Ahris-assisted isac system and multi-dimensional beam joint optimization method

Abstract

The application discloses an ISAC system and a multi-dimensional beam joint optimization method based on AHRIS assistance, wherein the system comprises a base station, an AHRIS and a communication user; a reflective amplifier is arranged in the AHRIS, an incident signal is first amplified by the reflective amplifier, and then is split into a reflection signal and a receiving signal by a radio frequency shunt; wherein the reflection signal is directly reflected outward by the AHRIS, and the receiving signal is superimposed on a radio frequency chain, and then is transmitted out by the AHRIS; during a downlink, the base station performs radar sensing and wireless communication with the assistance of the AHRIS, wherein the AHRIS first amplifies the signal transmitted by the base station, and then reflects the amplified signal to a sensing target and the communication user; during an uplink, a radar echo from a detection area is amplified by the AHRIS, and then is received and superimposed into a beam by the AHRIS, and finally is forwarded to the base station. The application can effectively improve the sensing / communication performance of the ISAC system.

Description

Technical Field

[0001] This invention relates to the field of mobile communication technology, and in particular to an AHRIS-assisted ISAC system and a multi-dimensional beam joint optimization method. Background Technology

[0002] Reconfigurable Smart Surfaces (RIS) are metamaterials composed of passive reflective elements that can control the propagation environment of radio waves by adjusting the amplitude and / or phase of the incident signal. Deploying RIS in an ISAC system can effectively improve signal transmission efficiency and propagation range, thereby improving the system's communication and sensing performance. Traditional RIS only have the ability to reflect signals and can be further divided into passive RIS and active RIS based on whether they can amplify the incident signal. Passive RIS can only adjust the phase of the incident signal and does not require an additional power supply. Active RIS can both adjust the phase shift and amplify the incident signal, thus effectively improving signal quality, but increasing system power consumption. Unlike traditional purely reflective RIS, Hybrid RIS (HRIS) combines passive RIS with a dynamic metasurface antenna, enabling it to both reflect and receive signals. The reflection matrix and receive vector of the HRIS correspond to the reflection and reception functions, respectively. In an HRIS-assisted ISAC system, the reflection matrix controls the HRIS element to reflect the composite signal transmitted by the base station (BS) to the sensing targets (STs) / communication users (CUs), while the receive vector controls the HRIS element to receive radar echoes from the detection area. Compared to a RIS (Reflection Array Indicator) with only a reflection matrix, an HRIS (Hyper-Ring Array Indicator) with dual beamforming can more precisely adjust different incident signals. Furthermore, all components of the HRIS are connected to the radio frequency (RF) chain via dedicated waveguides, so the received radar echoes are superimposed into a single beam within the HRIS before being relayed out. This operation effectively reduces path attenuation of the radar echo compared to a purely reflective RIS. Therefore, the HRIS significantly improves the communication and sensing performance of the ISAC (Information and Communication Control) system compared to a RIS.

[0003] However, due to the existence of double path loss and multiplicative decay, HRIS cannot effectively improve the performance of the ISAC system when STs are far from BS. Summary of the Invention

[0004] In order to at least partially solve one of the technical problems existing in the prior art, the purpose of this invention is to provide an AHRIS-assisted ISAC system and a multi-dimensional beam joint optimization method.

[0005] The first technical solution adopted in this invention is:

[0006] An AHRIS-assisted ISAC system includes a base station, an AHRIS, and communication users. The AHRIS is equipped with a reflective amplifier. The incident signal is first amplified by the reflective amplifier and then split into a reflected signal and a received signal by a radio frequency splitter. The reflected signal is directly reflected outward by the AHRIS, while the received signal is superimposed on the radio frequency chain and then forwarded by the AHRIS.

[0007] During the downlink, the base station performs radar sensing and wireless communication with the assistance of AHRIS. AHRIS first amplifies the signal sent by the base station and then reflects the amplified signal to the sensing target and the communication user. During the uplink, the radar echo from the detection area is amplified by AHRIS, then received by AHRIS and superimposed into a beam, and finally forwarded to the base station.

[0008] The second technical solution adopted in this invention is:

[0009] A multidimensional beam joint optimization method, applied to the aforementioned AHRIS-assisted ISAC system, includes the following steps:

[0010] Construct a system model for the ISAC system;

[0011] Construct channel model, signal model, sensing model and communication model based on system model;

[0012] The optimization problem is determined based on the constructed model;

[0013] Based on the optimization problem, the sensing beamforming matrix, communication beamforming matrix, AHRIS reflection matrix, and AHRIS receive vector are jointly optimized to maximize the overall sensing signal-to-interference-plus-noise ratio (SINR) of the system.

[0014] Furthermore, the system model for constructing the ISAC system includes:

[0015] An AHRIS is deployed between the base station and the equipment to establish a signal transmission link for radar sensing and wireless communication; the AHRIS with N elements is modeled as a uniform planar array, and the BS is modeled as an M-element uniform linear array; L sensing targets and K communication users are randomly distributed within a preset range.

[0016] Furthermore, the channel model is constructed as follows:

[0017] G and These are the AHRIS-BS channels in downlink and uplink, respectively; g r,k This represents the channel between AHRIS and the k-th communication user;

[0018] Assume that the large-scale fading experienced by all channels satisfies:

[0019] L(d)=-C0(d / d0) -l

[0020] In the formula, d is the communication distance, C0 represents the path loss at the reference distance d0 = 1, and l is the path fading index;

[0021] Rice fading was used as a small-scale fading model for all channels.

[0022] Furthermore, the signal model is constructed as follows:

[0023] To achieve the dual functions of sensing and communication, the signals transmitted by the base station include a sensing signal vector s. r and communication signal vector s c ,in and I M Let I be an M×M identity matrix. K Let H be a K×K identity matrix, and H denote the conjugate transpose.

[0024] Define W r and W c These are the sensing beamforming matrix and the communication beamforming matrix, respectively. The composite signal transmitted by the base station is represented as x = W. r s r +W c s c ;W r and W c satisfy P0 is the maximum transmit power of the base station.

[0025] Furthermore, the perception model and communication model are constructed as follows:

[0026] Sensing Model: Since radar echoes include signals reflected from both the sensed target and the interference source, the radar echo received by the base station is represented as:

[0027]

[0028] H l =α l a(θ l ,φ l )a(θ l ,φ l ) H

[0029]

[0030]

[0031]

[0032] In the formula, J is the number of interference sources; a(θ,φ) is the steering vector of the uniform planar array at angles θ and φ; α l It is a dual-path attenuation between AHRIS and the l-th sensing target. It is a dual-path attenuation between AHRIS and the j-th interference source; θ l and φ l These are the azimuth and elevation angles of the l-th sensing target relative to AHRIS, respectively. and These are the azimuth and elevation angles of the j-th interference source relative to AHRIS, respectively; Φ r and Φ s These represent the reflection matrix and the reception vector of AHRIS, respectively. It is the reflection phase shift vector. It represents the received phase shift vector, β represents the amplitude vector, ρ represents the distribution vector, I is a 1×N dimensional vector of all 1s, and ⊙ represents the Hadamard product; n r It is additive white Gaussian noise;

[0033] The sensing SINR corresponding to the l-th sensing target is:

[0034]

[0035] In the formula, δ 2 This represents the power of additive white Gaussian noise;

[0036] Communication model: For downlink communication, the signal received by the k-th user is represented as:

[0037]

[0038] In the formula, g k It is the equivalent channel between the base station and the k-th communication user, n r It is the additive white Gaussian noise corresponding to the k-th communication user; w k,c It is W c The communication beamforming vector corresponding to the k-th communication user, s k,c It is s c The communication signal vector corresponding to the k-th communication user; w k′,c It is W c The communication beamforming vector corresponding to the k′-th communication user, s k′,c It is s c The communication signal vector corresponding to the k′-th communication user; w m,r It is W r The sensing beamforming vector corresponding to the m-th target, s m,r It is s r The sensing signal vector corresponding to the m-th target;

[0039] The received SINR of the k-th communication user is:

[0040]

[0041] Furthermore, the expression for the optimization problem is:

[0042]

[0043] In the formula, W r and W c These are the sensing beamforming matrix and the communication beamforming matrix, respectively; φ r and Φ s Represent the reflection matrix and reception vector of AHRIS, respectively; γ sum It is the overall perceived SINR; γ l,r It is the perceived SINR corresponding to the l-th target, γ min,r It is the minimum perceptual SINR that each target must satisfy, γ k,c It is the received SINR corresponding to the k-th target, γ min,c It is the minimum received SINR that each user must meet; P r This is the power consumption of AHRIS when the signal is reflected, P s This is the power consumption of AHRIS when receiving signals, P e P1 represents the power consumption of the switching and control circuits and the DC bias power consumption of each AHRIS component, while P1 represents the battery capacity of the AHRIS. It is Φ r The magnitude of the nth element in the diagonal vector. It is Φ s The magnitude of the nth element, β max It is the maximum amplitude of each AHRIS unit;

[0044] Where C1 represents the total transmit power limit of the base station; C2 and C3 guarantee the minimum sensing SINR of each sensing target and the minimum receiving SINR of each communication user, respectively; C4 represents the power budget constraint of AHRIS; and C5 represents the energy conservation law in AHRIS.

[0045] Furthermore, the joint optimization of the sensing beamforming matrix, communication beamforming matrix, AHRIS reflection matrix, and AHRIS receive vector includes:

[0046] In Phase 1, the objective function of the optimization problem (P1) is transformed into an equivalent linear form using the Tinkelbach method; the highly coupled Φ... r and Φ s Replace with four independent parameters

[0047] In Phase Two, each parameter is solved individually based on the transformed problem, and the final optimization result is obtained through an alternating optimization algorithm.

[0048] Furthermore, after stage one, the optimization problem (P1) is transformed into optimization problem (P2):

[0049]

[0050] In the formula, ζ is a non-negative auxiliary parameter.

[0051] Furthermore, the second stage specifically includes:

[0052] The optimization problem (P2) is broken down into five subproblems, and corresponding optimizations are performed:

[0053] 1) Fixed Optimize beamforming matrix W r With the communication beamforming matrix W c ;

[0054] 2) Fixed Optimize reflection phase shift vector

[0055] 3) Fix Optimize the received phase shift vector

[0056] 4) Given Optimize the amplitude vector β;

[0057] 5) In obtaining Then, optimize the allocation vector ρ.

[0058] The beneficial effects of this invention are as follows: Addressing the degradation of the sensing signal-to-interference-plus-noise ratio (SINR) in ISAC systems caused by dual path loss and multiplicative attenuation effects, this invention proposes an active HRIS (AHRIS) structure. By amplifying the signal, AHRIS can effectively improve signal strength, thus enhancing the sensing / communication performance of the ISAC system more effectively than HRIS. Furthermore, to maximize the system's sensing SINR, this invention designs a joint optimization algorithm for the sensing beamforming matrix, communication beamforming matrix, AHRIS reflection matrix, and AHRIS receive vector. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following description is provided with accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a model diagram of the AHRIS-assisted ISAC system in an embodiment of the present invention;

[0061] Figure 2 This is a schematic diagram of the AHRIS structure proposed in the embodiments of the present invention;

[0062] Figure 3 This diagram illustrates the changes in the total perceived SINR compared to the total power consumption of the ISAC system in this embodiment of the invention when the proposed AHRIS structure and the proposed optimized allocation scheme, the traditional HRIS structure and the proposed optimized allocation scheme, the active RIS structure and the proposed optimized allocation scheme, the passive RIS structure and the proposed optimized allocation scheme, and the proposed AHRIS structure and the optimized scheme based on semidefinite programming are used respectively.

[0063] Figure 4 This diagram illustrates the changes in the total perceived SINR relative to the number of base station antennas in the ISAC system of this invention when using the AHRIS structure and the optimized allocation scheme proposed in this invention, the traditional HRIS structure and the optimized allocation scheme proposed in this invention, the active RIS structure and the optimized allocation scheme proposed in this invention, the passive RIS structure and the optimized allocation scheme proposed in this invention, and the AHRIS structure proposed in this invention and the optimized scheme based on semidefinite programming. Detailed Implementation

[0064] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0065] The terminology used in the embodiments of this application is for the purpose of describing specific embodiments only and is not intended to limit the embodiments of this application. The singular forms "a," "described," and "the" used in the embodiments of this application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. Furthermore, unless otherwise expressly limited, terms such as "set," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0066] In the description of this application, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0067] In the description of this application, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0068] In the description of this application, "and / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the related objects before and after it are in an "or" relationship.

[0069] Terminology Explanation:

[0070] ISAC: An abbreviation for Integrated Sensing and Communication.

[0071] RIS: An abbreviation for Reconfigurable Intelligence Surface.

[0072] AHRIS: Active Hybrid Reconfigurable Smart Surface.

[0073] BS: Base station.

[0074] STs: Perceived Targets.

[0075] Cus: Communication user.

[0076] In the first existing literature, the authors considered an HRIS-assisted multiple-input multiple-output (MIMO) ISAC system, in which the HRIS reflects the incident signal to the CU and receives the radar echo. They proposed an alternating optimization method that includes automatic gradient descent (AGD) and semidefinite relaxation (SDR) algorithms, which maximizes the radar's sensing SINR while ensuring the SINR requirements of each CU.

[0077] The second existing literature focuses on HRIS-assisted ISAC systems under non-line-of-sight (NLoS) conditions. In the system under consideration, the authors propose a parameter design method that maximizes the communication throughput of all CUs in the downlink by jointly optimizing transmit beamforming and HRIS beamforming.

[0078] In the third existing paper, the authors aim to maximize the total sensing SINR of an HRIS-assisted ISAC system by optimizing transmit beamforming, HRIS reflective beamforming, and HRIS receive beamforming. To address the non-convex optimization problem, the authors propose an efficient iterative algorithm based on Rayleigh quotient, Dinkelbach transform, and successful convex approximation (SCA) method.

[0079] However, while HRIS, as a RIS structure capable of both reflecting and receiving signals, can more precisely adjust for different incident signals and mitigate path attenuation experienced by radar echoes, it cannot effectively improve the performance of ISAC systems when STs are far from BS due to dual path loss and multiplicative attenuation. Therefore, it is necessary to design a more effective RIS structure for ISAC systems based on the existing HRIS structure, taking into account the characteristics of ISAC systems.

[0080] Furthermore, due to the complexity of the objective function and the high degree of parameter coupling, performance optimization of the AHRIS-assisted ISAC system is extremely challenging. Especially when optimizing the reflection matrix and receiver vector of AHRIS, existing solutions either optimize only the amplitude or, with a fixed amplitude, only the phase shift. To maximize the gain of AHRIS to the system, a solution that can simultaneously optimize both the amplitude and phase shift of AHRIS is needed.

[0081] To address the aforementioned problems, this invention proposes an AHRIS structure, which adds a reflective amplifier to the traditional HRIS, enabling it to reflect, receive, and amplify incident signals. Based on the proposed structure, this invention further proposes an AHRIS-assisted ISAC system framework. In the downlink, the BS performs radar sensing and wireless communication with the assistance of AHRIS, where AHRIS first amplifies the signal transmitted by the BS and then reflects it to STs and CUs. During the uplink, radar echoes from the detection area are amplified by AHRIS, then received by AHRIS and superimposed into a beam, which is finally forwarded to the BS. To maximize the overall sensing SINR of the system while meeting communication requirements, this invention proposes a joint optimization method for the sensing beamforming matrix, communication beamforming matrix, AHRIS reflection matrix, and AHRIS receive vector. Specifically, the joint optimization method consists of two stages. In stage one, this invention first uses the Dinkelbach method to transform the objective function of problem (P1) into an equivalent linear form. Then, the highly coupled Φ... r and Φ s Replace with four independent parameters In Phase Two, based on the transformed optimization problem, each parameter is solved individually, and the final optimization result is obtained through an alternating optimization algorithm. Simulation results show that, compared with other optimization schemes, the proposed AHRIS structure and optimized allocation scheme can effectively improve the overall sensing SINR of the system while meeting communication requirements.

[0082] The technical solution of the present invention will be explained in detail below with reference to the accompanying drawings and specific embodiments.

[0083] (1) System Model

[0084] As one embodiment, in the AHRIS-assisted ISAC system, the BS and AHRIS are located at (0m, 0m, 0m) and (0m, 20m, 2m), respectively. L STs and K CUs are randomly distributed within a circle with a radius of 8m centered at (0m, 20m, 0m). The AHRIS with N elements can be modeled as a uniform planar array, while the BS is an M-element uniform linear array.

[0085] like Figure 1 As shown, due to the obstruction of buildings, direct communication between the BS and the equipment is impossible. Therefore, in this embodiment, an AHRIS is deployed between the BS and the equipment to establish a signal transmission link for radar sensing and wireless communication. This AHRIS can reflect and receive incident signals, and also amplify them. Its structure is as follows... Figure 2As shown, the incident signal is first amplified by a reflective amplifier, and then split into a reflected signal and a received signal by an RF splitter. The reflected signal is directly reflected outward by the AHRIS, while the received signal is superimposed onto the RF chain and then forwarded by the AHRIS. Based on the characteristics of the AHRIS, this invention proposes a novel ISAC system transmission protocol. Specifically, in the downlink, the BS performs radar sensing and wireless communication with the assistance of the AHRIS, where the AHRIS first amplifies the signal transmitted by the BS and then reflects it to the STs and CUs. During the uplink, the radar echo from the detection area is amplified by the AHRIS, then received by the AHRIS and superimposed into a beam, which is finally forwarded to the BS.

[0086] (2) Channel Modeling

[0087] G∈C M×N and These are the AHRIS-BS channels in the downlink and uplink, respectively. r,k ∈C N×1 This represents the channel between AHRIS and the k-th CU. Assume that the large-scale fading experienced by all channels satisfies L(d) = -C0(d / d0). -l Where C0 = -30dB represents the path loss at the reference distance d0 = 1. l is the path fading index, with the path fading index values ​​between BS-AHRIS and AHRIS and the device set to 2.5 and 2, respectively. Furthermore, Ricean fading is used as a small-scale fading model for all channels, with a Ricean factor of 3dB. Since channel estimation errors are unavoidable, this paper considers a statistical error model for channel state information, G, and g r,k They can be represented as

[0088]

[0089]

[0090]

[0091] (3) Signal Model and Transmission Model

[0092] Signal Model: To achieve the dual functions of sensing and communication, the signal transmitted by the BS includes a sensing signal vector s. r =[s 1,r ,…,s M,r ] T and communication signal vector s c =[s 1,c ,…,s K,c ] T ,in and IM Let I be an M×M identity matrix. K Let W be a K×K identity matrix. To eliminate interference between signals, it is assumed that the sensed signal and the communication signal are statistically independent. r =[w 1,r ,…,w M,r ] and W c =[w 1,c ,…,w K,c Let be the sensing beamforming matrix and the communication beamforming matrix, respectively. Then, the composite signal transmitted by the BS can be expressed as x = W. r s r +W c s c , where W r and W c satisfy P0 is the maximum transmission power of the BS.

[0093] Based on the proposed transmission protocol and the defined composite signal, the sensing model and communication model can be described as follows:

[0094] Sensing Model: Since the radar echo includes signals reflected from both STs and interference sources, the radar echo received by the BS can be represented as follows:

[0095]

[0096] Where J is the number of interference sources, H l =α l a(θ l ,φ l )a(θ l ,φ l ) H , in It is the guide vector of a uniform planar array at angles θ and φ. It is a dual-path attenuation between AHRIS and the l-th ST (j-th interference source). and These are the azimuth and elevation angles of the l-th ST (j-th interference source) relative to AHRIS, respectively. and Let AHRIS's reflection matrix and reception vector be represented respectively, where and These are the amplitude and phase shift of the signal reflected (received) by the nth element, respectively. β max It is the maximum amplitude. This refers to the number of phase shift coefficients. For example... Figure 2 As shown, based on the internal structure of AHRIS, and They can be respectively equivalent to ρ n β n and (1-ρ n )β n , where β n ρ is the amplification factor of the reflective amplifier in the nth element. n This represents the portion of the signal reflected in the nth element. It can be deduced that... and in It is the reflection phase shift vector. is the received phase shift vector, β represents the amplitude vector, ρ represents the distribution vector, and Ι is a 1×N dimensional vector of all 1s. This represents the received additive white Gaussian noise. According to formula (4), the perceived SINR corresponding to the l-th ST can be modeled as:

[0097]

[0098] Communication Model: For downlink communication, the signal received by the k-th CU can be represented as:

[0099]

[0100] in It is the equivalent channel between the BS and the k-th CU. This is the additive white Gaussian noise corresponding to the k-th CU. According to formula (6), the received SINR of the k-th CU is:

[0101]

[0102] (4) Optimization problem

[0103] In the system under consideration, the objective of this invention is to jointly optimize the sensing beamforming matrix W. r =[w 1,r ,…,w M,r Communication beamforming matrix W c =[w 1,c ,…,w K,c ], AHRIS reflection matrix Φ r and AHRIS receive vector Φ s The goal is to maximize the total perceived SINR of the target while ensuring communication quality. Therefore, the mathematical expression for the optimization problem is (P1):

[0104]

[0105] In Equation (8), C1 represents the total transmit power limit of the BS. C2 and C3 guarantee the minimum sensing SINR for each ST and the minimum receiving SINR for each CU, respectively. C4 represents the power budget constraint of AHRIS, where P r =|Φ r G H (W r +W c )| 2 and These are the power consumption of AHRIS when reflecting and receiving signals, respectively, P. e This represents the power consumption of the switching and control circuits and the DC bias power consumption of each AHRIS component. P1 is the battery capacity of the AHRIS. C5 represents the law of conservation of energy in the AHRIS.

[0106] (5) Joint resource optimization allocation method

[0107] To effectively solve the optimization problem (P1), this embodiment proposes an alternating optimization algorithm framework based on Dinkelbach. Specifically, in stage one, the objective function of the problem (P1) is first transformed into an equivalent linear form using the Dinkelbach method. Then, the highly coupled Φ... r and Φ s Replace with four independent parameters In Phase Two, based on the transformed problem, each parameter is solved individually, and the final optimization result is obtained through an alternating optimization algorithm. The specific process is as follows:

[0108] 5.1) Phase 1: Transformation Optimization Problem

[0109] In this phase, the goal is to transform the optimization problem (P1) into a more tractable form using the Dinkelbach method and equivalent substitution.

[0110] First, since the objective function is in fractional form, the optimization problem (P1) is difficult to solve directly. Therefore, this embodiment uses the Dinkelbach method to transform it into:

[0111]

[0112] Here, ζ is a non-negative auxiliary parameter, which is determined by executing [the appropriate parameter] before each alternation iteration. Update. Definition and Since both are 1×1 dimensional scalars, according to The objective function (9) can be further simplified to:

[0113]

[0114] Furthermore, the highly coupled AHRIS reflection matrix Φ r and AHRIS receive vector Φ s This also greatly increases the complexity of solving problem (P1). To address this problem, the present invention is based on... and Use respectively Replace Φ r , Replace Φ s Therefore, the optimization problem (P1) can be transformed into the optimization problem (P2):

[0115]

[0116] Although the optimization problem is simplified, problem (P2) remains difficult to solve due to the presence of multiple coupling parameters and non-convex constraints C2, C3, C6, and C7. An efficient solution is to break down the original problem into several tractable subproblems and solve them using an alternating optimization algorithm.

[0117] 5.2) Phase Two: Perform Alternating Optimization

[0118] In this stage, this embodiment breaks down the optimization problem (P2) into five sub-problems and provides corresponding optimization solutions for each.

[0119] The specific process is as follows:

[0120] 1) Optimize the beamforming matrix W r With the communication beamforming matrix W c : in fixed Under the premise of W r and W c The optimization problem can be represented as (P3):

[0121]

[0122] in, and To address the non-convexity of constraints C2 and C3, this embodiment rewrites them as follows:

[0123]

[0124]

[0125] definition and Formulas (13) and (14) can be rewritten as follows:

[0126]

[0127]

[0128] Based on the above transformation, the optimization problem (P3) can be equivalent to (P3)0:

[0129]

[0130] To address the nonconvexity of constraints C15 and C16, this invention transforms the rank-1 constraints into penalty terms, and then reformulates the objective function of problem (P3)0 as follows:

[0131]

[0132] Where κ>0 is the punishment factor, M e (·) represents the largest eigenvalue of the matrix. However, in the penalty term... and This makes the objective function non-convex. To address this problem, this embodiment introduces the Continuous Convex Approximation (SCA) algorithm. Specifically, it utilizes the feasible point at the nth iteration... and The first-order Taylor expansion, and The upper bounds can be represented as follows:

[0133]

[0134]

[0135] Where D(·) is the eigenvector corresponding to the largest eigenvalue. Finally, the optimization problem (P3)0 can be written as (P3)1:

[0136]

[0137] Problem (P3)1 is a quadratic positive semidefinite programming (QSDP) problem, which can be solved using CVX. After the algorithm converges, the optimized w can be obtained using eigenvalue decomposition (EVD). m,r and w k,c .

[0138] 2) Optimize the reflection phase shift vector fixed Under the premise of:

[0139]

[0140] optimization The objective function can be expressed as:

[0141]

[0142] Among them, X m =diag(G H w m,r ⊙ρ T ⊙β T Its optimization problem is equivalent to (P4):

[0143]

[0144] in, and This problem can then be effectively solved using a fixed-point iteration method. The specific steps are as follows:

[0145] Step 1: Select feasible set Set the convergence threshold Δ and set t = 0.

[0146] Step 2: Update the next feasible set using the formula: Where unt(·) represents the normalization operation.

[0147] Step 3: Execute step 2 until...

[0148] Since the phase shift of AHRIS is discrete, while the obtained solution is continuous, it is necessary to convert the obtained continuous parameters after the algorithm converges. Mapping to discrete parameters Right now:

[0149]

[0150] in, and They represent and The nth element,

[0151] 3) Optimize the received phase shift vector fixed optimization The objective function can be expressed as:

[0152]

[0153] in, Similar to formula (23), for The optimization problem can be equivalent to (P5):

[0154]

[0155] in, Then, the problem (P5) can be solved using the fixed-point iteration method, and the analysis process is similar to that for solving problem (P4). Finally, continuous phase shift... Mapped to discrete phase shift using the following method

[0156]

[0157] in, and They represent and The nth element.

[0158] 4) Optimize the amplitude vector β: given The optimization problem for β can be expressed as (P6):

[0159]

[0160] in, and To solve the optimization problem (P6), this invention first simplifies the objective function in formula (29). The steps are as follows:

[0161]

[0162] According to vec((β) T β * ) T ) T =vec(β) H β) T and Formula (30) can be further rewritten as:

[0163]

[0164] in, and Now, the difficulty in solving the problem (P6) lies in how to express it using β. And seek an easier-to-solve alternative function to replace it. To solve this problem, the present invention addresses the feasible point. right Performing a second-order Taylor expansion, we obtain:

[0165]

[0166] in, Then, The upper bound can be represented as:

[0167]

[0168] in, (a) is based on the upper bound property of the Rayleigh quotient. Here, λ1 represents the upper bound of the largest eigenvalue of matrix F. Considering an N... 2 ×N 2 The eigenvalue decomposition of matrix F leads to high complexity. This invention uses λ1 = tr(F) to represent the upper bound of the maximum eigenvalue of matrix F. Furthermore, according to... This can be equivalent to:

[0169]

[0170] in, It is a vector reshaping operation that can transform 1×N vectors. 2 dimensional vector Reshape it into an N×N dimensional matrix. Based on the above transformation, it can be derived that... in This is a constant term. Therefore, the optimization problem (P6) can be transformed into (P6)0:

[0171]

[0172] To handle the non-convex constraints C2 and C3, this invention converts them into penalty terms and adds them to the objective function. Thus, problem (P6)0 is reformulated as (P6)1:

[0173]

[0174] After the above transformation, problem (P6)1 becomes a convex optimization problem, which can be solved using convex algorithms / toolboxes.

[0175] 5) Optimize the allocation vector ρ: after obtaining Then, the objective function for optimizing ρ can be expressed as:

[0176]

[0177] in, and because Since both cubic and quartic terms of ρ exist simultaneously, it is difficult to directly optimize ρ. Therefore, this invention chooses... The upper bound function is used as the substitution function to obtain a suboptimal solution. Because |(I-ρ)Z l,m ρT | 2 and Having similar expressions, this invention selects Let's analyze this as an example. The process is as follows:

[0178]

[0179] Based on the properties that the trace of a scalar is equal to itself and that the trace of a matrix tr(AB) = tr(BA), it can be deduced that... and Combining formulas (30) and (31), formula (38) can be further derived as:

[0180]

[0181] in, and Similar to the analysis of formulas (32) and (33), At the reference point The lower bound at that point can be deduced as:

[0182]

[0183] in, (b) is based on the lower bound property of the Rayleigh quotient, where λ² represents the matrix. The lower bound of the minimum eigenvalue. Therefore, it can be deduced that... The lower bound is:

[0184]

[0185] Similarly, The lower bound can be represented as:

[0186]

[0187] in,

[0188]

[0189]

[0190] λ3 is a matrix The lower bound of the minimum eigenvalue, Therefore, the optimization problem for ρ can be expressed as (P7):

[0191]

[0192] To ensure that ρ is a real number Real symmetric matrix Replacement. Combination ρ = ρ * The objective function of the optimization problem (P7) becomes ρZ1ρ H For the non-convex constraints C2 and C3, refer to formula (36) to convert them into penalty terms and add them to the objective function. Therefore, problem (P7) is reformulated as (P7)0:

[0193]

[0194] Problem (P7)0 has been transformed into a quadratic objective function maximization problem with convex constraints, which can be solved using the CVX toolbox.

[0195] Iterative optimization: Finally, And ρ are alternately optimized until F(ζ) approaches 0 and Convergence. The execution steps are as follows:

[0196] Initialization: i = 0, set the convergence threshold Δ.

[0197] Step 1: Execute the loop

[0198] Step 2:

[0199] Step 3: Optimize the sensing beamforming matrix With communication beamforming matrix By formula (21).

[0200] Step 4: Optimize the reflection phase shift vector By formula (25).

[0201] Step 5: Optimize the received phase shift vector By formula (28).

[0202] Step 6: Optimize the amplitude vector β i+1 By formula (36).

[0203] Step 7: Optimize the allocation vector ρ i+1 By formula (46).

[0204] Step 8: i=i+1.

[0205] Step 9: When F(ζ) i )≤Δ and End the loop.

[0206] Step 10: Update and

[0207] Step 11: Return optimization parameters As a solution to problem (P1).

[0208] (6) Simulation Experiment Results

[0209] In this embodiment, simulation analysis verifies the effectiveness of the proposed AHRIS structure and resource optimization configuration method in improving the sensing performance of the ISAC system, and compares them with different RIS structures and different optimization algorithm schemes. In the traditional HRIS structure, the HRIS can reflect or receive signals, but can only adjust the phase shift of the incident signal and cannot amplify the signal. In the active RIS structure, the RIS can only reflect signals, but can adjust the phase shift and amplify the signal. In the passive RIS structure, the RIS can only reflect signals and adjust the phase shift of the incident signal. In the semidefinite programming algorithm-based scheme, the optimization of the sensing beamforming matrix, communication beamforming matrix, reflection phase shift vector, and receiving phase shift vector is achieved by transforming the corresponding optimization problem into a semidefinite programming problem (SDP) through semidefinite relaxation (SDR) and then solving it. The optimization of the amplitude vector and allocation vector both adopt the method mentioned in this invention.

[0210] The simulation parameters are set as follows: K = 3, L = 3, J = 4, M = 6, N = 16. ,β max =10, Δ=10 -6 ,δ 2 = -70dBm,γ min,r =20dBm,γ min,c =5dB, P0 = 20dBm, P1 = 10dBm, P e = -15dBm,P sum =30dBm, τ=0.9.

[0211] To ensure fairness in the comparison, the total system power consumption P of all schemes is... sum Maintain consistency. Specifically, in the scheme employing AHRIS and active RIS, the total system power consumption is P. sum = P0 + P1. In the scheme using HRIS and passive RIS, the total system power budget is P. sum =P0+NP e .

[0212] Figure 3 This paper demonstrates the change in the total perceived SINR compared to the total system power consumption in an ISAC system using the AHRIS structure and optimization algorithm proposed in this invention. The results are compared with traditional HRIS structures, active RIS structures, passive RIS structures, and schemes based on semidefinite programming algorithms. Simulation results show that the total perceived SINR of the system using the proposed AHRIS structure and optimization scheme is significantly better than the other four schemes, proving the effectiveness of the proposed AHRIS structure and optimization scheme in improving perceived SINR.

[0213] Figure 4 This paper demonstrates how the overall perceived SINR changes relative to the number of base station antennas in an ISAC system using the proposed AHRIS structure and optimization algorithm, and compares it with traditional HRIS structures, active RIS structures, passive RIS structures, and schemes based on semidefinite programming algorithms. Simulation results show that the overall perceived SINR of the system using the proposed AHRIS structure and optimization scheme is significantly better than the other four schemes, proving the effectiveness of the proposed AHRIS structure and optimization scheme in improving perceived SINR.

[0214] (7) Beneficial effects

[0215] HRIS, as a RIS structure capable of reflecting and receiving signals, can more precisely adjust different incident signals and mitigate path attenuation experienced by radar echoes. However, due to double path loss and multiplicative attenuation, HRIS cannot effectively improve the performance of the ISAC system when the target is far from the base station. Therefore, this invention proposes an active HRIS (AHRIS) structure that combines a reflective amplifier with HRIS, enabling it to amplify signals while reflecting and receiving incident signals. By amplifying the signal, AHRIS can effectively improve signal strength, thus enhancing the sensing / communication performance of the ISAC system more effectively than HRIS. To demonstrate the effectiveness of the proposed AHRIS structure, this invention further considers an ISAC system framework based on AHRIS assistance. In the downlink, the BS performs radar sensing and wireless communication with the assistance of AHRIS, where AHRIS first amplifies the signal transmitted by the BS and then reflects it to STs and CUs. In the uplink, radar echoes from the detection area are amplified by AHRIS, then received by AHRIS and superimposed into a beam, and finally forwarded to the BS. Based on this framework, this invention proposes an efficient resource allocation method that maximizes the overall perceived signal-to-interference-plus-noise ratio (SINR) of the target while ensuring communication quality by jointly optimizing the sensing beamforming matrix, communication beamforming matrix, AHRIS reflection matrix, and AHRIS receive vector.

[0216] The present invention has the following main advantages:

[0217] 1) This invention proposes an AHRIS structure that adds a reflective amplifier to a traditional HRIS, enabling it to not only reflect and receive incident signals, but also amplify them.

[0218] 2) Based on the proposed AHRIS structure, this invention further proposes an AHRIS-assisted ISAC system, providing a new solution for future wireless network construction, and conducts research on optimizing the system's performance.

[0219] 3) Based on the proposed system, this invention proposes a method for joint optimization of the sensing beamforming matrix, communication beamforming matrix, AHRIS reflection matrix, and AHRIS receive vector, which can effectively improve the sensing SINR of the system while meeting communication requirements. Furthermore, unlike existing solutions that either optimize only the amplitude or, with a fixed amplitude, only the phase shift, this invention equates the AHRIS reflection matrix and AHRIS receive vector to four independent parameters, thereby achieving simultaneous optimization of both the AHRIS amplitude and phase shift.

[0220] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0221] The above embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made based on the essence of the content of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A multidimensional beam joint optimization method, characterized in that, Includes the following steps: Construct a system model for the ISAC system; Construct channel model, signal model, sensing model and communication model based on system model; The optimization problem is determined based on the constructed model; Based on the optimization problem, the sensing beamforming matrix, communication beamforming matrix, AHRIS reflection matrix, and AHRIS receive vector are jointly optimized to maximize the overall sensing signal-to-interference-plus-noise ratio of the system. The perception model and communication model are constructed as follows: Sensing Model: Since radar echoes include signals reflected from both the sensed target and the interference source, the radar echo received by the base station is represented as: In the formula, J It is the number of interference sources; It is a uniform planar array at an angle and The guide vector below; Is it AHRIS and the first l Dual path attenuation between perceived targets Is it AHRIS and the first j Dual-path attenuation between interference sources; and They are the first l The azimuth and elevation angles of the sensing target relative to AHRIS. and They are the first j The azimuth and elevation angles of the interference sources relative to AHRIS; and These represent the reflection matrix and the reception vector of AHRIS, respectively. It is the reflection phase shift vector. It receives the phase shift vector. Represents the amplitude vector. Represents the assignment vector. yes A dimensional vector of all 1s. Represents the Hadama product; It is additive white Gaussian noise; and These are the AHRIS-BS channels in downlink and uplink, respectively. To sense the beamforming matrix; For sensing signal vectors; No. l The sensing SINR corresponding to each sensing target is: In the formula, This represents the power of additive white Gaussian noise; Communication model: For downlink communication, the first k The signal received by a communication user is represented as follows: In the formula, Is the base station and the first k Equivalent channel between communication users It is the first k Additive white Gaussian noise corresponding to each communication user; yes The corresponding number in the middle k Communication beamforming vectors for each communication user yes The corresponding number in the middle k Communication signal vectors of each communication user; yes The corresponding number in the middle Communication beamforming vectors for each communication user yes The corresponding number in the middle Communication signal vectors of each communication user; yes The corresponding number in the middle m Perceived beamforming vector of a target yes The corresponding number in the middle m The sensing signal vector of each target; No. k The received SINR for each communication user is: The expression for the optimization problem is: In the formula, and These are the sensing beamforming matrix and the communication beamforming matrix, respectively. and These represent the reflection matrix and the reception vector of AHRIS, respectively. It is the overall perceived SINR; It is the first l The perceived SINR corresponding to each target It is the minimum perceived SINR that each target must meet. It is the first k The received SINR corresponding to each target It is the minimum received SINR that each user must meet; This is the power consumption of AHRIS when the signal is reflected. This refers to the power consumption of AHRIS when receiving signals. These are the power consumption of the switching and control circuitry of each AHRIS component, as well as the DC bias power consumption. This refers to the battery capacity of the AHRIS. yes The first diagonal vector n The magnitude of each element, yes The Middle n The magnitude of each element, It is the maximum amplitude of each AHRIS unit; This represents the maximum transmit power of the base station; Where C1 represents the total transmit power limit of the base station; C2 and C3 guarantee the minimum sensing SINR of each sensing target and the minimum receiving SINR of each communication user, respectively; C4 represents the power budget constraint of AHRIS; and C5 represents the energy conservation law in AHRIS.

2. The multidimensional beam joint optimization method according to claim 1, characterized in that, The system model for constructing the ISAC system includes: AHRIS is deployed between the base station and the equipment to establish a signal transmission link for radar sensing and wireless communication; which includes... N The AHRIS of each element is modeled as a uniform planar array, and BS is modeled as... M uniform linear array; L Individual perception targets and K The communication users are randomly distributed within a preset range.

3. The multidimensional beam joint optimization method according to claim 1, characterized in that, The channel model is constructed as follows: and These are the AHRIS-BS channels in downlink and uplink, respectively. Indicates AHRIS and the first k Channel between individual communication users; Assume that the large-scale fading experienced by all channels satisfies: In the formula, For communication distance, Indicates reference distance Road strength loss at the location; The path fading index; Rice fading was used as a small-scale fading model for all channels.

4. The multidimensional beam joint optimization method according to claim 1, characterized in that, The signal model is constructed as follows: To achieve the dual functions of sensing and communication, the signals transmitted by the base station include sensing signal vectors. and communication signal vector ,in and , for identity matrix for identity matrix Indicates conjugate transpose; definition and These are the sensing beamforming matrix and the communication beamforming matrix, respectively. The composite signal transmitted by the base station is represented as follows: ;in and satisfy , This represents the maximum transmission power of the base station.

5. The multidimensional beam joint optimization method according to claim 1, characterized in that, The joint optimization of the sensing beamforming matrix, communication beamforming matrix, AHRIS reflection matrix, and AHRIS receive vector includes: In Phase 1, the objective function of optimization problem P1 is transformed into an equivalent linear form using the Tinkelbach method; the highly coupled... and Replace with four independent parameters ; In Phase Two, each parameter is solved individually based on the transformed problem, and the final optimization result is obtained through an alternating optimization algorithm.

6. The multidimensional beam joint optimization method according to claim 5, characterized in that, After phase one, the optimization problem P1 is transformed into the optimization problem P2: In the formula, It is a non-negative auxiliary parameter.

7. The multidimensional beam joint optimization method according to claim 6, characterized in that, Phase two specifically includes: The optimization problem P2 is broken down into five subproblems, and corresponding optimizations are performed: 1) Fixed Optimize beamforming matrix With communication beamforming matrix ; 2) Fixed Optimize the reflection phase shift vector ; 3) Fix Optimize the received phase shift vector ; 4) Given Optimize amplitude vector ; 5) In obtaining Then, optimize the allocation vector. .

8. An AHRIS-assisted ISAC system for implementing the method described in any one of claims 1-7, characterized in that, It includes a base station, AHRIS, and communication users; the AHRIS is equipped with a reflective amplifier, the incident signal is first amplified by the reflective amplifier, and then split into a reflected signal and a received signal by the radio frequency splitter; the reflected signal is directly reflected outward by the AHRIS, while the received signal is superimposed on the radio frequency chain and then forwarded by the AHRIS. During the downlink, the base station performs radar sensing and wireless communication with the assistance of AHRIS. AHRIS first amplifies the signal sent by the base station and then reflects the amplified signal to the sensing target and the communication user. During the uplink, the radar echo from the detection area is amplified by AHRIS, then received by AHRIS and superimposed into a beam, and finally forwarded to the base station.

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