A ris-assisted isac network transmission method based on hardware damage and imperfect channel

Abstract

The application belongs to the technical field of wireless communication, and particularly relates to a RIS-assisted ISAC network transmission method based on hardware impairment and imperfect channel, which comprises the following steps: constructing an ISAC network system model based on RIS assistance; constructing a target function with the maximization of perceptual beamforming gain as an optimization target; constructing a perceptual beamforming gain maximization model in combination with four constraint conditions of user signal-to-noise ratio, eavesdropper signal-to-noise ratio, RIS amplitude and base station transmission power; and obtaining an optimal transmission scheme and performing data transmission by solving the perceptual beamforming gain maximization model; the application realizes the maximization of perceptual beamforming gain under the premise of considering HWIs and imperfect CSI, and can safely transmit user information while guaranteeing the robustness of the system.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a RIS-assisted ISAC network transmission method based on hardware impairments and imperfect channels. Background Technology

[0002] After decades of independent development, wireless communication and radar sensing technologies are now evolving towards deep integration. To address the challenges of increasingly scarce spectrum resources, high hardware costs, and low system energy efficiency, Integrated Sensing and Communication (ISAC), as an emerging system paradigm, is considered a key enabling technology for sixth-generation mobile communication (6G) and future intelligent networks. Compared to traditional discrete system designs, ISAC significantly improves system spectral efficiency, energy efficiency, and hardware utilization by sharing spectrum resources, hardware platforms, and unified waveforms. However, performance degradation is still unavoidable under harsh propagation conditions. Faced with such a complex electromagnetic environment, the Reconfigurable Intelligent Surface (RIS) technology, which has emerged in recent years, offers a potentially transformative approach to solving these problems. RIS is a two-dimensional planar array composed of a large number of low-cost, reconfigurable passive reflective elements. Each element can independently control the amplitude and phase of the incident signal, thereby actively reconfiguring the wireless propagation environment and effectively improving spectrum utilization efficiency and system robustness.

[0003] Introducing RIS into ISAC systems has significant research value and application prospects. RIS can not only enhance signal quality between the base station and users or sensing targets, but also improve sensing performance while enhancing physical layer secure transmission capabilities through precise beamforming. For example, existing research has explored using RIS to assist ISAC systems to optimize multi-user communication quality while meeting target sensing signal-to-noise ratio or estimation error constraints; or, under conditions of imperfect channel state information, to improve sensing accuracy and secure transmission performance by jointly designing active beamforming and RIS passive beamforming. These works have initially revealed the potential of RIS to synergistically enhance communication and sensing capabilities in ISAC systems.

[0004] However, most existing studies are based on idealized assumptions and fail to fully consider the impact of unavoidable hardware impairments (HWIs) and imperfect channel state information (CSI) in real-world systems. Hardware impairments such as transceiver nonlinearity and phase noise, as well as CSI uncertainties caused by channel estimation errors, user mobility, and environmental time-varying factors, severely restrict beamforming accuracy, thereby degrading overall system performance, especially secure transmission capabilities. Although a few studies have begun to focus on robust design in RIS-assisted communication systems, research on incorporating both hardware impairments and imperfect CSI into the RIS-assisted ISAC secure transmission framework and conducting joint optimization is still relatively lacking. Therefore, exploring the secure transmission mechanism of RIS-assisted ISAC systems under conditions closer to real-world systems has significant theoretical and practical value. Summary of the Invention

[0005] To address the above problems, this invention provides a RIS-assisted ISAC network transmission method based on hardware impairments and imperfect channels, characterized by the following steps:

[0006] S1. Construct an ISAC network system model based on RIS assistance, which includes a base station equipped with M antennas, a RIS and K single-antenna users. The RIS is equipped with N reflection units; at the same time, a sniper is used as the sensing target.

[0007] S2. Construct an objective function with the goal of maximizing the perceived beamforming gain; combine the four constraints of user signal-to-noise ratio, eavesdropper signal-to-noise ratio, RIS amplitude and base station transmit power to construct a model for maximizing the perceived beamforming gain.

[0008] S3. By solving the sensing beamforming gain maximization model, the optimal transmission scheme is obtained and data transmission is executed.

[0009] The beneficial effects of this invention are:

[0010] This invention addresses issues such as transceiver hardware aging, oscillator noise, and low-resolution digital-to-analog converters that impair the quality of transmitted and received signals. It also considers channel uncertainties encountered in practical applications and constructs an intelligent reflector-assisted sensing-integrated model. This model reflects the optimal sensing beam gain achieved with intelligent reflector assistance while ensuring secure user communication. It maximizes sensing beam gain while considering HWIs and imperfect CSI, enabling secure transmission of user information while maintaining system robustness, thus better aligning with real-world application scenarios. Attached Figure Description

[0011] Figure 1 This is a flowchart of the RIS-assisted ISAC network transmission method based on hardware impairment and imperfect channels in this invention.

[0012] Figure 2 This is a model diagram of the RIS-assisted ISAC network system based on hardware impairments and imperfect channels in this invention.

[0013] Figure 3 This is an iterative diagram of the sensing beam gain of the present invention and the comparative scheme;

[0014] Figure 4 This is a graph showing the relationship between the number of elements of the intelligent reflective surface and the sensing beam gain in the present invention and the comparative scheme. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figures 1-2 This invention provides a RIS-assisted ISAC network transmission method based on hardware impairments and imperfect channels, comprising the following steps:

[0017] S1. Construct an ISAC network system model based on RIS assistance.

[0018] Preferably, the embodiments of the present invention construct as follows: Figure 2 The ISAC network system model shown includes one base station (BS) equipped with M antennas, one RIS (Radio Reflector Array) and K single-antenna users. The RIS is equipped with N reflector elements. A single eavesdropper is used as the sensing target. The channels from the base station to the RIS, from the RIS to the users, and from the RIS to the eavesdropper are as follows: , , .

[0019] In practical deployment scenarios of RIS-assisted ISAC networks, there are actual hardware defects between base stations and user equipment, such as power amplifier nonlinear distortion and local oscillator phase noise. In addition, ISAC networks also have factors such as residual SIC error and randomness of wireless channels. Therefore, this invention comprehensively considers two key non-ideal factors in its modeling: hardware impairments (HWIs) and imperfect channel state information (CSI).

[0020] S2. Construct an objective function with the goal of maximizing the perceived beamforming gain; combine the four constraints of user signal-to-noise ratio, eavesdropper signal-to-noise ratio, RIS amplitude and base station transmit power to construct a model for maximizing the perceived beamforming gain.

[0021] Preferably, the sensing beamforming gain maximization model is expressed as:

[0022] ,

[0023] ,

[0024] ,

[0025] ,

[0026]

[0027] In the formula, This represents the reflection phase vector matrix of the RIS. Denotes the reflection phase vector of RIS, where This represents the reflection phase of the nth element. This represents the signal-to-noise ratio of user k's communication. This represents the minimum signal-to-noise ratio (SNR) for user k. This represents the signal-to-noise ratio of the eavesdropper's decoding of user k's signal. This represents the maximum signal-to-noise ratio (SNR) that the eavesdropper can decode from user k's signal; P represents the reflection unit vector in the nth row and nth column of the reflection phase vector matrix. BS This indicates the maximum allowable transmission power for the base station. Represents the communication beamforming matrix. This represents the communication beam assignment vector of the k-th user; Represents the radar beamforming matrix. Let represent the sensing beamforming vector of the m-th antenna of the base station. This represents the equivalent ISAC transmit beamforming matrix.

[0028] Preferably, in the user signal-to-noise ratio constraint C1:

[0029] ,

[0030] ,

[0031] ,

[0032] ,

[0033] In the formula, This indicates irrelevant signal interference for user k. This indicates base station hardware noise. Indicates user hardware noise. It represents the ratio of distortion noise power to signal power. The ratio of distorted noise power to undistorted received signal power. Indicates the noise variance of user k; This means transforming matrix R into a diagonal matrix, where .

[0034] In the eavesdropper signal-to-noise ratio constraint C2:

[0035] ,

[0036] ,

[0037] ,

[0038] In the formula, This indicates interference from irrelevant signals related to eavesdropping. This indicates base station hardware noise. The variance of the noise emitted by the eavesdropper. This indicates the channel from the base station to the eavesdropper.

[0039] S3. By solving the sensing beamforming gain maximization model, the optimal transmission scheme is obtained and data transmission is executed.

[0040] Preferably, step S3, which involves solving the sensing beamforming gain maximization model, includes:

[0041] S31. Construct a composite channel uncertainty model.

[0042] Preferably, this invention considers that channel uncertainty is unavoidable in practical applications, and traditional channel estimation methods can obtain channel state information between the base station and the smart reflector. However, due to user mobility and the complexity of the electromagnetic environment, accurately obtaining channel state information between the smart reflector and the user is challenging. Furthermore, since eavesdroppers cannot actively communicate with the base station, the base station also finds it difficult to obtain channel state information between the eavesdropper and the smart reflector. Therefore, this invention constructs a composite channel uncertainty model based on a finite uncertainty model, expressed as follows:

[0043] ,

[0044] ,

[0045] In the formula, This represents the estimated channel gain from RIS to user k. This represents the channel estimation error from RIS to user k. This represents the estimated channel gain from the RIS to the eavesdropper. This represents the channel estimation error from the RIS to the eavesdropper. This represents the set of RIS-user channel errors. Denotes the set of RIS-eavesdropper channel errors, ||·|| F express, This represents the estimated upper channel limit for user k. This represents the eavesdropper's estimated channel upper limit.

[0046] S32. Based on the composite channel uncertainty model, auxiliary variables are introduced to reconstruct the user signal-to-noise ratio constraint, the eavesdropper signal-to-noise ratio constraint, and the objective function.

[0047] Preferably, for user signal-to-noise ratio constraints, the present invention introduces auxiliary variables. satisfy Therefore, the user signal-to-noise ratio constraint is equivalent to

[0048] ,

[0049] ,

[0050] Further substitution The rewritten user signal-to-noise ratio constraint is equivalent to:

[0051] ,

[0052] ,

[0053] In the formula, , , , , , , , ; , .in:

[0054] ,

[0055] ,

[0056] ,

[0057] ,

[0058] ,

[0059] In the formula, This represents the covariance matrix of the transmitted signal allocated by the base station to the k-th communication user. The covariance matrix represents the radar sensing beamforming vector.

[0060] Similarly, an auxiliary variable is introduced to constrain the signal-to-noise ratio of the eavesdropper. satisfy Then the signal-to-noise ratio constraint for the eavesdropper is equivalent to:

[0061] ,

[0062] ,

[0063] Further substitution The rewritten eavesdropper signal-to-noise ratio constraint is equivalent to:

[0064] ,

[0065] ,

[0066] in, , , .

[0067] Introduce auxiliary variables into the objective function This makes it equivalent to:

[0068] ,

[0069] Further substitution ,get

[0070] ,

[0071] in,

[0072] , , .

[0073] S33. After completing step S32, the channel estimation error is separated according to the S-procedure lemma, and the reconstructed sensing beamforming gain maximization model is obtained.

[0074] Preferably, Lemma 1 (S-Procedure): Define a quadratic function ,in, , , ,and Then the condition The necessary and sufficient condition for its validity is that a scalar exists if and only if such a scalar exists. Makes the following linear matrix inequalities hold

[0075] ,

[0076] Using the S-procedure lemma described above, the channel error in the sensing beamforming gain maximization model is separated, and the sensing beamforming gain maximization problem is rewritten by defining nonnegative relaxation variables. The problem of maximizing perception beamforming gain can be transformed into:

[0077] ,

[0078] st ,

[0079] ,

[0080] ,

[0081] ,

[0082] ,

[0083] ,

[0084] ,

[0085] .

[0086] In the formula, Rank() represents the rank.

[0087] S34. Based on the reconstructed sensing beam gain shaping gain maximization model, the optimal transmission scheme is obtained by using the alternating optimization method.

[0088] Preferably, the process of solving the problem using the alternating optimization method includes:

[0089] S341. With the reflection phase of the RIS fixed, construct the base station transmit beamforming optimization problem, expressed as:

[0090] ,

[0091] ,

[0092] ,

[0093] ,

[0094] ,

[0095] ,

[0096] ,

[0097] S342. Apply semi-definite relaxation to the base station beamforming variables, and define... , , , , , .make The new transmit beamforming optimization problem is thus obtained as follows:

[0098] ,

[0099] ,

[0100] ,

[0101] ,

[0102] ,

[0103] ,

[0104] ,

[0105] ,

[0106] in , , , , , , , , , , , , , , , , , , , , I represents the identity matrix.

[0107] Therefore, if the obtained solution satisfies the first-order condition, then the SVD method can be applied to obtain... The optimal solution is not found elsewhere. In contrast, an approximate solution can be obtained based on the Gaussian randomization method.

[0108] S343. Transmit beamforming for fixed base stations, constructing a reflection phase optimization problem for RIS, expressed as:

[0109] ,

[0110] ,

[0111] ,

[0112] ,

[0113] ,

[0114] ,

[0115] ,

[0116] The above problem is transformed into a quadratic nonconvex form using the SVD method, that is, let ,but Transform into Similarly, let It can be converted into . This represents the equivalent channel direction component of a signal in a certain virtual space direction after it is reflected by the RIS. Let represent the equivalent channel direction component of the signal in a certain virtual space direction before it reaches the RIS, where i is the subscript of A, j is the j-th column of the left and right singular vectors, and v represents the optimized RIS phase vector. .

[0117] When the beamforming w is fixed, the objective function still contains a non-convex component, which requires handling non-convex mode constraints. This remains challenging. Therefore, we employ SDR for phase updates. To this end, we introduce a new variable. And the rank satisfies the constraint The RIS reflection phase optimization problem is reformulated as follows:

[0118] ,

[0119] ,

[0120] ,

[0121] ,

[0122] ,

[0123] ,

[0124] ,

[0125] ,

[0126] ,

[0127] When the rank-one constraint is relaxed, the global problem can be viewed as relating to linearity. The problem of constrained SDP. Obtaining the slack problem using CVX tools. After obtaining the solution, we apply the Gaussian randomization method or eigenvalue decomposition method to construct the rank-one solution. .

[0128] In one specific embodiment, the number of base station antennas is set to 6, the number of smart reflector elements is set to 49, and the number of users is designed to be 3. The path loss indices from the base station to the smart reflector, from the smart reflector to the user, and from the smart reflector to the eavesdropper are set to 2.2, 2.2, and 2.3, respectively. Channel bandwidth... The hardware impairment coefficients of the base station and the user terminal are set to... Gaussian noise power Assuming the base station is located at (0,0,0), the distance between the base station and the RIS is... The angle of arrival of the RIS relative to the base station is set to Distance between RIS and target The target's azimuth and elevation angles relative to the RIS are respectively RIS is close to users .

[0129] Figure 3 The iterative plots of the perceived beam gain of the present invention and the comparative scheme are illustrated, demonstrating the robustness of our method under different conditions. Figure 4 The relationship between the number of RIS elements and the sensing beam gain is described. While meeting the signal-to-noise ratio (SNR) requirements of both the user and the eavesdropper, it effectively characterizes the system's robustness. The beam gain increases with the number of RIS elements. Simulation results show that the beam gain of all schemes increases with the number of reflective elements, verifying the positive effect of RIS scaling on system performance. This is because installing more passive reflective elements provides more freedom in resource allocation, helping to achieve higher beamforming gain, thereby improving beam mode gain when the RIS phase shift is well adjusted.

[0130] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A RIS-assisted ISAC network transmission method based on hardware impairments and imperfect channels, characterized in that, Includes the following steps: S1. Construct an ISAC network system model based on RIS assistance, which includes a base station equipped with M antennas, a RIS and K single-antenna users. The RIS is equipped with N reflection units; at the same time, a sniper is used as the sensing target. The channels from the base station to the RIS, from the RIS to the user, and from the RIS to the eavesdropper are, in order: , , ; S2. Construct an objective function with the goal of maximizing the perceived beamforming gain; combining the four constraints of user signal-to-noise ratio, eavesdropper signal-to-noise ratio, RIS amplitude, and base station transmit power, construct a model for maximizing the perceived beamforming gain, expressed as: ， ， ， ， ， In the formula, This represents the reflection phase vector matrix of the RIS. This indicates the communication beamforming for user k. This represents the radar beamforming of the m-th antenna at the base station. This represents the signal-to-noise ratio of user k's communication. This represents the minimum signal-to-noise ratio (SNR) for user k. This represents the signal-to-noise ratio of the eavesdropper's decoding of user k's signal. This represents the maximum signal-to-noise ratio (SNR) that the eavesdropper can decode from user k's signal; P represents the reflection unit vector in the nth row and nth column of the reflection phase vector matrix. BS This indicates the maximum allowable transmit power for the base station; S3. By solving the sensing beamforming gain maximization model, the optimal transmission scheme is obtained and data transmission is executed.

2. The RIS-assisted ISAC network transmission method based on hardware impairments and imperfect channels according to claim 1, characterized in that, In the user signal-to-noise ratio constraint C1: ， ， ， ， In the formula, This indicates irrelevant signal interference for user k. This indicates base station hardware noise. Indicates user hardware noise. It represents the ratio of distortion noise power to signal power. This means transforming matrix R into a diagonal matrix, where , Represents the communication beamforming matrix. Represents the radar beamforming matrix. This represents the ratio of distorted noise power to undistorted received signal power. Represents the variance of user k-noise; In the eavesdropper signal-to-noise ratio constraint C2: ， ， ， In the formula, This indicates interference from irrelevant signals related to eavesdropping. This indicates base station hardware noise. The variance of the noise emitted by the eavesdropper. This indicates the channel from the base station to the eavesdropper.

3. The RIS-assisted ISAC network transmission method based on hardware impairments and imperfect channels according to claim 1, characterized in that, Step S3 involves solving the sensing beamforming gain maximization model, which includes: S31. Construct a composite channel uncertainty model; S32. Based on the composite channel uncertainty model, auxiliary variables are introduced to reconstruct the user signal-to-noise ratio constraint, the eavesdropper signal-to-noise ratio constraint, and the objective function; S33. After completing step S32, the channel estimation error is separated from the preliminary reconstructed sensing beam gain shaping gain maximization model according to the S-procedure lemma, and the reconstructed sensing beam gain shaping gain maximization model is obtained. S34. Based on the reconstructed sensing beam gain shaping gain maximization model, the optimal transmission scheme is obtained by using the alternating optimization method.

4. The RIS-assisted ISAC network transmission method based on hardware impairments and imperfect channels according to claim 3, characterized in that, The composite channel uncertainty model is expressed as: ， ， In the formula, This represents the estimated channel gain from RIS to user k. This represents the channel estimation error from RIS to user k. This represents the estimated channel gain from the RIS to the eavesdropper. This represents the channel estimation error from the RIS to the eavesdropper. This represents the set of RIS-user channel errors. Denotes the set of RIS-eavesdropper channel errors, ||·|| F express, This represents the estimated upper channel limit for user k. This represents the eavesdropper's estimated channel upper limit.

5. The RIS-assisted ISAC network transmission method based on hardware impairments and imperfect channels according to claim 4, characterized in that, Introducing auxiliary variables to constrain user signal-to-noise ratio and substitute The reconstructed user signal-to-noise ratio constraint is: ， ， In the formula, , , , , , , , ; , ,in, ， ， ， ， ； It represents the ratio of distortion noise power to signal power. The ratio of distorted noise power to undistorted received signal power. This represents the noise of the k-th user. This represents the covariance matrix of the transmitted signal allocated by the base station to the k-th communication user. The covariance matrix represents the radar sensing beamforming vector. , , , Rank() represents rank; Introducing auxiliary variables to constrain the signal-to-noise ratio of eavesdroppers and substitute The signal-to-noise ratio constraint for the reconstructed eavesdropper is: ， ， in, , , ; Introduce auxiliary variables into the objective function and substitute ,get: ， in, , , .

6. The RIS-assisted ISAC network transmission method based on hardware impairments and imperfect channels according to claim 5, characterized in that, Based on the reconstructed user signal-to-noise ratio constraint, eavesdropper signal-to-noise ratio constraint, and objective function, define non-negative relaxation variables. Using the S-procedure lemma, the model for maximizing the reconstructed sensing beamforming gain is obtained as follows: ， s.t. , ， ， ， ， ， ， ， In the formula, I represents the identity matrix.

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