WO2021147561A1 - 一种干扰抑制方法及装置 - Google Patents

一种干扰抑制方法及装置 Download PDF

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Publication number
WO2021147561A1
WO2021147561A1 PCT/CN2020/135793 CN2020135793W WO2021147561A1 WO 2021147561 A1 WO2021147561 A1 WO 2021147561A1 CN 2020135793 W CN2020135793 W CN 2020135793W WO 2021147561 A1 WO2021147561 A1 WO 2021147561A1
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matrix
interference suppression
base station
channel correlation
shaping
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PCT/CN2020/135793
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English (en)
French (fr)
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肖晶成
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大唐移动通信设备有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04J—MULTIPLEX COMMUNICATION
    • H04J11/00—Orthogonal multiplex systems, e.g. using WALSH codes
    • H04J11/0023—Interference mitigation or co-ordination
    • H04J11/0026—Interference mitigation or co-ordination of multi-user interference
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04J—MULTIPLEX COMMUNICATION
    • H04J11/00—Orthogonal multiplex systems, e.g. using WALSH codes

Definitions

  • This application relates to the field of communication technology, and in particular to an interference suppression method and device.
  • the beamforming technology is to make the antenna beam point as far as possible to the target direction, by weighting each antenna array element to improve the strength of the target signal.
  • the base station Based on the beamforming technology of a single terminal device, when the base station accesses multiple terminal devices, the base station needs to pair according to the service processing between the terminal devices to ensure the throughput per unit time.
  • the base station needs to pair according to the service processing between the terminal devices to ensure the throughput per unit time.
  • there are multiple terminal devices that reuse the same time-frequency resource and there will inevitably be interference between the terminal devices. Therefore, when multiple terminal devices perform multiple inputs or multiple outputs, the use of interference suppression methods can avoid interference between multiple terminal devices.
  • the base station usually adopts a zero-forcing algorithm to solve the interference suppression generated when multiple terminal equipment reuses time-frequency resources.
  • the method is as follows: the base station obtains the shaping coefficient of each terminal device after channel estimation for the multiple terminal devices that are accessed, and determines the shaping vector matrix according to the obtained shaping coefficients of the multiple terminal devices; The shaping vector matrix determines the channel correlation matrix of the multi-terminal device, and the interference suppression matrix is further determined through the inverse matrix of the channel correlation matrix; finally, the base station performs the interference suppression weight flow power and the antenna power according to the interference suppression matrix. Processing to achieve interference suppression.
  • the shaping coefficient of each terminal device is the weight of each antenna array element corresponding to the downlink transmission beam used by the base station to transmit to the terminal device; the dimension of the shaping vector matrix is related to the number of antennas of the base station and the total number of paired terminal devices. The number of flows is related.
  • the calculation amount of the base station is proportional to the square of the bandwidth of the terminal device, the number of antennas, and the number of streams.
  • the base station uses large-scale antenna technology, and the communication system supports multiple terminal equipment resource multiplexing (such as 16-stream signals), these make the calculation process of the interference suppression module of the base station more complicated and more computationally expensive. Large, resulting in more time-consuming entire interference suppression process.
  • the present application provides an interference suppression optimization method and device, which are used to reduce the time consumption of the interference suppression process.
  • the embodiments of the present application provide an interference suppression method and device.
  • the method specifically includes the following steps:
  • the base station obtains the shaping coefficient vectors of the paired n terminal devices, where the shaping coefficient vector of each terminal device is the shaping coefficient corresponding to the downlink transmission beam used for signal transmission with the terminal device managed by the base station Vector, n is an integer greater than 1;
  • the base station determines the channel correlation inverse matrix according to the channel correlation matrix, where the channel correlation inverse matrix is the inverse matrix of the channel correlation matrix;
  • the base station performs flow power adjustment processing on the channel correlation inverse matrix to obtain a channel correlation inverse matrix adjustment matrix
  • the base station adjusts the matrix according to the shaped vector matrix and the channel correlation inverse matrix to obtain an interference suppression matrix
  • the base station performs interference suppression processing on the n terminal devices according to the interference suppression matrix.
  • the channel correlation matrix conforms to the following formula: in, It is a conjugate matrix C n, C n is the vector matrix excipient.
  • the channel correlation inverse matrix conforms to the following formula: Wherein, R n is the channel correlation matrix.
  • the channel correlation inverse matrix adjustment matrix conforms to the following formula: Wherein, R inv is the channel correlation inverse matrix.
  • that the base station performs interference suppression processing on the n terminal devices according to the interference suppression matrix includes:
  • the base station uses the updated interference suppression matrix to perform interference suppression processing on the n terminal devices.
  • the updated interference suppression matrix conforms to the following formula:
  • the dimension of the shaped vector matrix is KaR ⁇ N layer , where KaR is the number of antennas of the base station, and N layer is the total number of streams of the n terminal devices.
  • an embodiment of the present application provides a base station, including:
  • the obtaining unit is configured to obtain the shaping coefficient vector of the paired n terminal devices, where the shaping coefficient vector of each terminal device corresponds to the downlink transmission beam managed by the base station for signal transmission with the terminal device
  • the shaping coefficient vector of, n is an integer greater than 1;
  • the processing unit is configured to determine a shaping vector matrix according to the n shaping coefficients, and calculate the channel correlation matrix of the n terminal devices according to the shaping vector matrix; and determine the channel correlation according to the channel correlation matrix An inverse matrix, wherein the channel correlation inverse matrix is the inverse matrix of the channel correlation matrix; the channel correlation inverse matrix is subjected to flow power adjustment processing to obtain a channel correlation inverse matrix adjustment matrix; according to the shaping vector matrix and The channel correlation inverse matrix adjusts the matrix to obtain an interference suppression matrix; and performs interference suppression processing on the n terminal devices according to the interference suppression matrix.
  • the processing unit determines the shaping vector matrix according to the n shaping coefficients, it is specifically configured to:
  • n shaped coefficients are formed into a combined vector matrix, and each column in the combined vector matrix is normalized by 2 norms to obtain the shaped vector matrix, and the shaped vector matrix conforms to the following formula:
  • C n [w 1 ',w 2 ',...,w n '], where w i 'is w i subjected to 2-norm normalization processing to obtain the shaped vector matrix, w i is the i-th
  • the shaping coefficient vector of each terminal device, i is an integer greater than 1 and less than n.
  • the channel correlation matrix conforms to the following formula: in, C n is the conjugate vector matrix, C n is the vector matrix excipient.
  • the channel correlation inverse matrix conforms to the following formula: R n is the channel correlation matrix.
  • the channel correlation inverse matrix adjustment matrix conforms to the following formula: R inv is the channel correlation inverse matrix.
  • the processing unit when performing interference suppression processing on the n terminal devices according to the interference suppression matrix, is specifically configured to:
  • the updated interference suppression matrix conforms to the following formula:
  • the dimension of the shaped vector matrix is KaR ⁇ N layer , where KaR is the number of antennas of the base station, and N layer is the total number of streams of the n terminal devices.
  • embodiments of the present application provide a computer-readable storage medium, including: the computer-readable storage medium stores a computer program, and when the computer program runs on an electronic device, the electronic device executes the above Any possible implementation of either aspect.
  • the embodiments of the present application provide a computer program, including instructions, which when run on a computer, cause the computer to execute any one of the possible implementations of any of the above aspects.
  • an embodiment of the present application provides a chip, which is used to read a computer program stored in a memory and execute any one of the possible implementation manners of any of the above aspects.
  • the base station obtains the shaping coefficient vector of the paired n terminal devices, where the shaping coefficient vector of each terminal device is managed by the base station and used to communicate with the terminal device.
  • the base station determines the shaping vector matrix according to the n shaping coefficients, and calculates the channel correlation matrix of the n terminal devices according to the shaping vector matrix; the base station determines the channel according to the channel correlation matrix Correlation inverse matrix, wherein the channel correlation inverse matrix is the inverse matrix of the channel correlation matrix; the base station performs flow power adjustment processing on the channel correlation inverse matrix to obtain a channel correlation inverse matrix adjustment matrix, and then according to the The shaping vector matrix and the channel correlation inverse matrix are adjusted to obtain an interference suppression matrix; the base station performs interference suppression processing on the n terminal devices according to the interference suppression matrix.
  • This method puts the flow power processing process of the interference suppression matrix after the inverse matrix, which greatly reduces the calculation amount of the interference suppression processing process, thereby reducing the time consumption of the system for performing interference suppression and improving the performance of the system.
  • FIG. 1 is a schematic diagram of the structure of a communication device in an embodiment of the application
  • FIG. 2 is a schematic diagram of the structure of a communication system in an embodiment of the application
  • FIG. 3 is a schematic flowchart of an existing interference suppression method
  • FIG. 4 is a schematic flowchart of an interference suppression method in an embodiment of this application.
  • FIG. 5 is a structural diagram of a base station provided in an embodiment of this application.
  • Fig. 6 is a structural diagram of a base station provided in an embodiment of the application.
  • the embodiment of the present application provides an interference suppression optimization method to reduce the time consumption of interference suppression and improve the processing performance of the system.
  • the method and device described in the present application are based on the same inventive concept, and because the method and the device have similar principles for solving the problem, the implementation of the device and the method can be referred to each other, and the repetition will not be repeated.
  • the base station obtains the shaping coefficient vector of the paired n terminal devices, where the shaping coefficient vector of each terminal device is managed by the base station for use in signal transmission with the terminal device.
  • the shaping coefficient vector corresponding to the downlink transmit beam of, n is an integer greater than 1;
  • the base station determines the shaping vector matrix according to the n shaping coefficients, and calculates the channel correlation matrix of the n terminal devices according to the shaping vector matrix; the base station determines the channel according to the channel correlation matrix Correlation inverse matrix, wherein the channel correlation inverse matrix is the inverse matrix of the channel correlation matrix; the base station performs flow power adjustment processing on the channel correlation inverse matrix to obtain a channel correlation inverse matrix adjustment matrix, and then according to the The shaping vector matrix and the channel correlation inverse matrix are adjusted to obtain an interference suppression matrix; the base station performs interference suppression processing on the n terminal devices according to the interference suppression matrix.
  • This method puts the flow power processing process of the interference suppression matrix after the inverse matrix, which greatly reduces the calculation amount of the interference suppression processing process, thereby reducing the time consumption of the system for performing interference suppression and improving the performance of the system.
  • a base station also called a network device, is a device deployed on a wireless access network to provide wireless communication functions.
  • base stations are: gNB, NR base station, evolved Node B (evolved Node B, eNB), transmission reception point (TRP), radio network controller (RNC), Node B (Node B, NB), base station controller (BSC), base transceiver station (BTS), home base station (for example, home evolved NodeB, or home Node B, HNB), or baseband unit ( base band unit, BBU), etc.
  • gNB gNode B
  • eNB evolved Node B
  • TRP transmission reception point
  • RNC radio network controller
  • Node B Node B
  • BSC base station controller
  • BTS base transceiver station
  • home base station for example, home evolved NodeB, or home Node B, HNB
  • baseband unit base band unit
  • the base station may include a centralized unit (CU) node and a distributed unit (DU) node.
  • CU centralized unit
  • DU distributed unit
  • This structure splits the protocol layer of the eNB in the long term evolution (LTE) system. Some of the protocol layer functions are placed under the centralized control of the CU, and some or all of the protocol layer functions are distributed in the DU. Centralized control of DU.
  • Terminal equipment is a device that provides users with voice and/or data connectivity.
  • the terminal equipment may also be called user equipment (UE), mobile station (mobile station, MS), mobile terminal (mobile terminal, MT), and so on.
  • UE user equipment
  • MS mobile station
  • MT mobile terminal
  • the terminal device may be a handheld device with a wireless connection function, a vehicle-mounted device, and the like.
  • some examples of terminal devices are: mobile phones (mobile phones), tablet computers, notebook computers, handheld computers, mobile internet devices (MID), wearable devices, virtual reality (VR) devices, augmented Augmented reality (AR) equipment, wireless terminals in industrial control, wireless terminals in self-driving (self-driving), wireless terminals in remote medical surgery, and smart grid (smart grid)
  • Beamforming is a signal preprocessing technology based on antenna arrays. Beamforming generates a directional beam by adjusting the weighting coefficient of each element in the antenna array, thereby obtaining significant array gain. As shown in Fig. 1, a communication device can obtain M beams in different directions through M sets of antenna array coefficients. In other words, one beam corresponds to a set of antenna array coefficients. The communication device can use any beam for signal transmission.
  • the base station shapes the beam in order to communicate with the terminal device.
  • the base station shapes the beam in order to communicate with the terminal device.
  • multiple terminal devices will have multiple inputs and multiple outputs.
  • Each terminal device shares the same time-frequency resource, which will cause interference between the terminal devices, making it impossible to achieve efficient normal service transmission and processing.
  • the processing unit of the base station performs interference suppression.
  • equalization is performed at the transmitting end of the base station, so that the weight of a terminal device is at the "null" position of the terminal device. Due to the complexity of the equalization algorithm, zero-forcing equalization is currently used, and the algorithm satisfies the following formula:
  • V n represents the suppression matrix
  • C n represents the shape vector matrix of n terminal devices
  • V n C n *R inv
  • the matrix dimension KaR ⁇ N layer .
  • the fifth and sixth steps are to prevent multiple terminal devices from exceeding the maximum transmission power of the device when multiplexed.
  • the key technologies are: 1. Ultra-large bandwidth, for example, the maximum bandwidth of the frequency band below 6GHz (Frequency range 1, FR1) in 5G is 900MHz, and FR2 The maximum bandwidth is 3250MHz; 2. Large-scale antenna technology, 5G uses a huge number of antennas, usually 64, 128, 256 antennas; 3. Multi-user resource multiplexing, some communication systems support 16-stream signal peak speed. These technologies bring huge challenges to the baseband digital processing unit of the base station. As a computationally intensive module in the baseband digital processing unit, interference suppression is proportional to the bandwidth and number of antennas, and proportional to the square of the number of streams. Therefore, the processing overhead of interference suppression in current communication systems is very large. Reducing the time-consuming processing of interference suppression can effectively improve the performance of the 5G system.
  • the current interference suppression algorithm does not consider the relationship between the data, the implementation steps are complex, the calculation is redundant, and the calculation amount is large. Therefore, the calculation process of interference suppression needs to be optimized from the perspective of the algorithm to reduce the time consumption of interference suppression and improve System performance.
  • an embodiment of the present application provides an interference suppression method. This method can be applied to the communication system as shown in FIG. 2. The flow of an interference suppression method provided by an embodiment of the present application will be described in detail below with reference to FIG. 4.
  • the base station obtains the shaping coefficient vector of the paired n terminal devices, where the shaping coefficient vector of each terminal device is the shaping coefficient vector managed by the base station for the downlink transmission beam used for signal transmission with the terminal device.
  • Shape coefficient vector, N is an integer greater than 1.
  • the 2 norm normalization process obtains the shaping vector matrix, w i is the shaping coefficient vector of the i-th terminal device, and i is an integer greater than 1 and less than n.
  • the base station determines a shaping vector matrix according to the n shaping coefficients, and calculates the channel correlation matrix of the n terminal devices according to the shaping vector matrix.
  • the channel correlation matrix conforms to the following formula: in, C n is the conjugate vector matrix, C n is the vector matrix excipient.
  • the channel correlation matrix of the n terminal devices is calculated as the channel correlation upper triangular matrix of the n terminal devices, excluding diagonals.
  • step S402 calculate the channel correlation upper triangular matrix of the n terminal devices, excluding the diagonal, the main reasons include: (1) Since the channel correlation matrix R n is a Hermitian matrix, as long as the value of the upper triangle of the matrix is known It can be inverted; (2) Each column of the shaped vector matrix C n is the result of 2-norm normalization, so the main diagonal of R n is 1, so no calculation is required.
  • the base station determines a channel correlation inverse matrix according to the channel correlation matrix, where the channel correlation inverse matrix is an inverse matrix of the channel correlation matrix.
  • the channel correlation inverse matrix conforms to the following formula: R n is the channel correlation matrix.
  • the base station performs flow power adjustment processing on the channel correlation inverse matrix to obtain a channel correlation inverse matrix adjustment matrix.
  • the channel correlation inverse matrix adjustment matrix conforms to the following formula: R inv is the channel correlation inverse matrix.
  • the base station adjusts the matrix according to the shaping vector matrix and the channel correlation inverse matrix to obtain an interference suppression matrix.
  • the base station performs interference suppression processing on the n terminal devices according to the interference suppression matrix; including: the base station performs antenna power adjustment processing on the interference suppression matrix.
  • the base station performs interference suppression processing on the n terminal devices according to the interference suppression matrix.
  • the updated interference suppression matrix conforms to the following formula:
  • the base station uses the updated interference suppression matrix to perform interference suppression processing on the N terminal devices.
  • step S402 the channel correlation matrix of the n terminal devices calculated by the base station is modified to the channel correlation upper triangular matrix of the n terminal devices, excluding the diagonal. It can reduce 4*M*N*(N+1)/2 multiplications and (4*M-2)*N*(N+1)/2 additions.
  • the base station adjusts the flow power of the channel correlation inverse matrix (step S404) to the inverse matrix (step S403), the base station of the existing scheme can adjust the flow power of the channel correlation inverse matrix for the reason.
  • the processing formula is derived as follows:
  • the interference suppression matrix V n ′ obtained after the flow power processing in the existing solution is equivalent to the interference suppression matrix V n obtained after the flow power processing in the present application.
  • the calculation amount in the fifth step of the existing solution is 4*M*N multiplications and (N+1)*M additions, but the calculation amount in step S404 in the solution of this application is 2*N* N multiplications, therefore, the reduced calculation amount is 4*M*N-2*N*N multiplications, and (N+1)*M additions.
  • the interference suppression method reduces a total of 10483200 times of multiplications and 9727536 times of additions. Assuming that the processor's main frequency is 1.8GHz, 2 multiplications or additions are executed per clock cycle, which reduces the total time by about 5.6ms. Processing time.
  • the interference suppression method provided by this application makes full use of the characteristics of data, uses the context of matrix transformation, merges matrix operations, reduces unnecessary calculations, and greatly reduces the amount of calculation in the interference suppression process. This reduces the time it takes for the system to perform interference suppression and improves the performance of the system.
  • an embodiment of the present application also provides a base station that implements an interference suppression method.
  • the structure of the base station is shown in FIG. 5 and includes an acquiring unit 501 and a processing unit 502.
  • the base station device can be applied to the communication system shown in FIG. 2 and can implement the interference suppression method shown in FIG. 4 above.
  • the function of each unit in the device 500 is introduced below.
  • the obtaining unit 501 is configured to obtain the shaping coefficient vector of the paired n terminal devices, where the shaping coefficient vector of each terminal device is a downlink transmission beam managed by the base station for signal transmission with the terminal device.
  • the shaping coefficient vector of each terminal device is a downlink transmission beam managed by the base station for signal transmission with the terminal device.
  • Corresponding shaping coefficient vector, n is an integer greater than 1;
  • the processing unit 502 is configured to determine a shaping vector matrix according to the n shaping coefficients, and calculate the channel correlation matrix of the n terminal devices according to the shaping vector matrix;
  • the channel correlation inverse matrix is the inverse matrix of the channel correlation matrix
  • the processing unit 502 determines the shaping vector matrix according to the n shaping coefficients for:
  • n shaped coefficients are formed into a combined vector matrix, and each column in the combined vector matrix is normalized by 2 norms to obtain the shaped vector matrix, and the shaped vector matrix conforms to the following formula:
  • C n [w 1 ',w 2 ',...,w n '], where w i 'is w i subjected to 2-norm normalization processing to obtain the shaped vector matrix, w i is the i-th
  • the shaping coefficient vector of each terminal device, i is an integer greater than 1 and less than n.
  • the channel correlation matrix conforms to the following formula: in, C n is the conjugate vector matrix, C n is the vector matrix excipient.
  • the channel correlation inverse matrix conforms to the following formula: R n is the channel correlation matrix.
  • the channel correlation inverse matrix adjustment matrix conforms to the following formula: R inv is the channel correlation inverse matrix.
  • the processing unit 502 performs interference suppression processing on the n terminal devices according to the interference suppression matrix for:
  • the updated interference suppression matrix conforms to the following formula:
  • the dimension of the shaped vector matrix is KaR ⁇ N layer , where KaR is the number of antennas of the base station, and N layer is the total number of streams of the n terminal devices.
  • an embodiment of the present application also provides a base station 600 that can implement an interference suppression method as shown in FIG. 3.
  • the base station 600 includes: a transceiver 601, a processor 602, and a memory 603. Wherein, the transceiver 601, the processor 602, and the memory 603 are connected to each other.
  • the transceiver 601, the processor 602, and the memory 603 are connected to each other through a bus 604.
  • the bus 604 may be a peripheral component interconnect standard (PCI) bus or an extended industry standard architecture (EISA) bus, etc.
  • PCI peripheral component interconnect standard
  • EISA extended industry standard architecture
  • the bus can be divided into an address bus, a data bus, a control bus, and so on.
  • the transceiver 601 is used to receive and send data to realize communication and interaction with other devices.
  • the processor 602 is configured to implement an interference suppression method as shown in FIG. 3.
  • the transceiver 601 is configured to obtain the shaping coefficient vector of the paired n terminal devices, where the shaping coefficient vector of each terminal device is a downlink transmission beam managed by the base station for signal transmission with the terminal device Corresponding shaping coefficient vector, n is an integer greater than 1;
  • the processor 602 is configured to determine a shaping vector matrix according to the n shaping coefficients, and calculate the channel correlation matrix of the n terminal devices according to the shaping vector matrix;
  • the channel correlation inverse matrix is the inverse matrix of the channel correlation matrix
  • the processor 602 determines a shaping vector matrix according to the n shaping coefficients for:
  • n shaped coefficients are formed into a combined vector matrix, and each column in the combined vector matrix is normalized by 2 norms to obtain the shaped vector matrix, and the shaped vector matrix conforms to the following formula:
  • C n [w 1 ',w 2 ',...,w n '], where w i 'is w i subjected to 2-norm normalization processing to obtain the shaped vector matrix, w i is the i-th
  • the shaping coefficient vector of each terminal device, i is an integer greater than 1 and less than n.
  • the channel correlation matrix conforms to the following formula: in, C n is the conjugate vector matrix, C n is the vector matrix excipient.
  • the channel correlation inverse matrix conforms to the following formula: R n is the channel correlation matrix.
  • the channel correlation inverse matrix adjustment matrix conforms to the following formula: R inv is the channel correlation inverse matrix.
  • the processor 602 performs interference suppression processing on the n terminal devices according to the interference suppression matrix for:
  • the updated interference suppression matrix conforms to the following formula:
  • the dimension of the shaped vector matrix is KaR ⁇ N layer , where KaR is the number of antennas of the base station, and N layer is the total number of streams of the n terminal devices.
  • the embodiments of the present application also provide a computer program, which when the computer program runs on a computer, causes the computer to execute the interference suppression method provided by the embodiment shown in FIG. 4.
  • the embodiments of the present application also provide a computer-readable storage medium in which a computer program is stored.
  • the computer program When the computer program is executed by a computer, the computer executes the implementation shown in FIG. 4 Example provides an interference suppression method.
  • the embodiment of the present application also provides a chip, which is used to read a computer program stored in a memory to implement an interference suppression method provided by the embodiment shown in FIG. 4.
  • the embodiments of the present application provide a chip system, which includes a processor, and is used to support a computer device to implement the functions related to the base station in the embodiment shown in FIG. 4.
  • the chip system further includes a memory, and the memory is used to store necessary programs and data of the computer device.
  • the chip system can be composed of chips, or include chips and other discrete devices.
  • the base station obtains the shaping coefficient vector of the paired n terminal devices, where the shaping coefficient vector of each terminal device is managed by the base station and used to communicate with the terminal.
  • the base station determines the shaping vector matrix according to the n shaping coefficients, and calculates the channel correlation matrix of the n terminal devices according to the shaping vector matrix; the base station determines the channel according to the channel correlation matrix Correlation inverse matrix, wherein the channel correlation inverse matrix is the inverse matrix of the channel correlation matrix; the base station performs flow power adjustment processing on the channel correlation inverse matrix to obtain a channel correlation inverse matrix adjustment matrix, and then according to the The shaping vector matrix and the channel correlation inverse matrix are adjusted to obtain an interference suppression matrix; the base station performs interference suppression processing on the n terminal devices according to the interference suppression matrix.
  • This method puts the flow power processing process of the interference suppression matrix after the inverse matrix, which greatly reduces the calculation amount of the interference suppression process, thereby reducing the time consumption of the system for performing interference suppression and improving the performance of the system.
  • this application can be provided as methods, systems, or computer program products. Therefore, this application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing equipment to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction device.
  • the device implements the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.
  • These computer program instructions can also be loaded on a computer or other programmable data processing equipment, so that a series of operation steps are executed on the computer or other programmable equipment to produce computer-implemented processing, so as to execute on the computer or other programmable equipment.
  • the instructions provide steps for implementing the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.

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Abstract

本申请实施例提供一种干扰抑制方法及装置，用于实现减少干扰抑制过程的耗时，该方法中，基站获取配对的n个终端设备的赋形系数向量，根据n个赋形系数确定赋形向量矩阵，并根据赋形向量矩阵，计算所述n个终端设备的信道相关矩阵；基站根据信道相关矩阵，确定信道相关逆矩阵；基站对信道相关逆矩阵进行流功率调整处理，得到信道相关逆矩阵调整矩阵，再根据赋形向量矩阵和信道相关逆矩阵调整矩阵，得到干扰抑制矩阵；基站根据干扰抑制矩阵对所述n个终端设备进行干扰抑制处理。该方法将干扰抑制矩阵的流功率处理过程放在逆矩阵之后，大大减少了干扰抑制处理过程的计算量，从而减少了系统执行干扰抑制的耗时，提高了系统的性能。

Description

一种干扰抑制方法及装置
相关申请的交叉引用
本申请要求在2020年01月20日提交中国专利局、申请号为202010067657.5、申请名称为“一种干扰抑制方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及通信技术领域，尤其涉及一种干扰抑制方法及装置。
背景技术
目前在移动通信系统中，随着终端设备数量增加，对通信的信道需求日益增长，使频谱资源的有限性问题日益凸显，而通过波束赋形技术是提高频谱资源复用率的一个重要手段。波束赋形技术是为了使天线的波束尽可能指向目标方向，通过对各天线阵元进行加权，以提高目标信号的强度。
在单个终端设备波束赋形技术基础上，当基站接入多个终端设备时，基站需要根据终端设备之间的业务处理进行配对，以保证单位时间内的吞吐量。然而，这就存在多个终端设备复用相同的时频资源，终端设备之间必然会存在干扰。因此，在多终端设备进行多输入或多输出时，采用干扰抑制方法可避免多终端设备之间产生的干扰。
目前基站通常采用迫零算法解决多终端设备复用时频资源时产生的干扰抑制。该方法为：基站对接入的多终端设备进行信道估计后，获得每个终端设备的赋形系数，并根据获得的多个终端设备的赋形系数确定赋形向量矩阵；然后基站根据所述赋形向量矩阵确定多终端设备的信道相关矩阵，通过该信道相关矩阵的逆矩阵，进一步确定干扰抑制矩阵；最后，所述基站根据所述干扰抑制矩阵对干扰抑制权值流功率和天线功率进行处理，从而实现干扰抑制。其中，每个终端设备的赋形系数为基站对该终端设备传输时使用的下行 发送波束对应的各天线阵元的权重；所述赋形向量矩阵的维度与基站的天线数和配对终端设备总流数有关。
显然，基站的计算量与终端设备的带宽、天线数以及流数的平方成正比。然而，随着追求更高的吞吐量。由于现有的通信系统支持超大带宽，基站使用大规模天线技术，且通信系统支持多终端设备资源复用(例如16流信号)，这些导致基站的干扰抑制模块的计算过程更加复杂，计算量较大，从而导致整个干扰抑制过程耗时较多。
发明内容
本申请提供了一种干扰抑制优化方法及装置，用以实现减少干扰抑制过程的耗时。
本申请实施例提供的具体技术方案如下：
第一方面，本申请实施例提供了一种干扰抑制方法及装置，该方法具体包括以下步骤：
基站获取配对的n个终端设备的赋形系数向量，其中，每个终端设备的赋形系数向量为所述基站管理的用于与该终端设备进行信号传输使用的下行发送波束对应的赋形系数向量，n为大于1的整数；
所述基站根据所述n个赋形系数确定赋形向量矩阵，并根据所述赋形向量矩阵，计算所述n个终端设备的信道相关矩阵；
所述基站根据所述信道相关矩阵，确定信道相关逆矩阵，其中，所述信道相关逆矩阵为所述信道相关矩阵的逆矩阵；
所述基站对所述信道相关逆矩阵进行流功率调整处理，得到信道相关逆矩阵调整矩阵；
所述基站根据所述赋形向量矩阵和所述信道相关逆矩阵调整矩阵，得到干扰抑制矩阵；
所述基站根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理。
在一个可能的实现方式中，所述基站根据所述n个赋形系数确定赋形向 量矩阵，包括：所述基站将所述n个赋形系数组成所述赋形向量矩阵，其中，所述赋形向量矩阵符合以下公式：C n＝[w 1,w 2,...,w n]，其中，w i为第i个终端设备的赋形系数向量，i为大于1且小于n的整数；或者
所述基站将所述n个赋形系数组成组合向量矩阵，对所述组合向量矩阵中的每一列进行2范数归一化处理，得到所述赋形向量矩阵，所述赋形向量矩阵符合以下公式：C n＝[w 1',w 2',...,w n']，其中，w i'为w i进行2范数归一化处理得到所述赋形向量矩阵，w i为第i个终端设备的赋形系数向量，i为大于1且小于n的整数。
在一个可能的实现方式中，所述信道相关矩阵符合以下公式：
Figure PCTCN2020135793-appb-000001
其中，
Figure PCTCN2020135793-appb-000002
为C n的共轭矩阵，C n为所述赋形向量矩阵。
在一个可能的实现方式中，所述信道相关逆矩阵符合以下公式：
Figure PCTCN2020135793-appb-000003
其中，R n为所述信道相关矩阵。
在一个可能的实现方式中，所述信道相关逆矩阵调整矩阵符合以下公式：
Figure PCTCN2020135793-appb-000004
其中，R inv为所述信道相关逆矩阵。
在一个可能的实现方式中，所述干扰抑制矩阵符合以下公式：V n＝C n*R inv,norm，其中，C n为所述赋形向量矩阵，R inv,norm所述信道相关逆矩阵调整矩阵。
在一个可能的实现方式中，所述基站根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理，包括：
所述基站根据所述干扰抑制矩阵，确定所述基站的天线的功率调整因子；
根据所述天线的功率调整因子，对所述干扰抑制矩阵进行天线功率调整处理，得到更新的干扰抑制矩阵；
所述基站使用所述更新的干扰抑制矩阵，对所述n个终端设备进行干扰抑制处理。
在一个可能的实现方式中，所述天线的功率调整因子符合以下公式： P ant＝sum(|V n| 2,2)，其中，V n为所述干扰抑制矩阵；
所述更新的干扰抑制矩阵，符合以下公式：
Figure PCTCN2020135793-appb-000005
在一个可能的实现方式中，所述赋形向量矩阵的维度为K aR×N layer，其中，K aR为所述基站的天线的数量，N layer为所述n个终端设备的总流数。
第二方面，本申请实施例提供了一种基站，包括：
获取单元，用于获取配对的n个终端设备的赋形系数向量，其中，每个终端设备的赋形系数向量为所述基站管理的用于与该终端设备进行信号传输使用的下行发送波束对应的赋形系数向量，n为大于1的整数；
处理单元，用于根据所述n个赋形系数确定赋形向量矩阵，并根据所述赋形向量矩阵，计算所述n个终端设备的信道相关矩阵；根据所述信道相关矩阵，确定信道相关逆矩阵，其中，所述信道相关逆矩阵为所述信道相关矩阵的逆矩阵；对所述信道相关逆矩阵进行流功率调整处理，得到信道相关逆矩阵调整矩阵；根据所述赋形向量矩阵和所述信道相关逆矩阵调整矩阵，得到干扰抑制矩阵；根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理。
在一个可能的实现方式中，所述处理单元，在根据所述n个赋形系数确定赋形向量矩阵时，具体用于：
将所述n个赋形系数组成所述赋形向量矩阵，其中，所述赋形向量矩阵符合以下公式：C n＝[w 1,w 2,...,w n]，其中，w i为第i个终端设备的赋形系数向量，i为大于1且小于n的整数；或者
将所述n个赋形系数组成组合向量矩阵，对所述组合向量矩阵中的每一列进行2范数归一化处理，得到所述赋形向量矩阵，所述赋形向量矩阵符合以下公式：C n＝[w 1',w 2',...,w n']，其中，w i'为w i进行2范数归一化处理得到所述赋形向量矩阵，w i为第i个终端设备的赋形系数向量，i为大于1且小于n的整数。
在一个可能的实现方式中，所述信道相关矩阵符合以下公式：
Figure PCTCN2020135793-appb-000006
其中，
Figure PCTCN2020135793-appb-000007
为C n的共轭向量矩阵，C n为所述赋形向量矩阵。
在一个可能的实现方式中，所述信道相关逆矩阵符合以下公式：
Figure PCTCN2020135793-appb-000008
R n为所述信道相关矩阵。
在一个可能的实现方式中，所述信道相关逆矩阵调整矩阵符合以下公式：
Figure PCTCN2020135793-appb-000009
R inv为所述信道相关逆矩阵。
在一个可能的实现方式中，所述干扰抑制矩阵符合以下公式：V n＝C n*R inv,norm，其中，C n为所述赋形向量矩阵，R inv,norm所述信道相关逆矩阵调整矩阵。
在一个可能的实现方式中，所述处理单元，在根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理时，具体用于：
根据所述干扰抑制矩阵，确定所述基站的天线的功率调整因子；
根据所述天线的功率调整因子，对所述干扰抑制矩阵进行天线功率调整处理，得到更新的干扰抑制矩阵；
使用所述更新的干扰抑制矩阵，对所述n个终端设备进行干扰抑制处理。
在一个可能的实现方式中，所述天线的功率调整因子符合以下公式：P ant＝sum(|V n| 2,2)，其中，V n为所述干扰抑制矩阵；
所述更新的干扰抑制矩阵，符合以下公式：
Figure PCTCN2020135793-appb-000010
在一个可能的实现方式中，所述赋形向量矩阵的维度为K aR×N layer，其中，K aR为所述基站的天线的数量，N layer为所述n个终端设备的总流数。
第三方面，本申请实施例提供了一种计算机可读存储介质，包括：所述计算机可读存储介质中存储有计算机程序，当计算机程序在电子设备上运行时，使得所述电子设备执行以上任一方面的任意一个可能实现方式。
第四方面，本申请实施例提供了一种计算机程序，包括指令，当所述指令在计算机上运行时，使得所述计算机执行以上任一方面的任意一个可能实现方式。
第五方面，本申请实施例提供了一种芯片，所述芯片用于读取存储器中存储的计算机程序，执行以上任一方面的任意一个可能实现方式。
通过以上描述，本申请实施例的技术方案中，基站获取配对的n个终端设备的赋形系数向量，其中，每个终端设备的赋形系数向量为所述基站管理的用于与该终端设备进行信号传输使用的下行发送波束对应的赋形系数向量，n为大于1的整数。
所述基站根据所述n个赋形系数确定赋形向量矩阵，并根据所述赋形向量矩阵，计算所述n个终端设备的信道相关矩阵；所述基站根据所述信道相关矩阵，确定信道相关逆矩阵，其中，所述信道相关逆矩阵为所述信道相关矩阵的逆矩阵；所述基站对所述信道相关逆矩阵进行流功率调整处理，得到信道相关逆矩阵调整矩阵，再根据所述赋形向量矩阵和所述信道相关逆矩阵调整矩阵，得到干扰抑制矩阵；所述基站根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理。该方法将干扰抑制矩阵的流功率处理过程放在逆矩阵之后，大大减少了干扰抑制处理过程的计算量，从而减少了系统执行干扰抑制的耗时，提高了系统的性能。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简要介绍，显而易见地，下面描述中的附图仅仅是本申请实施例的一些实施例，对于本领域的普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本申请实施例中通信设备结构示意图；
图2为本申请实施例中通信系统结构示意图；
图3为现有的一种干扰抑制方法的流程示意图；
图4为本申请实施例中一种干扰抑制方法的流程示意图；
图5为本申请实施例中提供的一种基站结构图；
图6为本申请实施例中提供的一种基站结构图。
具体实施方式
为了使本申请的目的、技术方案和优点更加清楚，下面将结合附图对本申请作进一步地详细描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其它实施例，都属于本申请保护的范围。
本申请实施例提供一种干扰抑制优化方法，用以减少干扰抑制的耗时，提升系统的处理性能。其中，本申请所述方法和装置基于同一发明构思，由于方法及装置解决问题的原理相似，因此装置与方法的实施可以相互参见，重复之处不再赘述。
本申请实施例的技术方案中，基站获取配对的n个终端设备的赋形系数向量，其中，每个终端设备的赋形系数向量为所述基站管理的用于与该终端设备进行信号传输使用的下行发送波束对应的赋形系数向量，n为大于1的整数；
所述基站根据所述n个赋形系数确定赋形向量矩阵，并根据所述赋形向量矩阵，计算所述n个终端设备的信道相关矩阵；所述基站根据所述信道相关矩阵，确定信道相关逆矩阵，其中，所述信道相关逆矩阵为所述信道相关矩阵的逆矩阵；所述基站对所述信道相关逆矩阵进行流功率调整处理，得到信道相关逆矩阵调整矩阵，再根据所述赋形向量矩阵和所述信道相关逆矩阵调整矩阵，得到干扰抑制矩阵；所述基站根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理。该方法将干扰抑制矩阵的流功率处理过程放在逆矩阵之后，大大减少了干扰抑制处理过程的计算量，从而减少了系统执行干扰抑制的耗时，提高了系统的性能。
以下先对本申请实施例中的部分用语进行解释说明，以便于本领域技术人员理解。
1、基站(base station，BS)，也可称为网络设备，是一种部署在无线接入网用以提供无线通信功能的装置。
目前，一些基站的举例为：gNB、NR基站、演进型节点B(evolved Node B，eNB)、传输接收点(transmission reception point，TRP)、无线网络控制器(radio network controller，RNC)、节点B(Node B，NB)、基站控制器(base station controller，BSC)、基站收发台(base transceiver station,BTS)、家庭基站(例如，home evolved NodeB，或home Node B，HNB)，或基带单元(base band unit，BBU)等。
另外，在一种网络结构中，所述基站可以包括集中单元(centralized unit，CU)节点和分布单元(distributed unit，DU)节点。这种结构将长期演进(long term evolution，LTE)系统中eNB的协议层拆分开，部分协议层的功能放在CU集中控制，剩下部分或全部协议层的功能分布在DU中，由CU集中控制DU。
2、终端设备，是一种向用户提供语音和/或数据连通性的设备。终端设备又可以称为用户设备(user equipment，UE)、移动台(mobile station，MS)、移动终端(mobile terminal，MT)等。
例如，终端设备可以为具有无线连接功能的手持式设备、车载设备等。目前，一些终端设备的举例为：手机(mobile phone)、平板电脑、笔记本电脑、掌上电脑、移动互联网设备(mobile internet device，MID)、可穿戴设备，虚拟现实(virtual reality，VR)设备、增强现实(augmented reality，AR)设备、工业控制(industrial control)中的无线终端、无人驾驶(self driving)中的无线终端、远程手术(remote medical surgery)中的无线终端、智能电网(smart grid)中的无线终端、运输安全(transportation safety)中的无线终端、智慧城市(smart city)中的无线终端、智慧家庭(smart home)中的无线终端等。
3、“或者”，描述关联对象的关联关系，例如，A或者B，可以表示：单独存在A，单独存在B这两种情况。
下面结合附图对本申请的实施例进行说明。
波束赋形是一种基于天线阵列的信号预处理技术，波束赋形通过调整天线阵列中每个阵元的加权系数，以产生具有指向性的波束，从而能够获得明 显的阵列增益。如图1所示，通信设备可以通过M组天线阵列系数，得到M个不同方向的波束，换句话说，一个波束对应一组天线阵列系数。该通信设备可以使用任一种波束进行信号传输。
图2所示的通信系统，基站为了实现和终端设备进行通信，对波束进行赋形，然而针对单个终端设备的波束赋形基础上，若出现多个终端设备的多输入和多输出，使得多个终端设备公用相同的时频资源，这将导致终端设备之间存在干扰，而无法实现高效进行正常的业务传输和处理。
为了避免出现多终端设备之间存在干扰，所述基站的处理单元通过执行干扰抑制，干扰抑制方法时在基站的发送端做均衡，使一个终端设备的权重处在终端设备的“零陷”位置，由于均衡算法的复杂性，目前均使用迫零均衡，算法满足以下公式：
Figure PCTCN2020135793-appb-000011
其中，V n表示抑制矩阵，C n表示n个终端设备的赋形向量矩阵，
Figure PCTCN2020135793-appb-000012
为C n的共轭向量。
目前的实现流程如下图3所示：
1)基站获取配对的n个终端设备的赋形系数向量：C n＝[w 1,w 2,...,w n]；矩阵维度为K aR×N layer，K aR为天线数，N layer为所有配对终端设备总流数。
2)计算信道相关矩阵：
Figure PCTCN2020135793-appb-000013
3)计算信道相关逆矩阵：
Figure PCTCN2020135793-appb-000014
矩阵维度为K aR×N layer。
4)生成终端设备的干扰抑制矩阵：V n＝C n*R inv，矩阵维度为K aR×N layer。
5)干扰抑制矩阵流功率处理：
1、计算每一流的功率调整因子：
P layer＝sum(|V n| 2,1)
2、流功率调整：
Figure PCTCN2020135793-appb-000015
6)干扰抑制矩阵天线功率处理：
1、计算每一天线的功率调整因子：
P ant＝sum(|V n'| 2,2)
2、天线功率调整：
Figure PCTCN2020135793-appb-000016
其中，第五和第六步骤是为了防止多个终端设备复用时超出设备的最大发送功率。
然而，在目前的通信系统中，为了追求更高的吞吐量采用关键技术有：1、超大带宽，例如，5G中在6GHz以下的频段(Frequency range 1，FR1)的最大带宽为900MHz，FR2的最大带宽为3250MHz；2、大规模天线技术，5G使用的天线数目巨大，通常为64、128、256天线；3、多用户资源复用，一些通信系统中支持16流信号峰速。这些技术给基站的基带数字处理单元带来巨大的挑战。干扰抑制作为基带数字处理单元中的一个计算密集型的模块，其计算量和带宽、天线数成正比，与流数的平方成正比，因此，目前通信系统中进行干扰抑制的处理开销很大，减少干扰抑制的处理耗时可以有效的提升5G系统性能。
目前的干扰抑制算法未考虑到数据之间的相互关系，实现步骤复杂，计算冗余，计算量大，因此，需从算法角度对干扰抑制的计算过程进行优化，以减少干扰抑制耗时，提升系统性能。
为了解决在以上的问题，本申请实施例提供了一种干扰抑制方法。该方法可以适用于如图2所示的通信系统中。下面参考图4对本申请实施例提供的一种干扰抑制方法的流程进行详细说明。
S401：基站获取配对的n个终端设备的赋形系数向量，其中，每个终端设备的赋形系数向量为所述基站管理的用于与该终端设备进行信号传输使用的下行发送波束对应的赋形系数向量，N为大于1的整数。
在一种实施方式中，所述基站根据所述n个赋形系数，所述n个赋形系数组成所述赋形向量矩阵，其中，所述赋形向量矩阵符合以下公式：C n＝[w 1,w 2,...,w n]，其中，w i为第i个终端设备的赋形系数向量，i为大于1且小于n的整数。
在另一种实施方式中，所述基站根据所述n个赋形系数，将所述N个赋形系数组成组合向量矩阵，对所述组合向量矩阵中的每一列进行2范数归一化处理，得到所述赋形向量矩阵，所述赋形向量矩阵符合以下公式：C n＝[w 1',w 2',...,w n']，其中，w i'为w i进行2范数归一化处理得到所述赋形向量矩阵，w i为第i个终端设备的赋形系数向量，i为大于1且小于n的整数。
S402：所述基站根据所述n个赋形系数确定赋形向量矩阵，并根据所述赋形向量矩阵，计算所述n个终端设备的信道相关矩阵。
在一种实施方式中，所述信道相关矩阵符合以下公式：
Figure PCTCN2020135793-appb-000017
其中，
Figure PCTCN2020135793-appb-000018
为C n的共轭向量矩阵，C n为所述赋形向量矩阵。
可选的，在计算所述n个终端设备的信道相关矩阵为所述n个终端设备的信道相关上三角矩阵，不包括对角线。
针对步骤S402中，计算所述n个终端设备的信道相关上三角矩阵，不包括对角线，主要原因包括：(1)由于信道相关矩阵R n是Hermitian矩阵，只要知道矩阵的上三角的值就可以求逆；(2)所述赋形向量矩阵C n的每一列都是经过2-范数归一化的结果，所以R n的主对角线为1，因此不用计算。
S403：所述基站根据所述信道相关矩阵，确定信道相关逆矩阵，其中，所述信道相关逆矩阵为所述信道相关矩阵的逆矩阵。
在一种实施方式中，所述信道相关逆矩阵符合以下公式：
Figure PCTCN2020135793-appb-000019
R n为所述信道相关矩阵。
S404：所述基站对所述信道相关逆矩阵进行流功率调整处理，得到信道相关逆矩阵调整矩阵。
在一种实施方式中，所述信道相关逆矩阵调整矩阵符合以下公式：
Figure PCTCN2020135793-appb-000020
R inv为所述信道相关逆矩阵。
S405：所述基站根据所述赋形向量矩阵和所述信道相关逆矩阵调整矩阵，得到干扰抑制矩阵。
在第一种实施方式中，所述扰抑制矩阵符合以下公式：V n＝C n*R inv,norm，其中，C n为所述赋形向量矩阵，R inv,norm所述信道相关逆矩阵调整矩阵。
S406：所述基站根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理；包括：所述基站对所述干扰抑制矩阵进行天线功率调整处理。
在一种实施方式中，所述基站根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理。包括：
所述基站根据所述干扰抑制矩阵，确定所述基站的天线的功率调整因子；
所述天线的功率调整因子符合以下公式：P ant＝sum(|V n| 2,2)，其中，V n为所述干扰抑制矩阵。
根据所述天线的功率调整因子，对所述干扰抑制矩阵进行天线功率调整处理，得到更新的干扰抑制矩阵；所述更新的干扰抑制矩阵，符合以下公式：
Figure PCTCN2020135793-appb-000021
所述基站使用所述更新的干扰抑制矩阵，对所述N个终端设备进行干扰抑制处理。
通过以上描述可知，在M天线N流的系统中，与目前现有方案相比，本申请提出的干扰抑制方法有以下改进：
1、步骤S402中将基站计算所述n个终端设备的信道相关矩阵修改为所述n个终端设备的信道相关上三角矩阵，不包括对角线。可以减少4*M*N*(N+1)/2次乘法和(4*M-2)*N*(N+1)/2次加法。
2、将基站对所述信道相关逆矩阵进行流功率调整处理(步骤S404)调整到逆矩阵(步骤S403)之后，原因时可以将现有方案的基站对所述信道相关逆矩阵进行流功率调整处理的公式做如下推导：
Figure PCTCN2020135793-appb-000022
可知现有方案中经过流功率处理后得到的干扰抑制矩阵V n'与本申请中流功率处理后得到的干扰抑制矩阵V n为等价的。对于M天线N流的系统，现有方案第五步骤的计算量为4*M*N次乘法和(N+1)*M加法，然而本申请方案中步骤S404的计算量为2*N*N次乘法，因此，减少的计算量为4*M*N-2*N*N次乘法，以及(N+1)*M次加法。
因此，以上两处改进总共减少了4*M*N*(N+1)/2+4*M*N-2*N*N次乘法，以及(4*M-2)*N*(N+1)/2+(N+1)*M次加法。
例如：在5G通信中，100M带宽(273PRB)、64天线、16流的峰速配置，即M＝64，N＝16。本申请提供的干扰抑制方法相比目前现有方法共减少10483200次乘法和9727536次加法，假设处理器的主频为1.8GHz，每个时钟周期执行2条乘法或加法，共减少约5.6ms的处理时间。
综上所述，本申请提供的一种干扰抑制方法充分利用了数据之间的特点，利用矩阵变换的前后关系，合并矩阵运算，减少不必要的计算，大大减少了干扰抑制处理过程的计算量，从而减少了系统执行干扰抑制的耗时，提高了系统的性能。
基于相同的技术构思，本申请实施例还提供了实现一种干扰抑制方法的基站，该基站的结构如图5所示，包括获取单元501、处理单元502。所述基站装置可以应用于图2所示的通信系统中，并可以实现以上图4所示的一种干扰抑制方法。下面对装置500中的各个单元的功能进行介绍。
获取单元501，用于获取配对的n个终端设备的赋形系数向量，其中，每个终端设备的赋形系数向量为所述基站管理的用于与该终端设备进行信号传输使用的下行发送波束对应的赋形系数向量，n为大于1的整数；
处理单元502，用于根据所述n个赋形系数确定赋形向量矩阵，并根据所述赋形向量矩阵，计算所述n个终端设备的信道相关矩阵；
根据所述信道相关矩阵，确定信道相关逆矩阵，其中，所述信道相关逆矩阵为所述信道相关矩阵的逆矩阵；对所述信道相关逆矩阵进行流功率调整处理，得到信道相关逆矩阵调整矩阵；根据所述赋形向量矩阵和所述信道相关逆矩阵调整矩阵，得到干扰抑制矩阵；根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理。
在一种实施方式中，所述处理单元502，在根据所述n个赋形系数确定赋形向量矩阵，用于：
将所述n个赋形系数组成所述赋形向量矩阵，其中，所述赋形向量矩阵符合以下公式：C n＝[w 1,w 2,...,w n]，其中，w i为第i个终端设备的赋形系数向量，i为大于1且小于n的整数；或者
将所述n个赋形系数组成组合向量矩阵，对所述组合向量矩阵中的每一列进行2范数归一化处理，得到所述赋形向量矩阵，所述赋形向量矩阵符合以下公式：C n＝[w 1',w 2',...,w n']，其中，w i'为w i进行2范数归一化处理得到所述赋形向量矩阵，w i为第i个终端设备的赋形系数向量，i为大于1且小于n的整数。
在一种实施方式中，所述信道相关矩阵符合以下公式：
Figure PCTCN2020135793-appb-000023
其中，
Figure PCTCN2020135793-appb-000024
为C n的共轭向量矩阵，C n为所述赋形向量矩阵。
在一种实施方式中，所述信道相关逆矩阵符合以下公式：
Figure PCTCN2020135793-appb-000025
R n为所述信道相关矩阵。
在一种实施方式中，所述信道相关逆矩阵调整矩阵符合以下公式：
Figure PCTCN2020135793-appb-000026
R inv为所述信道相关逆矩阵。
在一种实施方式中，所述干扰抑制矩阵符合以下公式：V n＝C n*R inv,norm，其中，C n为所述赋形向量矩阵，R inv,norm所述信道相关逆矩阵调整矩阵。
在一种实施方式中，所述处理单元502，在根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理，用于：
根据所述干扰抑制矩阵，确定所述基站的天线的功率调整因子；
根据所述天线的功率调整因子，对所述干扰抑制矩阵进行天线功率调整处理，得到更新的干扰抑制矩阵；
使用所述更新的干扰抑制矩阵，对所述n个终端设备进行干扰抑制处理。
在一种实施方式中，所述天线的功率调整因子符合以下公式：P ant＝sum(|V n| 2,2)，其中，V n为所述干扰抑制矩阵；
所述更新的干扰抑制矩阵，符合以下公式：
Figure PCTCN2020135793-appb-000027
在一种实施方式中，赋形向量矩阵的维度为K aR×N layer，其中，K aR为所述基站的天线的数量，N layer为所述n个终端设备的总流数。
参阅图6所示，基于相同的技术构思，本申请实施例还提供了一种基站600，可以实现如图3所示的一种干扰抑制的方法。所述基站600包括：收发器601、处理器602以及存储器603。其中，所述收发器601、所述处理器602以及所述存储器603之间相互连接。
可选的，所述收发器601、所述处理器602以及所述存储器603之间通过总线604相互连接。所述总线604可以是外设部件互连标准(peripheral component interconnect，PCI)总线或扩展工业标准结构(extended industry standard architecture，EISA)总线等。所述总线可以分为地址总线、数据总线、控制总线等。
所述收发器601，用于接收和发送数据，实现与其他设备之间的通信交互。
所述处理器602，用于实现如图3所示的一种干扰抑制方法。
下面对通过设备500中的各个设备的功能进行介绍。
收发器601，用于获取配对的n个终端设备的赋形系数向量，其中，每个终端设备的赋形系数向量为所述基站管理的用于与该终端设备进行信号传输 使用的下行发送波束对应的赋形系数向量，n为大于1的整数；
处理器602，用于根据所述n个赋形系数确定赋形向量矩阵，并根据所述赋形向量矩阵，计算所述n个终端设备的信道相关矩阵；
根据所述信道相关矩阵，确定信道相关逆矩阵，其中，所述信道相关逆矩阵为所述信道相关矩阵的逆矩阵；对所述信道相关逆矩阵进行流功率调整处理，得到信道相关逆矩阵调整矩阵；根据所述赋形向量矩阵和所述信道相关逆矩阵调整矩阵，得到干扰抑制矩阵；根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理。
在一种实施方式中，所述处理器602，在根据所述n个赋形系数确定赋形向量矩阵，用于：
将所述n个赋形系数组成所述赋形向量矩阵，其中，所述赋形向量矩阵符合以下公式：C n＝[w 1,w 2,...,w n]，其中，w i为第i个终端设备的赋形系数向量，i为大于1且小于n的整数；或者
将所述n个赋形系数组成组合向量矩阵，对所述组合向量矩阵中的每一列进行2范数归一化处理，得到所述赋形向量矩阵，所述赋形向量矩阵符合以下公式：C n＝[w 1',w 2',...,w n']，其中，w i'为w i进行2范数归一化处理得到所述赋形向量矩阵，w i为第i个终端设备的赋形系数向量，i为大于1且小于n的整数。
在一种实施方式中，所述信道相关矩阵符合以下公式：
Figure PCTCN2020135793-appb-000028
其中，
Figure PCTCN2020135793-appb-000029
为C n的共轭向量矩阵，C n为所述赋形向量矩阵。
在一种实施方式中，所述信道相关逆矩阵符合以下公式：
Figure PCTCN2020135793-appb-000030
R n为所述信道相关矩阵。
在一种实施方式中，所述信道相关逆矩阵调整矩阵符合以下公式：
Figure PCTCN2020135793-appb-000031
R inv为所述信道相关逆矩阵。
在一种实施方式中，所述干扰抑制矩阵符合以下公式：V n＝C n*R inv,norm， 其中，C n为所述赋形向量矩阵，R inv,norm所述信道相关逆矩阵调整矩阵。
在一种实施方式中，所述处理器602，在根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理，用于：
根据所述干扰抑制矩阵，确定所述基站的天线的功率调整因子；
根据所述天线的功率调整因子，对所述干扰抑制矩阵进行天线功率调整处理，得到更新的干扰抑制矩阵；
使用所述更新的干扰抑制矩阵，对所述n个终端设备进行干扰抑制处理。
在一种实施方式中，所述天线的功率调整因子符合以下公式：P ant＝sum(|V n| 2,2)，其中，V n为所述干扰抑制矩阵；
所述更新的干扰抑制矩阵，符合以下公式：
Figure PCTCN2020135793-appb-000032
在一种实施方式中，赋形向量矩阵的维度为K aR×N layer，其中，K aR为所述基站的天线的数量，N layer为所述n个终端设备的总流数。
基于以上实施例，本申请实施例还提供了一种计算机程序，当所述计算机程序在计算机上运行时，使得所述计算机执行图4所示的实施例提供的一种干扰抑制方法。
基于以上实施例，本申请实施例还提供了一种计算机可读存储介质，该计算机可读存储介质中存储有计算机程序，所述计算机程序被计算机执行时，使得计算机执行图4所示的实施例提供的一种干扰抑制方法。
基于以上实施例，本申请实施例还提供了一种芯片，所述芯片用于读取存储器中存储的计算机程序，实现图4所示的实施例提供的一种干扰抑制方法。
基于以上实施例，本申请实施例提供了一种芯片系统，该芯片系统包括处理器，用于支持计算机装置实现图4所示的实施例中基站所涉及的功能。在一种可能的设计中，所述芯片系统还包括存储器，所述存储器用于保存该计算机装置必要的程序和数据。该芯片系统，可以由芯片构成，也可以包含芯片和其他分立器件。
综上所述，本申请实施例的技术方案中，基站获取配对的n个终端设备的赋形系数向量，其中，每个终端设备的赋形系数向量为所述基站管理的用于与该终端设备进行信号传输使用的下行发送波束对应的赋形系数向量，n为大于1的整数；
所述基站根据所述n个赋形系数确定赋形向量矩阵，并根据所述赋形向量矩阵，计算所述n个终端设备的信道相关矩阵；所述基站根据所述信道相关矩阵，确定信道相关逆矩阵，其中，所述信道相关逆矩阵为所述信道相关矩阵的逆矩阵；所述基站对所述信道相关逆矩阵进行流功率调整处理，得到信道相关逆矩阵调整矩阵，再根据所述赋形向量矩阵和所述信道相关逆矩阵调整矩阵，得到干扰抑制矩阵；所述基站根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理。该方法将干扰抑制矩阵的流功率处理过程放在逆矩阵之后，大大减少了干扰抑制处理过程的计算量，从而减少了系统执行干扰抑制的耗时，提高了系统的性能。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器 中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
显然，本领域的技术人员可以对本申请进行各种改动和变型而不脱离本申请的精神和范围。这样，倘若本申请的这些修改和变型属于本申请权利要求及其等同技术的范围之内，则本申请也意图包含这些改动和变型在内。

Claims (21)

  1. 一种干扰抑制方法，其特征在于，包括：
    基站获取配对的n个终端设备的赋形系数向量，其中，每个终端设备的赋形系数向量为所述基站管理的用于与该终端设备进行信号传输使用的下行发送波束对应的赋形系数向量，n为大于1的整数；
    所述基站根据所述n个赋形系数确定赋形向量矩阵，并根据所述赋形向量矩阵，计算所述n个终端设备的信道相关矩阵；
    所述基站根据所述信道相关矩阵，确定信道相关逆矩阵，其中，所述信道相关逆矩阵为所述信道相关矩阵的逆矩阵；
    所述基站对所述信道相关逆矩阵进行流功率调整处理，得到信道相关逆矩阵调整矩阵；
    所述基站根据所述赋形向量矩阵和所述信道相关逆矩阵调整矩阵，得到干扰抑制矩阵；
    所述基站根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理。
  2. 如权利要求1所述的方法，其特征在于，所述基站根据所述n个赋形系数确定赋形向量矩阵，包括：
    所述基站将所述n个赋形系数组成所述赋形向量矩阵，其中，所述赋形向量矩阵符合以下公式：C n＝[w 1,w 2,...,w n]，其中，w i为第i个终端设备的赋形系数向量，i为大于1且小于n的整数；或者
    所述基站将所述n个赋形系数组成组合向量矩阵，对所述组合向量矩阵中的每一列进行2范数归一化处理，得到所述赋形向量矩阵，所述赋形向量矩阵符合以下公式：C n＝[w 1',w 2',...,w n']，其中，w i'为w i进行2范数归一化处理得到所述赋形向量矩阵，w i为第i个终端设备的赋形系数向量，i为大于1且小于n的整数。
  3. 如权利要求1所述的方法，其特征在于，所述信道相关矩阵符合以下公式：
    Figure PCTCN2020135793-appb-100001
    其中，
    Figure PCTCN2020135793-appb-100002
    为C n的共轭矩阵，C n为所述赋形向量矩阵。
  4. 如权利要求1所述的方法，其特征在于，所述信道相关逆矩阵符合以下公式：
    Figure PCTCN2020135793-appb-100003
    其中，R n为所述信道相关矩阵。
  5. 如权利要求1所述的方法，其特征在于，所述信道相关逆矩阵调整矩阵符合以下公式：
    Figure PCTCN2020135793-appb-100004
    其中，R inv为所述信道相关逆矩阵。
  6. 如权利要求1所述的方法，其特征在于，所述干扰抑制矩阵符合以下公式：V n＝C n*R inv,norm，其中，C n为所述赋形向量矩阵，R inv,norm所述信道相关逆矩阵调整矩阵。
  7. 如权利要求1所述的方法，其特征在于，所述基站根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理，包括：
    所述基站根据所述干扰抑制矩阵，确定所述基站的天线的功率调整因子；
    根据所述天线的功率调整因子，对所述干扰抑制矩阵进行天线功率调整处理，得到更新的干扰抑制矩阵；
    所述基站使用所述更新的干扰抑制矩阵，对所述n个终端设备进行干扰抑制处理。
  8. 如权利要求7所述的方法，其特征在于，所述天线的功率调整因子符合以下公式：P ant＝sum(|V n| 2,2)，其中，V n为所述干扰抑制矩阵；
    所述更新的干扰抑制矩阵，符合以下公式：
    Figure PCTCN2020135793-appb-100005
  9. 如权利要求1-8任一项所述的方法，其特征在于，所述赋形向量矩阵的维度为K aR×N layer，其中，K aR为所述基站的天线的数量，N layer为所述n个终端设备的总流数。
  10. 一种基站，其特征在于，包括：
    获取单元，用于获取配对的n个终端设备的赋形系数向量，其中，每个终端设备的赋形系数向量为所述基站管理的用于与该终端设备进行信号传输使用的下行发送波束对应的赋形系数向量，n为大于1的整数；
    处理单元，用于根据所述n个赋形系数确定赋形向量矩阵，并根据所述赋形向量矩阵，计算所述n个终端设备的信道相关矩阵；根据所述信道相关矩阵，确定信道相关逆矩阵，其中，所述信道相关逆矩阵为所述信道相关矩阵的逆矩阵；对所述信道相关逆矩阵进行流功率调整处理，得到信道相关逆矩阵调整矩阵；根据所述赋形向量矩阵和所述信道相关逆矩阵调整矩阵，得到干扰抑制矩阵；根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理。
  11. 如权利要求10所述的基站，其特征在于，所述处理单元，在根据所述n个赋形系数确定赋形向量矩阵时，具体用于：
    将所述n个赋形系数组成所述赋形向量矩阵，其中，所述赋形向量矩阵符合以下公式：C n＝[w 1,w 2,...,w n]，其中，w i为第i个终端设备的赋形系数向量，i为大于1且小于n的整数；或者
    将所述n个赋形系数组成组合向量矩阵，对所述组合向量矩阵中的每一列进行2范数归一化处理，得到所述赋形向量矩阵，所述赋形向量矩阵符合以下公式：C n＝[w 1',w 2',...,w n']，其中，w i'为w i进行2范数归一化处理得到所述赋形向量矩阵，w i为第i个终端设备的赋形系数向量，i为大于1且小于n的整数。
  12. 如权利要求10所述的基站，其特征在于，所述信道相关矩阵符合以下公式：
    Figure PCTCN2020135793-appb-100006
    其中，
    Figure PCTCN2020135793-appb-100007
    为C n的共轭向量矩阵，C n为所述赋形向量矩阵。
  13. 如权利要求10所述的基站，其特征在于，所述信道相关逆矩阵符合以下公式：
    Figure PCTCN2020135793-appb-100008
    R n为所述信道相关矩阵。
  14. 如权利要求10所述的基站，其特征在于，所述信道相关逆矩阵调整矩阵符合以下公式：
    Figure PCTCN2020135793-appb-100009
    R inv为所述信道相关逆矩阵。
  15. 如权利要求10所述的基站，其特征在于，所述干扰抑制矩阵符合以 下公式：V n＝C n*R inv,norm，其中，C n为所述赋形向量矩阵，R inv,norm所述信道相关逆矩阵调整矩阵。
  16. 如权利要求10所述的基站，其特征在于，所述处理单元，在根据所述干扰抑制矩阵对所述n个终端设备进行干扰抑制处理时，具体用于：
    根据所述干扰抑制矩阵，确定所述基站的天线的功率调整因子；
    根据所述天线的功率调整因子，对所述干扰抑制矩阵进行天线功率调整处理，得到更新的干扰抑制矩阵；
    使用所述更新的干扰抑制矩阵，对所述n个终端设备进行干扰抑制处理。
  17. 如权利要求16所述的基站，其特征在于，所述天线的功率调整因子符合以下公式：P ant＝sum(|V n| 2,2)，其中，V n为所述干扰抑制矩阵；
    所述更新的干扰抑制矩阵，符合以下公式：
    Figure PCTCN2020135793-appb-100010
  18. 如权利要求10-17任一项所述的基站，其特征在于，所述赋形向量矩阵的维度为K aR×N layer，其中，K aR为所述基站的天线的数量，N layer为所述n个终端设备的总流数。
  19. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质中存储有计算机程序，当计算机程序在电子设备上运行时，使得所述电子设备执行如权利要求1-9任一项所述的方法。
  20. 一种计算机程序，其特征在于，包括指令，当所述指令在计算机上运行时，使得所述计算机执行如权利要求1-9任一项所述的方法。
  21. 一种芯片，其特征在于，所述芯片用于读取存储器中存储的计算机程序，执行如权利要求1-9任一项所述的方法。
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