Large-scale MIMO (Multiple-Input Multiple-Output) general-inductance integrated joint precoding and power distribution method and system

Abstract

The invention discloses a large-scale MIMO (Multiple-Input Multiple-Output) communication-sensing integrated joint precoding and power distribution method and system, relates to the field of wireless communication, and aims to solve the problem that the existing method cannot give consideration to both improvement of communication rate and high-precision estimation of a target. The method is technically characterized by comprising the following steps of: 1, establishing a large-scale MIMO (Multiple Input Multiple Output) general sensing integrated base station downlink joint beam forming pre-coding system model; step 2, establishing minimization interference of joint power distribution and a Cramer-Rao bound ISAC downlink precoding optimization model; 3, initializing quantum positions of particles, and constructing a fitness function according to the established optimization model; 4, selecting an optimal quantum position; 5, judging the decay type of the particles, and generating new particles; 6, mapping the updated quantum positions of the particles into positions, calculating fitness function values of all the updated particles, and updating the global optimal quantum position; and step 7, performing iterative optimization to obtain an optimal ISAC joint precoding and power distribution scheme.

Description

A method and system for integrated precoding and power allocation of large-scale MIMO sensing Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically, to a method and system for large-scale MIMO integrated sensing and precoding and power allocation. Background Technology

[0002] In recent years, with the increasing number of global communication devices, there is a need to explore spectrum resources to meet the growing demands of communication services. Integrated Sensing and Communication (ISAC) technology aims to achieve spectrum resource sharing between radar and communication, and by designing an integrated technical solution to simultaneously realize communication and radar sensing functions, it has become one of the key technologies for next-generation wireless communication. Massive Multiple Input Multiple Output (MIMO) deploys a large number of antennas at the base station, achieving interference suppression through spatial multiplexing, and improving the spectral efficiency and data rate of wireless communication systems. Therefore, massive MIMO integrated sensing and communication systems can provide high degrees of freedom, thus offering advantages in communication efficiency, target localization, multi-user and multi-target communication, and parameter estimation.

[0003] Integrating communication and sensing functions in massive MIMO inevitably introduces complex interference. Therefore, it is necessary to ensure that the radar and communication systems do not interfere with each other while operating simultaneously on the same frequency. This interference can be eliminated through precoding design at the transmitter, achieving joint optimization of communication and sensing performance. Direction of Arrival (DOA) estimation is the final step in localization. To achieve accurate localization, massive MIMO integrated sensing networks must provide localization accuracy similar to MIMO radar. The Cramér-Rao lower bound (CRLB) is often used to estimate the upper limit of sensing performance in target localization studies, and the estimation error typically needs to be within 1 degree.

[0004] Existing integrated sensing downlink precoding designs can be divided into null-space projection methods and precoding designs based on convex optimization methods. Null-space projection refers to precoding the sensing target in the orthogonal subspace of the communication user's channel. However, there is a possibility that the sensing target may be misaligned with the null space, leading to the target being unidentifiable. Convex optimization methods can avoid this. However, precoding based on convex optimization methods has significant engineering implementation difficulties, and transforming a non-convex optimization problem into a convex one cannot guarantee the global optimality of the original problem, thus limiting its application in complex scenarios. Null-space projection-based sensing target precoding methods have closed-form solutions, which can reduce hardware complexity. Therefore, considering large-scale MIMO integrated sensing precoding designs with closed-form solutions has significant research value and significance.

[0005] A review of existing technical literature revealed that Ozan Alp Topal et al., in their paper "Multi-Target Integrated Sensing and Communications in Massive MIMO Systems" published in IEEE Wireless Communications Letters (2025, 14(2): 345-349), proposed a precoding method for ISAC multi-target scenarios that minimizes DOA estimation error while satisfying communication rate constraints, but only considered joint sensing precoding and power allocation. Minghe Zhu et al., in their paper "Information and Sensing Beamforming Optimization for Multi-User Multi-TargetMIMO ISAC Systems" published in the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2023, pp: 1-5), proposed a method for minimizing the weighted Cramer-Rao bound for scenarios where communication and radar base stations are deployed separately, but this method only optimizes precoding for sensing signals and cannot simultaneously improve communication rate.

[0006] It is evident that existing integrated sensing precoding methods typically focus on precoding the sensed target to achieve high-precision positioning while meeting communication requirements. However, while minimizing the Cramer-Rao bound as the integrated sensing optimization objective achieves target localization, it sacrifices communication rate performance, making it difficult to meet the high transmission rate requirements of next-generation wireless communication networks. Summary of the Invention

[0007] The technical problem to be solved by this invention is:

[0008] Existing methods cannot simultaneously address the issues of improving communication rates and achieving high-precision target estimation.

[0009] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0010] To address the shortcomings and deficiencies of existing large-scale MIMO integrated sensing precoding methods, this invention designs a joint precoding and power allocation method with closed-form solutions for multi-objective, multi-user coexistence scenarios. This method accurately estimates the target while suppressing interference, and employs a quantum energy valley mechanism to achieve high-precision solutions to this hybrid optimization problem, thus overcoming the application limitations of existing large-scale MIMO integrated sensing precoding methods.

[0011] This invention provides a method for large-scale MIMO integrated sensing precoding and power allocation, comprising the following steps:

[0012] Step 1: Establish a model of a large-scale MIMO integrated sensing base station downlink joint beamforming precoding system;

[0013] Step 2: Establish a joint power allocation model that minimizes interference and optimizes the downlink precoding of the ISAC with Cramer-Rao boundary;

[0014] Step 3: Initialize the quantum position of each particle and construct the fitness function based on the established optimization model;

[0015] Step 4: Calculate the fitness value to obtain the enriched energy level of the particle, select the quantum position of the particle with the smallest enriched energy level as the optimal quantum position, and calculate the relevant parameters.

[0016] Step 5: Determine the decay type of the particle based on its neutron enrichment level and stability level, and generate new particles.

[0017] Step 6: Map the updated quantum position of the particle to a position, calculate the fitness function value of all particles after the update, and update the global optimal quantum position;

[0018] Step 7: Determine if the maximum number of iterations has been reached. If not, set the iteration count to zero. Return to step four; if the target is reached, terminate the loop iteration, output the global optimal quantum position, and obtain the optimal ISAC joint precoding and power allocation scheme through mapping transformation.

[0019] Furthermore, step one includes the following process:

[0020] Large-scale MIMO integrated sensing base station includes Root transmitting antenna and The number of receiver antennas forms an array with an element spacing of [missing information]. A uniform linear array, where user communication and far-field target estimation are performed at the same base station, assuming there are [missing information - likely referring to a location or area in space]. Single-antenna communication users and Multiple far-field sensing targets exist simultaneously. , The sensing and communication signals transmitted by the base station are within the same time-frequency resource block, and the channel state information is perfectly known; in the first... The base station's transmitted signal during the next sampling is represented as follows: , , This represents the maximum number of sampling points, where, , Indicates the first Precoded vectors for each user This indicates taking the transpose. , Indicates the first Beamforming vector of a sensing target , Indicates the first During the second sampling, the base station transmitted to the... Communication signals of individual users Indicates the first During the second sampling, the base station transmitted to the... The sensing signals of each target are obtained from the signals transmitted by the base station, which yields the base station downlink joint beamforming precoding matrix. , Indicates taking the conjugate transpose; the first The communication signals received by each user are , For base station to user The channel vector, The mean and variance are 0. Gaussian white noise, other communication users and sensing targets in the scene will affect the base station and users. Interference in communication , ,user The signal-to-noise ratio of the received signal is expressed as The integrated sensing base station uses echo signals to perform DOA estimation on the sensed target. The DOA corresponding to each far-field sensing target is: The sensing signal received by the base station is represented as ,in, Indicates the first The amplitude of the perceived target. Indicates the first The guidance vector of a perceived target. Represents the imaginary unit. Indicates the spacing between array elements. For the signal wavelength, This indicates that the expression follows a pattern with a mean of 0 and a variance of 0. A normally distributed Gaussian white noise vector for A 3D array manifold matrix; the vector of unknown deterministic parameters of the perceived target is... , , , Indicates taking the real part, The Fisher information matrix, representing the imaginary part and unknown parameters, is expressed as follows: , , , , , , , Indicates taking the conjugate. , , This represents the sum of the diagonal elements. Indicates the noise variance. The integrated sensing base station can sense the angle of the target. The estimated Craméro boundary is composed of Calculations show that This indicates taking the inverse.

[0021] Furthermore, step two includes the following process:

[0022] For communication users in the downlink Linear precoding method is used for integrated base station and user Interference between the two Suppression is performed by selecting Maximum Ratio Transmission (MRT) precoding and Regular Zero Forcing (RZF) precoding when targeting users. When using MRT precoding, the normalized precoding vector is represented as follows: , Represents the vector magnitude, when for the user When using RZF precoding, the normalized precoding vector is represented as follows: ,in, for 3D channel state information matrix, Represents the regularization factor. for An identity matrix of dimension; for the perceived target The integrated radar uses maximum ratio transmission (MRT) precoding and null-space zero-forcing (Null-ZF) precoding to detect targets. Beamforming is performed when the target is sensed. When using MRT precoding, the normalized precoding vector is represented as follows: When the integrated radar uses Null-ZF precoding to detect targets, the normalized precoding vector is represented as follows: , for An identity matrix of dimension; the downlink joint beamforming precoding matrix of an integrated base station is represented by a normalized precoding vector as follows: ,in, and These represent the base station's communication with the user. and perceived target Allocated transmission power, and These represent the base station's communication with the user. and perceived target The normalized precoding vector, , , Let the downlink power allocation vector of the integrated base station be denoted as . Then, the lower bound of the DOA estimation performance of the large-scale MIMO sensing system is expressed as . , Represents the array manifold matrix. Establish a communication-aware downlink precoding optimization model for joint power allocation. Constraint 1 is Constraint 2 is ,in This represents the maximum transmit power of the base station, and constraint 3 is... .

[0023] Furthermore, step three includes the following process:

[0024] First, set the number of particles to... The maximum number of iterations is ; in the In the nth iteration, the 1st The quantum position of each particle is The position obtained after position mapping is ,when At that time, the quantum positions of all particles in the first generation... Dimension initialized to ,in, for A uniformly random number between, when At that time, the particle's first The mapping relationship of dimensional positions is as follows: , express Random numbers that follow a uniform distribution between , when At that time, the particle's first Dimensional position mapping relationship is ;

[0025] when At that time, the first The first particle Dimensional location represents communication user The precoding choice is: ;when At that time, the first The first particle 3D position representation of the perceived target The precoding choice is: ,when At that time, the first The first particle The position represents the downlink power allocation of the integrated base station, the third position. The power allocation vector of each particle is represented as: , obtained the The second iteration Downlink Joint Beamforming Precoding Matrix of an Integrated Base Station for Individual Particles , No. The fitness function of each particle is ,in and This represents the weighting factor, which also satisfies the constraints.

[0026] Furthermore, step four includes the following process:

[0027] Calculate the first The fitness values ​​of all particles in the population in the next iteration are used as the fitness values ​​of the next iteration. Neutron enrichment levels of individual particles The particle with the smallest fitness value is selected as the first. The optimal particle in the next iteration, and the optimal enrichment energy level are denoted as... At this point, the quantum position of the optimal particle is The corresponding optimal particle position is mapped as The worst enrichment energy level corresponding to the maximum fitness value is denoted as , No. The central particle of the next iteration is represented as The central particle's first Weiyou Calculations show that ;Calculate the first based on the enrichment energy level The enrichment boundary of the next iteration , No. The second iteration The stability of a particle is expressed as ,in, for Calculate the uniform random number between the two values. The Euclidean distance of a particle from other particles The one with the smallest distance The average of the particles is obtained by taking the particle size. The nearest neighbor particle, for Integers between the nearest neighbors are... .

[0028] Furthermore, step five includes the following process:

[0029] Compare the neutron enrichment level with the boundary value, if If the particle undergoes decay, then... , indicating particle occurrence and decay, in which Represents particles stability, express Random numbers that follow a uniform distribution between each other, the th The iteration factor for the next iteration is Take a set without repetition middle The elements constitute a vector. ,in At this time, the particles occur The decay produces two new particles, one of which is the first. The quantum position is updated to ,in, , The first particle represents the optimal particle. Dimension, a set of non-repeating values. middle The elements constitute a vector. ,in Another The quantum position is updated to ,in, , Represents particles The neighboring particle's first Wei; if , indicating that it has occurred decay, particle release The ray forms two particles, and after decomposition, the first particle... The quantum rotation angles are updated to: , ,in, and They are respectively The particles are updated using a simulated quantum rotation gate, where random numbers following a uniform distribution are used. occur The quantum positions at the time of decay are respectively , ;

[0030] if , representing particles When the enrichment level is less than the boundary value, the optimal particle either receives or emits electrons. The quantum rotation angle is updated to Updating quantum position using simulated quantum rotation gate .

[0031] Furthermore, step six includes the following process:

[0032] For the g+1 generation particles The update, if The quantum positions of the two new particles produced by the decay process of the particle will then be determined. and Mapped to position and Calculate the fitness value and compare it. If Then continue with the particle Comparison of enrichment energy levels, if Then the particle The updated quantum position is The updated enrichment level is Otherwise, no update; if ,and Then the particle The updated quantum position is The updated enrichment level is Otherwise, it will not be updated;

[0033] if Set the fitness value of the new particle to match that of the particle. The comparison of enriched energy levels will determine the quantum position of the new particle. Mapped to position And calculate the fitness value, if Then the particle The updated quantum position is The updated enrichment level is Otherwise, it will not be updated;

[0034] Update the quantum positions and enriched energy levels of all particles in the population to obtain the globally optimal quantum positions up to generation g+1. .

[0035] This invention provides a large-scale MIMO integrated sensing and co-coding and power allocation system. The system has a program module corresponding to the steps of any of the above-described technical solutions, and executes the steps in the above-described large-scale MIMO integrated sensing and co-coding and power allocation method during runtime.

[0036] This invention provides a computer-readable storage medium storing a computer program configured to, when invoked by a processor, implement the steps of the large-scale MIMO inductive joint precoding and power allocation method described in any of the above technical solutions.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] (1) To address the problem that existing large-scale MIMO integrated sensing precoding methods cannot simultaneously improve communication rate and target high-precision estimation, a more robust large-scale MIMO integrated sensing joint power allocation precoding method based on quantum energy valley mechanism is designed. When multiple targets and multiple users coexist, a joint null space precoding scheme with closed-form solution and a direct coding scheme are designed, and the hybrid optimization problem is solved by using the quantum energy valley algorithm.

[0039] (2) The large-scale MIMO integrated sensing precoding method designed in this invention designs a precoding optimization model with joint power allocation, minimizes interference and Cramer-Rao bound, reduces communication interference and parameter estimation lower bound by selecting RZF and MRT precoding for users and Null-ZF and MRT precoding for sensing targets, and the designed quantum energy valley mechanism can solve the optimization target with high precision, realize the joint optimization of communication rate and target estimation accuracy, and provide ideas and methods for the design and engineering implementation of integrated sensing precoding system. Attached Figure Description

[0040] Figure 1 is a flowchart of the large-scale MIMO integrated inductive joint precoding and power allocation method in an embodiment of the present invention;

[0041] Figure 2 is a graph showing the relationship between the root mean square error of direction of arrival estimation and the number of sensed targets in an embodiment of the present invention.

[0042] Figure 3 is a graph showing the relationship between the average transmission rate and the number of sensing targets in an embodiment of the present invention. Detailed Implementation

[0043] To enable those skilled in the art to better understand the present invention, exemplary embodiments or examples of the present invention will be described below in conjunction with the accompanying drawings. Obviously, the described embodiments or examples are merely some, not all, of the embodiments or examples of the present invention. All other embodiments or examples obtained by those skilled in the art based on the embodiments or examples of the present invention without inventive effort should fall within the scope of protection of the present invention.

[0044] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0045] Referring to Figure 1, the present invention provides a method for large-scale MIMO integrated sensing precoding and power allocation, comprising the following steps:

[0046] Step 1: Establish a model of a large-scale MIMO integrated sensing base station downlink joint beamforming precoding system.

[0047] Large-scale MIMO integrated sensing base station includes Root transmitting antenna and The number of receiver antennas forms an array with an element spacing of [missing information]. A uniform linear array, where user communication and far-field target estimation are performed at the same base station, assuming there are [missing information - likely referring to a location or area in space]. Single-antenna communication users and Several far-field sensing targets exist simultaneously. , The sensing and communication signals transmitted by the base station are within the same time-frequency resource block, and the channel state information is perfectly known; in the first... The base station's transmitted signal during the next sampling is represented as follows: , , This represents the maximum number of sampling points, where, , Indicates the first Precoded vectors for each user This indicates taking the transpose. , Indicates the first Beamforming vector of a sensing target , Indicates the first During the second sampling, the base station transmitted to the... Communication signals of individual users Indicates the first During the second sampling, the base station transmitted to the... The sensing signals of each target are obtained from the signals transmitted by the base station, which yields the base station downlink joint beamforming precoding matrix. , Indicates taking the conjugate transpose; the first The communication signals received by each user are , For base station to user The channel vector, The mean and variance are 0. Gaussian white noise, other communication users and sensing targets in the scene will affect the base station and users. Interference in communication , ,user The signal-to-noise ratio of the received signal is expressed as The integrated sensing base station uses echo signals to perform DOA estimation on the sensed target. The DOA corresponding to each far-field sensing target is: The sensing signal received by the base station is represented as ,in, Indicates the first The amplitude of the perceived target. Indicates the first The guidance vector of a perceived target. Represents the imaginary unit. Indicates the spacing between array elements. For the signal wavelength, This indicates that the expression follows a pattern with a mean of 0 and a variance of 0. A normally distributed Gaussian white noise vector for A 3D array manifold matrix; the vector of unknown deterministic parameters of the perceived target is... , , , Indicates taking the real part, The Fisher information matrix, representing the imaginary part and unknown parameters, is expressed as follows: , , , , , , , Indicates taking the conjugate. , , This represents the sum of the diagonal elements. Indicates the noise variance. The integrated sensing base station can sense the angle of the target. The estimated Craméro boundary is composed of Calculations show that This indicates taking the inverse.

[0048] Step 2: Establish a joint power allocation model that minimizes interference and optimizes the downlink precoding of the ISAC (Interference-Induced Colour) boundary (ISAC). The specific steps are as follows:

[0049] For communication users in the downlink Linear precoding method is used for integrated base station and user Interference between the two Suppression is performed by selecting Maximum Ratio Transmission (MRT) precoding and Regular Zero Forcing (RZF) precoding when targeting users. When using MRT precoding, the normalized precoding vector is represented as follows: , Represents the vector magnitude, when for the user When using RZF precoding, the normalized precoding vector is represented as follows: ,in, for 3D channel state information matrix, Represents the channel state information matrix Take the conjugate transpose. Represents the regularization factor. for An identity matrix of dimension; for the perceived target Maximum Ratio Transmission (MRT) precoding and Null-ZF precoding are used to target sensing in an integrated radar system. Beamforming is performed when the target is sensed. When using MRT precoding, the normalized precoding vector is represented as follows: When the integrated radar uses Null-ZF precoding to detect targets, the normalized precoding vector is represented as follows: , for An identity matrix of dimension; the downlink joint beamforming precoding matrix of an integrated base station is represented by a normalized precoding vector as follows: ,in, and These represent the base station's communication with the user. and perceived target Allocated transmission power, and These represent the base station's communication with the user. and perceived target The normalized precoding vector, , , Let the downlink power allocation vector of the integrated base station be denoted as . Then, the lower bound of the DOA estimation performance of the large-scale MIMO sensing system is expressed as . , Represents the array manifold matrix. Establish a communication-aware downlink precoding optimization model for joint power allocation. The constraint at this time is Constraint 2 is ,in This represents the maximum transmit power of the base station, and constraint 3 is... .

[0050] Step 3: Initialize the quantum position of each particle and construct the fitness function based on the established optimization model. The specific steps are as follows:

[0051] First, set the number of particles to... The maximum number of iterations is ; in the In the nth iteration, the 1st The quantum position of each particle is The position obtained after position mapping is ,when At that time, the quantum positions of all particles in the first generation... Dimension initialized to ,in, for A uniformly random number between, when At that time, the particle's first The mapping relationship of dimensional positions is as follows: , express Random numbers that follow a uniform distribution between , when At that time, the particle's first Dimensional position mapping relationship is ;

[0052] when At that time, the first The first particle Dimensional location represents communication user The precoding choice is: ;when At that time, the first The first particle 3D position representation of the perceived target The precoding choice is: ,when At that time, the first The first particle The position represents the downlink power allocation of the integrated base station, the third position. The power allocation vector of each particle is represented as: , obtained the The second iteration Downlink Joint Beamforming Precoding Matrix of an Integrated Base Station for Individual Particles , No. The fitness function of each particle is ,in and This represents the weighting factor, which also satisfies the constraints.

[0053] Step 4: Calculate the fitness value to obtain the enriched energy level of the particle, select the quantum position of the particle with the smallest enriched energy level as the optimal quantum position, and calculate the relevant parameters. The specific steps are as follows:

[0054] Calculate the first The fitness values ​​of all particles in the population in the next iteration are used as the fitness values ​​of the next iteration. Neutron enrichment levels of individual particles The particle with the smallest fitness value is selected as the first. The optimal particle in the next iteration, and the optimal enrichment energy level are denoted as... At this point, the quantum position of the optimal particle is The corresponding optimal particle position is mapped as The worst enrichment energy level corresponding to the maximum fitness value is denoted as , No. The central particle of the next iteration is represented as The central particle's first Weiyou Calculations show that Calculate the first energy level based on the enrichment energy level. The enrichment boundary of the next iteration , No. The second iteration The stability of a particle is expressed as ,in, for Calculate the uniform random number between the two values. The Euclidean distance of a particle from other particles The one with the smallest distance The average of the particles is obtained by taking the particle size. The nearest neighbor particle, for Integers between the nearest neighbors are... .

[0055] Step 5: Determine the decay type of the particle based on its neutron enrichment level and stability level, and generate new particles. The specific steps are as follows:

[0056] Compare the neutron enrichment level with the boundary value, if If the particle undergoes decay, then... , indicating particle occurrence and decay, in which Represents particles stability, express Random numbers that follow a uniform distribution between each other, the th The iteration factor for the next iteration is Take a set without repetition middle The elements constitute a vector. ,in At this time, the particles occur The decay produces two new particles, one of which is the first. The quantum position is updated to ,in, , The first particle represents the optimal particle. Dimension, a set of non-repeating values. middle The elements constitute a vector. , in Another The quantum position is updated to ,in, , Represents particles The neighboring particle's first Wei; if , indicating that it has occurred decay, particle release The ray forms two particles, and after decomposition, the first particle... The quantum rotation angles are updated to: , ,in, and They are respectively The particles are updated using a simulated quantum rotation gate, where random numbers following a uniform distribution are used. occur The quantum positions at the time of decay are respectively , .

[0057] if This indicates that the particle When the enrichment level is less than the boundary value, the optimal particle either receives or emits electrons. The quantum rotation angle is updated to Updating quantum position using simulated quantum rotation gate .

[0058] Step 6: Map the updated quantum position of the particle to a position, calculate the fitness function value of all particles after the update, and update the global optimal quantum position.

[0059] For the g+1 generation particles The update, if The quantum positions of the two new particles produced by the decay process of the particle will then be determined. and Mapped to position and Calculate the fitness value and compare it. If Then continue with the particle Comparison of enrichment energy levels, if Then the particle The updated quantum position is The updated enrichment level is Otherwise, no update; if ,and Then the particle The updated quantum position is The updated enrichment level is Otherwise, it will not be updated.

[0060] if At this point, simply set the fitness value of the new particle to the same value as the original particle. The comparison of enriched energy levels will determine the quantum position of the new particle. Mapped to position And calculate the fitness value, if Then the particle The updated quantum position is The updated enrichment level is Otherwise, it will not be updated.

[0061] Update the quantum positions and enriched energy levels of all particles in the population to obtain the globally optimal quantum positions up to generation g+1. .

[0062] Step 7: Determine if the maximum number of iterations has been reached. If it is not achieved, then Return to step four; if the target is reached, terminate the loop iteration and output the globally optimal quantum position. After mapping transformation, the globally optimal position is the optimal ISAC joint precoding and power allocation scheme.

[0063] The integrated precoding and power allocation method (algorithm) for large-scale MIMO sensing proposed in this invention is the underlying technical core of this invention, and various products can be derived based on the algorithm.

[0064] Based on the method proposed in this invention, a large-scale MIMO integrated sensing and precoding and power allocation system is developed using a programming language. This system has program modules corresponding to the steps of the above-mentioned technical solution, and executes the steps in the above-mentioned large-scale MIMO integrated sensing and precoding and power allocation method during runtime.

[0065] The developed system (software) computer program is stored on a computer-readable storage medium. This computer program is configured to implement the steps of the aforementioned large-scale MIMO integrated inductive precoding and power allocation method when invoked by a processor. In other words, the invention is materialized on a carrier, becoming a computer program product.

[0066] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, application-specific integrated circuits (ASICs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0067] The computational programs (also referred to as programs, software, software applications, or code) of this invention include machine instructions of a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device PLD) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0068] The effects of the present invention will be illustrated below using specific embodiments.

[0069] Example 1

[0070] In this embodiment, the large-scale MIMO sensor-integrated joint precoding and power allocation method based on the quantum energy valley mechanism of the present invention is denoted as QEVO; the ISAC joint precoding and power allocation method based on the energy valley optimization algorithm is denoted as EVO; and the ISAC joint precoding and power allocation method based on the particle swarm optimization algorithm is denoted as PSO. The EVO related parameter settings refer to "Energy Valley Optimizer: A Novel Metaheuristic Algorithm for Global and Engineering Optimization" published by M. Azizi et al. in Scientific Reports (2023, 1(13):226); the PSO related parameters refer to "Joint Channel Allocation and Power Control Based on PSO for Cellular Networks with D2D Communications" published by J. Xu et al. in Computer Networks (2018, 3(113):104-119).

[0071] In the simulation experiment, consider a square simulated area with a side length of 500 m, with the base station located at the center of the area, at coordinates [coordinates missing]. The center coordinates of the communication user's location target Randomly generated within a 75 m square area, the target's center coordinates are sensed. Randomly generated within a 75 m square area, the maximum output power of the base station is... Number of communication users Number of transmitting and receiving antennas in an integrated base station Minimum signal-to-interference-plus-noise ratio for communication users The element spacing of both the transmitting and receiving arrays is half a wavelength, enabling target sensing. amplitude The path loss of the communication channel was modeled using the 3GPP Urban Microcell model, with a carrier frequency of 1.9 GHz, a bandwidth of 20 MHz, and a noise variance of [missing information]. for Maximum number of sampling points The channel is a correlated Rayleigh fading channel. , , , Given the spatial correlation matrix, the channel state information is perfectly known. The regularization factor for RZF precoding is... The weighting factor of the fitness function , .

[0072] The parameters for the quantum energy valley mechanism are set as follows: number of particles. Maximum number of iterations The number of Monte Carlo experiments was 20.

[0073] Figures 2 and 3 respectively show the number of perceived targets. The curves showing the relationship between the root mean square error and the user's average signal-to-interference-plus-noise ratio (SINNR) as the number of sensed targets increases from 1 to 7 demonstrate that the method designed in this invention can optimize target estimation with high accuracy. By selecting different precoding schemes and power allocation, communication interference and estimation errors are reduced, achieving joint optimization of communication rate and target estimation accuracy.

[0074] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A method for integrated precoding and power allocation of large-scale MIMO sensing, characterized in that, The process includes the following steps: Step 1: Establish a joint beamforming precoding system model for the downlink of a large-scale MIMO integrated sensing base station; Step 2: Establish a joint power allocation model for minimizing interference and a Cramer-Rao boundary ISAC downlink precoding optimization model; Step 3: Initialize the quantum position of each particle and construct a fitness function based on the established optimization model; Step 4: Calculate the enriched energy level of the particle by calculating the fitness value, select the quantum position of the particle with the smallest enriched energy level as the optimal quantum position, and calculate relevant parameters; Step 5: Determine the decay type of the particle based on its neutron enrichment level and stability level, and generate new particles; Step 6: Map the updated quantum position of the particle to a position, calculate the updated fitness function value of all particles, and update the global optimal quantum position; Step 7: Determine if the maximum number of iterations has been reached. If not, set the iteration count to zero. Return to step four; if the target is reached, terminate the loop iteration, output the global optimal quantum position, and obtain the optimal ISAC joint precoding and power allocation scheme through mapping transformation.

2. The method according to claim 1, characterized in that, Step one includes the following process: A large-scale MIMO integrated sensing base station includes... Root transmitting antenna and The number of receiving antennas forms an array with an element spacing of [missing information]. A uniform linear array, where user communication and far-field target estimation are performed at the same base station, assuming there are [missing information - likely referring to a location or area in space]. Single-antenna communication users and Multiple far-field sensing targets exist simultaneously. ， The sensing and communication signals transmitted by the base station are within the same time-frequency resource block, and the channel state information is perfectly known; in the first... The base station's transmitted signal during the next sampling is represented as follows: ， ， This represents the maximum number of sampling points, where, ， Indicates the first Precoded vectors for each user This indicates taking the transpose. ， Indicates the first Beamforming vector of a sensing target ， Indicates the first During the second sampling, the base station transmitted to the... Communication signals of individual users Indicates the first During the second sampling, the base station transmitted to the... The sensing signals of each target are obtained from the signals transmitted by the base station, which yields the base station downlink joint beamforming precoding matrix. ， Indicates taking the conjugate transpose; the first The communication signals received by each user are ， For base station to user The channel vector, The mean and variance are 0. Gaussian white noise, other communication users and sensing targets in the scene will affect the base station and users. Interference in communication ， ,user The signal-to-noise ratio of the received signal is expressed as The integrated sensing base station uses echo signals to perform DOA estimation on the sensed target. The DOA corresponding to each far-field sensing target is: The sensing signal received by the base station is represented as ,in, Indicates the first The amplitude of the perceived target. Indicates the first The guidance vector of a perceived target. Represents the imaginary unit. Indicates the spacing between array elements. For the signal wavelength, This indicates that the expression follows a pattern with a mean of 0 and a variance of 0. A normally distributed Gaussian white noise vector for A 3D array manifold matrix; the vector of unknown deterministic parameters of the perceived target is... ， ， ， Indicates taking the real part, The Fisher information matrix, representing the imaginary part and unknown parameters, is expressed as follows: ， ， ， ， ， ， ， Indicates taking the conjugate. ， ， This represents the sum of the diagonal elements. Indicates the noise variance. The integrated sensing base station can sense the angle of the target. The estimated Craméro boundary is composed of Calculations show that This indicates taking the inverse.

3. The method according to claim 2, characterized in that, Step two includes the following process: for communication users in the downlink Linear precoding method is used for integrated base station and user Interference between the two Suppression is performed by selecting Maximum Ratio Transmission (MRT) precoding and Regular Zero Forcing (RZF) precoding when targeting users. When using MRT precoding, the normalized precoding vector is represented as follows: ， Represents the vector magnitude, when for the user When using RZF precoding, the normalized precoding vector is represented as follows: ,in, for 3D channel state information matrix, Represents the regularization factor. for An identity matrix of dimension; for the perceived target The integrated radar uses maximum ratio transmission (MRT) precoding and null-space zero-forcing (Null-ZF) precoding to detect targets. Beamforming is performed when the target is sensed. When using MRT precoding, the normalized precoding vector is represented as follows: When the integrated radar uses Null-ZF precoding to detect targets, the normalized precoding vector is represented as follows: ， for An identity matrix of dimension; the downlink joint beamforming precoding matrix of an integrated base station is represented by a normalized precoding vector as follows: ,in, and These represent the base station's communication with the user. and perceived target Allocated transmission power, and These represent the base station's communication with the user. and perceived target The normalized precoding vector, ， ， Let the downlink power allocation vector of the integrated base station be denoted as . Then, the lower bound of the DOA estimation performance of the large-scale MIMO sensing system is expressed as . ， Represents the array manifold matrix. Establish a communication-aware downlink precoding optimization model for joint power allocation. Constraint 1 is Constraint 2 is ,in This represents the maximum transmit power of the base station, and constraint 3 is... 。 4. The method according to claim 3, characterized in that, Step three includes the following process: First, set the number of particles to... The maximum number of iterations is ; in the In the nth iteration, the 1st The quantum position of each particle is The position obtained after position mapping is ,when At that time, the quantum positions of all particles in the first generation... Dimension initialized to ,in, for A uniformly random number between, when At that time, the particle's first The mapping relationship of dimensional positions is as follows: ， express Random numbers that follow a uniform distribution between , when At that time, the particle's first Dimensional position mapping relationship is ;when At that time, the first The first particle Dimensional location represents communication user The precoding choice is: ;when At that time, the first The first particle 3D position representation of the perceived target The precoding choice is: ,when At that time, the first The first particle The position represents the downlink power allocation of the integrated base station, the third position. The power allocation vector of each particle is represented as: , obtained the The second iteration Downlink Joint Beamforming Precoding Matrix of an Integrated Base Station for Individual Particles , the The fitness function of each particle is ,in and This represents the weighting factor, which also satisfies the constraints.

5. The method according to claim 4, characterized in that, Step four includes the following process: Calculate the... The fitness values ​​of all particles in the population in the next iteration are used as the fitness values ​​of the next iteration. Neutron enrichment levels of individual particles The particle with the smallest fitness value is selected as the first. The optimal particle in the next iteration, and the optimal enrichment energy level are denoted as... At this point, the quantum position of the optimal particle is The corresponding optimal particle position is mapped as The worst enrichment energy level corresponding to the maximum fitness value is denoted as , the The central particle of the next iteration is represented as The central particle's first Weiyou Calculations show that ;Calculate the first based on the enrichment energy level The enrichment boundary of the next iteration , the The second iteration The stability of a particle is expressed as ,in, for Calculate the uniform random number between the two values. The Euclidean distance of a particle from other particles The one with the smallest distance The average of the particles is obtained by taking the particle size. The nearest neighbor particle, for Integers between the nearest neighbors are... 。 6. The method according to claim 5, characterized in that, Step five includes the following process: comparing the neutron enrichment level with the boundary value, if... If the particle undergoes decay, then... , indicating particle occurrence and decay, in which Represents particles stability, express Random numbers that follow a uniform distribution between each other, the th The iteration factor for the next iteration is Take a set without repetition middle The elements constitute a vector. ,in At this time, the particles occur The decay produces two new particles, one of which is the first. The quantum position is updated to ,in, ， The first particle represents the optimal particle. Dimension, a set of non-repeating values. middle The elements constitute a vector. ,in Another The quantum position is updated to ,in, ， Represents particles The neighboring particle's first Wei; if , indicating that it has occurred decay, particle release The ray forms two particles, and after decomposition, the first particle... The quantum rotation angles are updated to: ， ,in, and They are respectively The particles are updated using a simulated quantum rotation gate, where random numbers following a uniform distribution are used. occur The quantum positions at the time of decay are respectively ， ;if , representing particles When the enrichment level is less than the boundary value, the optimal particle either receives or emits electrons. The quantum rotation angle is updated to Updating quantum position using simulated quantum rotation gate 。 7. The method according to claim 6, characterized in that, Step six includes the following process: for the (g+1)th generation particle The update, if The quantum positions of the two new particles produced by the decay process of the particle will then be determined. and Mapped to position and Calculate the fitness value and compare it. If Then continue with the particle Comparison of enrichment energy levels, if Then the particle The updated quantum position is The updated enrichment level is Otherwise, no update; if ,and Then the particle The updated quantum position is The updated enrichment level is Otherwise, do not update; if Set the fitness value of the new particle to match that of the particle. The comparison of enriched energy levels will determine the quantum position of the new particle. Mapped to position And calculate the fitness value, if Then the particle The updated quantum position is The updated enrichment level is Otherwise, do not update; update the quantum positions and enriched energy levels of all particles in the population to obtain the globally optimal quantum positions up to generation g+1. 。 8. A large-scale MIMO integrated sensing precoding and power distribution system, characterized in that, The system has a program module corresponding to the steps of the method described in any one of claims 1 to 7, and executes the steps in the above-described large-scale MIMO inductive joint precoding and power allocation method when running.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program configured to, when invoked by a processor, implement the steps of the large-scale MIMO inductively coupled precoding and power allocation method as described in any one of claims 1 to 7.

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