CN118174756A

CN118174756A - Random signal system-oriented general sense integrated precoding method, equipment and medium

Info

Publication number: CN118174756A
Application number: CN202410597353.8A
Authority: CN
Inventors: 刘凡; 卢仕航; 袁伟杰; 董福王; 李韫鑫
Original assignee: Southwest University of Science and Technology
Current assignee: Southwest University of Science and Technology
Priority date: 2024-05-14
Filing date: 2024-05-14
Publication date: 2024-06-11

Abstract

The embodiment of the application relates to the technical field of wireless communication and discloses a random signal system-oriented all-in-one precoding method, equipment and medium. Compared with the traditional scheme of adding a perception sensor to an existing communication network architecture to realize communication perception dual-function, the method provided by the embodiment of the application considers the influence of random signals on the perception performance and the communication performance when constructing the target precoding matrix, does not need to change the existing communication network architecture, realizes the communication perception dual-function only by means of the existing communication network architecture, and further remarkably reduces the cost of integrating the perception function into an existing network.

Description

Random signal system-oriented general sense integrated precoding method, equipment and medium

Technical Field

The present application relates to the field of wireless communications technologies, and in particular, to a random signal system-oriented integrative precoding method, an electronic device, and a storage medium.

Background

Communication awareness Integration (ISAC) systems are capable of dual functions of communication and awareness. In a conventional multiple-input multiple-output (MIMO) scenario, a communication system needs to transmit signals as randomly as possible to ensure communication performance, and a radar sensing system needs to determine signals as well as possible to ensure sensing performance. The existing ISAC system precoding method generally adds hardware such as a radar sensor and the like in the existing network hardware architecture to realize the perception performance of the system, so that the existing hardware architecture can be greatly changed. The ISAC system wants to perform both communication and sensing functions by integrating signals, which must be random signals. Existing ISAC system precoding methods will assume an ISAC signal as a deterministic signal, e.g., assuming that the length of the transmitted signal transmitted is long enough or approaching infinity such that the sample covariance matrix of the signal is approximately equal to the statistical covariance matrix of the signal. But the effect of the random signal on the perceptual performance is neglected by assuming the ISAC signal as the determined signal.

Disclosure of Invention

The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides the universal integrated precoding method, the electronic equipment and the storage medium for the random signal system, which can realize the communication sensing dual-function of the ISAC system without changing the prior communication network architecture, thereby obviously reducing the cost of integrating the sensing function into the current network.

In order to achieve the above object, a first aspect of an embodiment of the present application provides a method for precoding a sense of unity for a random signal system, including:

Acquiring a plurality of random signals and an initial precoding matrix;

Calculating according to the plurality of signal matrixes and the initial precoding matrix to obtain average perception performance data; wherein each of said signal matrices characterizes one of said random signals;

determining communication performance data according to the initial precoding matrix;

Performing parameter adjustment on the initial precoding matrix according to the average perception performance data and the communication performance data to obtain a target precoding matrix;

And carrying out signal coding according to the target pre-coding matrix and the random signal to obtain a target transmitting signal so as to carry out communication and perception according to the target transmitting signal.

According to the general sense integrated precoding method for the random signal system, which is provided by the embodiment of the application, the average perception performance data is obtained by constructing a plurality of random signals and the initial precoding matrix, the average perception performance data can describe the perception performance of the system, then the communication performance data is determined according to the initial precoding matrix, and the communication performance data can describe the communication performance of the system. And then, according to the average perception performance data and the communication performance data, carrying out parameter adjustment on the initial precoding matrix to obtain a target precoding matrix, wherein the target precoding matrix can balance the perception performance and the communication performance at the same time. And finally, carrying out communication signal coding on the random signal by utilizing the target pre-coding matrix to obtain a target transmitting signal. Compared with the traditional scheme of adding a perception sensor to an existing communication network architecture to realize a communication perception dual function, the method provided by the embodiment of the application considers the influence of random signals on the perception performance and the communication performance when constructing a target precoding matrix, particularly realizes the perception evaluation by the average perception performance data obtained by calculating a plurality of random signals and an initial precoding matrix, realizes the communication evaluation by the communication performance data of the plurality of random signals under the initial precoding matrix, and can be obtained by depending on the existing communication network architecture without adding other devices such as the perception sensor, namely without changing the existing communication network architecture, thereby obviously reducing the cost of integrating the perception function into the existing network.

In some embodiments, the calculating according to the plurality of signal matrices and the initial precoding matrix to obtain average perceptual performance data includes:

acquiring perceived noise power and the number of antennas of a base station receiving antenna;

Calculating according to the perceived noise power, the number of antennas, the signal matrix and the initial precoding matrix to obtain least square error data corresponding to each random signal;

And calculating an average value according to the least square error data corresponding to the random signals to obtain the average perception performance data.

In some embodiments, the calculating according to the perceived noise power, the number of antennas, the signal matrix and the initial precoding matrix to obtain the least square error data corresponding to each random signal includes:

Determining a first conjugate transpose of the signal matrix and a second conjugate transpose of the initial precoding matrix;

Performing matrix multiplication according to the initial precoding matrix, the signal matrix, the first conjugate transpose matrix and the second conjugate transpose matrix to obtain a first intermediate matrix;

Performing trace number operation on the inverse matrix of the first intermediate matrix to obtain the trace number of the inverse matrix;

And multiplying the perceived noise power, the number of antennas and the trace number to obtain the least square error data.

In some embodiments, the performing parameter adjustment on the initial precoding matrix according to the average perceptual performance data and the communication performance data to obtain a target precoding matrix includes:

Extracting perception performance sub-data corresponding to each random signal from the average perception performance data; determining a communication rate constraint condition and a communication transmission power constraint condition according to the communication performance data;

Constructing a first diagonal matrix to be optimized according to the initial precoding matrix;

According to a preset communication channel matrix, a first diagonal matrix to be optimized, a displacement matrix and a left singular matrix of a signal matrix, performing matrix multiplication to obtain a first precoding matrix;

And replacing the initial precoding matrix with the first precoding matrix, and carrying out parameter adjustment on the first precoding matrix according to the perception performance sub-data, the communication rate constraint condition and the communication transmission power constraint condition to obtain a target precoding matrix corresponding to each random signal.

acquiring a reference signal, perceived noise power and the number of antennas of a base station receiving antenna; wherein, the reference matrix representing the reference signal is multiplied by the conjugate transpose matrix of the reference matrix to form an identity matrix;

Calculating according to the reference matrix, the perceived noise power, the number of antennas and the initial precoding matrix to obtain reference perceived performance data;

converting the average perception performance data according to the reference perception performance data to obtain converted perception performance data; determining a communication rate constraint condition and a communication transmission power constraint condition according to the communication performance data;

Constructing a second diagonal matrix to be optimized according to the initial precoding matrix, and multiplying the second diagonal matrix to be optimized and a preset unitary matrix according to a preset communication channel matrix and the second diagonal matrix to be optimized to obtain a second precoding matrix;

And replacing the initial precoding matrix with the second precoding matrix, and performing parameter adjustment on the second precoding matrix according to the converted perceptual performance data, the communication rate constraint condition and the communication transmitting power constraint condition to obtain a target precoding matrix.

In some embodiments, before replacing the initial precoding matrix with the first precoding matrix, performing parameter adjustment on the first precoding matrix according to the perceptual performance sub-data, the communication rate constraint condition, and the communication transmission power constraint condition to obtain a target precoding matrix corresponding to each random signal, the method further includes:

coding according to the first precoding matrix and the signal matrix to obtain an intermediate transmitting signal;

Acquiring an echo signal, wherein the echo signal is a signal reflected by the intermediate transmitting signal through a preset object;

performing estimation calculation according to the echo signals and the intermediate transmitting signals to obtain estimation of a sensing channel matrix;

According to the estimated quantity, carrying out frame length compensation on the random signal to obtain a compensated signal;

replacing the random signal with the compensated signal to update the average perceptual performance data.

In some embodiments, the communication performance data comprises communication rate data; the determining communication performance data according to the initial precoding matrix includes:

Determining a third conjugate transpose of the initial precoding matrix and a fourth conjugate transpose of a preset communication channel matrix;

Calculating according to a preset communication noise power matrix, the preset communication channel matrix, the initial precoding matrix, the third conjugate transpose matrix and the fourth conjugate transpose matrix to obtain a second intermediate matrix; the preset communication noise power matrix characterizes communication noise power of a preset communication channel;

Calculating according to a preset identity matrix and the second intermediate matrix to obtain a third intermediate matrix;

And performing determinant logarithmic calculation on the third intermediate matrix to obtain the communication rate data.

In some embodiments, the communication performance data further comprises communication power data; the determining communication performance data according to the initial precoding matrix further includes:

Calculating the initial precoding matrix by using a Fu Luo Beini Usta norm function to obtain an intermediate value;

And square calculation is carried out on the intermediate value, so that the communication power data are obtained.

To achieve the above objective, a second aspect of an embodiment of the present application provides an electronic device, including a memory and a processor, where the memory stores a computer program, and the processor implements the method for precoding a sense of unity for a random signal system according to the technical scheme of the first aspect when executing the computer program.

To achieve the above object, a third aspect of the embodiments of the present application provides a computer readable storage medium, where a computer program is stored, where the computer program when executed by a processor implements a method for universal precoding for a random signal system according to the first aspect of the present application.

Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Drawings

The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate and do not limit the application.

FIG. 1 is a schematic diagram of an application scenario of a MIMO-ISAC system;

fig. 2 is an optional flowchart of a method for precoding a sense of openness of a random signal system according to an embodiment of the present application;

fig. 3 is a specific flowchart of step S200 in fig. 2;

Fig. 4 is a specific flowchart of step S220 in fig. 3;

fig. 5 is a specific flowchart of step S300 in fig. 2;

FIG. 6 is another specific flowchart of step S300 in FIG. 2;

fig. 7 is a specific flowchart of step S400 in fig. 2;

FIG. 8 is another specific flowchart of step S400 in FIG. 2;

FIG. 9 is a specific flowchart of signal compensation before step S400 in FIG. 2;

FIG. 10 is a graph of perceived performance provided by an embodiment of the present application;

FIG. 11 is another perceived performance graph provided by an embodiment of the present application;

FIG. 12 is a schematic diagram of power distribution provided by an embodiment of the present application;

FIG. 13 is a general flowchart of a sense-of-general precoding method provided by an embodiment of the present application;

fig. 14 is a schematic hardware structure of an electronic device according to an embodiment of the present application.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the application only and is not intended to be limiting of the application.

First, several nouns involved in the present application are parsed:

MIMO (Multiple Input, multiple Output): the multi-input multi-output is an abstract mathematical model for describing multi-path transmission of a multi-antenna wireless communication system, and can utilize a plurality of antennas of a transmitting end to independently transmit signals, and simultaneously, the plurality of antennas are used for receiving and recovering the original information at a receiving end. MIMO technology has attracted much attention in recent years because it can greatly increase the data throughput and transmission distance of a system without increasing the bandwidth or the total transmission power consumption. The core concept of MIMO is to effectively increase the spectrum efficiency of a wireless communication system by using the spatial degrees of freedom provided by multiple transmit antennas and multiple receive antennas, so as to increase the transmission rate and improve the communication quality.

ISAC (INTEGRATED SENSING AND Communications): i.e. communication awareness integration. The system cooperatively implements sensing and communication functions by sharing the same frequency band and hardware. The ISAC system realizes high-precision positioning service based on a communication and perception fusion technology, obtains the position information of equipment based on a reference signal in communication on one hand, and obtains distance, angle and speed information based on the perception of time delay, angle and Doppler information of a reflected wireless signal on the other hand.

Gaussian codebook (Gaussian code book): is an encoding scheme used to describe or represent a signal, where the signal is typically represented as a set of symbols or codewords. These codewords are typically extracted from a gaussian distribution (Gaussian distribution), and in vector quantization, a gaussian codebook is used to divide a continuous signal space into a set of discrete codewords. Each codeword represents a particular signal point and the entire set forms a codebook. This method can be used to compress the signal in order to represent the signal with fewer bits, while still maintaining good reconstruction capabilities of the original signal.

Freude Luo Beini us Norm (Frobenius Norm): is a form of Norm (Norm) of a matrix, referring to the square root of the sum of the squares of the matrix elements, commonly used to measure the size of the matrix. For a matrix A, its Fr Luo Beini Usness norm is shown in equation (1):

（1），

Where a _ij is an element of the ith row and jth column of the matrix a, m is the number of rows of the matrix, and n is the number of columns of the matrix.

Dual variables: often referred to as new variables introduced in the dual problem. For an original optimization problem, an optimal solution to the original problem can be solved by constructing a dual problem. In some optimization algorithms, the dual variables may also be used to represent lagrangian multipliers or generalized lagrangian multipliers for constructing lagrangian functions or generalized lagrangian functions to solve constrained optimization problems or non-convex optimization problems.

Today, the international telecommunications union determines the integrated communication awareness (ISAC) as one of six key scenarios of 6G, which marks the progress of ISAC from theoretical research to practical application.

In one aspect, in conventional MIMO communication systems, random transmit signals are typically employed to carry more useful information, such as gaussian signals. The transmitting end performs precoding processing on the transmitting signal according to the known channel state Information CSI (CHANNEL STATE Information) and the statistical Information (such as statistical covariance matrix) of the transmitting signal, so that the transmission rate and the link reliability of the communication system can be improved. On the other hand, conventional MIMO radar sensing systems generally use a determination signal to implement target detection and parameter estimation, which requires that the sensing signal has a specific structure (such as a chirp signal) to obtain good sensing performance, and the radar sensing system performs precoding processing on the sensing signal by using a sample covariance matrix of the determination signal, so as to improve the detection and estimation performance of the system. It will be appreciated that there is a substantial distinction between the need for signals for communication systems and sensing systems, i.e. communication systems want to transmit signals as "random" as possible to carry more useful information, while sensing systems want to transmit signals as "deterministic" as possible to ensure sensing performance. In ISAC systems, this communication and perceptual performance tradeoff mechanism due to signal characteristics is referred to as a "random-determination" tradeoff. It will be appreciated that in an ISAC system where the communication and sensing functions are implemented by an integrated signal, the signal must be a random signal.

The signal precoding schemes capable of realizing the communication and perception dual functions currently include an integrated signal precoding scheme with perception as a center, an integrated signal precoding scheme with communication as a center and a communication and perception joint precoding scheme based on superposition signals:

The perceptually centered integrated signal precoding scheme employs deterministic radar signals with good perceptual performance for precoding designs such as frequency modulated continuous wave signals, transverse mode signals, etc. Based on this, perceptual performance is maximized, e.g., radar SNR (Signal to Interference plus Noise Ratio, signal-to-noise ratio), detection probability, relative entropy function, etc., while meeting communication constraints. The scheme needs to utilize deterministic radar signals, does not fully consider the influence of signal randomness on the perception performance under an ISAC system, and ignores the loss of the real perception performance.

Communication-centric integrated signal precoding schemes typically perform precoding designs based on random communication signals by assuming that the length of the signal is long enough that the precoding design is simply a sample covariance matrix of the design signal. Based on this, communication performance, e.g., communication SNR, communication rate, etc., is maximized while perceptual constraints are satisfied. This scheme assumes that the transmit signal length is long enough or approaches infinity, however blindly increasing the signal length increases the base station buffer load and computational complexity.

The communication and perception joint precoding scheme based on the superposition signals carries out joint precoding design based on the weighted superposition of radar signals and communication signals, meanwhile, the radar perception signals and the communication signals are assumed to be independent, and joint precoding matrixes meeting communication performance and perception performance are respectively designed. The scheme has high design complexity and larger deployment difficulty, and the communication and perception compete with limited transmitting power, so that the communication and perception performances are restricted.

The current precoding schemes all assume that the signal length of the signal transmitted by the ISAC system is long enough or approaches infinity, so that the sample covariance matrix of the signal is approximately equal to the statistical covariance matrix of the signal, namely, the ISAC system is assumed to transmit the signal deterministically, and the signal precoding scheme realized under the assumption ignores the randomness of the ISAC signal and ignores the influence of the randomness of the signal on the perception performance.

Based on the above, the embodiment of the application provides a random signal system-oriented general sense integrated precoding method, electronic equipment and a storage medium, aiming at realizing the communication sensing dual-function of an ISAC system by precoding a random signal in consideration of the influence of the randomness of the ISAC signal on the sensing performance.

Referring to fig. 1, fig. 1 is a schematic diagram of an application scenario of a classical multi-antenna MIMO-ISAC system. The ISAC base station transmits the encoded signal to serve a certain communication user, while the encoded signal is reflected by the perceived target object to form an echo signal. And the ISAC base station receives the echo signals and estimates a target response matrix according to the echo signals and the communication perception signal model so as to realize the perception performance of the system.

It should be noted that, the ISAC base station is provided with N _t transmitting antennas and N _r base station receiving antennas, where the transmitting antennas are used for transmitting the encoded signals, and the base station receiving antennas are used for receiving the echo signals and uplink data of the user. The single communication user is configured with N _u user receiving antennas, i.e. receiving antennas of the multi-antenna communication user side, for receiving the encoded signals sent by the ISAC base station.

The communication perception signal model is a classical Gaussian linear model, and is specifically shown as a formula (2) and a formula (3):

（2），

（3），

Wherein the matrix Representing a communication reception signal matrix, matrix/>Representing a matrix of perceptually received signals, matrix/>Representing a matrix of point-to-point MIMO communication channels, matrix/>Representing a target response matrix to be estimated, matrix/>And matrix/>Additive white gaussian noise matrix, matrix/>, respectively representing communication and perceptual signalsRepresenting the precoded transmit signal, the structure of matrix X may be represented by equation (4):

（4），

Wherein, Representing precoding matrix,/>Representing a matrix characterizing a random ISAC signal. /(I)Refers to a complex domain whose superscript indicates the dimension of the complex domain, e.g./>Refers to the dimensions of the complex domain are/>Row L and column.

The embodiment of the application provides a random signal system-oriented all-in-one precoding method, electronic equipment and storage medium, which are applied to the above scenes and are concretely described by the following embodiment.

Fig. 2 is an optional flowchart of a method for precoding a sense of a random signal system according to an embodiment of the present application, where the method in fig. 2 may include, but is not limited to, steps S100 to S500.

Step S100, a plurality of random signals and an initial precoding matrix are acquired.

Step S200, calculating according to the plurality of signal matrixes and the initial precoding matrix to obtain average perception performance data. Wherein each signal matrix characterizes a random signal.

Step S300, determining communication performance data according to the initial precoding matrix.

And step S400, carrying out parameter adjustment on the initial precoding matrix according to the average perception performance data and the communication performance data to obtain a target precoding matrix.

And S500, performing signal coding according to the target precoding matrix and the random signal to obtain a target transmitting signal so as to perform communication and perception according to the target transmitting signal.

The steps S100 to S500 shown in the embodiment of the application ensure the communication rate and the communication power and simultaneously minimize the average perception performance under random signals on the basis of the existing communication network architecture, can realize the estimation of a target response matrix by depending on the existing communication system architecture, further realize the communication perception dual-function, does not need to greatly change the existing hardware equipment architecture, can obviously reduce the cost of integrating the perception function into the existing network, and multiplexes wireless resources by a communication channel and a perception channel, thereby improving the performance and the resource utilization rate of the integrated system.

In step S100 of some embodiments, the random signals are random transmission signals selected by the ISAC base station and not yet subjected to coding processing, and the random signals are gaussian signals. It should be noted that the random signal is selected from gaussian codebooks { S ₁,S₂,…,S_N }, where N is the number of codebooks, i.e. the number of symbols to be transmitted in the communication channel. The initial precoding matrix W is an unoptimized precoding matrix.

Referring to fig. 3, in step S200 of some embodiments, step S200 may include, but is not limited to, steps S210 to S230:

step S210, obtaining the perceived noise power and the number of antennas of the base station receiving antenna.

Step S220, calculating according to the perceived noise power, the number of antennas, the signal matrix and the initial precoding matrix to obtain the least square error data corresponding to each random signal.

Step S230, average value calculation is carried out according to least square error data corresponding to a plurality of random signals, and average perception performance data is obtained.

In step S210 of some embodiments, the relevant parameters are obtained during the initialization of the MIMO-ISAC system parameters, including perceived noise powerThe number of antennas N _r and the signal length L of the base station receiving antennas, etc. Perceived noise power/>The degree of interference and noise impact on the quality of the signal in the perceived channel is described, generally assuming additive white gaussian noise.

Referring to fig. 4, in step S220 of some embodiments, step S220 may include, but is not limited to, steps S221 to S224:

In step S221, a first conjugate transpose of the signal matrix and a second conjugate transpose of the initial precoding matrix are determined.

In step S222, matrix multiplication is performed according to the initial precoding matrix, the signal matrix, the first conjugate transpose matrix, and the second conjugate transpose matrix, so as to obtain a first intermediate matrix.

Step S223, performing trace number operation on the inverse matrix of the first intermediate matrix to obtain the trace number of the inverse matrix.

In step S224, the least square error data is obtained by multiplying the perceived noise power, the number of antennas and the trace number.

In step S221 of some embodiments, it is understood that each signal matrix S characterizes a random signal. And performing conjugate transposition operation on the signal matrix S and the initial pre-coding matrix W respectively to obtain a first conjugate transpose matrix S ^H of the signal matrix and a second conjugate transpose matrix W ^H of the initial pre-coding matrix.

In step S222 of some embodiments, matrix multiplication is performed according to the initial precoding matrix W, the signal matrix S, the first conjugate transpose matrix S ^H, and the second conjugate transpose matrix W ^H, so as to obtain a first intermediate matrix. The structure of the first intermediate matrix may be formed by the product of the matrices described above (i.e) And (3) representing.

In step S223 of some embodiments, the first intermediate matrix is inverted to obtain an inverse matrix of the first intermediate matrix, and then the first intermediate matrix is subjected to trace count, i.e. the sum of elements on a main diagonal of the inverse matrix is obtained. The trace number can be determined byAnd (3) representing.

In step S224 of some embodiments, the least squares error data J _LS may be represented by equation (5):

（5），

Wherein, To perceive noise power, N _r is the number of antennas of the base station's receive antennas. It should be noted that J _LS may be regarded as the least squares error data under the given signal implementation, which is the perceptual performance metric index based on the determined signal in the conventional precoding scheme, since the present invention is a random ISAC signal system, the original least squares error data is no longer applicable as the perceptual performance metric, and a new perceptual performance metric index, that is, the average perceptual data mentioned in step S230, needs to be defined based on the least squares error data to adapt to the random ISAC signal system.

Through steps S221 to S224 illustrated in the embodiment of the present application, the least square error data implemented by each random signal is obtained according to the signal matrix S and the precoding matrix WProviding the necessary data basis for the subsequent definition of new perceptual performance metrics.

In step S230 of some embodiments, each realization of the plurality of random signals can result in a plurality of least squares error dataAveraging to obtain traversal least square error data, namely average perception performance data/>Wherein average perceptual performance data/>Can be represented by formula (6):

（6），

Wherein, For sensing noise power, N _r is the number of antennas of the base station receiving antenna, E _s is average calculation, and the least squares error data/>, for a plurality of least squares error data are expressed in formula (6)And (5) averaging. It will be appreciated that since conventional sensing systems implement sensing functions by adding radar or other sensors, these sensing systems do not sense with random signals, and the randomness of the ISAC signals results in the sensing performance metric being a random variable with respect to the random signals defining average sensing performance data/>The method is used for measuring the new perception performance under the random signal system, and can obtain the accurate value of the perception performance loss of the random ISAC signal system compared with the determination signal system, thereby more accurately reflecting the perception performance realized by the system. Average perceptual performance data/>The lower the value of (2) is, the response matrix/>, to the targetThe more accurate the estimate of (c) and thus the perceived performance of the system.

Through the steps S210 to S230 illustrated in the embodiment of the present application, the average perceived performance is precisely defined in consideration of the randomness of the ISAC signal, so that the perceived performance of the system under the random ISAC signal system can be more precisely described.

In step S300 of some embodiments, communication performance data is determined according to the initial precoding matrix, wherein the communication performance data includes communication rate data and communication power data, the communication rate data is a communication rate of the point-to-point MIMO communication system, and the communication power data is a communication power of the point-to-point MIMO communication system.

Referring to fig. 5, regarding the calculation of the communication rate data, step S300 may include, but is not limited to, steps S310 to S340:

step S310, determining a third conjugate transpose of the initial precoding matrix and a fourth conjugate transpose of the preset communication channel matrix.

Step S320, calculating according to the preset communication noise power matrix, the preset communication channel matrix, the initial precoding matrix, the third conjugate transpose matrix, and the fourth conjugate transpose matrix to obtain a second intermediate matrix.

Step S330, calculating according to the preset identity matrix and the second intermediate matrix to obtain a third intermediate matrix.

Step S340, determinant logarithm calculation is carried out on the third intermediate matrix, and communication rate data is obtained.

In step S310 of some embodiments, a third conjugate transpose, i.e., the conjugate transpose of the initial precoding matrixPresetting a communication channel matrixNamely a point-to-point MIMO communication channel matrix, and a fourth conjugate transpose matrix is a preset communication channel matrix/>Conjugated transposed matrix/>。

In step S320 of some embodiments, a communication noise power matrix is presetI.e. a matrix characterizing the communication noise power, which describes the extent to which the quality of the signal in the communication channel is affected by interference and noise, typically assumed to be additive white gaussian noise. Due to the calculation of the subsequent second intermediate matrix, the preset communication noise power needs to be converted into a matrix formally, and the preset communication noise power matrix/>The specific structure (such as the number of rows and columns) of the system may be changed according to the actual application scenario, which is not limited by the embodiment of the present application. Preset communication noise power matrix/>Is a negative power of two, presets the communication channel matrix/>Initial precoding matrix/>Third conjugate transpose matrix/>And a fourth conjugate transpose matrix/>Multiplying to obtain a second intermediate matrix, wherein the second intermediate matrix structure can be represented by/>And (3) representing.

In step S330 of some embodiments, an identity matrix is presetIs a dimension/>Where N _u refers to the number of communication user receive antennas. Adding the preset identity matrix and the second intermediate matrix to obtain a third intermediate matrix, wherein the structure of the third intermediate matrix can be represented by/>And (3) representing.

In step S340 of some embodiments, the calculation of the third intermediate matrix is specifically as shown in formula (7):

（7），

In the MIMO communication system, R (W) represents a communication rate function taking the initial precoding matrix W as a variable, and the present application updates the initial precoding matrix W later, so that a communication rate corresponding to the current implementation scenario of the initial precoding matrix W, that is, communication rate data, can be obtained according to the functional relationship, and a specific implementation process for updating the initial precoding matrix W will be described in the following specific embodiments. The meaning of log det (logarithm of THE DETERMINANT) is a logarithmic determinant, representing the natural logarithmic value of a certain square determinant calculated.

Through steps S310 to S340 illustrated in the embodiment of the present application, the communication rate of the system under the implementation of the current precoding matrix can be accurately calculated based on the precoding matrix.

Referring to fig. 6, regarding the calculation of the communication power data, step S300 may include, but is not limited to, steps S350 to S360:

Step S350, calculating an initial precoding matrix by using a Fu Luo Beini Us norm function to obtain an intermediate value.

In step S360, the intermediate value is squared to obtain communication power data.

In step S350 of some embodiments, the intermediate value is。

In step S360 of some embodiments, the communication power data may be composed ofAnd (3) representing.

Through steps S350 to S360 illustrated in the embodiment of the present application, the communication power of the ISAC system under the implementation of the current precoding matrix can be accurately calculated based on the precoding matrix.

It should be noted that step S400 will be based on the average perceptual performance dataAnd adjusting the initial precoding matrix, and selecting an optimal precoding matrix to ensure that the ISAC system can achieve optimal perception performance and ensure the communication rate and the communication power of the system. The process essentially solves an optimization problem as shown in equation (8):

（8），

Wherein, For preset communication rate threshold,/>For the preset transmit power threshold, s.t. is an abbreviation of "subject to" (subject to the constraint of the term.) and is a label that introduces a constraint. The optimization problem is that the precoding matrix is adjusted, and the average perception performance data/>Minimizing, and further obtaining the optimal precoding matrix/>. Simultaneously meeting two constraint conditions, namely a communication rate constraint condition and a communication transmission power constraint condition, namely, under the current precoding matrix realization, communication rate data/>Greater than or equal to a preset communication rate threshold/>Communication power data is less than or equal to a preset transmit power threshold/>。

In step S400 of some embodiments, the performance data is perceived according to the averageAnd carrying out parameter adjustment on the initial precoding matrix by the communication performance data to obtain a target precoding matrix, wherein the target precoding matrix is the optimal solution of the initial precoding matrix, namely the optimal precoding matrix. For the selection of the optimal precoding matrix, the embodiment of the application respectively provides two schemes of DDP (Data-DEPENDENT PRECODING) Data dependent precoding and DIP (Data-INDEPENDENT PRECODING) Data independent precoding, and the two precoding schemes are respectively described in detail below.

The DDP data dependent precoding scheme utilizes implementations of the ISAC base station known random signals each time to obtain a corresponding optimal precoding matrix for each random signal. Referring to fig. 7, step S400 may include, but is not limited to, steps S410 to S440:

step S410, extracting the perception performance sub-data corresponding to each random signal from the average perception performance data; a communication rate constraint and a communication transmit power constraint are determined based on the communication performance data.

Step S420, constructing a first diagonal matrix to be optimized according to the initial precoding matrix.

Step S430, performing matrix multiplication according to a preset communication channel matrix, a first diagonal matrix to be optimized, a replacement matrix and a left singular matrix of a signal matrix to obtain a first precoding matrix.

Step S440, the initial precoding matrix is replaced by the first precoding matrix, and parameter adjustment is carried out on the first precoding matrix according to the perception performance sub-data, the communication rate constraint condition and the communication transmitting power constraint condition, so as to obtain a target precoding matrix corresponding to each random signal.

In step S410 of some embodiments, it should be noted that a random signal is selected from the gaussian codebook { S ₁,S₂,…,S_N }, and the signal matrix corresponding to the random signal is recorded asN is a subscript, the values are 1 to N, and/>The corresponding conjugate transpose matrix is labeled/>And/>The corresponding initial precoding matrix is denoted/>And/>The corresponding optimal precoding matrix is noted as. In this scenario, the average perceptual performance data/>Converted from equation (6) to a form as shown in equation (9):

（9），

Where N is the number of random signals in the gaussian codebook and tr is the trace number (trace) operation. The above optimization problem shown in formula (8) is referred to as a first optimization problem, and it can be understood that in this precoding scheme, the first optimization problem can be converted from the form shown in formula (8) into N parallel second optimization problems, specifically, the second optimization problem is shown in formula (10):

（10），

Wherein the perception performance sub-data is Each perceptual performance sub-data corresponds to a random signal. /(I)Is a lower bound of achievable communication rate and satisfies the communication rate constraint, i.e., is not less than a preset communication rate threshold/>. Communication transmit power constraint, i.e. communication power data/>Less than or equal to a preset transmit power threshold/>。

In step S420 of some embodiments, according to the initial precoding matrixConstructing a first diagonal matrix to be optimizedSpecifically, the initial precoding matrix/>Only the elements of the main diagonal need to be optimized, and the rest elements are determined, so that a first diagonal matrix/>, to be optimized, is constructedMain diagonal elements of (a) and initial precoding matrix/>The main diagonal elements of (2) are identical, and the remaining elements are 0.

In step S430 of some embodiments, a communication channel matrix is pre-set according to the defaultFirst diagonal matrix to be optimizedPermutation matrix/>Left singular matrix/>, of sum signal matrixAnd multiplying the matrixes to obtain a first precoding matrix. Specifically, in the second optimization problem, the optimal precoding matrix/>The structure of (2) is shown in formula (11):

（11），

Wherein, Is a preset communication channel matrix/>Right singular matrix,/>Is the signal matrix/>Is a left singular matrix of (c) in (c),I.e./>Conjugated permutation matrix of/>Is a permutation matrix with all the anti-diagonal elements being 1 and all the remaining elements being 0,/>Is the first diagonal matrix to be optimized/>Is a solution to the optimization of (3).

It will be appreciated that the first diagonal matrix to be optimized at step S430Not yet the optimal solution, in this case matrix product structure/>The precoding matrix structure is not optimal, and the precoding matrix with the matrix product structure is the first precoding matrix before the optimization is completed.

In step S440 of some embodiments, the initial precoding matrix in the second optimization problem described above is replaced with the first precoding matrixThe second optimization problem can be converted into a third optimization problem by the form shown in formula (10), specifically, the third optimization problem is shown in formula (12):

（12），

wherein the first diagonal matrix to be optimized The sum of squares of diagonal elements of (a) represents the communication power data, and the first diagonal matrix to be optimized/>Square operation can be performed to obtain matrix/>，/>For matrix/>In (i, i) < th > diagonal element,/>For matrix/>The (i, i) th diagonal element in the product result, it should be noted that matrix/>For signal matrix/>Diagonal matrix of matrix/>For matrix/>Transposed matrix of/>For equivalent signal gain, i is from 1 to N _t, and in some embodiments, the number of user receive antennas N _u is less than the number of ISAC base station transmit antennas N _t.

It should be noted that the third optimization problem is a convex problem, which can be directly solved by computer software, a program, an open source library, etc., such as numerical software CVX (Convex Optimization), MOSEK, gurobi, etc., which is not limited by the present application. In addition, the third optimization problem can be solved by the Lagrange dual method, the optimal dual variable is obtained by solving the dual problem, and then the optimal solution of the third optimization problem is obtained by fast iteration of an ellipsoidal method. Specifically, when the value of i is 1 to N _u, in the third optimization problem, the allocation form of the optimal communication power at each diagonal element may be as shown in equation (13):

=/>+/>，i = 1, 2, ...,/>（13），

When i has a value of N _u +1 to N _t, in the third optimization problem, the allocation form of the optimal power in each diagonal element may be as shown in formula (14):

=/>，i =/>+1,/>+2, ...,/>（14），

Wherein, =/>，/>=/>，/>=/>，/>=/>，，（/>，/>) Representing the dual variables, the dual problem may be obtained by solving a third optimization problem by an ellipsoidal method.

Determining the optimal communication power can determine the optimal matrix to be optimizedAnd then determining the optimal precoding matrix/> according to the formula (11)Finally, N optimal precoding matrixes are obtained, and the N optimal precoding matrixes are in one-to-one correspondence with the random signals.

Through the steps S410 to S440 illustrated in the embodiments of the present application, a corresponding optimal precoding matrix can be obtained for each random signal, and under the condition of guaranteeing the communication rate and the communication power of the system, the communication power is adjusted in real time in each implementation of the random signal, so as to further optimize the perceptual performance, and by adopting the data-dependent precoding scheme, the system can obtain good perceptual performance.

The DIP data independent precoding scheme only needs one optimal precoding matrix, and all random signals share the same precoding matrix, so that the complexity of realizing signal coding by the system is greatly reduced. Regarding the DIP data independent precoding scheme, referring to fig. 8, step S400 may include, but is not limited to, steps S810 to S850:

Step S810, acquiring a reference signal, perceived noise power and the number of antennas of a base station receiving antenna.

Step S820, calculating according to the reference matrix, the perceived noise power, the number of antennas and the initial precoding matrix to obtain the reference perceived performance data.

Step S830, converting the average perceptual performance data according to the reference perceptual performance data to obtain converted perceptual performance data, and determining a communication rate constraint condition and a communication transmission power constraint condition according to the communication performance data.

Step S840, constructing a second diagonal matrix to be optimized according to the initial precoding matrix, and multiplying the second diagonal matrix to be optimized by a preset unitary matrix according to the preset communication channel matrix, the second diagonal matrix to be optimized and the preset unitary matrix to obtain a second precoding matrix.

Step S850, the initial precoding matrix is replaced by the second precoding matrix, and parameter adjustment is carried out on the second precoding matrix according to the converted perception performance data, the communication rate constraint condition and the communication transmitting power constraint condition, so as to obtain a target precoding matrix.

In step S810 of some embodiments, a reference matrix characterizing a reference signal is multiplied by a conjugate transpose of the reference matrix to form an identity matrix, where the reference signal may be an orthogonal pilot signal of a conventional channel estimation.

In step S820 of some embodiments, the average perceptual performance data is calculated from the violin (Jensen) inequalityForm transformation is performed as shown in formula (15):

（15），

Due to the initial precoding matrix And reference signal matrix/>Independent of each other, and/>Where L is the signal length and I is the identity matrix, thus averaging the perceptual performance data/>The relationship shown in the formula (16) is:

（16），

Average perceptual performance data using random signal perceptions Will be greater than or equal to the least squares error of sensing using the reference signal, i.e. reference sensing performance data/>Since the reference matrix K belongs to the coincidence/>Thus referencing one of the matrices of perceptual performance data/>The structure of (2) is shown in formula (17):

（17），

in step S830 of some embodiments, performance data is perceived according to a reference For average perceptual performance data/>Converting to obtain converted perceptual performance data, specifically, in a scenario where a DIP data independent precoding scheme is applied, the original first optimization problem can be converted into converted perceptual performance data by a form of formula (8), namely, a fourth optimization problem, where the form of the converted perceptual performance data is shown in formula (18):

（18），

wherein the communication rate constraint is communication rate data Greater than or equal to a preset communication rate threshold/>Communication transmission power constraint conditions, namely that communication power data is smaller than or equal to a preset transmission power threshold/>, are adopted。

In step S840 of some embodiments, according to a preset communication channel matrixSecond diagonal matrix to be optimized/>Preset unitary matrix/>Matrix multiplication is carried out to obtain a second precoding matrix, and in particular, in a fourth optimization problem, the optimal precoding matrix/>The structure of (2) is shown in formula (19):

（19），

Wherein, For presetting the communication channel matrix/>Right singular matrix,/>Diagonal matrix to be optimized for the second, diagonal elements thereof and initial precoding matrix/>Is the same as the main diagonal element of (1), the remaining elements are 0,/>Is the second diagonal matrix/>Is the best solution of/>For presetting unitary matrix and/>May be any unitary matrix,/>For/>Is a complex matrix of the matrix. It can be appreciated that the second diagonal matrix/>, to be optimized at step S840Not yet optimal solution, in which case the matrix product structureThe precoding matrix structure is not optimal, and the precoding matrix with the matrix product structure is the second precoding matrix before the optimization is completed.

In step S850 of some embodiments, the initial precoding matrix is replaced with the second precoding matrixParameter adjustment is carried out on the second precoding matrix according to the converted perception performance data, and a target precoding matrix/> isobtainedSpecifically, the second precoding matrix structure is substituted into the converted perceptual performance data, and the converted perceptual performance data is converted into a fifth optimization problem by the form of formula (19), wherein the fifth optimization problem is as shown in formula (20): /(I)

（20），

Wherein,Is communication power data to be optimized, and the value of i is 1 to N _t.

In fact, the fifth optimization problem is a special case of the third optimization problem, and for solving the fifth optimization problem, the optimal communication power can be obtained by the Lagrangian dual method, so as to obtain the optimal precoding matrix。

It should be noted that, although the system can obtain better perceptual performance by adopting the Data Dependent Precoding (DDP) scheme, the complexity is higher than that of the Data Independent (DIP) precoding scheme because the precoding matrix needs to be adjusted in real time in each transmission of the random signal, and the implementation complexity of the system can be greatly reduced by using an optimal precoding matrix for all the random signals together through the steps S810 to S850 illustrated in the embodiment of the present application.

In step S500 of some embodiments, signal encoding is performed according to the target precoding matrix and the random signal, and the random signal is encoded according to formula (4) to obtain a target transmission signal, and further communication and sensing are performed according to the target transmission signal. Specifically, if a data-dependent precoding method is employed, a signal matrix for each random signalTarget precoding matrix corresponding thereto/>Multiplying and coding to obtain the target transmitting signal. If a data independent precoding method is adopted, the signal matrix/>, of each random signalAre all identical to the same target precoding matrix/>Multiplying and coding to obtain the target transmitting signal.

Referring to fig. 9, before step S440 in some embodiments, the method for precoding the sense of unity for the random signal system further includes, but is not limited to, steps S910 to S950:

Step S910, performing coding according to the first precoding matrix and the signal matrix to obtain an intermediate transmission signal.

In step S920, an echo signal is acquired.

In step S930, an estimation operation is performed according to the echo signal and the intermediate transmission signal to obtain an estimation of the perceived channel matrix.

Step S940, according to the estimated quantity, frame length compensation is carried out on the random signal, and a compensated signal is obtained.

In step S950, the random signal is replaced by the compensated signal to update the average perceptual performance data.

In step S910 of some embodiments, a first precoding matrix and a signal matrix are usedThe coding is performed, i.e. according to formula (4), the first precoding matrix is matrix multiplied with the current signal matrix S to obtain an intermediate transmission signal, so that the intermediate transmission signal has the structure of WS.

In step S920 of some embodiments, the echo signalThe intermediate emission signal is a signal reflected by a preset object, namely a perception target object, wherein the perception target object can be a person or a vehicle, and the application is not limited to the above.

In step S930 of some embodiments, based on the echo signalPerforming estimation calculation with the intermediate transmitting signal to obtain estimation/>, of the sensing channel matrix. In particular, the estimation/>, of the perceptual channel matrixA least squares estimation is used as shown in equation (21):

（21），

Wherein, Is echo signal,/>For the estimation of the perceptual channel matrix,/>For matrix/>Is a pseudo-inverse of (a).

In step S940 of some embodiments, according to the estimated amountAnd performing frame length compensation on the current random signal, namely increasing the coherent frame length of the current random signal, namely the signal length L, to obtain a compensated signal.

Specifically, when the scheme of selecting the optimal precoding matrix is the data-dependent precoding method, each time it is generatedIt is necessary to adjust the primary/>Coding to obtain intermediate transmitting signals, transmitting the intermediate transmitting signals to obtain echo signals, performing estimation calculation, and adjusting the random signals to be transmitted once according to the estimationAnd further can determine the value corresponding to the average perceptual performance J _ELS to achieve the compensation of perceptual performance.

When the scheme of selecting the optimal precoding matrix is the data-independent precoding method, further analysis and inference are needed, please refer to the description of step S820, wherein the average value is usedConforms to the inverse Wishat (Wishare) distribution, wherein the mean/>As shown in equation (22):

=/>（22），

Wherein, Finger pair/>Taking the inverse, L is the signal length, and N _t is the number of base station transmit antennas. From the inverse weisal distribution, the form of the average perceptual performance data J _ELS can be converted into a form as shown in formula (23):

（23），

Wherein L is the signal length, To perceive noise power, N _r is the number of antennas of the base station receive antennas and N _t is the number of base station transmit antennas. It should be noted that the perceptual performance of the system using the deterministic signal is the best, and in the embodiment of the present application, the reference signal is deterministic, and the compensation of the signal is performed to make the perceptual performance of the system approach to the perceptual performance of the system using the reference signal, the perceptual performance using the random signal is represented by the average perceptual performance data J _ELS, and the perceptual performance using the reference signal is represented by the reference perceptual performance data J _K. Based on this, the following formula (24) can be obtained:

（24），

the average perceptual performance data J _ELS will be in accordance with The scaling law approaches the reference perceptual performance data J _K, by which the signal length L ₀ required to reach the desired perceptual performance can be determined, thereby compensating for the perceptual performance loss.

For example, the value of the expected perceived performance is set, which is known as the value of J _ELS in equation (24), J _K and N _t, the required signal length L ₀ is calculated and compared with the actual random signal length L, if L ₀ is less than or equal to L, no compensation is required for the signal, and if L ₀ is greater than L, the current signal length needs to be increased to L ₀, so that the ISAC system can achieve the expected perceived performance.

Whether a data-dependent precoding scheme or a data-independent precoding scheme is adopted, the estimation is required to be obtained after waiting for receiving the echo signals when the MIMO-ISAC system is actually implemented.

In step S950 of some embodiments, the random signal is replaced with the compensated signal to update the average perceptual performance data。

Traversing the steps S910 to S950 until the average perceptual performance data is completedIs described.

It should be noted that, through steps S910 to S950 illustrated in the embodiment of the present application, the method proposes that the perceived performance loss caused by the random signal is compensated by improving the length of the coherent data frame, so as to implement accurate compensation for the perceived performance, and improve the utilization rate of system resources.

Illustratively, the perceptual performance effects of the random signal oriented all-in-one precoding scheme according to the embodiments of the present application will be described with reference to fig. 10 to 12.

A single ISAC base station is provided which configures 8 transmit antennas, 8 base station receive antennas, and a communication user assumes that there are 4 user receive antennas for which channel state information is known.

Referring to fig. 10, fig. 10 illustrates a perceptual performance curve of a gaussian signal in a Data Independent Precoding (DIP) scheme and a Data Dependent (DDP) precoding scheme with respect to a transmission SNR in a scene where only perception is considered, and a reference signal capable of achieving an optimal perceptual performance is taken as a comparison scheme. The abscissa variable in fig. 10 is the transmitted signal-to-noise ratio, the ordinate variable is the normalized estimation error, i.e. the average perceptual performance data J _ELS, the semi-unitary signal, i.e. the reference signal, is deterministic, and the gaussian signal is a random signal.

For example, when the default transmission signal-to-noise ratio is 5dB and the signal length L is 16, there is a 3dB interval between the estimation error corresponding to the random signal using the DIP scheme and the estimation error of the reference signal, and the estimation error corresponding to the DDP scheme is closer to the estimation error of the reference signal than the estimation error using the DIP scheme, which means that the perceptual performance achieved by the DDP scheme is closer to the perceptual performance of the reference signal, i.e., has better perceptual performance.

When the signal length L is 48, referring to fig. 10, the solid line and the dotted line are a set of contrast, where the solid line is still the perceptual performance curve of the reference signal, the dotted line represents the perceptual performance curve of the DDP/DIP pre-encoded signal, and the difference between the two is reduced compared with the perceptual performance case when the signal length L is 16, and similarly, when the signal length L is 256, only one dot-dotted line remains in the figure, which means that the perceptual performance curve of the DDP/DIP pre-encoded signal and the perceptual performance curve of the reference signal overlap, indicating that the signal length L is increased, and the DDP/DIP pre-encoded signal gradually achieves the optimal perceptual performance.

Referring to fig. 11, fig. 11 illustrates a perceived performance curve of a gaussian signal in a Data Independent Precoding (DIP) scheme and a Data Dependent (DDP) precoding scheme with respect to a communication rate in a sense-all ISAC scenario. Taking two sets of data with signal length L of 16 and 32 as a control, it can be found that the gap between the DDP scheme and the DIP scheme is reduced when the signal length is 32, compared with the case where the signal length is 16. The DDP scheme is better than DIP scheme under different data frame lengths, because the former uses information realized by random signals each time, but as the data frame length (i.e. the signal length L) increases, the randomness of the signals also decreases, and the information realized each time has limited help to improve the perceptual performance, so that the perceptual performance difference between the two is reduced.

Referring to fig. 12, fig. 12 is a power distribution comparison diagram of the power distribution strategy and the perceived and communication optimal strategy according to the present application.

Setting the preset communication rate threshold to 14.8 bps/Hz, it was found that in the optimal case of communication, a water-filling power allocation strategy would be used to allocate transmit power to each of the communication sub-channels 1 to 4, and in the perceptually optimal case, an average power allocation strategy would be used to allocate transmit power to each of the sub-channels 1 to 8. The DIP scheme does not take into account the random implementations of the signals and therefore will equally distribute power among the remaining purely perceptual sub-channels 5 to 8 after the communication constraints are met. However, the DDP scheme takes into account the random fluctuation characteristics of the random signal and adaptively allocates power according to the communication channel and the strength of the transmission signal.

Next, referring to fig. 13, fig. 13 is a general flowchart of a precoding method for a random signal in a MIMO-ISAC system according to the present application Jing Jinhang.

Firstly, initializing parameters of a system, and setting initial signal length of a random signalPresetting a communication rate thresholdAnd preset transmit power threshold/>. Each base station transmitting antenna has independent Gaussian codebook/>,...,/>Simultaneously for a preset communication channel matrix/>Singular value decomposition is carried out to obtain a preset communication channel matrix/>Left singular matrix and right singular matrix of (a). And then based on the average perceptual performance data/>And optimizing the initial precoding matrix by communication performance data, wherein a data-dependent precoding or data-independent precoding scheme can be selected according to the operation level and the hardware level of an actual system, before the optimal precoding matrix is acquired, random signals are coded to form coded signals to be transmitted, then the coded signals are reflected by the surface of a perception target to form echo signals, after the base station receives the echo signals, a signal processor performs estimation calculation according to the echo signals, and then the system increases the signal length L according to the estimation, namely increases the coherent data frame length, so that the perception performance of the system is compensated. And the coded signals are transmitted to communication users through communication channels to realize communication functions. And finally, selecting an optimal precoding matrix, and encoding the random signal to obtain a target transmitting signal, so that the perception performance of the system is optimal under the constraint of a preset communication rate and communication power.

In the embodiment of the application, the sensing is realized by using random communication signals, and on the premise of ensuring the communication performance index, the sensing function is effectively realized by selecting the optimal precoding structure, the inhibition of the signal randomness to the sensing performance is reduced, and meanwhile, no additional hardware equipment is required to be introduced, so that the ISAC realization cost is greatly reduced, and the basic engineering requirement of realizing the integrated sense of all-in-one based on the existing network structure can be met.

The embodiment of the application also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of any one of the random signal system-oriented sense-of-general integral precoding methods when executing the computer program.

The computer program is stored in the memory, and the processor executes the at least one computer program to implement the random signal regime oriented all-in-one precoding method described above. The electronic device may be any intelligent terminal including a mobile phone, a tablet computer, a Personal digital assistant (Personal DIGITAL ASSISTANT, PDA), a vehicle-mounted computer, and the like.

An electronic device according to an embodiment of the present application is described in detail below with reference to fig. 14.

Referring to fig. 14, fig. 14 illustrates a hardware structure of an electronic device according to another embodiment, where the electronic device includes:

The processor 1410 may be implemented by a general purpose central processing unit (Central Processing Unit, CPU), a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solutions provided by the embodiments of the present disclosure;

The Memory 1420 may be implemented in the form of a Read Only Memory (ROM), a static storage device, a dynamic storage device, or a random access Memory (Random Access Memory, RAM). The memory 1420 may store an operating system and other application programs, and when the technical solution provided in the embodiments of the present disclosure is implemented by software or firmware, relevant program codes are stored in the memory 1420, and the processor 1410 invokes and executes the sense-of-general-with-one precoding method for a random signal system in the embodiments of the present disclosure;

an input/output interface 1430 for implementing information input and output;

the communication interface 1440 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g. USB, network cable, etc.), or may implement communication in a wireless manner (e.g. mobile network, WIFI, bluetooth, etc.);

bus 1450 to transfer information between components of the device (e.g., processor 1410, memory 1420, input/output interface 1430, and communication interface 1440);

wherein processor 1410, memory 1420, input/output interface 1430, and communication interface 1440 enable communication connections among each other within the device via a bus 1450.

The embodiment of the application also provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, and the computer program realizes the steps of any one of the random signal system-oriented sense-of-general integral precoding methods when being executed by a processor.

The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The embodiments described in the embodiments of the present application are for more clearly describing the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application, and those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

The terms "first," "second," "third," "fourth," and the like in the description of the application and in the above figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Claims

1. The universal integrated precoding method for the random signal system is characterized by comprising the following steps of:

Acquiring a plurality of random signals and an initial precoding matrix;

2. The method for integrative precoding of the sense of general for a random signal system according to claim 1, wherein the calculating according to the plurality of signal matrices and the initial precoding matrix to obtain average sensing performance data comprises:

3. The method for integrative precoding facing to random signal system according to claim 2, wherein the calculating according to the perceived noise power, the number of antennas, the signal matrix and the initial precoding matrix to obtain the least square error data corresponding to each random signal comprises:

4. The method for integrative precoding of the sense of general system for random signal according to claim 1, wherein the performing parameter adjustment on the initial precoding matrix according to the average sensing performance data and the communication performance data to obtain a target precoding matrix comprises:

extracting perception performance sub-data corresponding to each random signal from the average perception performance data; determining a communication rate constraint condition and a communication transmitting power constraint condition according to the communication performance data;

Performing matrix multiplication according to a preset communication channel matrix, the first diagonal matrix to be optimized, a displacement matrix and a left singular matrix of the signal matrix to obtain a first precoding matrix;

And replacing the initial precoding matrix with the first precoding matrix, and performing parameter adjustment on the first precoding matrix according to the perception performance sub-data, the communication rate constraint condition and the communication transmitting power constraint condition to obtain the target precoding matrix corresponding to each random signal.

5. The method for integrative precoding of the sense of general system for random signal according to claim 1, wherein the performing parameter adjustment on the initial precoding matrix according to the average sensing performance data and the communication performance data to obtain a target precoding matrix comprises:

Acquiring a reference signal, perceived noise power and the number of antennas of a base station receiving antenna; wherein a reference matrix characterizing the reference signal is multiplied by a conjugate transpose of the reference matrix to form an identity matrix;

Calculating according to the reference matrix, the perceived noise power, the antenna number and the initial precoding matrix to obtain reference perceived performance data;

Converting the average perception performance data according to the reference perception performance data to obtain converted perception performance data; determining a communication rate constraint condition and a communication transmitting power constraint condition according to the communication performance data;

constructing a second diagonal matrix to be optimized according to the initial precoding matrix, and multiplying the matrix according to a preset communication channel matrix, the second diagonal matrix to be optimized and a preset unitary matrix to obtain a second precoding matrix;

And replacing the initial precoding matrix with the second precoding matrix, and performing parameter adjustment on the second precoding matrix according to the converted perceptual performance data, the communication rate constraint condition and the communication transmitting power constraint condition to obtain the target precoding matrix.

6. The method for integrative precoding of the sense of looking at a random signal system according to claim 4, wherein before said replacing the initial precoding matrix with the first precoding matrix, performing parameter adjustment on the first precoding matrix according to the perceptual performance sub-data, the communication rate constraint condition, and the communication transmission power constraint condition to obtain the target precoding matrix corresponding to each random signal, the method further comprises:

7. The method for integrative precoding of sense of a random signal system according to claim 1, wherein the communication performance data includes communication rate data; the determining communication performance data according to the initial precoding matrix includes:

8. The method for precoding the sense of unity for a random signal system according to claim 1, wherein the communication performance data further includes communication power data; the determining communication performance data according to the initial precoding matrix further includes:

9. An electronic device, characterized in that the electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the sense of general integral precoding method facing to the random signal system according to any one of claims 1 to 8 when executing the computer program.

10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the random signal regime oriented all-in-one precoding method of any one of claims 1 to 8.