not shown) to be updated. The output of each of the layers (e.g., 556, 558, 560, 562, 564) may serve as an input of a subsequent layer of the layers (e.g., 556, 558, 560, 562, 564) in the deep convolutional network 550 to learn a hierarchical feature representation from the input data 552 (e.g., image, audio, video, sensor data, and / or other input data) provided in the first of the convolutional 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 that the input data contains a feature from the set of features.

[0072]

[0084] As noted above, Figures 3-5 are provided as examples. Other examples may differ from those described with respect to Figures 3-5.

[0073]

[0085] As described above, inter-cell interference can cause signal (e.g., signal-to-interference-plus-noise ratio (SINR)) degradation for a user. The signal degradation for a user can be significant, especially when the user is at the cell edge. Furthermore, with the introduction of massive multiple-input multiple-output (MIMO) antennas in next-generation base stations (e.g., gNBs), this inter-cell interference can be highly directional and highly variable over time. Unfortunately, highly directional interference at the cell edge can reduce data rates and adversely affect user experience. Furthermore, the high variability of interference makes it more difficult to implement link adaptation, such as predicting a supportable modulation and coding scheme (MCS). This is challenging for latency-sensitive applications that have a limited delay budget. This limited delay budget can be insufficient to recover packets through a hybrid automatic repeat request (HARQ) procedure when the selected modulation and coding scheme (MCS) is not accurate.

[0074]

[0086] 6A and 6B illustrate a communication network in which spatial interference experienced by a user equipment (UE) is based on downlink transmission beams from neighbor base stations to neighbor UEs, according to an aspect of the disclosure. As shown in FIG. 6A, in a first interference scenario 600, a first UE 120-1 communicates with a first base station 110-1 through downlink transmission beam i. Similarly, a second UE 120-2 communicates with a neighbor base station 110-2 through downlink transmission beam j. In this example, spatial inter-cell interference from downlink transmission beam j on downlink transmission beam i causes minimal signal degradation (e.g., SINR=20 dB) at the first UE 120-1.

[0075]

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

[0076]

[0088] FIG. 7 is a diagram of a communication network 700 illustrating signal strength measurements of downlink transmission beams from neighbor base stations to enable spatial inter-cell interference aware downlink cooperation according to an aspect of the disclosure. According to an aspect of the disclosure, a neural network is trained to infer the impact of potential transmission beams of neighbor base stations (e.g., gNBs) on potential victim UEs. In this example, victim user equipment (UE1) 120-1 communicates with serving base station (gNB1) 110-1 on downlink transmission beam i. Unfortunately, victim UE 1120-1 experiences significant interference from downlink transmission beam k of neighbor base station (gNB2) 110-2 that is used to communicate with second user equipment (UE2) 120-2.

[0077]

[0089] In this example, spatial inter-cell interference for victim UE 1120-1 is mitigated by cooperation with neighbor base station gNB2 110-2. For example, neighbor base station gNB2 110-2 may transmit on downlink beam j to avoid transmitting energy in the direction of downlink transmission beam k on resources serving victim UE 1120-1. Aspects of the present disclosure cooperate with neighbor base station gNB2 110-2 to transmit on downlink beam j on resources used to serve victim UE 1120-1 to avoid interference for victim UE 1110-1.

[0078]

[0090] According to aspects of the disclosure, a neural network of serving base station gNB1 110-1 is trained to infer the impact of a downlink transmission beam of neighbor base station gNB2 110-2 on victim UE 1120-1. In these aspects of the disclosure, serving base station gNB1 110-1 coordinates with neighbor base station gNB2 110-2 to prohibit communication of downlink transmission beam k by neighbor base station gNB2 110-2 on time / frequency resources used to serve victim UE 1120-1. In some configurations, prediction of inter-cell interference is performed using machine learning with a neural processing engine (NPE), for example, as shown in FIG. 8.

[0079]

[0091] 8 is a block diagram of a network including a neural processing engine configured for neural network-based prediction of spatial inter-cell interference-based distribution to enable spatial inter-cell interference-aware downlink cooperation according to an aspect of the present disclosure. In an aspect of the present disclosure, a database stores information regarding the impact of potential transmission beams of neighbor base stations (e.g., gNBs) on potential victim UEs. 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 lookup to determine the potential impact from neighbor base stations on potential victim UEs.

[0080]

[0092] FIG. 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 for implementing a neural network. In this example, the location block 830 indicates a UE location 802 (X) to the neural processing engine 810. Also, a neighbor cell transmit precoder 804 (T) (e.g., a channel state information (CSI) beam index) is input from the neighbor cell to the neural processing engine 810. 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.

[0081]

[0093] In aspects of the 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 reports provide neighbor cell signal strength for each beam of the neighbor base station. For example, the signal strength measurement reports may be based on CSI reports, including, for example, SINR information or reference signal received power (RSRP) information. In some aspects of the disclosure, the UE CSI-RS measurement reports may provide SINR information, where the signal strength corresponds to the serving cell and the interference corresponds to each beam of the neighbor base station.

[0082]

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

[0083]

[0095] As shown in FIG. 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 in a known codebook or may be a precoder weight. In an aspect of the 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), a 5G NR location server, etc. Alternatively, the UE location 802 may be determined based on a set of metrics representing the location of the UE 120 within the serving cell. For example, the set of metrics representative of the location of the UE 120 within the serving cell may include a serving cell reference signal received power (RSRP) signal, a serving cell precoder (e.g., a precoding matrix indicator (PMI)) indicating the strongest transmit beam direction, a serving cell channel quality indicator (CQI), and / or a path loss estimate for the channel between the serving cell and the UE 120. Additionally, the UE location 802 may be determined based on other UE sensor information. In some aspects, the UE location 802 may be a geolocation.

[0084]

[0096] In an aspect of the present disclosure, the training of the neural network may be performed at various nodes. For example, the training of the neural network may be performed at the serving cell. In this example, an interference measurement report is sent by the UE 120 to the serving cell base station 110 with information about the UE location 802. A position estimate of the UE 120 may be performed at the serving cell base station 110 or communicated to the serving cell base station 110 by a separate location server. Alternatively, a UE location indicator metric is reported by the UE 120 to the serving cell to determine the UE location 802.

[0085]

[0097] In some aspects of the disclosure, the training of the neural network is performed in a neighbor cell(s), such as the neighbor base station gNB2 110-2 shown in FIG. 7. In these aspects of the disclosure, the serving cell base station gNB1 110-1 sends the UE location 802 and the interference measurement report to the neighbor base station gNB2 110-2 to perform the training of the neural network in the neighbor base station gNB2 110-2. In other aspects of the disclosure, the training of the neural network is performed in a centralized node. In these aspects of the disclosure, the serving cell base station gNB1 110-1 sends the UE location 802, the serving cell identity (ID), the neighbor cell ID, and the interference measurement report to the centralized node to perform the training of the neural network in the centralized node.

[0086]

[0098] Once the neural network is trained to infer the impact of the neighbor base station's transmit beam on the victim UE, the network device (e.g., gNB) cooperates with the neighbor base station. In these aspects of the disclosure, the network device cooperation may prevent communication of the downlink transmit beam by the neighbor base station on the time / frequency resources used to serve the victim UE. For example, as shown in FIG. 7, the UE 1120-1 experiences significant interference from the downlink transmit beam k of the neighbor base station (gNB2) 110-2. In this example, the 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 on the resources used to serve the UE 1120-1.

[0087]

[0099] According to aspects of the present disclosure, undesirable neighbor cell beams are identified based on some criteria that follow the predicted interference-based distribution. For example, the criteria may be an average or percentile exceeding a predefined threshold. Furthermore, this spatial interference characterization may be used to coordinate scheduling between nearby cells, such as neighbor base station gNB2 110-2, to prevent high interference events, for example, as shown in FIGS. 9-11.

[0088]

[0100] FIG. 9 is a timing diagram illustrating an example process implemented, for example, by UEs 120 (120-1, ..., 120-N), base station 110-1 of the 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 in a serving cell 900 in accordance with various aspects of the present disclosure.

[0089]

[0101] According to aspects of the disclosure, the base station 110-1 of the serving cell 900 predicts potential downlink interference experienced by the UEs 120 (120-1, ..., 120-N) in the serving cell 900 from different potential downlink transmission beams of the neighbor cells 950 (950-1, ..., 950-N). For example, the base station 110-1 of the serving cell 900 identifies a subset of served UEs 120 for which the potential adverse impact caused by inter-cell downlink interference from the neighbor cells 950 exceeds a predetermined UE interference threshold. That is, the neighbor cells 950 may include potentially interfering neighbor cells. This identification of the potential victim subset of UEs may also include additional criteria, such as the type of traffic the UEs are 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.

[0090]

[0102] At time t0, the base station 110-1 of the serving cell 900 sends a request message to the potentially interfering neighbor cell 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 cell 950 is requested to avoid. Additionally, the request message may indicate the requested time / frequency resources (e.g., time slots / resource blocks (RBs)) for which protection is requested. For example, the request message may indicate the amount of resources to be protected. The base station 110-1 may determine how much bandwidth the vulnerable UE needs, e.g., 1 / 4 of the allocated resources. In some implementations, a predefined set of time / frequency resources is configured for each cell, and thus the signaling of the request message at time t0 may simply refer to the index of the proposed set of resources. Alternatively, the selection of the time / frequency resources is left as a decision for the neighbor cell 950.

[0091]

[0103] At time t1, the base stations 110-2 to N of the aggressor neighbor cell 950 respond with response message(s) that are received by the base station 110-1 of the serving cell 900 at time t1. The response message may be an acceptance of the proposal indicated by the request message. For example, the response message may indicate an agreement to limit the transmitted energy in the identified beam direction for the mentioned 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 t1 may include an alternative proposal for a different set of resources when the proposal (e.g., serving proposal) in the request message from the serving cell 900 is not accepted.

[0092]

[0104] The serving cell 900 may periodically repeat the spatial inter-cell downlink interference prediction when the victim UE 120 changes location, channel conditions change, and / or traffic demands change. For example, the UE 120 may move closer to the serving base station 110-1, or the UE may enter an idle mode. At time t2, the serving cell 900 may send an updated request message to the neighbor cell 950 based on the updated prediction for the new conditions. Furthermore, at time t3, the neighbor cell 950 may respond to the updated request message with an updated response message as well. When the interference threat for the victim UE 120 disappears, at time t4, the serving cell 900 may send a cancel request message to stop resource protection. For example, the serving cell 900 may send a cancel request message at time t4 when the victim UE 120 enters an idle mode.

[0093]

[0105] FIG. 10 is a timing diagram illustrating an example process implemented, for example, by UEs 120 (120-1, ..., 120-N), base station 110-1 of serving cell 900, and base station 110-2 of neighbor cell 950 for spatial inter-cell interference aware downlink coordination in neighbor cell 950 in accordance with various aspects of the present disclosure.

[0094]

[0106] In aspects of the disclosure, prediction of spatial inter-cell downlink interference is performed in a neighbor cell 950, sometimes referred to as an aggressor neighbor cell 950. In these aspects of the disclosure, 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 t0, 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 (1) the location(s) of the vulnerable UE(s), (2) a UE interference tolerance threshold, and / or (3) a subset of resource demands.

[0095]

[0107] The request message sent by the base station 110-1 of the serving cell 900 at time t0 may omit the resource demand, which is optional. When the request message omits the resource demand, the protected resources are determined by the base station 110-2 of the aggressor neighbor cell 950. Furthermore, instead of geolocation, the UE's location may be represented by some set of metrics that represent the UE's location. Furthermore, there may be a predefined set of time / frequency resources configured for each cell. In this configuration, the signaling of the request message sent at time t0 may simply refer to an index of the proposed set of resources that satisfy the UE resource demand.

[0096]

[0108] In these aspects of the disclosure, the base station 110-2 of the aggressor neighbor cell 950, in response to the request message at time t0, predicts whether interference from the transmission beam of the aggressor neighbor cell 950 will cause a detrimental effect on the victim UE that exceeds a UE interference threshold. In response to the predicted detrimental effect, at time t1, 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 prohibited beam directions for the victim UE's requested time / frequency resources. Potentially, the response message at time t1 includes a proposal for time / frequency resources for the protected UE, such as a resource set to be protected, or an alternative proposal (if the resource request is not accepted).

[0097]

[0109] In some implementations, the base station 110-1 of the serving cell 900 periodically evaluates the location and vulnerability of the protected UE 120 and the protected resources when the UE 120 changes location, channel conditions change, or 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 the new conditions. After predicting whether downlink interference will be harmful 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 sending an updated response message. In this example, when the interference threat to the set of vulnerable UEs 120 disappears, at time t4, the base station 110-1 of the serving cell 900 may send a cancellation message to the aggressor neighbor cell 950 to terminate resource protection.

[0098]

[0110] FIG. 11 is a timing diagram illustrating an example process implemented, for example, by UEs 120 (120-1, ..., 120-N), a base station 110-1 of a serving cell 900, the central node, and a base station 110-2 of an aggressor neighbor cell 950 for spatial inter-cell interference aware downlink coordination at a central node 960 in accordance with various aspects of the present disclosure.

[0099]

[0111] In some implementations, the prediction may be performed in a central node 960, such as the 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 t1, the base station 110-1 of the serving cell 900 sends a message to the central node 960 regarding the identified vulnerable UE. The message may indicate all or a subset of: (1) the location(s) of the vulnerable UE(s), (2) the interference threshold of the vulnerable UE, and (3) the traffic load. As described above, the UE location may be represented using a combination of (1) the UE's (x,y,z) coordinates (e.g., geolocation) from a positioning source, and / or (2) a set of metrics that represent the UE's location 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. Additionally, the set of metrics representative of the UE location may include a reference signal received power (RSRP) of the serving cell 900, a serving cell precoder (e.g., strongest transmit beam direction), a serving cell channel quality indicator (CQI), and / or a path loss estimate between the serving cell 900 and the UE 120. Other UE sensor information may also be included in the vulnerable UE message at time t0.

[0100]

[0112] During operation, the central node 960 predicts whether the degradation caused by interference from the downlink transmission beam of the aggressor neighbor cell 950 to the victim UE 120 exceeds the UE interference threshold. At time t1, the central node 960 transmits a cooperation request message to the aggressor neighbor cell 950 when the interference of the neighbor cell downlink transmission beam exceeds the UE interference threshold. The request message may include a proposal to protect the UE by limiting the transmitted energy in a specified beam direction for the requested time / frequency resource mentioned 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 that 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 a set of protected resources for the protected UE.

[0101]

[0113] 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 in the UE's location, the UE's channel conditions, and / or traffic demands for the UE has been detected. 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 the updated protected resources. At time t6, the central node 960 sends a response message to the serving cell 900 that may inform the base station 110-1 of the updated set of protected resources.

[0102]

[0114] When the interference threat disappears, at time t7, the serving cell 900 may send a cancellation message to the central node 960 and trigger a termination message to terminate resource protection at time t8. Some implementations including a central node may be beneficial when multiple aggressor neighbor cells are potentially causing interference to a victim UE.

[0103]

[0115] 12 is a flow diagram illustrating an example process 1200, for example, implemented 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.

[0104]

[0116] As shown in FIG. 12, in some aspects, process 1200 includes predicting spatial inter-cell downlink interference experienced by a UE (block 1202). For example, a base station (e.g., using controller / processor 240 and / or memory 242) can predict spatial downlink inter-cell downlink interference. The prediction may be performed at a serving base station, an aggressor base station, or a central node. In some aspects, the prediction is based on a location of the UE, a UE interference tolerance threshold, and / or a resource demand for the UE.

[0105]

[0117] In some aspects, the process 1200 further includes communicating with the second network device to reduce spatial inter-cell downlink interference in the direction of the UE by protecting resources across the selected resource set (block 1204). For example, the base station (e.g., using antennas 234, DEMOD / MOD 232, TX MIMO processor 230, transmit processor 220, controller / processor 240, and / or memory 242) can communicate with the second network device to reduce spatial inter-cell downlink interference in the direction of the UE. In some aspects, the protected resources include prohibited beam indexes, time / frequency resources to protect, and / or an amount of resources to protect. Exemplary Aspects

[0118] 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 spatial inter-cell downlink interference in a direction of the UE by protecting resources across a selected resource set.

[0106]

[0119] Aspect 2: The method of aspect 1, wherein 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, a UE whose predicted inter-cell downlink interference exceeds a predetermined threshold; and sending a request message to the second network device.

[0107]

[0120] Aspect 3: The method of aspect 1 or 2, wherein the request message indicates a beam that causes excessive interference.

[0108]

[0121] Aspect 4: The method of aspect 1 or 2, wherein the request message indicates time / frequency resources to be protected.

[0109]

[0122] Aspect 5: The method of aspect 1 or 2, wherein the request message indicates an amount of resources to be protected, the amount being determined based on a traffic demand of the UE.

[0110]

[0123] Aspect 6: The method of aspect 1 or 2, wherein the request message indicates an index of the selected resource set within a predefined list of resource sets.

[0111]

[0124] Aspect 7: The method of aspect 1 or 2, further comprising receiving a response message indicating acceptance of the proposal indicated by the request message.

[0112]

[0125]

[0023] 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.

[0113]

[0126] Aspect 9: The method of aspect 1 or 2, further comprising updating the request message in response to an updated forecast based on an updated UE location, updated channel conditions for the UE, and / or updated traffic demand for the UE.

[0114]

[0127] Aspect 10: The method of any of aspects 1 to 9, wherein the first network device comprises a neighbor cell and the second network device comprises a serving cell, and the method further comprises 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 sending a response message to the second network device.

[0115]

[0128] Aspect 11: The method of any of aspects 1 to 10, further comprising receiving an update of a UE location, a UE interference tolerance threshold, and / or a resource demand for the UE from the second network device.

[0116]

[0129] Aspect 12: The method of aspect 1, wherein the first network device comprises a central node and the second network device comprises a serving cell, and the method further comprises 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, sending a second request message to an aggressor neighbor cell requesting resource protection, and sending a response message to the second network device indicating the resources to be protected.

[0117]

[0130] Aspect 13: The method of aspect 12, further comprising updating the forecasting based on an updated UE location, updated channel conditions for the UE, and / or updated resource demands for the UE.

[0118]

[0131] 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 spatial downlink inter-cell interference in a direction of the UE by protecting resources across a selected resource set.

[0119]

[0132] Aspect 15: The apparatus of aspect 14, wherein 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, a UE for which predicted spatial downlink inter-cell interference exceeds a predetermined threshold; and means for transmitting a request message to the second network device.

[0120]

[0133]

[0041] Aspect 16: The apparatus of aspect 15, further comprising means for receiving a response message indicating acceptance of the proposal indicated by the request message.

[0121]

[0134]

[0041] 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.

[0122]

[0135] Aspect 18: The apparatus of aspect 15, further comprising means for updating the request message in response to an updated forecast based on an updated UE location, updated channel conditions for the UE, and / or updated traffic demand for the UE.

[0123]

[0136] Aspect 19: The apparatus of aspect 14, wherein the first network device comprises a neighbor cell and the second network device comprises a serving cell, and the apparatus further comprises means for 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, means for predicting based on the request message, and means for sending a response message to the second network device.

[0124]

[0137]

[0046] Aspect 20: The apparatus of aspect 19, further comprising means for receiving an update of a UE location, a UE interference tolerance threshold, and / or a resource demand for the UE from the second network device.

[0125]

[0138] Aspect 21: The apparatus of aspect 14, wherein the first network device comprises a central node and the second network device comprises a serving cell, and the apparatus further comprises means for 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, means for predicting based on the first request message, means for sending a second request message to an aggressor neighbor cell requesting resource protection, and means for sending a response message to the second network device indicating the resources to be protected.

[0126]

[0139] Aspect 22: The apparatus of aspect 21, further comprising means for updating the forecasting based on an updated UE location, updated channel conditions for the UE, and / or updated resource demands for the UE.

[0127]

[0140] Aspect 23: A first network device comprising a processor, a memory coupled to the processor, and instructions stored in the memory, the instructions, when executed by the processor, are operable 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 spatial inter-cell downlink interference in a direction of the UE by protecting resources across a selected resource set.

[0128]

[0141] Aspect 24: The first network device of aspect 23, wherein the first network device comprises a serving cell and the second network device comprises a potentially interfering neighbor cell, and the instructions further cause the first network device to select a UE whose predicted inter-cell downlink interference exceeds a predetermined threshold and to send a request message to the second network device.

[0129]

[0142] Aspect 25: The first network device of aspect 23, wherein the first network device comprises a neighbor cell and the second network device comprises a serving cell, and 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, make a prediction based on the request message, and send a response message to the second network device.

[0130]

[0143] Aspect 26: The first network device of aspect 23, wherein the first network device comprises a central node and the second network device comprises a serving cell, and 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, make a prediction based on the first request message, send a second request message to the aggressor neighbor cell requesting resource protection, and send a response message to the second network device indicating the resources to be protected.

[0131]

[0144] Aspect 27: The first network device of aspect 26, wherein the instructions further cause the first network device to update the forecasting based on an updated UE location, updated channel conditions for the UE, and / or updated resource demands for the UE.

[0132]

[0145] Aspect 28: A non-transitory computer-readable medium having program code recorded thereon, the program code being executed by a processor of a first network device and comprising: program code for predicting spatial inter-cell downlink interference experienced by a UE; and program code for communicating with a second network device to reduce spatial inter-cell downlink interference in a direction of the UE by protecting resources across a selected resource set.

[0133]

[0146] Aspect 29: The non-transitory computer-readable medium of aspect 28, wherein the first network device comprises a neighbor cell and the second network device comprises a serving cell, and the non-transitory computer-readable medium further comprises program code for 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, program code for predicting based on the request message, and program code for sending a response message to the second network device.

[0134]

[0147] Aspect 30: The non-transitory computer readable medium of aspect 28, wherein the first network device comprises a central node and the second network device comprises a serving cell, and the non-transitory computer readable medium further comprises program code for 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, program code for predicting based on the first request message, program code for sending a second request message to an aggressor neighbor cell requesting resource protection, and program code for sending a response message to the second network device indicating the resources to be protected.

[0135]

[0148] The above disclosure provides illustration and description, and is not intended to be exhaustive or to limit the embodiments to the precise forms disclosed. Modifications and variations may be made in light of the above disclosure or acquired from practice of the embodiments.

[0136]

[0149] The term "component" as used is intended to be interpreted broadly as hardware, firmware, and / or a combination of hardware and software. The processors used may be implemented in hardware, firmware, and / or a combination of hardware and software.

[0137]

[0150] Several aspects are described with respect to thresholds: Meeting a threshold used may 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, etc., depending on the context.

[0138]

[0151] It will be apparent that the described systems and / or methods may be implemented in different forms of hardware, firmware, and / or combinations of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods is not intended to limit the aspects. Thus, the operation and behavior of the systems and / or methods will be described without reference to specific software code, and it will be understood that software and hardware may be designed to implement the systems and / or methods based at least in part on the description.

[0139]

[0152] Although certain combinations of features have been recited in the claims and / or disclosed herein, these combinations do not limit the disclosure of the various aspects. Indeed, many of these features may be combined in ways not specifically recited in the claims and / or disclosed herein. Although each dependent claim described below may depend directly on only one claim, the disclosure of the various aspects includes each dependent claim in combination with any other claims in the claims. 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 include a, b, c, ab, ac, bc, and abc, as well as any combination with multiple identical elements (e.g., aa, aaa, aab, aac, abb, acc, bb, bbb, bbc, cc, and ccc, or any other order of a, b, and c).

[0140]

[0153] No element, act, or instruction used should be construed as critical or essential unless expressly described as such. Additionally, the articles "a" and "an" used are inclusive of one or more items and may be used interchangeably with "one or more." Additionally, the terms "set" and "group" used are inclusive of one or more items (e.g., related items, unrelated items, combinations of related and unrelated items, etc.) and may be used interchangeably with "one or more." Where only one item is intended, the phrase "only one" or similar language is used. Additionally, the terms "has," "have," "having," and the like used are intended to be open-ended terms. Additionally, the phrase "based on" is intended to mean "based at least in part on," unless otherwise specified.

Claims

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

2. The first network device comprises a serving cell and the second network device comprises a potentially interfering neighbor cell, and the method comprises: selecting, by the first network device, the UE for which the predicted inter-cell downlink interference exceeds a predefined threshold; sending a request message to the second network device; The method of claim 1 further comprising:

3. the request message indicates a beam that causes excessive interference, or the request message indicates time and / or frequency resources to be protected; or the request message indicates an amount of resources to be protected, the amount being determined based on traffic demands of the UE; or the request message indicates an index of the selected resource set within a predefined list of resource sets; or receiving a response message indicating acceptance of the proposal indicated by the request message; or receiving a response message indicating an alternative proposal for a different set of resources; or 3. The method of claim 2, further comprising: updating the request message in response to an updated forecast based on an updated UE location, updated channel conditions for the UE, and / or updated traffic demands for the UE.

4. The first network device comprises a neighbor cell and the second network device comprises a serving cell, and the method comprises: receiving a request message from the second network device indicating a location of the UE, a UE interference tolerance threshold, and / or a resource need for the UE; predicting based on the request message; sending a response message to the second network device; and preferably further comprising 2. The method of claim 1, further comprising receiving an update of the UE location, the UE interference tolerance threshold, and / or the resource demand for the UE from the second network device.

5. The first network device comprises a central node and the second network device comprises a serving cell, and the method comprises: 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 need for the UE; predicting based on the first request message; sending a second request message to the aggressor neighbor cell requesting resource protection; sending a response message to the second network device indicating the protected resources; and preferably further comprising 10. The method of claim 1, further comprising updating the predicting based on an updated UE location, updated channel conditions for the UE, and / or updated resource demands for the UE.

6. 1. An apparatus for wireless communication by a first network device, comprising: means for predicting spatial downlink inter-cell interference experienced by a UE; 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 over a selected resource set; An apparatus comprising:

7. The first network device comprises a serving cell and the second network device comprises a potentially interfering neighbor cell, and the apparatus comprises: means for selecting, by the first network device, the UE for which the predicted spatial downlink inter-cell interference exceeds a predetermined threshold; means for transmitting a request message to the second network device; The apparatus of claim 6 further comprising:

8. means for receiving a response message indicating acceptance of the proposal indicated by said request message; or means for receiving a response message indicating alternative proposals for different sets of resources; or means for updating the request message in response to an updated forecast based on an updated UE location, updated channel conditions for the UE, and / or updated traffic demand for the UE. The apparatus of claim 7 further comprising:

9. The first network device comprises a neighbor cell, the second network device comprises a serving cell, and the apparatus further comprises: means for receiving a request message from the second network device indicating a location of the UE, a UE interference tolerance threshold, and / or a resource need for the UE; means for predicting based on the request message; means for transmitting a response message to the second network device; and preferably further comprising 7. The apparatus of claim 6, further comprising: means for receiving an update of the UE location, the UE interference tolerance threshold, and / or the resource demand for the UE from the second network device.

10. The first network device comprises a central node, the second network device comprises a serving cell, and the apparatus comprises: means for 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 need for the UE; means for predicting based on the first request message; means for transmitting a second request message to an aggressor neighbor cell requesting resource protection; means for transmitting a response message to the second network device indicating resources to be protected; and preferably further comprising 7. The apparatus of claim 6, further comprising means for updating the predicting based on an updated UE location, updated channel conditions for the UE, and / or updated resource demands for the UE.

11. A processor; a memory coupled to the processor; instructions stored in the memory; a first network device comprising: predicting spatial inter-cell downlink interference experienced by a UE; communicating with a second network device to reduce the spatial inter-cell downlink interference in a direction of the UE by protecting resources across a selected resource set; A first network device operable to cause

12. The first network device is a serving cell, and the second network device comprises a potentially interfering neighbor cell, and the instructions include for the first network device to: selecting the UE for which the predicted inter-cell downlink interference exceeds a predetermined threshold; sending a request message to the second network device; or a neighbor cell and the second network device comprises a serving cell, and the instructions further comprise: receiving a request message from the second network device indicating a location of the UE, a UE interference tolerance threshold, and / or a resource need for the UE; predicting based on the request message; sending a response message to the second network device; The first network device of claim 11 , further comprising:

13. The first network device comprises a central node and the second network device comprises a serving cell, the instructions 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 need for the UE; predicting based on the first request message; sending a second request message to the aggressor neighbor cell requesting resource protection; sending a response message to the second network device indicating the protected resources; and preferably further comprising:

12. The first network device of claim 11, wherein 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 updated resource demands for the UE.

14. A non-transitory computer readable medium having program code recorded thereon, the program code being executed by a processor of a first network device; program code for predicting spatial inter-cell downlink interference experienced by a UE; program code 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 a selected resource set; 1. A non-transitory computer-readable medium comprising:

15. The first network device is a neighbor cell, the second network device comprising a serving cell, and the non-transitory computer-readable medium comprising: program code for receiving a request message from the second network device indicating a location of the UE, a UE interference tolerance threshold, and / or a resource need for the UE; program code for making a prediction based on the request message; program code for sending a response message to the second network device; or a central node, the second network device comprising a serving cell, and the non-transitory computer-readable medium comprising: program code for 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 need for the UE; program code for predicting based on the first request message; program code for sending a second request message to an aggressor neighbor cell requesting resource protection; program code for transmitting a response message to the second network device indicating protected resources; 15. The non-transitory computer-readable medium of claim 14, further comprising:

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