CN-121984618-A - ISAC near-field channel robust beam forming optimization method and device based on MMSE estimation

CN121984618ACN 121984618 ACN121984618 ACN 121984618ACN-121984618-A

Abstract

The invention provides an ISAC near-field channel robust beam forming optimization method and device based on MMSE estimation, which comprise the steps of carrying out system modeling and signal configuration on an ISAC system, adopting a spherical wave model to accurately model a near-field communication channel, calculating to obtain SINR of a user, introducing interrupt probability constraint, establishing a perception model under far-field conditions, adopting an MMSE criterion to estimate a target response matrix to obtain a perception MSE, introducing an enhanced waveform to promote the perception DoF, updating the SINR, introducing an enhanced covariance matrix, taking the minimum perception MSE as an optimization target, constructing an ISAC near-field channel robust beam forming optimization problem according to the updated interrupt probability constraint, the total power constraint and the semi-positive constraint as constraint conditions, converting a non-convex problem into a convex form through a semi-fixed relaxation SDR (synchronous dynamic random number) combined sphere limit and an optimization framework of S-lemma, and conforming to a linear matrix inequality LMI constraint, and solving to obtain a beam forming optimization matrix. The invention can optimize beam forming.

Inventors

ZHANG RONGHUI
MA ZHANGCHAO
SUN MENGJIN
ZHAO YANG
WEI LIYUAN
WANG JIANQUAN
SUN LEI
CHEN NA
FU MEIXIA
WANG QU

Assignees

北京科技大学

Dates

Publication Date: 20260505
Application Date: 20251223

Claims (10)

1. An ISAC near-field channel robust beamforming optimization method based on MMSE estimation, the method comprising: s1, performing system modeling and signal configuration on an integrated sensing and communication (ISAC) system, wherein a transmitting signal matrix of the ISAC system is used for realizing communication and sensing functions at the same time; S2, accurately modeling a near field communication channel by adopting a spherical wave model according to the transmitting signal matrix, capturing distance and angle dependence, calculating to obtain a signal-to-interference-and-noise ratio (SINR) of a user according to the channel matrix obtained by modeling, and introducing interrupt probability constraint according to the SINR; s3, establishing a perception model under far field conditions according to the emission signal matrix, and estimating a target response matrix of the perception model by adopting a Minimum Mean Square Error (MMSE) criterion to obtain a perception Mean Square Error (MSE); S4, introducing an enhanced waveform to the transmitting signal matrix to improve the perception freedom DoF, and updating the SINR according to the enhanced waveform; S5, introducing an enhanced covariance matrix according to the enhanced waveform, taking a minimized perceived MSE as an optimization target, and constructing an ISAC near-field channel robust beam forming optimization problem by taking an outage probability constraint, a total power constraint and a semi-positive constraint updated according to the updated SINR as constraint conditions; S6, converting the non-convex problem into a convex form through combining a semi-definite relaxation SDR with a sphere limit and an optimization framework of S-lemma for the robust beamforming optimization problem of the ISAC near-field channel, and solving to obtain a beamforming optimization matrix by obeying the LMI constraint of the linear matrix inequality.
2. The method according to claim 1, wherein S1 specifically comprises: The ISAC system is configured with Multiple transmitting antennas A plurality of receiving antennas, wherein To ensure that information loss is avoided in the perception task, and a single base station simultaneously serves Single antenna communication user and sensing environment target by reflected echo, setting To support the generation of independent beams to different users; Transmitting signal matrix , Is of frame length and is used for realizing communication and perception functions simultaneously, and is specifically expressed as Wherein For the beamforming matrix, each To aim at the first The beam vector of the user is set, A data signal matrix satisfying I is an identity matrix, signal orthogonality is ensured, and a signal covariance matrix is 。
3. The method according to claim 2, wherein S2 comprises in particular: for the first The users are positioned in the Fresnel area of the antenna array under the high frequency band, and the channel vectors Expressed as: (1) Wherein the method comprises the steps of In order for the complex gain to be achieved, For the near field steering vector, the specific expansion is as follows: (2) Wherein the distance function The method comprises the following steps: (3) Wherein the method comprises the steps of For the antenna spacing to be the same, As the carrier wavelength is used, In order to be able to take the angle of departure, The model captures the distance and angle dependence of the spherical wavefront for the radial distance; the user receives the signal as Wherein In the form of a channel matrix, Is additive Gaussian white noise, obeys to zero mean and variance In the actual deployment environment, taking incomplete channel state information (MesCSI) into consideration, modeling as Wherein In order to estimate the channel(s), In the event of a gaussian error, For the error covariance matrix, calculate to get the first The SINR of the user is: (4) introducing interrupt probability constraint to embody the communication performance of the quantization system: (5) Wherein the method comprises the steps of As a result of the minimum SINR threshold value, Is the maximum outage probability.
4. A method according to claim 3, wherein S3 comprises: The perception task is assumed to be applicable to a long-distance target under far-field conditions, and the received echo signals are as follows: (6) Wherein the method comprises the steps of For the response matrix of the object to be obtained, In order to have an interference response matrix, Is additive Gaussian white noise, variance Target response matrix Wherein For the number of scattering points, for the unknown, For the reflection coefficient, obeying Swerling a model, unit variance gaussian, In order for the angle to be the same, And Steering vectors for reception of far field transmissions, interference response matrix , For interference reflection coefficients, the covariance matrix is defined as And ; Estimation using MMSE criterion To obtain perceptual performance: (7) the perceived MSE is thus: (8)。
5. the method according to claim 4, wherein S4 specifically comprises: Because of the base signal Rank of Severely limiting perceived DoF, resulting in a higher MSE, thus introducing an enhanced waveform: (9) Wherein the method comprises the steps of For the additional beamforming matrix(s), For additional data stream, satisfy ; The update SINR is: (10)。
6. The method according to claim 5, wherein S5 specifically comprises: Introducing an enhanced covariance matrix: (11) Substituting formula (11) into (8) and defining Wherein Is that Obtained by Cholesky decomposition, neglected and The MSE reduces to an irrelevant constant term: (12) wherein, the parameters are defined as follows: normalization factor, wherein Representing the frame length of the frame, Representing the perceived noise power of the light source, Representing a receiving antenna for balancing perceived signal strength; A normalized form of target covariance for capturing the relative intensities of the target and the interference; The second normalization covariance is used for simplifying matrix inversion operation; the constraint conditions comprise interrupt probability constraint (5) and total power constraint Wherein For total transmit power budget, and semi-positive constraint ; The problem of robust beamforming optimization for constructing ISAC near field channels is as follows: (13) s.t. (14) (15)。
7. the method according to claim 6, wherein S6 specifically comprises: The problem of the robust beam forming optimization of the ISAC near-field channel is a non-convex problem, and an SDR method is adopted to reconstruct the problem so as to obtain a feasible solution: First define And Then obtain ; Then introducing auxiliary variables Using Schur's complement principle on it, the constraint of the variable M is equivalent to the LMI constraint, and the original objective function (13) is then reconverted to While the variables are The following constraints need to be obeyed: (16) and then reconstructing the outage probability constraint (14): First order Wherein Then the constraint is equivalent to Wherein the parameters are expanded as follows: ; ; ; Approximating the constraint domain by employing a sphere boundary method Wherein , Is distributed in a chi-square manner For defining the radius of the error sphere, ensuring that the resulting condition is probabilistic Covering the error space and applying the S-lemma theorem by introducing auxiliary variables Converting the outage probability constraint (14) to an LMI constraint: (17) And after reconstruction in constraint (15), constraint with rank of 1 Relaxation was performed, expressed as: (18) the final convex optimization problem is: (19) s.t. (16)、(17)、(18) and solving the convex optimization problem through a CVX tool to obtain a beam forming optimization matrix.
8. An ISAC near field channel robust beamforming optimization apparatus based on MMSE estimation, the apparatus comprising: The system modeling module is used for carrying out system modeling and signal configuration on an integrated sensing and communication (ISAC) system, and a transmitting signal matrix of the ISAC system is used for realizing communication and sensing functions at the same time; the communication channel modeling module is used for accurately modeling a near field communication channel by adopting a spherical wave model according to the emission signal matrix, capturing distance and angle dependence, calculating a signal-to-interference-plus-noise ratio (SINR) of a user according to the channel matrix obtained by modeling, and introducing interrupt probability constraint according to the SINR; The sensing model building module is used for building a sensing model under far field conditions according to the transmitting signal matrix, and estimating a target response matrix of the sensing model by adopting a Minimum Mean Square Error (MMSE) criterion to obtain a sensing Mean Square Error (MSE); The introducing module is used for introducing an enhanced waveform to the transmitting signal matrix so as to improve the perception freedom DoF, and updating the SINR according to the enhanced waveform; The construction module is used for introducing an enhanced covariance matrix according to the enhanced waveform, taking the minimum perceived MSE as an optimization target, taking interrupt probability constraint, total power constraint and semi-positive constraint updated according to the updated SINR as constraint conditions, and constructing an ISAC near-field channel robust beam forming optimization problem; And the solving module is used for solving the ISAC near-field channel robust beamforming optimization problem, converting the non-convex problem into a convex form through combining a semi-definite relaxation SDR with a sphere limit and an optimization framework of S-lemma, and solving the non-convex problem under the LMI constraint of a linear matrix inequality to obtain a beamforming optimization matrix.
9. An electronic device comprising a processor and a memory having at least one instruction stored therein, wherein the at least one instruction is loaded and executed by the processor to implement an ISAC near field channel robust beamforming optimization method based on MMSE estimation as claimed in any of claims 1-7.
10. A computer readable storage medium having stored therein at least one instruction loaded and executed by a processor to implement the ISAC near field channel robust beamforming optimization method based on MMSE estimation of any of claims 1-7.

Description

ISAC near-field channel robust beam forming optimization method and device based on MMSE estimation Technical Field The invention relates to the technical field of wireless communication and radar sensing, in particular to an ISAC near-field channel robust beam forming optimization method and device based on MMSE estimation. Background With the rapid development of wireless networks to 6G systems, integrated sensing and Communication (ISAC) systems have become a core paradigm of next generation wireless networks, and improve dual-function efficiency by simultaneously implementing information transmission and environment sensing. The system can be applied to the fields of automatic driving vehicles, intelligent city systems, automatic industrial processes and the like, and provides high-reliability low-delay communication and accurate environment sensing capability. Unlike conventional wireless systems, ISACs utilize shared spectrum and hardware resources, improving spectrum efficiency and reducing deployment costs, solving spectrum congestion problems. Advances in high frequency technology such as millimeter wave and large-scale antenna arrays provide ISACs with higher communication throughput and accurate sensing capabilities. In the prior art, the beam forming optimization of the ISAC system is mainly based on far field assumption, a plane wave model is used, and serious errors are caused in high frequency bands, particularly when a user is located in a near field region of a large-scale antenna array, because spherical waves are dominant in the near field environment, besides, the defects of the prior art also comprise that imperfect channel state Information (CHANNEL STATE Information, CSI) is not considered, the interference is easy to influence by environment, robustness is reduced, the minimum mean square error (Minimum Mean Squared Error, MMSE) criterion is used for perception performance, but the target estimation error is increased due to the fact that the target estimation error is not optimized, and the interruption probability is not fully considered due to communication QoS constraint, so that performance is unstable in actual deployment. Disclosure of Invention In order to solve the technical problems in the prior art, the invention provides an ISAC near-field channel robust beam forming optimization method and device based on MMSE estimation, wherein the technical scheme is as follows: in one aspect, an ISAC near-field channel robust beamforming optimization method based on MMSE estimation is provided, the method comprising: s1, performing system modeling and signal configuration on an integrated sensing and communication (ISAC) system, wherein a transmitting signal matrix of the ISAC system is used for realizing communication and sensing functions at the same time; S2, accurately modeling a near field communication channel by adopting a spherical wave model according to the transmitting signal matrix, capturing distance and angle dependence, calculating to obtain a signal-to-interference-and-noise ratio (SINR) of a user according to the channel matrix obtained by modeling, and introducing interrupt probability constraint according to the SINR; s3, establishing a perception model under far field conditions according to the emission signal matrix, and estimating a target response matrix of the perception model by adopting a Minimum Mean Square Error (MMSE) criterion to obtain a perception Mean Square Error (MSE); S4, introducing an enhanced waveform to the transmitting signal matrix to improve the perception freedom DoF, and updating the SINR according to the enhanced waveform; S5, introducing an enhanced covariance matrix according to the enhanced waveform, taking a minimized perceived MSE as an optimization target, and constructing an ISAC near-field channel robust beam forming optimization problem by taking an outage probability constraint, a total power constraint and a semi-positive constraint updated according to the updated SINR as constraint conditions; S6, converting the non-convex problem into a convex form through combining a semi-definite relaxation SDR with a sphere limit and an optimization framework of S-lemma for the robust beamforming optimization problem of the ISAC near-field channel, and solving to obtain a beamforming optimization matrix by obeying the LMI constraint of the linear matrix inequality. Optionally, the S1 specifically includes: The ISAC system is configured with Multiple transmitting antennasA plurality of receiving antennas, whereinTo ensure that information loss is avoided in the perception task, and a single base station simultaneously servesSingle antenna communication user and sensing environment target by reflected echo, settingTo support the generation of independent beams to different users; Transmitting signal matrix ,Is of frame length and is used for realizing communication and perception functions simultaneously, and is specifically expressed asWh