Reciprocity assisted interference suppression via eigen beamforming

Abstract

The invention discloses a method and a network node for reciprocity assisted interference suppression via eigen beamforming. According to one aspect, a method includes determining an LxM channel matrix by selecting L rows of the NxM channel matrix or L linear combinations of rows of the NxM channel matrix, M being the number of antennas for downlink transmissions, N being the number of antennas received by a wireless device, L being the number of layers. The method further includes determining an MxM pre-processing matrix based on a characteristic decomposition of the MxM interference covariance matrix. The method further includes determining an MxL RAIT precoder matrix based on an inverse of an LxL matrix, the LxL matrix being a first matrix product based on an MxL Hermitian transpose of the LxM channel matrix, the MxM pre-processing matrix, and the LxM channel matrix.

Description

Technical Field

[0001] The present application relates to wireless communications, and more particularly to reciprocity-assisted interference suppression via eigenbeamforming to address inter-cell interference. Background Art

[0002] The 3rd Generation Partnership Project (3GPP) has developed and is developing standards for the 4th generation (4G) (also known as Long Term Evolution (LTE)) and 5th generation (5G) (also known as New Radio (NR)) wireless communication systems. These systems provide, among other functions, broadband communications between network nodes such as base stations and mobile wireless devices (WDs), as well as communications between network nodes and between WDs. The 6th generation (6G) wireless communication systems are also under development.

[0003] Managing inter-cell interference can significantly improve the throughput of cellular systems, especially in dense deployment scenarios. For multiple-input multiple-output (MIMO) systems, downlink inter-cell interference management can be achieved by designing downlink precoders to reduce the transmission power of users in neighboring cells in the spatial direction. A closed-form solution to maximize the signal-to-leakage-plus-noise ratio (SLNR) has been proposed by designing an interference-aware downlink precoder using an estimate of the target receiver channel and the interference covariance matrix.

[0004] The traditional implementation of interference-aware downlink precoder calculation requires inverting the interference covariance matrix with a dimension equal to the number of antennas at the base station. For active antenna systems (AAS) with a large number of antennas, such as AIR 6488, the computational complexity of the matrix inversion may be too high to be practically implemented. Summary of the invention

[0005] Some embodiments advantageously provide methods and network nodes for reciprocity-assisted interference suppression via eigenbeamforming.

[0006] Some embodiments provide an optional implementation for interference-aware downlink precoder calculation, which has significantly reduced computational complexity compared to traditional solutions. The proposed implementation utilizes a subset of eigenvectors and corresponding eigenvalues ​​of the interference covariance matrix to construct a channel preprocessing matrix for directing downlink transmissions away from directions that cause interference to users in neighboring cells. In some embodiments, the precoder is implemented by multiplying the downlink channel estimate of the WD in the desired cell with the preprocessing matrix. Therefore, interference leakage reduction can be achieved without inverting the full-dimensional interference covariance matrix. Based on the number of eigenvectors used to construct the channel preprocessing matrix, the proposed implementation can provide adjustable interference leakage reduction.

[0007] Some embodiments include an interference-aware downlink precoding algorithm that utilizes a subset of eigenvectors and associated eigenvalues ​​of an interference covariance matrix to reduce out-of-cell interference leakage to neighboring cells.

[0008] Some embodiments include a method at a network node, comprising:

[0009] -Measure the signal received from users in the same channel cell at the network node antenna port during the transmission time interval (TTI);

[0010] - Calculate the spatial covariance matrix of the measured signal;

[0011] - calculating a preprocessing matrix based on the spatial covariance matrix;

[0012] - estimating the MIMO channels of users in the cell transmitting in the TTI for the selected transmission level;

[0013] - transforming the estimated channel using the pre-processing matrix; and / or

[0014] - Calculate the downlink (DL) precoder using the transformed channel matrix.

[0015] Some embodiments provide a low complexity implementation (compared to other possible solutions) for downlink interference-aware precoding, avoiding the inversion of the full-dimensional interference covariance matrix. In particular, some embodiments use a subset of eigenvectors and corresponding eigenvalues ​​of the interference covariance matrix to construct a downlink precoder, thereby enabling downlink inter-cell interference suppression.

[0016] According to one aspect, a method in a network node is provided, the network node being configured to determine a reciprocity-aided interference aware transmission (RAIT) precoder matrix to precode a downlink transmission to a wireless (WD). The method includes determining an LxM channel matrix by one of the following: selecting L rows of an NxM channel matrix, and selecting L linear combinations of the rows of the NxM channel matrix, M is the number of antennas for downlink transmission at the network node, N is the number of receiving antennas at the WD, and L is an integer less than M. The method also includes determining an MxM preprocessing matrix based at least in part on an eigendecomposition of an MxM interference covariance matrix. The method further includes determining an MxL RAIT precoder matrix based at least in part on an inverse of the LxL matrix, the LxL matrix being based at least in part on a first matrix product of an MxL Hermitian transpose of the LxM channel matrix, the MxM preprocessing matrix, and the LxM channel matrix.

[0017] In some embodiments, the method further comprises determining an MxM eigenvector matrix and M associated eigenvalues ​​based at least in part on an eigendecomposition of the MxM interference covariance matrix, each of the M associated eigenvalues ​​representing interference power, and each eigenvector of the MxM eigenvector matrix representing a different propagation direction of the interference power. In some embodiments, the MxM preprocessing matrix is ​​based at least in part on an estimated noise power associated with a subset of the MK eigenvectors of the MxM interference covariance matrix, K being an integer less than M. In some embodiments, the estimated noise power in each dimension is based at least in part on a difference between a first trace of the MxM interference covariance matrix and a second trace of a KxK diagonal matrix of eigenvalues ​​corresponding to K selected eigenvectors of the MxM eigenvector matrix.

[0018] In some embodiments, K selected eigenvectors are selected so that the sum of the corresponding eigenvalues ​​is a given percentage of the total interference power. In some embodiments, the K selected eigenvectors correspond to a set of K largest eigenvalues. In some embodiments, L is the number of transmission layers, which is less than or equal to the number of receive antennas at the WD. In some embodiments, the MxL RAIT precoder matrix is ​​determined at least in part based on the second matrix product of the MxM preprocessing matrix, the MxL Hermitian transpose of the LxM channel matrix, and the inverse of the LxL matrix. In some embodiments, the MxM preprocessing matrix is ​​based at least in part on the MxK matrix of the selected eigenvectors, the KxK diagonal modified eigenvalue matrix, and the KxM Hermitian transpose of the MxK selected eigenvector matrix, and K is an integer less than M. In some embodiments, the MxM preprocessing matrix is ​​determined at least in part by the following calculation:

[0019]

[0020] Where V(f,t) is the MxM preprocessing matrix, I M is the MxM identity matrix, is the MxK matrix of K selected eigenvectors from the MxM interference covariance matrix, is a KxK diagonal matrix whose i-th element is given by Given, δ 2 is the minimum mean square error regularization factor, σ i (f,t) is the noise power associated with the i-th eigenvector of the MxM interference covariance matrix, is the estimated noise power in each dimension, tr{Λ(f,t)} is the trace of the interference covariance matrix, is the trace of the KxK matrix of the dominant eigenvalues ​​corresponding to the K selected eigenvectors.

[0021] According to another aspect, a network node is provided, which is configured to determine a reciprocity-assisted interference-aware transmission (RAIT) precoder matrix to precode a downlink transmission to a wireless WD. The network node includes a processing circuit configured to: determine an LxM channel matrix by one of the following: selecting L rows of an NxM channel matrix, and selecting L linear combinations of the rows of the NxM channel matrix, M is the number of antennas used for downlink transmission at the network node, N is the number of receiving antennas at the WD, and L is an integer less than M. The processing circuit is further configured to determine an MxM preprocessing matrix based at least in part on an eigendecomposition of an MxM interference covariance matrix. The processing circuit is also configured to determine an MxL RAIT precoder matrix based at least in part on an inverse of the LxL matrix, the LxL matrix being based at least in part on a first matrix product of an MxL Hermitian transpose of the LxM channel matrix, the MxM preprocessing matrix, and the LxM channel matrix.

[0022] According to this aspect, in some embodiments, the processing circuit is further configured to determine an MxM eigenvector matrix and M associated eigenvalues ​​based at least in part on an eigendecomposition of an MxM interference covariance matrix, each of the M associated eigenvalues ​​representing interference power, and each eigenvector of the MxM eigenvector matrix represents a different propagation direction of the interference power represented by the corresponding eigenvalue. In some embodiments, the MxM preprocessing matrix is ​​based at least in part on an estimated noise power associated with a subset of MK eigenvectors of the MxM eigenvector matrix, K being an integer less than M. In some embodiments, the estimated noise power of each dimension is based at least in part on the difference between the first trace of the MxM interference covariance matrix and the second trace of the KxK diagonal matrix of eigenvalues ​​corresponding to the K selected eigenvectors of the MxM eigenvector matrix. In some embodiments, the K selected eigenvectors are selected so that the sum of the corresponding eigenvalues ​​is a given percentage of the total interference power. In some embodiments, the K selected eigenvectors correspond to a set of K largest eigenvalues. In some embodiments, L is the number of transmission layers, which is less than or equal to the number of receive antennas at the WD. In some embodiments, the MxL RAIT precoder matrix is ​​determined based at least in part on a second matrix product of an MxM preprocessing matrix, an MxL Hermitian transpose of an LxM channel matrix, and an inverse of the LxL matrix. In some embodiments, the MxM preprocessing matrix is ​​based at least in part on a third matrix product of an MxK matrix of selected eigenvectors, a KxK diagonally modified eigenvalue matrix, and a KxM Hermitian transpose of an MxK eigenvector matrix, K being an integer less than M. In some embodiments, the MxM preprocessing matrix is ​​determined at least in part by the following calculation:

[0023]

[0024] Where V(f,t) is the MxM preprocessing matrix, I M is the MxM identity matrix, is the MxK matrix of K selected eigenvectors from the MxM interference covariance matrix, is a KxK diagonal matrix whose i-th element is given by Given, δ 2 is the minimum mean square error regularization factor, σ i (f,t) is the noise power associated with the i-th eigenvector of the MxM interference covariance matrix, is the estimated noise power in each dimension, tr{Λ(f,t)} is the trace of the interference covariance matrix, is the trace of the KxK matrix of the principal eigenvalues ​​corresponding to the K selected eigenvectors. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The full meaning of the present embodiment and its attendant advantages and features will be more readily understood by referring to the following detailed description in conjunction with the accompanying drawings, in which:

[0026] Figure 1 is a schematic diagram showing an example network architecture of a communication system connected to a host via an intermediate network according to the principles of the present application;

[0027] Figure 2 is a block diagram of a host communicating with a wireless device over at least a partially wireless connection via a network node according to some embodiments of the present application;

[0028] Figure 3 is a flowchart illustrating an example method implemented in a communication system including a host, a network node, and a wireless device to execute a client application at a wireless device according to some embodiments of the present application;

[0029] Figure 4 is a flowchart illustrating an example method implemented in a communication system including a host, a network node and a wireless device to receive user data at a wireless device according to some embodiments of the present application;

[0030] Figure 5 is a flowchart illustrating an example method implemented in a communication system including a host, a network node and a wireless device to receive user data from a wireless device at the host according to some embodiments of the present application;

[0031] Figure 6 is a flowchart illustrating an example method implemented in a communication system including a host, a network node, and a wireless device to receive user data at a host according to some embodiments of the present application;

[0032] Figure 7is a flow chart of an example process in a network node for reciprocity-assisted interference suppression via eigenbeamforming according to the principles set forth herein;

[0033] Figure 8 is a block diagram of a RAIT precoder unit constructed according to the principles disclosed herein; and

[0034] Fig. 9 is the cell throughput determined by the RAIT precoder unit Figure 8 Example plot comparing the number of eigenvectors used for the RAIT precoder unit. DETAILED DESCRIPTION

[0035] Before describing the exemplary embodiments in detail, it should be noted that the embodiments reside primarily in a combination of device components and processing steps related to reciprocity-assisted interference suppression via eigenbeamforming. Therefore, conventional symbols are used in the drawings to appropriately represent components, and only those specific details relevant to understanding the embodiments are shown so as not to obscure the present application with those details that are obvious to those of ordinary skill in the art having the benefit of the description herein. Like numbers represent like elements throughout the specification.

[0036] As used herein, relational terms, such as "first" and "second", "top" and "bottom", etc., can be used individually to distinguish one entity or element from another entity or element, without necessarily requiring or implying any physical or logical relationship or order between such entities or elements. The terms used herein are only used for the purpose of describing specific embodiments and are not intended to limit the concepts described herein. As used herein, the singular forms "a, an" and "the" are intended to also include plural forms, unless the context clearly indicates otherwise. It will be further understood that the terms "include", "have" and / or "comprise" when used herein refer to the presence of the features, wholes, steps, operations, elements and / or components mentioned, without excluding the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0037] In the embodiments described herein, connection terms "in communication with" and the like may be used to indicate electrical or data communication, which may be achieved, for example, by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling, or optical signaling. Those of ordinary skill in the art will appreciate that multiple components may interoperate, and modifications and variations in achieving electrical and data communication are possible.

[0038] In some embodiments described herein, the terms "coupled," "connected," and the like may be used herein to indicate a connection, although not necessarily a direct connection, and may include wired and / or wireless connections.

[0039] The term "network node" used herein may be any kind of network node included in a radio network, and may further include any of the following: a base station (BS), a radio base station, a base transceiver station (BTS), a base station controller (BSC), a radio network controller (RNC), a gNode B (gNB), an evolved Node B (eNB or eNodeB), a Node B, a multi-standard radio (MSR) radio node such as an MSR BS, a multi-cell / multicast coordination entity (MCE), an integrated access and backhaul (IAB) node, a relay node, a host node controlling a relay, a radio access point (AP), a transmission point, a transmission node, a remote radio unit (RRU), a remote radio head (RRH), a core network node (e.g., a mobile management entity (MME), a self-organizing network (SON) node, a coordination node, a positioning node, an MDT node, etc.), an external node (e.g., a third-party node, a node outside the current network), a node in a distributed antenna system (DAS), a spectrum access system (SAS) node, an element management system (EMS), etc. The network node may also include a test device. As used herein, the term "radio node" may also be used to refer to a wireless device (WD), such as a wireless device (WD) or a radio network node.

[0040] In some embodiments, the non-limiting terms wireless device (WD) or user equipment (UD) are used interchangeably. The WD here can be any type of wireless device capable of communicating with a network node or another WD via a radio signal, such as a wireless device (WD). The WD can also be a radio communication device, a target device, a device-to-device (D2D) WD, a machine type WD, or a WD capable of machine-to-machine communication (M2M), a low-cost and / or low-complexity WD, a sensor equipped with a WD, a tablet computer, a mobile terminal, a smart phone, an embedded laptop computer (LEE), a device installed on a laptop computer (LME), a USB dongle, a user premises equipment (CPE), an Internet of Things (loT) device, or a narrowband loT (NB-IOT) device, etc.

[0041] Moreover, in some embodiments, the general term "radio network node" is used. It can be any type of radio network node, which can include any of the following: base station, radio base station, base transceiver station, base station controller, network controller, RNC, evolved Node B (eNB), Node B, gNB, multi-cell / multicast coordination entity (MCE), IAB node, relay node, access point, radio access point, remote radio unit (RRU), remote radio head (RRH).

[0042] Note that although terminology from one particular wireless system (e.g., 3GPP LTE and / or New Radio (NR)) is used in this application, this should not be seen as limiting the scope of this application to only the above-mentioned systems. Other wireless systems may also benefit from utilizing the ideas covered in the content of this application, including but not limited to Wideband Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMax), Ultra Mobile Broadband (UMB), and Global System for Mobile Communications (GSM).

[0043] Further note that the functions described herein as being performed by a wireless device or network node may be distributed across multiple wireless devices and / or network nodes. In other words, it is contemplated that the functions of the network nodes and wireless devices described herein are not limited to being performed by a single physical device, and in fact, may be distributed across several physical devices.

[0044] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those commonly understood by those of ordinary skill in the art to which this application belongs. It will be further understood that the terms used herein should be interpreted as having a meaning consistent with their meaning in the context of this specification and the relevant field, and will not be interpreted in an idealized or overly formal sense unless explicitly defined as such herein.

[0045] Some embodiments provide reciprocity-assisted interference suppression via eigenbeamforming. Reference is now made to the drawings, wherein like elements are designated with like reference numerals. Figure 1 1 is a schematic diagram of a communication system 10 according to an embodiment, for example, a 3GPP-type cellular network that can support standards such as LTE and / or NR (5G), which includes an access network 12 such as a radio access network and a core network 14. The access network 12 includes a plurality of network nodes 16a, 16b, 16c (collectively referred to as network nodes 16), such as NBs, eNBs, gNBs or other types of wireless access points, each of which defines a corresponding coverage area 18a, 18b, 18c (collectively referred to as coverage area 18). Each network node 16a, 16b, 16c can be connected to the core network 14 via a wired or wireless connection 20. A first wireless device (WD) 22a located in the coverage area 18a is configured to be wirelessly connected to or paged by the corresponding network node 16a. A second WD 22b in the coverage area 18b can be wirelessly connected to the corresponding network node 16b. Although multiple WDs 22a, 22b (collectively referred to as wireless devices 22) are illustrated in this example, the disclosed embodiments are equally applicable to situations where a single WD is in the coverage area or a single WD is connected to a corresponding network node 16. Note that although only two WDs 22 and three network nodes 16 are shown for convenience, the communication system may include more WDs 22 and network nodes 16.

[0046] Moreover, it is contemplated that the WD 22 may communicate simultaneously and / or be configured to communicate separately with more than one network node 16 and more than one type of network node 16. For example, the WD 22 may have dual connectivity with a network node 16 supporting LTE and the same or different network nodes 16 supporting NR. As an example, the WD 22 may be able to communicate with an eNB for LTE / E-UTRAN and a gNB for NR / NG-RAN.

[0047] The communication system 10 itself can be connected to a host 24, which can be implemented in hardware and / or software of a stand-alone server, a cloud-implemented server, a distributed server, or as a processing resource in a server cluster. The host 24 can be owned or controlled by a service provider, or can be operated by or on behalf of a service provider. The connections 26, 28 between the communication system 10 and the host 24 can extend directly from the core network 14 to the host 24, or can extend via an optional intermediate network 30. The intermediate network 30 can be a public network, a private network, or a managed network, or a combination of more than one. If present, the intermediate network 30 can be a backbone network or the Internet. In some embodiments, the intermediate network 30 may include two or more subnetworks (not shown).

[0048] Figure 1 The communication system as a whole realizes the connection between one of the connected WD 22a, 22b and the host 24. Connectivity can be described as over-the-top (OTT) connection. The host 24 and the connected WD 22a, 22b are configured to transmit data and / or signaling via the OTT connection, using the access network 12, the core network 14, any intermediate network 30 and possible other infrastructure (not shown) as intermediaries. At least some of the participating communication devices through which the OTT connection passes do not know the routes of uplink and downlink communications, and in this sense, the OTT connection is transparent. For example, the network node 16 may not be notified or need not be notified of the route through which the incoming downlink communication of the data originating from the host 24 is forwarded (e.g., switched) to the connected WD22a. Similarly, the network node 16 does not need to know the future route of the outgoing uplink communication originating from the WD 22a toward the host 24.

[0049] The network node 16 is configured to include a reciprocity-assisted interference-aware transmission (RAIT) precoder unit 32, which is configured to determine a RAIT precoder based at least in part on an eigendecomposition of an M×M interference covariance matrix to determine an M×M preprocessing matrix and an inverse of an L×L matrix, wherein the L×L matrix is ​​a product of an M×M channel matrix, an M×M preprocessing matrix, and an MxL Hermitian transpose of the L×M channel matrix.

[0050] Now refer to Figure 2 Describe an example implementation of the WD 22, network node 16, and host 24 discussed in the preceding paragraphs according to an embodiment. In the communication system 10, the host 24 includes hardware (HW) 38, which includes a communication interface 40, configured to establish and maintain a wired or wireless connection with the interface of different communication devices of the communication system 10. The host 24 also includes a processing circuit 42, which may have storage and / or processing capabilities. The processing circuit 42 may include a processor 44 and a memory 46. In particular, as a supplement or alternative to a processor such as a central processing unit and a memory, the processing circuit 42 may include an integrated circuit for processing and / or control, for example, one or more processors and / or processor cores and / or FPGAs (field programmable gate arrays) and / or ASICs (application-specific integrated circuits) suitable for executing instructions. The processor 44 may be configured to access (e.g., write to and / or read from) a memory 46, which may include any kind of volatile and / or non-volatile memory, such as cache and / or buffer memory and / or RAM (random access memory) and / or ROM (read only memory) and / or optical memory and / or EPROM (erasable programmable read-only memory).

[0051] Processing circuit 42 can be configured to control any method and / or process described herein and / or cause such method and / or process to be performed, for example, by host 24. Processor 44 corresponds to one or more processors 44 for performing the functions of host 24 described herein. Host 24 includes memory 46, which is configured to store data, programming software code and / or other information described herein. In some embodiments, software 48 and / or host application 50 may include instructions that, when executed by processor 44 and / or processing circuit 42, cause processor 44 and / or processing circuit 42 to perform the process described herein with respect to host 24. The instructions may be software associated with host 24.

[0052] The software 48 may be executed by the processing circuit 42. The software 48 includes a host application 50. The host application 50 is operable to provide services to a remote user (such as a WD 22 connected via an OTT connection 52 that terminates at the WD 22 and the host 24). When providing services to the remote user, the host application 50 may provide user data transmitted using the OTT connection 52. The "user data" may be data and information described herein as implementing the described functions. In one embodiment, the host 24 may be configured to provide control and functionality to a service provider, and may be operated by or on behalf of the service provider. The processing circuit 42 of the host 24 may enable the host 24 to observe, monitor, control, send to and / or receive from the network node 16 and / or the wireless device 22.

[0053] The communication system 10 also includes a network node 16 provided in the communication system 10 and includes hardware 58 that enables it to communicate with the host 24 and the WD 22. The hardware 58 may include a communication interface 60 for establishing and maintaining a wired or wireless connection to the interface of different communication devices of the communication system 10, and a radio interface 62 for establishing and maintaining at least a wireless connection 64 with the WD 22 located in the coverage area 18 served by the network node 16. The radio interface 62 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and / or one or more RF transceivers. The communication interface 60 may be configured to facilitate a connection 66 to the host 24. The connection 66 may be direct, or may be through the core network 14 of the communication system 10 and / or through one or more intermediate networks 30 external to the communication system 10.

[0054] In the illustrated embodiment, the hardware 58 of the network node 16 further includes a processing circuit 68. The processing circuit 68 may include a processor 70 and a memory 72. In particular, in addition to or in lieu of a processor such as a central processing unit and a memory, the processing circuit 68 may include an integrated circuit for processing and / or control, for example, one or more processors and / or processor cores and / or an FPGA (field programmable gate array) and / or an ASIC (application specific integrated circuit) adapted to execute instructions. The processor 70 may be configured to access (e.g., write and / or read) the memory 72, which may include any kind of volatile and / or non-volatile memory, for example, a cache and / or buffer memory and / or a RAM (random access memory) and / or a ROM (read only memory) and / or an optical memory and / or an EPROM (erasable programmable read only memory).

[0055] Therefore, the network node 16 further has software 74, which is stored internally, for example, in the memory 72 or stored in an external memory (e.g., a database, a storage array, a network storage device, etc.) that the network node 16 can access via an external connection. The software 74 can be executed by the processing circuit 68. The processing circuit 68 can be configured to control any method and / or process described herein and / or cause such a method and / or process to be performed, for example, by the network node 16. The processor 70 corresponds to one or more processors 70 for performing the network node 16 functions described herein. The memory 72 is configured to store data, programming software code, and / or other information described herein. In some embodiments, the software 74 may include instructions that, when executed by the processor 70 and / or the processing circuit 68, cause the processor 70 and / or the processing circuit 68 to perform the process described herein with respect to the network node 16. For example, the processing circuit 68 of the network node 16 may include a reciprocity-assisted interference-aware transmission (RAIT) precoder unit 32, which is configured to determine a RAIT precoder based at least in part on an eigendecomposition of an M×M interference covariance matrix to determine an M×M preprocessing matrix and an inverse of an L×L matrix, wherein the L×L matrix is ​​the product of an L×M channel matrix, an M×M preprocessing matrix, and an MxL Hermitian transpose of the L×M channel matrix.

[0056] The communication system 10 also includes the already mentioned WD 22. The WD 22 may have hardware 80, which may include a radio interface 82, configured to establish and maintain a wireless connection 64 with a network node 16 serving, for example, a coverage area 18 of a cell in which the WD 22 is currently located. The radio interface 82 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers and / or one or more RF transceivers.

[0057] The hardware 80 of the WD 22 further includes a processing circuit 84. The processing circuit 84 may include a processor 86 and a memory 88. In particular, as a supplement or alternative to a processor and a memory such as a central processing unit, the processing circuit 84 may include an integrated circuit for processing and / or control, for example, one or more processors and / or processor cores and / or FPGAs (field programmable gate arrays) and / or ASICs (application-specific integrated circuits) adapted to execute instructions. The processor 86 may be configured to access (e.g., write and / or read) the memory 88, which may include any kind of volatile and / or non-volatile memory, for example, a cache and / or buffer memory and / or a RAM (random access memory) and / or a ROM (read-only memory) and / or an optical memory and / or an EPROM (erasable programmable read-only memory).

[0058] Therefore, WD 22 may further include software 90, which is stored in, for example, a memory 88 at WD 22, or in an external memory (e.g., a database, a storage array, a network storage device, etc.) accessible by WD 22. Software 90 may be executed by processing circuit 84. Software 90 may include a client application 92. Client application 92 is operable to provide services to human or non-human users via WD 22 with the support of host 24. In host 24, the executing host application 50 may communicate with the executing client application 92 via an OTT connection 52 terminated at WD 22 and host 24. When providing services to users, client application 92 may receive request data from host application 50 and provide user data in response to the request data. OTT connection 52 may transmit both request data and user data. Client application 92 may interact with the user to generate the user data it provides.

[0059] The processing circuit 84 can be configured to control any method and / or process described herein and / or cause such method and / or process to be performed, for example, by the WD 22. The processor 86 corresponds to one or more processors 86 for performing the WD 22 functions described herein. The WD 22 includes a memory 88 configured to store data, programming software code, and / or other information described herein. In some embodiments, the software 90 and / or the client application 92 may include instructions that, when executed by the processor 86 and / or the processing circuit 84, cause the processor 86 and / or the processing circuit 84 to perform the process described herein with respect to the WD 22.

[0060] In some embodiments, the internal workings of network node 16, WD 22, and host 24 may be as follows: Figure 2 shown, and independently, the surrounding network topology can be Figure 1 network topology.

[0061] exist Figure 2 In FIG, an OTT connection 52 has been abstractly drawn to illustrate communication between a host 24 and a wireless device 22 via a network node 16, without explicitly involving any intermediate devices and the precise message routing via these devices. The network infrastructure can determine the routing, which can be configured to be hidden from the WD 22 or from the service provider operating the host 24, or from both. When the OTT connection 52 is active, the network infrastructure can further take decisions by which it dynamically changes the routing (e.g., based on load balancing considerations or network reconfiguration).

[0062] The wireless connection 64 between the WD 22 and the network node 16 is provided throughout this application in accordance with the teachings of the described embodiments. One or more of the various embodiments improves the performance of the OTT service provided to the WD 22 using the OTT connection 52, of which the wireless connection 64 may form the final part. More specifically, the teachings of some of these embodiments may improve data rates, latency, and / or power consumption, and thereby provide benefits such as reduced user waiting time, looser restrictions on file size, better responsiveness, extended battery life, and the like.

[0063] In some embodiments, a measurement process may be provided for the purpose of monitoring data rates, delays, and other factors improved by one or more embodiments. There may also be an optional network function for reconfiguring the OTT connection 52 between the host 24 and the WD 22 in response to changes in the measurement results. The measurement process and / or network function for reconfiguring the OTT connection 52 may be implemented in the software 48 of the host 24 or in the software 90 of the WD 22 or in both. In an embodiment, a sensor (not shown) may be deployed in or associated with a communication device through which the OTT connection 52 passes; the sensor may participate in the measurement process by providing the value of the monitored quantity exemplified above, or providing software 48, 90 based on which the value of other physical quantities of the monitored quantity is calculated or estimated. The reconfiguration of the OTT connection 52 may include message formats, retransmission settings, preferred routes, etc.; the reconfiguration does not need to affect the network node 16, and it may be unknown or imperceptible to the network node 16. Some such procedures and functions may be known and implemented in the art. In some embodiments, the measurement may involve proprietary WD signaling that prompts the host 24 to measure throughput, propagation time, delay, etc. In some embodiments, the measurements may be implemented such that the software 48 , 90 causes a message to be transmitted using the OTT connection 52 , particularly an empty or “dummy” message, while it monitors propagation time, errors, etc.

[0064] Thus, in some embodiments, host 24 includes processing circuitry 42 configured to provide user data and communication interface 40 configured to forward the user data to a cellular network for transmission to WD 22. In some embodiments, the cellular network also includes a network node 16 having a radio interface 62. In some embodiments, network node 16 is configured to, and / or processing circuitry 68 of network node 16 is configured to, perform the functions and / or methods described herein for preparing / initiating / maintaining / supporting / terminating transmissions to WD 22, and / or preparing / terminating / maintaining / supporting / terminating in receiving transmissions from WD 22.

[0065] In some embodiments, host 24 includes processing circuitry 42 and communication interface 40 configured to receive user data originating from transmission from WD 22 to network node 16. In some embodiments, WD 22 is configured to and / or includes a radio interface 82 and / or processing circuitry 84 configured to perform the functions and / or methods described herein for preparing / initiating / maintaining / supporting / terminating transmissions to network node 16 and / or preparing / terminating / maintaining / supporting / terminating in receiving transmissions from network node 16.

[0066] Although Figure 1 and Figure 2 Various "units" such as the RAIT precoder unit 32 are shown as being within a corresponding processor, but it is contemplated that these units may be implemented such that a portion of the unit is stored in a corresponding memory within the processing circuit. In other words, the unit may be implemented within the processing circuit in the form of hardware or a combination of hardware and software.

[0067] Figure 3 is a flow chart illustrating an example method implemented in a communication system, such as, for example, Figure 1 and Figure 2 The communication system may include a host 24, a network node 16 and a WD 22, which may be a reference Figure 2 Those described herein. In a first step of the method, host 24 provides user data (box S100). In an optional sub-step of the first step, host 24 provides user data by executing a host application, such as host application 50 (box S102). In a second step, host 24 initiates a transmission carrying user data to WD 22 (box S104). In an optional third step, in accordance with the teachings of the embodiments described throughout this application, network node 16 transmits the user data carried in the transmission initiated by host 24 to WD 22 (box S106). In an optional fourth step, WD 22 executes a client application associated with host application 50 executed by host 24, such as, for example, client application 92 (box S108).

[0068] Figure 4 is a flow chart illustrating an example method implemented in a communication system, such as, for example, Figure 1 The communication system may include a host 24, a network node 16 and a WD 22, which may be a reference Figure 1 and Figure 2Those described herein. In a first step of the method, host 24 provides user data (block S110). In an optional substep (not shown), host 24 provides user data by executing a host application, such as host application 50. In a second step, host 24 initiates a transmission carrying user data to WD 22 (block S112). According to the teachings throughout the embodiments described herein, the transmission may be via network node 16. In an optional third step, WD 22 receives the user data carried in the transmission (block S114).

[0069] Figure 5 is a flow chart illustrating an example method implemented in a communication system, such as, for example, Figure 1 The communication system may include a host 24, a network node 16 and a WD 22, which may be a reference Figure 1 and Figure 2 Those described. In the optional first step of the method, WD 22 receives input data provided by host 24 (box S116). In the optional sub-step of the first step, WD 22 executes client application 92, which provides user data in response to the received input data provided by host 24 (box S118). Additionally or alternatively, in the optional second step, WD 22 provides user data (box S120). In the optional sub-step of the second step, WD provides user data by executing a client application such as, for example, client application 92 (box S122). When providing user data, the executed client application 92 can further consider the user input received from the user. Regardless of the specific manner of providing user data, WD 22 can initiate the transmission of user data to host 24 in an optional third sub-step (box S124). In the fourth step of the method, according to the teachings of the embodiments described throughout this application, host 24 receives user data transmitted from WD 22 (box S126).

[0070] Figure 6 is a flow chart illustrating an example method implemented in a communication system, such as, for example, Figure 1 The communication system may include a host 24, a network node 16 and a WD 22, which may be a reference Figure 1 and Figure 2 In an optional first step of the method, in accordance with the teachings of the embodiments described throughout this application, the network node 16 receives user data from the WD 22 (block S128). In an optional second step, the network node 16 initiates transmission of the received user data to the host 24 (block S130). In a third step, the host 24 receives the user data carried in the transmission initiated by the network node 16 (block S132).

[0071] Figure 7 Flowchart of an example process in a network node 16 for reciprocity-assisted interference suppression via eigenbeamforming. One or more blocks described herein may be performed by one or more elements of the network node 16, such as by one or more of the processing circuit 68 (including the RAIT precoder unit 32), the processor 70, the radio interface 62, and / or the communication interface 60. The network node 16 is configured, for example, via the processing circuit 68 and / or the processor 70 and / or the radio interface 62 and / or the communication interface 60 to: determine the LxM channel matrix by one of the following: selecting L rows of the NxM channel matrix, and selecting L linear combinations of the rows of the NxM channel matrix, M is the number of antennas for downlink transmission at the network node, N is the number of receiving antennas at the WD, and L is an integer less than M (block S134). The process also includes determining an MxM preprocessing matrix based at least in part on the eigendecomposition of the MxM interference covariance matrix (block S136). The process further includes determining an MxL RAIT precoder matrix based at least in part on an inverse of the LxL matrix, the LxL matrix based at least in part on a first matrix product of the LxM channel matrix, the MxM preprocessing matrix, and the MxL Hermitian transpose of the LxM channel matrix (block S138).

[0072] In some embodiments, the method further comprises: determining an MxM eigenvector matrix and M associated eigenvalues ​​based at least in part on an eigendecomposition of an MxM interference covariance matrix, each of the M associated eigenvalues ​​representing interference power, and each eigenvector of the MxM eigenvector matrix representing a different propagation direction of interference power. In some embodiments, the MxM preprocessing matrix is ​​based at least in part on an estimated noise power associated with a subset of MK eigenvectors of the MxM interference covariance matrix, K being an integer less than M. In some embodiments, the estimated noise power of each dimension is based at least in part on the difference between the first trace of the MxM interference covariance matrix and the second trace of the KxK diagonal matrix of eigenvalues ​​corresponding to the K selected eigenvectors of the MxM eigenvector matrix. In some embodiments, the K selected eigenvectors are selected so that the sum of the corresponding eigenvalues ​​is a given percentage of the total interference power. In some embodiments, the K selected eigenvectors correspond to a set of K largest eigenvalues. In some embodiments, L is the number of transmission layers less than or equal to the number of receive antennas at the WD. In some embodiments, the MxL RAIT precoder matrix is ​​determined based at least in part on a second matrix product of an MxM preprocessing matrix, an MxL Hermitian transpose of an LxM channel matrix, and an inverse of the LxL matrix. In some embodiments, the MxM preprocessing matrix is ​​based at least in part on a third matrix product of an MxK matrix of selected eigenvectors, a KxK diagonally modified eigenvalue matrix, and a KxM Hermitian transpose of an MxK eigenvector matrix, K being an integer less than M. In some embodiments, the MxM preprocessing matrix is ​​determined at least in part by the following calculation:

[0073]

[0074] Where V(f,t) is the MxM preprocessing matrix, I M is the MxM identity matrix, is the MxK matrix of K selected eigenvectors from the MxM interference covariance matrix, is a KxK diagonal matrix whose i-th element is given by Given, δ 2 is the minimum mean square error regularization factor, σ i (f,t) is the noise power associated with the i-th eigenvector of the MxM interference covariance matrix, is the estimated noise power in each dimension, tr{Λ(f,t)} is the trace of the interference covariance matrix, is the trace of the KxK matrix of the dominant eigenvalues ​​corresponding to the K selected eigenvectors.

[0075] Having described the general process flow of the arrangements of the present application and provided examples of hardware and software arrangements for implementing the processes and functions of the present application, the following section provides details and examples of arrangements for reciprocity-assisted interference suppression via eigenbeamforming.

[0076] System Model

[0077] Consider a network node 16, such as a base station, employing an M-element antenna array, communicating with a WD 22 equipped with N receive antennas. Let the N×M matrix H(f,t) denote the matrix containing the coefficients of the downlink channel from the base station to the WD 22 at frequency f and time t. In a time division duplex (TDD) system assuming channel reciprocity, channel estimates are available at the base station, for example, from uplink channel sounding transmissions, and are used to select precoding coefficients to transmit downlink data. Channel estimates can also be obtained using quantized feedback from the WD 22 for use by the base station in downlink precoding, for example, beamforming based on type 1 and type 2 codebooks in NR.

[0078] The network node 16, such as a base station, uses an M×L precoding matrix To transmit L≤min{M,N} spatial layers (streams) to WD 22. Assume the matrix is normalized so that i=1,...,L, where Representation Matrix The i-th column of H represents the Hermitian transpose of the matrix. The N×1 received signal vector at WD 22 is given by:

[0079]

[0080] Among them, u(f,t)=[u 0 (f,t)..u L-1 (f,t)] T is an L×1 vector containing the transmitted symbols on L spatial layers, () T Denoting the transpose of the matrix, the L×L diagonal matrix A(f,t) is given by: p i is the power allocated to the i-th layer, and n(f,t) is the interference plus noise received at WD 22.

[0081] Reciprocity-Assisted Transfer

[0082] Precoding matrix It can be selected based on the minimum mean square error (MMSE) design criterion. The denormalized precoder W is calculated as follows RAT (f, t) to calculate the reciprocity-assisted transmission (RAT) precoder:

[0083]

[0084] Among them, δ 2 (f,t) is the MMSE regularization factor, I L is the L×L identity matrix, is the L×M matrix corresponding to the downstream channels after port mixing / selection. The selected channel matrix It can be constructed by selecting some rows of the full-dimensional downlink channel H(f,t) based on some selection criteria, for example, by selecting the rows with the highest norm. Optionally, the selected channel matrix It can be constructed by mixing the rows of the full-dimensional channel H(f,t), that is, where S(f,t) is an L×N port mixing matrix. The rank-LMMSE precoder is Given, and from the matrix W RAT (f, t) by scaling each column so that its norm is equal to And get.

[0085] Reciprocity-assisted interference-aware transmission

[0086] In dense deployment scenarios, where network nodes 16, such as base stations, are located in close proximity, a WD 22 may receive significant interference from downlink transmissions of neighboring network nodes 16 (e.g., base stations), which results in a significant reduction in downlink throughput, especially for WDs 22 located at the edge of a coverage area 18 (e.g., a cell) of the network nodes 16. Interference-aware downlink transmission schemes design precoding matrices that maximize the received signal power at a target WD 22 while controlling interference caused at WDs 22 in neighboring cells.

[0087] Let the M×M matrix Λ(f, t) represent the downlink interference covariance matrix, i.e., the covariance of the downlink channel vector to the out-of-cell WD 22. In a time division duplex (TDD) system, the matrix Λ(f, t) can be estimated based on the uplink signal received at the sounding transmission time, for example, by subtracting the expected received signal vector of the sounding sequence transmitted by the cell-attached WD 22 from the received uplink signal vector. This produces samples of the out-of-cell interference vector. The interference covariance matrix is ​​then obtained by calculating the second-order statistics of the out-of-cell interference vector samples.

[0088] The reciprocity-assisted interference-aware transmission (RAIT) precoder can be calculated by calculating the unnormalized precoder W RAIT (f, t) to calculate:

[0089]

[0090] Normalized RAIT precoder By W RAIT (f,t) by scaling each of its columns so that its norm is equal to And get.

[0091] Eigenbeamforming reciprocity-assisted interference-aware transmission

[0092] Assume that the eigendecomposition of the interference covariance matrix Λ(f,t) is given by:

[0093] Λ(f,t)=E(f,t)∑(f,t)E H (f,t)

[0094] Where E(f, t) is an M×M matrix containing the eigenvectors of Λ(f, t), and ∑(f, t) is an M×M diagonal matrix containing the corresponding eigenvalues ​​on its main diagonal. The unnormalized eigen RAIT precoder can be calculated by the RAIT precoder unit 32 by considering the (L+M)×M combined channels plus the interference eigenvectors

[0095]

[0096] And calculate the first L layers of the corresponding unnormalized RAT precoder, that is:

[0097]

[0098] Among them, the operator Select the first L columns of the matrix. Normalized feature precoder Can be W RAIT,EIG (f,t) by scaling each column so that its norm is equal to And get.

[0099] Note that it is possible to only apply The above expression is calculated by taking the first L columns of the inverse of , which is a (L+M)×(L+M) matrix. Structured as:

[0100]

[0101] The fact that can be used to avoid the above matrix inversion.

[0102] Then, the following equation for the inverse of a Hermitian 2X2 block matrix can be used:

[0103]

[0104] By applying the above matrix equation to And considering that due to the operator Only the first L columns of the matrix inverse need to be calculated, the feature precoder W RAIT,EIG (f,t) can be written as:

[0105]

[0106] in, is an M×M diagonal matrix, and the i-th diagonal element is given by Given, and λ i (f,t) is the i-th eigenvalue of Λ(f,t).

[0107] W RAIT,EIG (f,t) can be further simplified as:

[0108]

[0109] Among them, the M×M matrix V(f,t)=I M -E(f,t)X(f,t)E H (f, t), therefore, a matrix inversion can be performed on the L×L matrix.

[0110] Calculation of eigenvectors

[0111] Even though the characteristic RAIT beamformer, W RAIT,EIG The computation of (f, t) can be determined using the inverse of an L×L matrix, and computing the full eigenvalue decomposition of the M×M matrix Λ(f, t) to estimate the M eigenvectors in E(f, t) and the associated eigenvalues ​​in ∑(f, t) requires more computation than is actually necessary. The computational complexity can be significantly reduced by exploiting the fact that in typical dense deployments, the interference covariance matrix is ​​spatially colored and the interference is usually located in the reduced dimensional signal subspace.

[0112] set up represents the M×K matrix containing the dominant K eigenvectors of the matrix Λ(f,t), Denote a K×K matrix including the corresponding eigenvalues. The dominant eigenvector may be selected so that the sum of the corresponding eigenvalues ​​contains a given percentage of the total interference power, for example, 90% of the total interference power. Let represents the M×(MK) matrix including the remaining eigenvectors, represents a (MK)×(MK) diagonal matrix including the corresponding eigenvalues ​​on the main diagonal, that is:

[0113]

[0114] Assumptions contains the noise subspace, and thus in, is the noise power in each dimension and can be estimated as follows:

[0115]

[0116] Where tr{.} represents the trace of the matrix. Using the above decomposition of E(f,t) and ∑(f,t), the expression holds:

[0117]

[0118] Here, the diagonal structure of the matrix X(f,t) and the following fact are used:

[0119]

[0120] Therefore, W RAIT,EIG The matrix V(f,t) in the above expression of (f,t) can be written as:

[0121]

[0122] in, and is a K×K diagonal matrix whose i-th diagonal element is given by Given.

[0123] Figure 8 A block diagram of one example of a RAIT precoder unit 32 for determining a RAIT precoder is shown. Figure 8 The example of the RAIT precoder unit 32 shown includes an eigenvector estimation unit 96 to estimate K eigenvectors and corresponding eigenvalues ​​based on eigendecomposition. A noise power estimation unit 98 estimates the noise power of each dimension The pre-processing matrix unit 100 determines the channel pre-processing matrix V(f, t). The channel matrix unit 102 receives the downlink channel matrix and the preprocessing matrix V(f,t) and determine the processed channel matrix The precoding matrix unit 104 determines the RAIT precoding matrix W based on the processed channel matrix RAIT,EIG (f,t).

[0124] Compared to W, which requires an M×M matrix inversion RAIT (f, t), the proposed algorithm uses the K eigenvectors and eigenvalues ​​of the interference covariance matrix to calculate the channel preprocessing matrix V(f, t), and involves the inverse of a typically much smaller L×L matrix. The algorithm uses the processed channel matrix and the inverse calculation preconditioner W for the L×L matrix RAIT,EIG (f,t): Numerical simulation

[0125] Fig. 9 The performance of the proposed beam reduction technique using numerical simulations of an example TDD system with 36MHz bandwidth, 30kHz subcarrier spacing and 3.5GHz carrier frequency is shown in Figure . The simulation is for a multi-cell deployment scenario with 3 sites and 3 cells per site, where the inter-site distance is 200m, and 36 WD 22s are randomly placed in the simulation area, each WD 22 is equipped with 4 antennas. The 5G spatial correlation model urban macro channel model for non-line-of-sight communication is adopted. The antenna configuration at the base station is an active antenna system (AAS) 4x8x2 configuration. The traffic model for the downlink is selected as a full buffer model. Channel estimates are obtained using a full-band sounding reference signal, which is transmitted from one antenna port every 6 milliseconds by each WD 22, and antenna port switching is enabled, that is, complete channel information is obtained from all ports every 24 milliseconds. The interference covariance matrix of each cell is estimated based on the difference between the received sounding reference signal (SRS) and the reconstructed signal corresponding to the SRS transmission from the cell-attached WD 22 residual.

[0126] Fig. 9 The average downlink cell throughput of the proposed method is shown versus the number of eigenvectors used in computing the channel preconditioning matrix V(f,t). Fig. 9 Also shown is the downlink cell throughput using a RAT precoder without any out-of-cell interference mitigation capability. Fig. 9 It is shown that as the number of eigenvectors increases, the downlink cell throughput increases with the interference suppression capability. Note that the full-dimensional interference suppression capability of the full-dimensional RAIT algorithm (which corresponds to the selection of K=64) is achieved using K=48 eigenvectors. In addition, Fig. 9 It is shown that using a smaller number of eigenvectors can still provide significant improvement in cell throughput over RAT beamforming, e.g., using K=24 eigenvectors achieves 50% of the performance gain achieved by full-dimensional RAIT (over RAT beamforming).

[0127] As will be appreciated by those skilled in the art, the concepts described herein may be embodied as methods, data processing systems, computer program products, and / or computer storage media storing executable computer programs. Thus, the concepts described herein may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects, all of which are generally referred to herein as "circuits" or "modules". Any process, step, action, and / or function described herein may be performed and / or associated with a corresponding module, which may be implemented in software and / or firmware and / or hardware. In addition, the present application may take the form of a computer program product on a tangible computer-usable storage medium having a computer program code embodied in the medium that can be executed by a computer. Any suitable tangible computer-readable medium may be utilized, including a hard disk, a CD-ROM, an electronic storage device, an optical storage device, or a magnetic storage device.

[0128] Some embodiments are described herein with reference to flowcharts and / or block diagrams of methods, systems, and computer program products. It should be understood that each block in the flowcharts and / or block diagrams and combinations of blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer (to thereby create a special-purpose computer), a special-purpose computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device create the instructions for implementation in the flowcharts and / or block diagrams. Figure 1 means of performing functions / actions specified in one or more boxes.

[0129] These computer program instructions may also be stored in a computer-readable memory or storage medium, which may guide a computer or other programmable data processing device to function in a specific manner, so that the instructions stored in the computer-readable memory generate instructions including implementing the flowchart and / or block diagram. Figure 1 An article of manufacture that is an instruction means for performing the functions / actions specified in one or more blocks.

[0130] Computer program instructions may also be loaded onto a computer or other programmable data processing device to cause a series of operating steps to be performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device are loaded onto the computer or other programmable data processing device. The computer or other programmable device provides steps for implementing the functions / actions specified in the flowchart and / or block diagram box or multiple boxes.

[0131] It should be understood that the functions / actions indicated in the blocks may not occur in the order indicated in the operational diagram. For example, two blocks shown in succession may actually be executed substantially simultaneously, or the blocks may sometimes be executed in the reverse order, depending on the functions / actions involved. Although some figures include arrows on communication paths to illustrate the primary direction of communication, it should be understood that communication may occur in the direction opposite to the arrows depicted.

[0132] Computer program code for carrying out operations of the concepts described herein may be written in languages ​​such as Python, Or C++ object-oriented programming language is written. However, the computer program code for performing the operation of the present application can also be written in a conventional procedural programming language, such as "C" programming language. The program code can be executed completely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer or completely on a remote computer. In the latter case, the remote computer can be connected to the user's computer through a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, by using the Internet of an Internet service provider).

[0133] Many different embodiments have been disclosed herein in conjunction with the above description and accompanying drawings. It should be understood that literally describing and illustrating every combination and subcombination of these embodiments would be overly repetitive and confusing. Therefore, all embodiments may be combined in any manner and / or combination, and this specification (including the accompanying drawings) should be interpreted as constituting a complete written description of all combinations and subcombinations of the embodiments described herein and the manner and process of making and using them, and should support claims to any such combination or subcombination.

[0134] Abbreviations used in the foregoing description include:

[0135] abbreviation explain

[0136] AAS Active Antenna System

[0137] MIMO Multiple Input Multiple Output

[0138] MMSE Minimum Mean Square Error

[0139] RAT Reciprocity Assisted Transmission

[0140] RAIT Reciprocity-Assisted Interference-Aware Transmission

[0141] SLNR Signal to Leakage plus Noise Ratio

[0142] TDD Time Domain Duplex

[0143] UE User Equipment

[0144] WD Wireless Devices

[0145] Those skilled in the art will appreciate that the embodiments described herein are not limited to what is specifically shown and described above. In addition, unless otherwise mentioned above, it should be noted that all drawings are not to scale. Various modifications and variations are possible according to the above teachings without departing from the scope of the appended claims.

Claims

1. A method in a network node (16), the network node being configured to determine a reciprocity-assisted interference-aware transmission (RAIT) precoder matrix for precoding a downlink transmission to a wireless WD (22), the method comprising: The LxM channel matrix is ​​determined (S134) by one of the following: Select L rows of the NxM channel matrix; as well as Select L linear combinations of the rows of the NxM channel matrix; M is the number of antennas used for the downlink transmission at the network node (16), N is the number of receiving antennas at the WD (22), and L is an integer less than M; determining (S136) an MxM pre-processing matrix based at least in part on an eigen-decomposition of the MxM interference covariance matrix; and An MxL RAIT precoder matrix is ​​determined (S138) based at least in part on an inverse of an LxL matrix based at least in part on a first matrix product of the LxM channel matrix, the MxM preprocessing matrix, and an MxL Hermitian transpose of the LxM channel matrix.

2. The method according to claim 1 also includes determining an MxM eigenvector matrix and M associated eigenvalues ​​based at least in part on the eigendecomposition of the MxM interference covariance matrix, each of the M associated eigenvalues ​​representing interference power, and each eigenvector of the MxM eigenvector matrix representing a different propagation direction of the interference power.

3. The method according to claim 2, wherein: The MxM pre-processing matrix is ​​based at least in part on an estimated noise power associated with a subset of MK eigenvectors of the MxM interference covariance matrix, K being an integer less than M.

4. The method according to claim 3, wherein: The estimated noise power in each dimension is based at least in part on a difference between a first trace of the MxM interference covariance matrix and a second trace of a KxK diagonal matrix of eigenvalues ​​corresponding to K selected eigenvectors of the MxM eigenvector matrix.

5. The method according to claim 4, wherein: The K selected eigenvectors are selected such that a sum of the corresponding eigenvalues ​​is a given percentage of the total interference power.

6. The method according to claim 4, wherein: The K selected eigenvectors correspond to the set of K largest eigenvalues.

7. The method according to any one of claims 1 to 6, wherein: L is the number of transmission layers, which is less than or equal to the number of receive antennas at the WD.

8. The method according to claims 1-7, wherein: The MxL RAIT precoder matrix is ​​determined based at least in part on a second matrix product of the MxM preprocessing matrix, an MxL Hermitian transpose of the LxM channel matrix, and an inverse of the LxL matrix.

9. The method according to any one of claims 1 to 8, wherein: The MxM preconditioning matrix is ​​based at least in part on a third matrix product of an MxK matrix of selected eigenvectors, a KxK diagonal modified eigenvalue matrix, and a KxM Hermitian transpose of the MxK matrix of selected eigenvectors, K being an integer less than M.

10. The method according to claim 1, wherein: The MxM preprocessing matrix is ​​determined at least in part by the following calculation: Where V(f,t) is the MxM preprocessing matrix, I M is the MxM identity matrix, is the MxK matrix of K selected eigenvectors from the MxM interference covariance matrix, is a KxK diagonal matrix whose i-th element is given by Given, δ 2 is the minimum mean square error regularization factor, σ i (f,t) is the noise power associated with the i-th eigenvector of the MxM interference covariance matrix, is the estimated noise power in each dimension, tr{Λ(f,t)} is the trace of the interference covariance matrix, is the trace of the KxK matrix of the dominant eigenvalues ​​corresponding to the K selected eigenvectors.

11. A network node (16) configured to determine a reciprocity-assisted interference-aware transmission (RAIT) precoder matrix to precode a downlink transmission to a wireless WD (22), the network node (16) comprising a processing circuit (68) configured to: The LxM channel matrix is ​​determined by one of the following: Selecting L rows of an NxM channel matrix; and Select L linear combinations of the rows of the NxM channel matrices; M is the number of antennas used for downlink transmission at the network node (16), N is the number of receiving antennas at the WD (22), and L is an integer less than M; determining an MxM preprocessing matrix based at least in part on an eigendecomposition of the MxM interference covariance matrix; as well as An MxL RAIT precoder matrix is ​​determined based at least in part on an inverse of an LxL matrix based at least in part on a first matrix product of the LxM channel matrix, the MxM preprocessing matrix, and an MxL Hermitian transpose of the LxM channel matrix.

12. The network node (16) according to claim 11, wherein: The processing circuit (68) is also configured to determine an MxM eigenvector matrix and M associated eigenvalues ​​based at least in part on an eigendecomposition of the MxM interference covariance matrix, each of the M associated eigenvalues ​​representing an interference power, and each eigenvector of the MxM eigenvector matrix represents a different propagation direction of the interference power represented by the corresponding eigenvalue.

13. The network node (16) according to claim 12, wherein: The MxM pre-processing matrix is ​​based at least in part on an estimated noise power associated with a subset of MK eigenvectors of the MxM eigenvector matrix, K being an integer less than M.

14. The network node (16) according to claim 13, wherein: The estimated noise power in each dimension is based at least in part on a difference between a first trace of the MxM interference covariance matrix and a second trace of a KxK diagonal matrix of eigenvalues ​​corresponding to K selected eigenvectors of the MxM eigenvector matrix.

15. The network node (16) according to claim 14, wherein: The K selected eigenvectors are selected such that a sum of the corresponding eigenvalues ​​is a given percentage of the total interference power.

16. The network node (16) according to claim 14, wherein: The K selected eigenvectors correspond to the set of K largest eigenvalues.

17. The network node (16) according to any of claims 11-16, wherein L is the number of transmission layers which is less than or equal to the number of receive antennas at the wireless device.

18. The network node (16) according to claims 11-17, wherein: The MxL RAIT precoder matrix is ​​determined based at least in part on a second matrix product of the MxM preprocessing matrix, an MxL Hermitian transpose of the LxM channel matrix, and an inverse of the LxL matrix.

19. The network node (16) according to any one of claims 11 to 18, wherein: The MxM preconditioning matrix is ​​based at least in part on a third matrix product of an MxK matrix of selected eigenvectors, a KxK diagonal modified eigenvalue matrix, and a KxM Hermitian transpose of the MxK eigenvector matrix, K being an integer less than M.

20. The network node (16) of claim 11, wherein: The MxM pre-processing matrix is ​​determined at least in part by the following calculation: Where V(f,t) is the MxM preprocessing matrix, I M is the MxM identity matrix, is the MxK matrix of K selected eigenvectors from the MxM interference covariance matrix, is a KxK diagonal matrix whose i-th element is given by Given, δ 2 is the minimum mean square error regularization factor, σ i (f,t) is the noise power associated with the i-th eigenvector of the MxM interference covariance matrix, is the estimated noise power in each dimension, tr{Λ(f,t)} is the trace of the interference covariance matrix, is the trace of the KxK matrix of the dominant eigenvalues ​​corresponding to the K selected eigenvectors.

Images