ASAC system based on AHRIS assistance and multi-dimensional beam joint optimization method

Abstract

The invention discloses an ISAC system based on AHRIS assistance and a multi-dimensional beam joint optimization method. The system comprises a base station, an AHRIS and a communication user. A reflective amplifier is arranged in the AHRIS, and an incident signal is firstly amplified by the reflective amplifier and then split into a reflected signal and a received signal by the radio frequency shunt; wherein the reflected signal is directly reflected outwards by the AHRIS, and the received signal is superposed on the radio frequency chain and then is forwarded by the AHRIS; in the downlink period, the base station performs radar sensing and wireless communication with the assistance of the AHRIS, and the AHRIS firstly amplifies a signal sent by the base station and then reflects the amplified signal to a sensing target and a communication user; in the uplink period, radar echoes from a detection area are amplified by the AHRIS, then received and superposed by the AHRIS to form a beam, and finally forwarded to the base station. According to the invention, the sensing / communication performance of the ISAC system can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and in particular to an ISAC system based on AHRIS assistance and a multi-dimensional beam joint optimization method. Background Art

[0002] A reconfigurable smart surface (RIS) is a metamaterial composed of passive reflective elements that can manipulate the radio propagation environment by adjusting the amplitude and / or phase of the incident signal. Deploying a RIS in an ISAC system can effectively improve signal transmission efficiency and range, thereby enhancing the system's communication and perception performance. Traditional RISs only reflect signals and can be further categorized as passive or active, depending on whether they can amplify the incident signal. Passive RISs only adjust the phase of the incident signal and do not require an additional power supply. Active RISs can both adjust the phase shift and amplify the incident signal, effectively improving signal quality but increasing system power consumption. Unlike traditional, purely reflective RISs, hybrid RISs (HRISs) combine passive RISs with dynamic metasurface antennas, enabling them to both reflect and receive signals. The reflection matrix and receive vector of an HRIS correspond to the reflection and receive functions, respectively. In an HRIS-assisted ISAC system, the reflection matrix controls the HRIS elements to reflect the composite signal transmitted by the base station (BS) toward sensing targets (STs) / communication users (CUs), while the receive vector controls the HRIS elements to receive radar echoes from the detection area. Compared to a reflective matrix-only RIS, the HRIS with dual beamforming can more precisely adjust to varying incoming signals. Furthermore, all HRIS components are connected to the radio frequency (RF) chain via dedicated waveguides. Therefore, received radar returns are summed into a single beam within the HRIS and then forwarded by the HRIS. This effectively mitigates path attenuation of radar returns compared to a purely reflective RIS. Consequently, the HRIS can significantly improve the communication and perception performance of the ISAC system compared to a RIS.

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

[0004] In order to solve at least one of the technical problems existing in the prior art to a certain extent, the object of the present invention is to provide an ISAC system and a multi-dimensional beam joint optimization method based on AHRIS assistance.

[0005] The first technical solution adopted by the present invention is:

[0006] An ISAC system based on AHRIS assistance includes a base station, an AHRIS, and a communication user. The AHRIS is equipped with a reflection amplifier. An incident signal is first amplified by the reflection 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, where AHRIS first amplifies the signal sent by the base station and then reflects the amplified signal to the sensing target and communication user; during the uplink, the radar echo from the detection area is amplified by AHRIS, then received and superimposed into a beam by AHRIS, and finally forwarded to the base station.

[0008] The second technical solution adopted by the present invention is:

[0009] A multi-dimensional beam joint optimization method, applied to the above-mentioned ISAC system based on AHRIS assistance, includes the following steps:

[0010] Construct the system model of ISAC system;

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

[0012] Determine the optimization problem based on the constructed model;

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

[0014] Furthermore, the construction of the ISAC system model includes:

[0015] An AHRIS is deployed between the base station and the device 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 are AHRIS-BS channels in downlink and uplink respectively; g r,k represents the channel between AHRIS and the kth communication user;

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

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

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

[0021] All channels use Rician fading as the small-scale fading model.

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

[0023] In order to realize the dual functions of perception and communication, the signal transmitted by the base station contains the perception signal vector s r and the communication signal vector s c ,in and I M is the M×M identity matrix, I K is the K×K identity matrix, H represents the conjugate transpose;

[0024] Define W r and W c are the sensing beamforming matrix and the communication beamforming matrix respectively. The composite signal transmitted by the base station is expressed 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] Perception model: Since the radar echo includes signals reflected by both the perceived target and the interference source, the radar echo received by the base station is expressed as:

[0027]

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

[0029]

[0030]

[0031]

[0032] Where 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 is the double path attenuation between AHRIS and the lth sensing target, is the double path attenuation between AHRIS and the jth interferer; θ l and φ l are the azimuth and elevation of the lth perceived target relative to AHRIS, and are the azimuth and elevation angles of the jth interference source relative to AHRIS; Φ r and Φ s Represent the reflection matrix and receiving vector of AHRIS respectively; is the reflection phase shift vector, is the received phase shift vector, β represents the amplitude vector, ρ represents the distribution vector, I is a 1×N-dimensional all-one vector, ⊙ represents the Hadamard product; n r is additive white Gaussian noise;

[0033] The perceived SINR corresponding to the lth perception target is:

[0034]

[0035] Where, δ 2 represents the power of additive white Gaussian noise;

[0036] Communication model: For downlink communication, the signal received by the kth communication user is expressed as:

[0037]

[0038] Where g k is the equivalent channel between the base station and the kth communication user, n r is the additive white Gaussian noise corresponding to the kth communication user; w k,c It's W c The communication beamforming vector corresponding to the kth communication user, s k,c It is c The communication signal vector corresponding to the kth communication user; w k′,c It's W c The communication beamforming vector corresponding to the k′th communication user, s k′,c It is c The communication signal vector corresponding to the k′th communication user; w m,r It's W r The perceptual beamforming vector corresponding to the mth target, s m,r It is r The perception signal vector corresponding to the mth target;

[0039] The received SINR of the kth communication user is:

[0040]

[0041] Furthermore, the optimization problem is expressed as:

[0042]

[0043] Where W r and W c are the sensing beamforming matrix and the communication beamforming matrix respectively; φ r and Φ s Respectively represent the reflection matrix and receiving vector of AHRIS; γ sum is the total perceived SINR; γ l,r is the perceived SINR corresponding to the lth target, γ min,r is the minimum perceived SINR that each target must meet, γ k,c is the received SINR corresponding to the kth target, γ min,c is the minimum receiving SINR that each user needs to meet; P r is the power consumption of AHRIS when reflecting the signal, P s is the power consumption of AHRIS when receiving signals, P e is the power consumption of the switch and control circuits and the DC bias power consumption of each AHRIS component, and P1 is the battery capacity of the AHRIS; is Φ r The magnitude of the nth element in the diagonal vector, is Φ s The magnitude of the nth element in , β max is the maximum amplitude of each AHRIS unit;

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

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

[0046] In the first stage, the objective function of the optimization problem (P1) is transformed into an equivalent linear form using the Dinkelbach method; the highly coupled Φ r and Φ s Replaced with four separate parameters

[0047] In the second stage, according to the transformed problem, each parameter is solved separately, and the final optimization result is obtained through the alternating optimization algorithm.

[0048] Furthermore, after stage 1, the optimization problem (P1) is transformed into the optimization problem (P2):

[0049]

[0050] Where ζ is a non-negative auxiliary parameter.

[0051] Furthermore, the second stage specifically includes:

[0052] The optimization problem (P2) is divided into five sub-problems and optimized accordingly:

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

[0054] 2) Fixed Optimizing the reflection phase shift vector

[0055] 3) Fixation Optimize the receiving phase shift vector

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

[0057] 5) After obtaining After that, the allocation vector ρ is optimized.

[0058] The present invention addresses the problem of perceived signal-to-interference-and-noise ratio degradation in ISAC systems caused by dual path loss and multiplicative fading. It proposes an active HRIS (AHRIS) architecture. By amplifying the signal, AHRIS effectively increases signal strength, thereby improving the ISAC system's perception and communication performance compared to HRIS. Furthermore, to maximize the system's perceived SINR, the present invention designs a joint optimization algorithm for the perception beamforming matrix, communication beamforming matrix, AHRIS reflection matrix, and AHRIS receive vector. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present invention or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly describing 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 any creative work.

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

[0061] Figure 2 Schematic diagram of the AHRIS structure proposed in an embodiment of the present invention;

[0062] Figure 3 Schematic diagram showing changes in the total perceived SINR of the system compared to the total system power consumption when the ISAC system in an embodiment of the present invention respectively adopts the AHRIS structure proposed in the present invention and the optimized allocation scheme proposed in the present invention, the traditional HRIS structure and the optimized allocation scheme proposed in the present invention, the active RIS structure and the optimized allocation scheme proposed in the present invention, the passive RIS structure and the optimized allocation scheme proposed in the present invention, and the AHRIS structure proposed in the present invention and the optimization scheme based on semi-definite programming;

[0063] Figure 4 Schematic diagram showing the change in the total perceived SINR of the system compared to the number of base station antennas when the ISAC system adopts the AHRIS structure proposed in the present invention and the optimized allocation scheme proposed in the present invention, the traditional HRIS structure and the optimized allocation scheme proposed in the present invention, the active RIS structure and the optimized allocation scheme proposed in the present invention, the passive RIS structure and the optimized allocation scheme proposed in the present invention, and the AHRIS structure proposed in the present invention and the optimization scheme based on semi-definite programming in embodiments of the present invention. DETAILED DESCRIPTION

[0064] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application. For the step numbers in the following embodiments, they are provided only for the convenience of explanation and are not intended to limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0065] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the embodiments of the present application. The singular forms of "a", "said", and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise clearly defined, words such as setting, installing, and connecting should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above words in the present invention in combination with the specific content of the technical solution.

[0066] In the description of this application, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships 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, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on this application.

[0067] In the description of this application, "several" means one or more, "many" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The terms "first" and "second" are used solely to distinguish technical features and are not to be construed as indicating or implying relative importance, or as implicitly specifying the number or order of the technical features indicated.

[0068] In the description of this application, "and / or" describes the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0069] Explanation of terms:

[0070] ISAC: abbreviation of Integrated Sensing and Communication, integrated communication and perception technology.

[0071] RIS: abbreviation of Reconfigurable Intelligence Surface, reconfigurable intelligent surface.

[0072] AHRIS: Active hybrid reconfigurable smart surfaces.

[0073] BS: Base station.

[0074] STs: sensory targets.

[0075] Cus: Communication user.

[0076] In the first existing paper, the authors considered an HRIS-assisted multiple-input multiple-output (MIMO) ISAC system, where the HRIS reflects the incident signal to the CU and receives the radar echo. They proposed an alternating optimization method consisting of an automatic gradient descent (AGD) method and a semi-definite relaxation (SDR) algorithm to maximize the radar's perceived SINR while ensuring the SINR requirement of each CU.

[0077] In the second existing literature, the focus is on the HRIS-assisted ISAC system under non-line-of-sight (NLoS) conditions. In the considered system, the authors proposed a parameter design method to maximize 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 perceived SINR of an HRIS-assisted ISAC system by optimizing transmit beamforming, HRIS-assisted reflection beamforming, and HRIS-assisted receive beamforming. To address the non-convex optimization problem, the authors propose an efficient iterative algorithm based on the Rayleigh quotient, the Dinkelbach transform, and the successive convex approximation (SCA) method.

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

[0080] Furthermore, optimizing the performance of AHRIS-assisted ISAC systems is extremely challenging due to the complex objective functions and highly coupled parameters. Specifically, when optimizing the AHRIS reflection matrix and receive vector, existing solutions either optimize only the amplitude or, with a fixed amplitude, only the phase shift. To maximize the AHRIS system gain, a solution is needed that can simultaneously optimize both the AHRIS amplitude and phase shift.

[0081] In response to the above problems, the present invention proposes an AHRIS structure, which adds a reflective amplifier to the traditional HRIS, so that it has the ability to reflect, receive and amplify the incident signal. Based on the proposed structure, the present 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 sent by the BS and then reflects it to STs and CUs. During the uplink, the radar echo from the detection area is amplified by the AHRIS, then received and superimposed into a beam by the AHRIS, and finally forwarded to the BS. In order to maximize the total perception SINR of the system while meeting the communication requirements, the present invention proposes a joint optimization method for the perception beamforming matrix, the communication beamforming matrix, the AHRIS reflection matrix and the AHRIS receiving vector. Specifically, the joint optimization method is divided into two stages. In stage one, the present invention first uses the Dinkelbach method to convert the objective function of problem (P1) into an equivalent linear form. Then, the highly coupled Φ r and Φ s Replaced with four separate parameters In the second phase, 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 optimization allocation scheme can effectively improve the overall perceived SINR of the system while meeting communication requirements.

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

[0083] (1) System model

[0084] As an example, in an 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 in the figure, due to the obstruction of the building, the BS and the device cannot communicate directly. Therefore, in this embodiment, an AHRIS is deployed between the BS and the device to establish a signal transmission link for radar sensing and wireless communication. The AHRIS can reflect and receive the incident signal, and can also amplify the incident signal. 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 on the RF chain and then forwarded by the AHRIS. Based on the characteristics of the AHRIS, the present invention proposes a new ISAC system transmission protocol. Specifically, in the downlink, the base station performs radar sensing and wireless communication with the assistance of the AHRIS. The AHRIS first amplifies the signal transmitted by the base station 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 and superimposed into a beam by the AHRIS, and finally forwarded to the base station.

[0086] (2) Channel Modeling

[0087] G∈C M×N and are the AHRIS-BS channels in downlink and uplink respectively. r,k ∈C N×1 Then represents the channel between AHRIS and the kth 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 exponent, and the path fading exponent values ​​for BS-AHRIS and AHRIS and the device are set to 2.5 and 2, respectively. In addition, all channels use Rice fading as a small-scale fading model with a Rice factor of 3dB. Since channel estimation errors are inevitable, this paper considers a statistical error model for the channel state information, G, and g r,k Can be expressed as

[0088]

[0089]

[0090]

[0091] (3) Signal model and transmission model

[0092] Signal model: In order to achieve the dual functions of perception and communication, the signal transmitted by the BS contains the perception signal vector s r =[s 1,r ,…,s M,r ] T and the communication signal vector s c =[s 1,c ,…,s K,c ] T ,in and IM is the M×M identity matrix, I K is a K×K unit matrix. In order to eliminate the interference between signals, it is assumed that the perception signal and the communication signal are statistically independent of each other. Define W r =[w 1,r ,…,w M,r ] and W c =[w 1,c ,…,w K,c ] are 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 transmit power of the BS.

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

[0094] Perception model: Since the radar echo includes signals reflected by both STs and interference sources, the radar echo received by the BS can be expressed as

[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 is the steering vector of the uniform planar array at angles θ and φ. is the double path attenuation between AHRIS and the lth ST (jth interferer), and are the azimuth and elevation angles of the lth ST (jth interference source) relative to the AHRIS, respectively. and Represent the reflection matrix and receiving vector of AHRIS respectively, where and are the amplitude and phase shift of the signal reflected (received) by the nth element. max is the maximum amplitude, is the number of phase shift coefficients. Figure 2 As shown, according to the internal structure of AHRIS, and can be equivalent to ρ n β n and (1-ρ n )β n , where β n is the gain factor of the reflective amplifier in the nth element, ρ n represents the reflected signal portion of the nth element. It can be deduced that and in is the reflected phase shift vector, is the received phase shift vector, β represents the amplitude vector, ρ represents the allocation vector, and Ι is a 1×N-dimensional all-one vector. represents the received additive white Gaussian noise. According to formula (4), the perceived SINR corresponding to the lth ST can be modeled as:

[0097]

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

[0099]

[0100] in is the equivalent channel between BS and k-th CU, 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 issues

[0103] Under the considered system, the goal of this invention is to optimize the perceptual beamforming matrix W by jointly 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 receiving vector Φ s , while ensuring the communication quality, the target total perceived SINR is maximized. Therefore, the mathematical expression of the optimization problem is (P1):

[0104]

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

[0106] (5) Joint resource optimization allocation method

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

[0108] 5.1) Phase 1: Conversion Optimization Problem

[0109] In this phase, the goal is to transform the optimization problem (P1) into a more tractable form by 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 convert it into:

[0111]

[0112] Among them, ζ is a non-negative auxiliary parameter, which is executed before each alternating iteration. Update. Definition and Because both are 1×1 scalars, according to The objective function (9) can be further simplified as:

[0113]

[0114] In addition, the highly coupled AHRIS reflection matrix Φ r and AHRIS receiving vector Φ s This also greatly increases the complexity of solving the problem (P1). In order to solve this problem, the present invention is based on and Use separately 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) is still difficult to solve due to the presence of multiple coupled parameters and non-convex constraints C2, C3, C6, and C7. An efficient solution is to split the original problem into multiple tractable subproblems and solve them using an alternating optimization algorithm.

[0117] 5.2) Phase 2: Performing Alternating Optimization

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

[0119] The specific process is:

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

[0121]

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

[0123]

[0124]

[0125] definition and Formula (13) and formula (14) can be rewritten as:

[0126]

[0127]

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

[0129]

[0130] In order to solve the non-convexity of constraints C15 and C16, the present invention converts the rank-1 constraint into a penalty term and then reformulates the objective function of problem (P3)0 as:

[0131]

[0132] Among them, κ>0 is the punishment factor, M e (·) represents the maximum eigenvalue of the matrix. However, the penalty term and The objective function becomes a non-convex function. To address this problem, this embodiment introduces the continuous convex approximation (SCA) algorithm. Specifically, the nth iteration is used to find the feasible point and The first-order Taylor expansion of and The upper bounds of can be expressed as:

[0133]

[0134]

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

[0136]

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

[0138] 2) Optimize the reflection phase shift vector fixed On 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 Then, the problem can be effectively solved by the fixed point iteration method. The specific steps are:

[0145] Step 1: Select a feasible set Converge threshold Δ and set t=0.

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

[0147] Step 3: Follow step 2 until

[0148] Since the phase shift of AHRIS is discrete, and the solution is continuous, after the algorithm converges, the continuous parameters need to be converted to Mapping into discrete parameters Right now:

[0149]

[0150] in, and Respectively and The nth element of

[0151] 3) Optimize the receiving 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 fixed point iteration method can be used to solve problem (P5), and the analysis process is similar to that of solving problem (P4). Mapping to discrete phase shifts is done by

[0156]

[0157] in, and Respectively and The nth element of .

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

[0159]

[0160] in, and In order to solve the optimization problem (P6), the present 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 problem (P6) lies in how to express it in terms of β And seek an alternative function that is easier to solve to replace In order to solve this problem, the present invention is feasible right Performing a second-order Taylor expansion, we obtain:

[0165]

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

[0167]

[0168] in, (a) is based on the upper bound property of the Rayleigh quotient. Where λ1 represents the upper bound of the maximum eigenvalue of the matrix F. Considering an N 2 ×N 2 The eigendecomposition of will bring high complexity. The present invention uses λ1=tr(F) to represent the upper limit of the maximum eigenvalue of the matrix F. In addition, according to Can be equivalent to:

[0169]

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

[0171]

[0172] In order to deal with the non-convex constraints C2 and C3, the present invention converts them into penalty terms and adds them to the objective function. In this way, the 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 a convex algorithm / toolbox.

[0175] 5) Optimize the allocation vector ρ: After obtaining After that, the objective function of optimizing ρ can be expressed as:

[0176]

[0177] in, and because There are both cubic and quartic terms of ρ in , which makes it difficult to optimize ρ directly. Therefore, the present invention selects The upper bound function of is used as an alternative function to obtain a suboptimal solution. |(I-ρ)Z in l,m ρT | 2 and With similar expressions, the present invention uses As an example analysis The process is as follows:

[0178]

[0179] According to the property that the trace of a scalar is equal to itself and the trace of a matrix tr(AB)=tr(BA), we can deduce that and Combining formulas (30) and (31), formula (38) can be further deduced as:

[0180]

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

[0182]

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

[0184]

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

[0186]

[0187] in,

[0188]

[0189]

[0190] λ3 is the 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 Replace. Combine 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, the problem (P7) is reformulated as (P7)0:

[0193]

[0194] Problem (P7)0 becomes a quadratic objective function maximization problem with convex constraints and can be solved using the CVX toolbox.

[0195] Iterative optimization: Finally, and ρ are optimized alternately until F(ζ) is close to 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 perceptual beamforming matrix and communication beamforming matrix Through formula (21).

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

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

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

[0203] Step 7: Optimize the allocation vector ρ i+1 Through 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 to optimized parameters As a solution to problem (P1).

[0208] (6) Simulation experiment results

[0209] In this embodiment, simulation analysis verified the effectiveness of the proposed AHRIS structure and resource optimization configuration method in improving the perception performance of the ISAC system, and compared different RIS structures and different optimization algorithm solutions. In a 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 an active RIS structure, the RIS can only reflect signals, but can adjust the phase shift and amplify the signal. In a passive RIS structure, the RIS can only reflect signals and adjust the phase shift of the incident signal. In the semi-definite programming algorithm solution, the optimization of the sensing beamforming matrix, the communication beamforming matrix, the reflection phase shift vector, and the reception phase shift vector is achieved by converting the corresponding optimization problem into a semi-definite programming problem (SDP) through semi-definite relaxation (SDR) and then solving it. The optimization of the amplitude vector and the allocation vector both adopt the methods mentioned in this invention.

[0210] The relevant parameters of the simulation experiment 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] In order to ensure the fairness of the comparison, the total system power consumption P of all solutions is sum Specifically, in the solution using 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 how the overall perceived SINR of the system changes relative to total system power consumption when using the proposed AHRIS structure and optimization algorithm in an ISAC system. The paper also compares this with traditional HRIS structures, active RIS structures, passive RIS structures, and a solution based on a semi-definite programming algorithm. The simulation graphs show that the proposed AHRIS structure and optimization scheme significantly outperforms the other four schemes in terms of overall perceived SINR, demonstrating its effectiveness in improving perceived SINR.

[0213] Figure 4 This paper demonstrates how the overall perceived SINR of the system changes with the number of base station antennas in an ISAC system using the proposed AHRIS structure and optimization algorithm. The paper also compares this with traditional HRIS structures, active RIS structures, passive RIS structures, and a solution based on a semi-definite programming algorithm. The simulation graphs show that the proposed AHRIS structure and optimization scheme significantly outperforms the other four schemes in terms of overall perceived SINR, demonstrating its effectiveness in improving perceived SINR.

[0214] (7) Beneficial effects

[0215] As a RIS structure capable of both reflecting and receiving signals, the HRIS can more precisely adjust to varying incident signals and mitigate the path attenuation experienced by radar echoes. However, due to dual path loss and multiplicative attenuation, the HRIS cannot effectively improve ISAC system performance when the target is far from the base station. Therefore, this paper proposes an active HRIS (AHRIS) structure that combines a reflective amplifier with the HRIS, enabling it to amplify the signal while reflecting and receiving the incident signal. By amplifying the signal, the AHRIS effectively increases signal strength, thereby improving the ISAC system's perception and communication performance compared to the HRIS. To demonstrate the effectiveness of the proposed AHRIS structure, this paper further considers an AHRIS-assisted ISAC system framework. In the downlink, the base station performs radar perception and wireless communication with the assistance of the AHRIS. The AHRIS first amplifies the signal transmitted by the base station and then reflects it to the STs and CUs. In the uplink, radar echoes from the detection area are amplified by the AHRIS, then received and superimposed into a beam, and finally forwarded to the base station. Based on this framework, the present invention proposes an efficient resource allocation method, which can maximize the target total perceived signal-to-interference-and-noise ratio (SINR) while ensuring communication quality by jointly optimizing the perception beamforming matrix, communication beamforming matrix, AHRIS reflection matrix, and AHRIS receive vector.

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

[0217] 1) The present invention proposes an AHRIS structure, which adds a reflective amplifier to the traditional HRIS, so that it can not only reflect and receive the incident signal, but also amplify the signal.

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

[0219] 3) Based on the proposed system, this paper proposes a method for jointly optimizing the sensing beamforming matrix, communication beamforming matrix, AHRIS reflection matrix, and AHRIS receive vector. This method effectively improves the system's perceived SINR while meeting communication requirements. Unlike existing solutions that either optimize only amplitude or only phase shift while maintaining a fixed amplitude, this paper equates the AHRIS reflection matrix and AHRIS receive vector into four independent parameters, enabling simultaneous optimization of both AHRIS amplitude and phase shift.

[0220] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0221] The above embodiments are intended only to illustrate the technical concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the contents of the present invention and implement them accordingly. They are not intended to limit the scope of protection of the present invention. Any equivalent changes or modifications made based on the essence of the present invention are intended to be covered by the scope of protection of the present invention.

Claims

1. An ISAC system based on AHRIS assistance, characterized by: It includes a base station, an AHRIS, and a communication user. The AHRIS is equipped with a reflection amplifier. The incident signal is first amplified by the reflection 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. During the downlink, the base station performs radar sensing and wireless communication with the assistance of AHRIS, where AHRIS first amplifies the signal sent by the base station and then reflects the amplified signal to the sensing target and communication user; during the uplink, the radar echo from the detection area is amplified by AHRIS, then received and superimposed into a beam by AHRIS, and finally forwarded to the base station.

2. A multi-dimensional beam joint optimization method, applied to the AHRIS-assisted ISAC system according to claim 1, characterized in that: The following steps are involved: Construct the system model of ISAC system; Construct channel model, signal model, perception model and communication model based on the system model; Determine the optimization problem based on the constructed model; Based on the optimization problem, the perception beamforming matrix, communication beamforming matrix, AHRIS reflection matrix and AHRIS receiving vector are jointly optimized to maximize the overall perception signal-to-interference-and-noise ratio of the system.

3. The multi-dimensional beam joint optimization method according to claim 2, characterized in that: The system model for constructing the ISAC system includes: An AHRIS is deployed between the base station and the device 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.

4. The multi-dimensional beam joint optimization method according to claim 2, characterized in that: The channel model is constructed as follows: G and are AHRIS-BS channels in downlink and uplink respectively; g r,k represents the channel between AHRIS and the kth communication user; Assume that the large-scale fading experienced by all channels satisfies: L(d)=-C0(d / d0) -l Where d is the communication distance, C0 represents the path loss at the reference distance d0 = 1; l is the path fading index; All channels use Rician fading as the small-scale fading model.

5. The multi-dimensional beam joint optimization method according to claim 2, characterized in that: The signal model is constructed as follows: In order to realize the dual functions of perception and communication, the signal transmitted by the base station contains the perception signal vector s r and the communication signal vector s c ,in and I M is the M×M identity matrix, I K is the K×K identity matrix, H represents the conjugate transpose; Define W r and W c are the sensing beamforming matrix and the communication beamforming matrix respectively. The composite signal transmitted by the base station is expressed 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.

6. The multi-dimensional beam joint optimization method according to claim 2, characterized in that: The perception model and communication model are constructed as follows: Perception model: Since the radar echo includes signals reflected by both the perceived target and the interference source, the radar echo received by the base station is expressed as: H l =a l a(θ l ,f l )a(θ l ,f l ) H Where 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 is the double path attenuation between AHRIS and the lth sensing target, is the double path attenuation between AHRIS and the jth interferer; θ l and φ l are the azimuth and elevation of the lth perceived target relative to AHRIS, and are the azimuth and elevation angles of the jth interference source relative to AHRIS; Φ r and Φ s Represent the reflection matrix and receiving vector of AHRIS respectively; is the reflection phase shift vector, is the received phase shift vector, β represents the amplitude vector, k represents the allocation vector, I is a 1×N-dimensional all-one vector, ⊙ represents the Hadamard product; n r is additive white Gaussian noise; The perceived SINR corresponding to the lth perception target is: Where, δ 2 represents the power of additive white Gaussian noise; Communication model: For downlink communication, the signal received by the kth communication user is expressed as: Where g k is the equivalent channel between the base station and the kth communication user, n r is the additive white Gaussian noise corresponding to the kth communication user; w k,c It's W c The communication beamforming vector corresponding to the kth communication user, s k,c It is c The communication signal vector corresponding to the kth communication user; w k′,c It's W c The communication beamforming vector corresponding to the k′th communication user, s k′,c It is c The communication signal vector corresponding to the k′th communication user; w m,r It's W r The perceptual beamforming vector corresponding to the mth target, s m,r It is r The perception signal vector corresponding to the mth target; The received SINR of the kth communication user is:

7. The multi-dimensional beam joint optimization method according to claim 2, characterized in that: The expression of the optimization problem is: C4:P r +P s +NP e ≤P1, Where W r and W c are the sensing beamforming matrix and the communication beamforming matrix respectively; Φ r and Φ s Respectively represent the reflection matrix and receiving vector of AHRIS; γ sum is the total perceived SINR; γ l,r is the perceived SINR corresponding to the lth target, γ min,r is the minimum perceived SINR that each target must meet, γ k,c is the received SINR corresponding to the kth target, γ min,c is the minimum receiving SINR that each user needs to meet; P r is the power consumption of AHRIS when reflecting the signal, P s is the power consumption of AHRIS when receiving signals, P e is the power consumption of the switch and control circuits and the DC bias power consumption of each AHRIS component, and P1 is the battery capacity of the AHRIS; is Φ r The magnitude of the nth element in the diagonal vector, is Φ s The amplitude of the nth element in , β max is the maximum amplitude of each AHRIS unit; Among them, C1 represents the total transmit power limit of the base station; C2 and C3 guarantee the minimum perceived SINR of each sensing target and the minimum received SINR of each communication user, respectively; C4 represents the power budget constraint of AHRIS; C5 represents the energy conservation law in AHRIS.

8. The multi-dimensional beam joint optimization method according to claim 2, characterized in that: The joint optimization of the sensing beamforming matrix, the communication beamforming matrix, the AHRIS reflection matrix, and the AHRIS receive vector includes: In the first stage, the objective function of the optimization problem (P1) is transformed into an equivalent linear form using the Dinkelbach method; the highly coupled Φ r and Φ s Replaced with four separate parameters In the second stage, according to the transformed problem, each parameter is solved separately, and the final optimization result is obtained through the alternating optimization algorithm.

9. The multi-dimensional beam joint optimization method according to claim 8, characterized in that: After stage 1, the optimization problem (P1) is transformed into the optimization problem (P2): stC1-C4, Where ζ is a non-negative auxiliary parameter.

10. The multi-dimensional beam joint optimization method according to claim 9, characterized in that: The second stage specifically includes: The optimization problem (P2) is divided into five sub-problems and optimized accordingly: 1) Fixed Optimize the beamforming matrix W r and the communication beamforming matrix W c ; 2) Fixed Optimizing the reflection phase shift vector 3) Fixation Optimize the receiving phase shift vector 4) Given Optimize the amplitude vector β; 5) After obtaining After that, the allocation vector ρ is optimized.

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