CN-121984547-A - Multi-reflection point-based honeycomb-free large-scale MIMO (multiple input multiple output) general sense integrated system precoding method and device

CN121984547ACN 121984547 ACN121984547 ACN 121984547ACN-121984547-A

Abstract

The invention discloses a honeycomb-free large-scale MIMO (multiple input multiple output) sense integrated system precoding method and device based on multiple reflection points, which belong to the technical field of wireless communication and comprise the steps of calculating the speed and the transmitting power of an access point and the perceived signal-to-noise ratio of the access point to a perceived target direction of a communication user based on a pre-constructed honeycomb-free large-scale MIMO sense integrated system model; and maximizing the rate sum of communication users by optimizing a precoding matrix of the access point by taking the transmitting power of the access point and the perceived signal-to-noise ratio of the access point in the perceived target direction as constraints, wherein the honeycomb-free large-scale MIMO (multiple input multiple output) sense integrated system model comprises a downlink communication channel model and a radar perceived target model. The invention can maximize the rate sum of communication users by optimizing the sending precoding matrix of the access point, thereby effectively improving the environment sensing capability and achieving the purpose of simultaneously improving the communication performance and the sensing performance.

Inventors

ZHANG QI
TANG PULE
ZHANG JUN
CAI SHU

Assignees

南京邮电大学

Dates

Publication Date: 20260505
Application Date: 20251231

Claims (10)

1. A honeycomb-free large-scale MIMO (multiple input multiple output) sense integrated system precoding method based on multiple reflection points is characterized by comprising the following steps: Calculating the speed and the transmitting power of the access point and the perceived signal-to-noise ratio of the access point in the perceived target direction of the communication user based on a pre-constructed honeycomb-free large-scale MIMO (multiple input multiple output) sense integrated system model; the method comprises the steps that the transmission power of an access point and the perceived signal-to-noise ratio in the direction from the access point to a perceived target are taken as constraints, and the rate sum of communication users is maximized by optimizing a precoding matrix of the access point; the honeycomb-free large-scale MIMO (multiple input multiple output) sense integrated system model comprises a downlink communication channel model and a radar sense target model.
2. The method for precoding a cellular-free massive MIMO-sense integrated system according to claim 1, wherein the cellular-free massive MIMO-sense integrated system comprises: multiple access points, each equipped with Dual function antenna for transmitting/receiving, serving together Each access point is connected with a CPU through a return link, and the user and the sensing target are respectively communicated and sensed with the access point.
3. The non-cellular massive MIMO-generic integrated system precoding method of claim 1, wherein the expression of the downlink communication channel model is: ; Wherein, the Represent the first Access point number A communication channel model between the individual communication users, Represent the first Access point number Small scale fading between the individual communication users, Represent the first Access point number The angle between the users of the communication, Represent the first Access point number A priori known large scale fading between individual communication users, , , For the total number of access points in the cellular-free massive MIMO-generic integrated system, The method is the total number of communication users in the cellular-free large-scale MIMO communication integrated system.
4. The non-cellular massive MIMO-sense integrated system precoding method of claim 3, wherein the radar-aware target model comprises: Definition of the first embodiment The sense-of-general integrated signal sent by each access point is The expression is as follows: ; Wherein, the Is the first Precoding matrices transmitted by the individual access points, Represent the first Access point to the first The precoding matrix of the individual communication users, Representing the data symbol vectors sent by the access point to the communication user, Indicating all access points to the first A sense of general integrated signal sent by each communication user; First, the The received signals of the individual communication users are: ; Wherein, the Represent the first Signals received by individual communication users from all access points, Represent the first Access point to the first The channel state information vectors of the individual communication subscribers, Represents the conjugate transpose of the object, Represent the first Additive white gaussian noise received by individual communication users, Represent the first Precoding matrices for each access point to other communication users or perceived targets, And the integrated communication signal represents the sense integrated signal sent by all access points to other communication users or perception targets.
5. The method for precoding a cellular-free massive MIMO-generic integrated system according to claim 4, wherein calculating a rate sum of communication users, a transmission power of an access point, and a perceived signal-to-noise ratio of the access point in a perceived target direction based on a pre-constructed cellular-free massive MIMO-generic integrated system model comprises: calculating the first step in the honeycomb-free large-scale MIMO (multiple input multiple output) sense-all integrated system according to the downlink communication channel model and the radar sense target model Signal-to-interference-and-noise ratio of individual communication users : ; Wherein, the , Representing a modulo operation on the plurality; Calculating the rate sum of all communication users according to the signal-to-interference-and-noise ratio of all communication users : ; Wherein, the Represent the first Communication rates of individual communication users; Calculate the first Transmit power of individual access points : ; Channel modeling is carried out on the downlink perception channel model, so as to obtain a perception channel between the access point and the perception target, and the expression is as follows: ; Wherein, the Represent the first A perceived channel matrix of access points and perceived targets, To perceive the total number of valid reflection points on the target, Represent the first The combined perceived channel gain of each access point obeys a mean value of 0 and a variance of 0 Is used for the complex gaussian distribution of (c), Represent the first Access point number The steering vectors between the individual reflection points, Represent the first Access point number A steering vector between the reflection points; Then the first The received signals for the individual access points are expressed as: ; Wherein, the , Represent the first All echo signals received by the access points that are reflected by the reflection points, Represent the first Additive white gaussian noise received by each access point; Installing a radar receiver at each access point for preliminary processing of the reflected echo signals, then Output of radar receiver at access point The method comprises the following steps: ; Wherein, the Represent the first Receiver vectors of radar receivers at the access points; By means of Maximizing the signal-to-noise ratio of the echo signal, optimizing the receiver vector The expression of (2) is: ; if all access points transmit the output signals of the radar receiver to the CPU through the lossless backhaul link, the total output of the radar receiver of all access points The method comprises the following steps: ; optimal receiver vector Substituting the total output of the radar receiver Obtaining the perceived signal-to-noise ratio of the access point in the perceived target direction The formula is as follows: ; Wherein, the The representation is made of the mathematical expectation, Representing a trace-out operation on the parameter, Representation of Is a function of the variance of (a), For the number of antennas at the access point in a cellular-free massive MIMO-enabled integrated system, Represent the first Access point to the first Precoding matrix of individual communication users.
6. The method for precoding a cellular-free massive MIMO-generic integrated system according to claim 1, wherein the maximizing the rate sum of communication users by optimizing a precoding matrix of an access point with a constraint of a transmission power of the access point and a perceived signal-to-noise ratio in a perceived target direction of the access point comprises: the rate sum of the maximized communication user is converted into an optimization problem P1, and the expression is as follows: ; Wherein, the To express the first Access point to the first The precoding matrix of the individual communication users, Representing the sum of the rates of the communicating users, Represent the first The signal-to-interference-and-noise ratio of the individual communication subscribers, For the total number of communication users in the cellular-free massive MIMO communication system, For a perceived signal-to-noise ratio in the direction of the access point to the perceived target, To perceive the lowest threshold value of the signal-to-noise ratio, Is the first The transmit power of the individual access points, Representing the maximum power constraints of the access point, , The method is the total number of access points in the honeycomb-free large-scale MIMO communication integrated system; and solving an optimization problem P1 by using a weighted minimum mean square error algorithm, an alternating iterative algorithm and a semi-positive relaxation algorithm to obtain an optimal solution of the precoding matrix of the access point and the communication user rate sum.
7. The method for precoding the honeycomb-free massive MIMO-generic integrated system according to claim 6, wherein the solving the optimization problem P1 by using a weighted minimum mean square error algorithm, an alternate iterative algorithm and a semi-positive relaxation algorithm to obtain an optimal solution of the sum of the precoding matrix and the communication user rate of the access point comprises: The optimization problem P1 is equivalent to the optimization problem P2, and the expression is as follows: ; Wherein, the Representing a determinant operation on the parameters, Represents the conjugate transpose of the object, Representing all access points to the first The channel state information of the individual communication users, Represent the first Access point to the first The channel state information of the individual communication users, Representing all access points to the first The precoding matrix of the individual communication users, Generating a block diagonal matrix in a representation manner; Maximizing communication user rate and translating to minimizing weighted mean square error by introducing auxiliary variables using equivalent transforms in weighted least mean square error algorithm , The optimization problem P2 is equivalent to the optimization problem P3, and the expression is as follows: ; Wherein: ; ; ; ; Wherein, the Representing a trace-out operation on the parameter, A precoding matrix is sent to indicate all access points to other users or perception targets; the objective function of the optimization problem P3 is respectively for , And Are convex functions, and an optimization problem P3 is solved by adopting an alternate iterative optimization algorithm, and is fixed And The optimal form of the two is used, and the optimal precoding is obtained by solving the optimization problem P4 Will be As an input of an optimization problem P3, solving a precoding matrix by an alternate iterative optimization algorithm; the expression of the optimization problem P4 is as follows: ; the optimization problem P4 is converted into a convex problem by using a semi-positive relaxation method, and an optimization problem P5 is obtained, wherein the expression is as follows: ; Wherein: ; ; ; ; ; ; ; Wherein, the Represent the first Access point to the first The channel state information vectors of the individual communication subscribers, Represent the first Access point to the first The channel state information vectors of the individual communication subscribers, Is that First order identity matrix The number of rows of the device is, Is a matrix of units which is a matrix of units, To create a function of the block diagonal matrix, To perceive the total number of valid reflection points on the target, Represent the first Combined perceived channel gain for individual access points Is a function of the variance of (a), For the number of antennas at the access point in a cellular-free massive MIMO-enabled integrated system, Represent the first Access point number A steering vector between the reflection points; And solving the optimization problem P5 by using a CVX tool package to obtain an optimal solution of the precoding matrix of the access point and the sum of the communication user rates.
8. A honeycomb-free large-scale MIMO general sense integrated system precoding device based on multiple reflection points is characterized by comprising: The data processing module is used for calculating the speed and the transmitting power of the communication user and the perceived signal-to-noise ratio of the access point in the perceived target direction based on a pre-constructed honeycomb-free large-scale MIMO (multiple input multiple output) sense integrated system model; The optimization module is used for maximizing the rate sum of communication users by optimizing a precoding matrix of the access point by taking the transmitting power of the access point and the perceived signal-to-noise ratio of the access point in the perceived target direction as constraints; the honeycomb-free large-scale MIMO (multiple input multiple output) sense integrated system model comprises a downlink communication channel model and a radar sense target model.
9. A computer readable storage medium having stored thereon a computer program/instructions, which when executed by a processor, performs the steps of the multi-reflection point based cellular massive MIMO-generic integrated system precoding method of any of claims 1-7.
10. A computer program device comprising computer program/instructions which, when executed by a processor, implement the steps of the multi-reflection point based cellular massive MIMO-sense all-in-one system precoding method of any one of claims 1-7.

Description

Multi-reflection point-based honeycomb-free large-scale MIMO (multiple input multiple output) general sense integrated system precoding method and device Technical Field The invention relates to a honeycomb-free large-scale MIMO (multiple input multiple output) sense integrated system precoding method and device based on multiple reflection points, and belongs to the technical field of wireless communication. Background With the increasing emerging applications of social demands, the fifth generation mobile communication system (Fifth Generation Mobile Communication System, 5G) has failed to meet new service requirements. Taking the next generation holographic invisible transmission as an example, the required data rate of the Ethernet bit and microsecond delay far exceed the bearing limit of the 5G millimeter wave technology. The key to solving the problem is the sixth generation mobile communication system (Sixth Generation Mobile Communication System, 6G) or the future research application of the communication system beyond 6G. To meet various performance requirements in the 6G era, researchers have proposed a cellular-free large-scale Multiple-Input Multiple-Output (MIMO) system. The system eliminates the limit of the cell boundary and is one of the main application scenes of 6G. The honeycomb-free large-scale MIMO system introduces the idea of taking users as the center, a plurality of distributed access points are deployed, the same time-frequency resources are utilized for serving all users, the distance between the access points and the users is effectively shortened, the space macro diversity gain is obtained, the path loss is greatly reduced, and the favorable propagation brought by a large number of access points is utilized to reduce multi-user interference, so that the whole area is uniformly covered, the user experience is greatly improved, and the method has important supporting effects on the typical application scene and key technical indexes of 6G. The 6G is connected with the intelligent interconnection of the person, the machine and the object, and can realize the transition from mobile interconnection to everything interconnection and even everything intelligent interconnection. 6G expects to support brand new services such as holographic communication, digital twin, augmented reality and the like through interaction of communication, perception and calculation, expands vertical application scenes such as smart city, smart traffic, intelligent manufacturing and the like, and promotes the deep combination of the real physical world and the virtual digital world. In order to support the emerging services, the perception capability has become an endogenous requirement of the 6G system, and the air interface design is promoted to evolve from a pure transmission function to transmission and perception integration. Communication perception integration (hereinafter referred to as "communication perception integration") generally refers to an electronic system design method that integrates design perception and communication systems, thereby more effectively utilizing crowded hardware resources, and even realizing synergy between two functions, and an enabling technology for realizing the method. Compared with the traditional independent communication and sensing system, the communication and sensing integrated system combines the two to realize complementary advantages, not only obtains integrated gain, but also obtains additional cooperative gain, and is widely regarded as one of key technologies of the next generation mobile communication network. The honeycomb-free large-scale MIMO and general sense integrated combination has the advantages that on one hand, the method is beneficial to a large-scale antenna array, extremely high space resolution capability can be provided, great potential is provided in the aspect of improving the perception performance, and on the other hand, the method assists in channel state information acquisition and beam forming design by endowing the communication system with the endophytic perception capability, and improves the communication service quality and efficiency. Therefore, the honeycomb-free large-scale MIMO communication integrated system is expected to realize high-speed communication and high-precision perception at the same time. However, the research results of combining the honeycomb-free large-scale MIMO and the sense-of-general integrated system are relatively few at present, and the problem that the access point in the honeycomb-free large-scale MIMO sense-of-general integrated system has limited acquisition of the sensing information exists in practical application. Disclosure of Invention The invention aims to provide a honeycomb-free large-scale MIMO (multiple input multiple output) sense-through integrated system precoding method and device based on multiple reflection points, which take the transmitting power of an access point and the sen