A beamforming method for an IRS-assisted DFRC system under a correlated channel

Abstract

The application discloses a beam forming method of an IRS-assisted DFRC system under a related channel, comprising the following steps: constructing a communication reachable rate model under IRS assistance, a radar detection power model of a target direction, a DFRC system related channel model and an adaptive user grouping strategy; constructing a beam forming optimization problem based on the above-mentioned models and strategies, which is an optimization problem with the active beam former at the DFRC base station and the passive beam former at the IRS as solving parameters, with the maximum weighted sum rate of the communication users and the detection power at the radar target as the target of optimization under the conditions of satisfying the power constraint and the IRS phase shift constraint; acquiring relevant information required for solving the beam forming optimization problem; and solving the beam forming optimization problem based on the acquired relevant information to obtain a beam forming scheme. The application can improve the system communication performance while minimizing the radar performance loss and improving the system performance.

Description

A beamforming method for an IRS-assisted DFRC system under correlated channels Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a beamforming method for a DFRC (Dual-functional Radar Communication) system assisted by an IRS (Intelligent Reflecting Surface) under correlated channels. Background Technology

[0002] As Multiple-Input Multiple-Output (MIMO) technology matures across various fields, academia has begun exploring the integration of MIMO radar and MIMO communication, leveraging the spatial freedom offered by multi-antenna technology to achieve radar-communication integration. In civilian applications, with the development of transportation, intelligent driving, vehicle collision avoidance, and vehicle positioning functions in intelligent transportation systems require radar and communication equipment. In military applications, combat platforms need to be equipped with various electronic devices such as communication and radar to enhance their information warfare capabilities. Therefore, combining radar and communication equipment to form a DFRC system allows it to communicate with downlink users while simultaneously detecting radar targets. This not only improves space utilization but also reduces equipment and energy costs and enhances operational flexibility.

[0003] A key feature of DFRC systems is that radar and communication systems share the same hardware platform to transmit beams, thereby achieving simultaneous radar detection and communication functions. Therefore, beamforming design is an important research direction for DFRC systems. In beamforming design, by utilizing spatial degrees of freedom to perform beamforming on radar targets and communication users, different beams with the same waveform can achieve dual functions of communication and radar, potentially supporting higher data rates while ensuring radar performance. Furthermore, the communication rate of combined beamforming is unaffected by pulse repetition frequency, enabling efficient multi-user communication.

[0004] However, beamforming design requires a trade-off between radar and communication performance. Especially under the influence of correlated channels, the performance of the communication system is more significantly affected. Therefore, it is necessary to reduce radar performance to improve communication performance, which results in a loss of radar performance. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this invention provides a beamforming method for an IRS-assisted DFRC system under correlated channels.

[0006] The technical problem to be solved by this invention is achieved through the following technical solution:

[0007] A beamforming method for an IRS-assisted DFRC system under correlated channels, the system comprising: a DFRC base station, a smart reflector IRS, communication users, and a radar target; wherein, the IRS is used to improve the wireless propagation environment to assist communication between the DFRC base station and the communication users;

[0008] The method includes:

[0009] Construct a communication reachability rate model for communication users with IRS assistance;

[0010] A radar detection power model is constructed in the target direction;

[0011] Construct a relevant channel model for the DFRC system;

[0012] An adaptive user grouping strategy is constructed, in which relevant communication users are divided into different user groups;

[0013] Based on the communication achievable rate model, the radar detection power model, the DFRC system related channel model, and the adaptive user grouping strategy, a beamforming optimization problem is constructed. The beamforming optimization problem is an optimization problem that, under the conditions of satisfying power constraints and IRS phase shift constraints, uses the active beamformer at the DFRC base station and the passive beamformer at the IRS as solution parameters, and aims to maximize the weighted sum rate of communication users and the detection power at the radar target.

[0014] Obtain the relevant information needed to solve the beamforming optimization problem; the relevant information includes known channel parameters and radar target angle;

[0015] Based on the obtained relevant information, the beamforming optimization problem is solved to obtain a beamforming scheme.

[0016] Preferably, the communication reachable rate model is expressed as:

[0017] ,

[0018] ;

[0019] in, This represents the reachable rate at the k-th communication user. This represents the signal-to-interference-plus-noise ratio (SIR) of the k-th communication user. , This represents the channel parameters from the DFRC base station to the k-th communication user, with the superscript H indicating the conjugate transpose. This represents the channel parameters from the IRS to the k-th communication user. This represents the channel parameters from the DFRC base station to the IRS; For the passive beamforming matrix on the IRS, , It is the amplitude reflection coefficient of the array element with index n in the IRS. is the phase-shift reflection coefficient of the array element with index n in the IRS; M is the number of antennas in the uniform linear array equipped in the DFRC base station; N is the number of reflection array elements equipped in the IRS; and K is the number of communication users. Describes the linear precoder of the j-th communication user. This represents the linear precoding of the k-th communication user. Additive Gaussian noise at the k-th communication user that follows a complex Gaussian distribution The variance.

[0020] Preferably, the radar detection power model is as follows:

[0021] ;

[0022] in, It is the direction vector of the transmitting antenna array, and the superscript H indicates the conjugate transpose; It is the covariance matrix of the radar waveform. For radar operating wavelength, The element spacing of the DFRC base station. This represents the radar target angle, where N is the number of reflector array elements equipped on the IRS. Indicates radar detection power. Let K represent the linear precoding of the k-th communication user, where K is the number of communication users.

[0023] Preferably, the DFRC system related channel model includes: a semi-correlated non-line-of-sight Rayleigh channel model between the DFRC base station and the communication user, a semi-correlated non-line-of-sight Rayleigh channel model between the IRS and the communication user, and a Ricean channel model for the baseband equivalent channel between the DFRC base station and the IRS; wherein,

[0024] The semi-correlated non-line-of-sight Rayleigh channel model between the DFRC base station and the k-th communication user is expressed as:

[0025] ;

[0026] The semi-correlated non-line-of-sight Rayleigh channel model between the IRS and the k-th communication user is expressed as:

[0027] ;

[0028] The baseband equivalent channel between the DFRC base station and the IRS is modeled using the Ricean channel model, expressed as:

[0029] ;

[0030] in, This represents the channel parameters from the DFRC base station to the k-th communication user, with the superscript H indicating the conjugate transpose. This represents the channel parameters from the IRS to the k-th communication user. This represents the channel parameters between the DFRC base station and the IRS; It is the path loss between the DFRC base station and the k-th communication user. It is the path loss between the IRS and the k-th communication user. It is the path loss between the DFRC base station and the IRS; This represents the Rayleigh fading channel vector between the DFRC base station and the k-th communication user, which follows zero mean and unit variance. Represents the Rayleigh fading channel vector between the IRS and the k-th communication user, which follows zero mean and unit variance; This represents the communication between the DFRC base station and the k-th communication user, including... The steering matrix of each steering vector. This represents the communication between the IRS and the k-th user, including... A steering matrix for each steering vector; It is the Rice factor. This represents the line-of-sight component between the DFRC base station and the IRS. , , , Indicates the angle of arrival of the IRS. The departure angle of the DFRC base station is represented by M, the number of antennas in the uniform linear array equipped by the DFRC base station is represented by N, and the superscript T indicates matrix transpose. This represents the non-line-of-sight component between the DFRC base station and the IRS. It follows a complex normal distribution with a mean of zero and a unit variance.

[0031] Preferably, the adaptive user grouping strategy includes:

[0032] Calculate the channel correlation coefficient between every two communication users;

[0033] For each pair of communication users whose channel correlation coefficient is higher than a preset threshold, two different user groups are created for the pair of communication users, and the pair of communication users are assigned to the two created user groups respectively;

[0034] For each remaining ungrouped communication user, calculate the sum of the channel correlation coefficients between that communication user and all communication users in each user group, and then assign that communication user to the user group with the minimum sum of channel correlation coefficients.

[0035] The channel correlation coefficient is defined as:

[0036] ;

[0037] in, Represents the channel correlation coefficient. and These represent the channel parameters of the i-th and j-th communication users, respectively. This indicates the search for the L2 norm.

[0038] Preferably, under the adaptive user grouping strategy, the reachable weighted sum rate of all communication users is expressed as:

[0039] ,

[0040] ;

[0041] in, This represents the reachable weighted sum rate of all K communication users, which are divided into G groups. This represents the reachable weighted sum rate of all communication users in the g-th user group. It represents the weight of the k-th communication user within the g-th user group. This indicates the proportion of time the IRS spends configuring the IRS phase shift in each scheduling cycle. This represents the reachable rate at the k-th communication user within the g-th user group. This represents the number of communication users in the g-th user group.

[0042] Preferably, the beamforming optimization problem is expressed as:

[0043]

[0044] in, This represents the beamforming matrix of the active beamformer at the DFRC base station. This represents the beamforming matrix of the passive beamformer at the IRS; all K communication users are divided into G groups; This indicates the proportion of time the IRS spends configuring the IRS phase shift in each scheduling cycle. This represents the number of communicating users in the g-th user group. For regularization parameters, It represents the weight of the k-th communication user within the g-th user group. This represents the reachable rate at the k-th communication user within the g-th user group. This represents the direction vector of the transmitting antenna array. This represents the linear precoding of the k-th communication user. This represents a function for constructing a diagonal matrix. Where N is the maximum transmit power of the DFRC base station, and N is the number of reflector array elements equipped in the IRS. Indicates length is The vector, M, is the number of antennas in the uniform linear array equipped in the DFRC base station. , It is the amplitude reflection coefficient of the array element with index n in the IRS. It is the phase-shift reflection coefficient of the array element with index n in the IRS.

[0045] Preferably, the beamforming optimization problem is solved based on the acquired relevant information to obtain a beamforming scheme, including:

[0046] The beamforming optimization problem is transformed into a first subproblem and a second subproblem, and the obtained relevant information is substituted into them. The first subproblem is an optimization problem with the active beamformer at the DFRC base station as the solution parameter, aiming to minimize the weighted mean square error of the communication users and maximize the detection power at the radar target. The second subproblem is an optimization problem with the passive beamformer at the IRS as the solution parameter, aiming to maximize the weighted sum rate of the communication users and the detection power at the radar target.

[0047] For each user group, the beamforming optimization problem is iteratively optimized by alternately solving the first subproblem and the second subproblem, so as to obtain the beamforming scheme for the user group when the optimization objective of the beamforming optimization problem is achieved; wherein, in each iteration, the solution parameters of the first subproblem are solved first. Then based on the solution Solve the second subproblem .

[0048] Preferably, the first subproblem is expressed as:

[0049]

[0050] in, , , This represents the mean square error at the output of the receiver with the minimum mean square error for the k-th communication user. , , Represents the identity matrix. , This represents the channel parameters from the DFRC base station to the k-th communication user, with the superscript H indicating the conjugate transpose. This represents the channel parameters from the IRS to the k-th communication user. This represents the channel parameters from the DFRC base station to the IRS; This represents the linear precoder of the k-th communication user. Describes the linear precoder of the j-th communication user. Additive Gaussian noise at the k-th communication user that follows a complex Gaussian distribution variance For functions that take the real part of complex numbers, This indicates a linear decoder.

[0051] Preferably, the second subproblem is expressed as:

[0052]

[0053] in, , It is a function that vectorizes a matrix. , , , and All are auxiliary variables, and the superscript * indicates taking the conjugate.

[0054] The beamforming method for an IRS-assisted DFRC system under correlated channels provided by this invention divides users into different groups based on an adaptive user grouping strategy. By optimizing active and passive beamforming, it maximizes the weighted sum rate of communication users and the detection power at the radar target. This improves the system's communication performance while minimizing radar performance loss, ensuring the performance of both radar and communication. It also satisfies power constraints and IRS phase shift restrictions, thus effectively improving system performance.

[0055] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0056] Figure 1 is a schematic diagram of the structure of the IRS-assisted DFRC system under the relevant channel in an embodiment of the present invention;

[0057] Figure 2 is a flowchart of a beamforming method for an IRS-assisted DFRC system under a correlated channel according to an embodiment of the present invention;

[0058] Figure 3 is a flowchart of the iterative optimization of the beamforming optimization problem by alternately solving the first sub-problem and the second sub-problem in an embodiment of the present invention;

[0059] Figure 4 illustrates the impact of spatial correlation on the performance of the radar-communication integrated system under two different scenarios.

[0060] Figure 5 illustrates the impact of user grouping and IRS assistance on system communication performance;

[0061] Figure 6 shows the impact of IRS assistance on the system radar performance under different grouping thresholds;

[0062] Figure 7 illustrates the impact of the number of IRS array elements on the system communication performance;

[0063] Figure 8 illustrates the impact of the number of IRS array elements on the system's radar performance. Detailed Implementation

[0064] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0065] This invention provides a beamforming method for an IRS-assisted DFRC system under correlated channel conditions. Considering the influence of correlated channels, a smart reflector is used to assist the beamforming design in the integrated radar-communication system. Referring to Figure 1, the system includes: a DFRC base station (BS), a smart reflector IRS, communication users, and a radar target. The IRS is used to improve the wireless propagation environment to assist communication between the DFRC base station and the communication users. The DFRC base station is equipped with a uniform linear array (ULA) of M antennas, and the IRS is equipped with N reflector elements, serving K single-antenna communication users. Since the communication users are located in a complex urban environment, severe path loss occurs during transmission due to multipath effects. Therefore, the IRS is used to improve the wireless propagation environment to assist communication between the DFRC base station and the communication users. Furthermore, this integrated system also uses radar tracking to beamform at an azimuth angle of... The MIMO radar tracks a radar target at a specific location. Specifically, the MIMO radar transmits a separate waveform on each antenna, providing the advantage of waveform diversity and allowing for greater freedom in system design. During the detection phase, the MIMO radar transmits spatially orthogonal waveforms to form an omnidirectional beammap, searching for potential targets across the entire angular domain to obtain prior position information of the radar target. Then, using the prior position information of the radar target as the direction of interest, a directional beam is formed to obtain more accurate observations. The beamforming method provided in this embodiment is a method for designing this directional beam.

[0066] As shown in Figure 2, the beamforming method for an IRS-assisted DFRC system under a related channel provided in this embodiment of the invention includes the following steps:

[0067] S10: Construct a communication reachability rate model for communication users with IRS assistance.

[0068] For communication users, with the assistance of the IRS, the received signal of the k-th user... It can be represented as:

[0069] ;

[0070] in, This represents the communication channel from the DFRC base station to the k-th user. This represents the channel parameters from the IRS to the k-th communication user. For the passive beamforming matrix on the IRS, Among them, , It is the amplitude reflection coefficient of the array element with index n in the IRS. It is the phase-shift reflection coefficient of the array element with index n in the IRS; the superscript H indicates conjugate transpose; This represents the channel parameters from the DFRC base station to the IRS. This represents the linear precoding of the k-th communication user. This represents the communication symbol of the k-th communication user. Describes the linear precoder of the j-th communication user. This represents the communication symbol of the j-th communication user. The additive Gaussian noise at communication user k is... Additive Gaussian noise at the k-th communication user that follows a complex Gaussian distribution The variance is given by K, where K is the number of communication users, M is the number of antennas in the uniform linear array of the DFRC base station, and N is the number of reflective array elements in the IRS.

[0071] The SINR (signal-to-interference-plus-noise ratio) at the k-th communication user is:

[0072]

[0073] in, , This represents the signal-to-interference-plus-noise ratio (SINR) of the k-th communication user. For explanations of the other parameters, please refer to the above text.

[0074] According to Shannon's formula, the achievable rate at the k-th communication user is... It can be represented as:

[0075] ;

[0076] Therefore, the constructed communication reachability rate model can be expressed as:

[0077] ,

[0078] .

[0079] It is understandable that the communication reachability model given here is the communication reachability model for the k-th communication user.

[0080] S20: The radar detection power model constructed in the direction of the target.

[0081] Specifically, in order to improve system communication performance while minimizing radar performance loss, the radar waveform can be designed to maximize the detection power in the radar target direction. Therefore, the constructed radar detection power model is expressed as follows:

[0082] ;

[0083] in, It is the direction vector of the transmitting antenna array, where j in the exponent of the natural base e is the imaginary part of the complex number. It is the covariance matrix of the radar waveform. For radar operating wavelength, The element spacing of the DFRC base station. Indicates the radar target angle. This indicates the radar detection power. The superscript T indicates matrix transpose. For explanations of other parameters, please refer to the above text.

[0084] S30: Construct the relevant channel model for the DFRC system.

[0085] Generally speaking, fading correlation channel matrix Modeled as:

[0086] ;

[0087] in It receives the correlation matrix. It is the emission correlation matrix. It is a random matrix with independent, zero-mean, unit-variance complex terms, assumed to be a complex Gaussian distribution that leads to correlated Rayleigh fading. Indicates taking the expected value. Represents the trace function.

[0088] Specifically, in the implementation of this invention, since communication users suffer severe path loss due to multipath effects during transmission, the channels between the DFRC base station and the user, as well as the channels between the IRS and the user, are modeled as semi-correlated non-line-of-sight (NLoS) Rayleigh fading channels, where the fading is correlated on the DFRC base station side and the IRS side, but uncorrelated on the communication user side.

[0089] Therefore, the direct channel of the k-th user Modeled as:

[0090] ;

[0091] in, It is the path loss between the DFRC base station and the k-th communication user. This represents the Rayleigh fading channel vector between the DFRC base station and the k-th communication user, which follows zero mean and unit variance. This represents the communication between the DFRC base station and the k-th communication user, including... A steering matrix with 10 steering vectors, each vector corresponding to a specific direction of departure (DoD). The steering matrix is ​​in ULA form. Represented as:

[0092] ;

[0093] in, , This represents the i-th DoD of the k-th communication user. It is assumed that DoDs are randomly and independently distributed in the regions represented as... In the angular spread, the width of the angular spread represents the degree of channel correlation. In this embodiment of the invention, the width of the angular spread is fixed.

[0094] Correspondingly, the semi-correlated non-line-of-sight Rayleigh channel model between the IRS and the k-th communication user is expressed as:

[0095] ;

[0096] in, It is the path loss between the IRS and the k-th communication user. Represents the Rayleigh fading channel vector between the IRS and the k-th communication user, which follows zero mean and unit variance. This represents the communication between the IRS and the k-th user, including... The steering matrix of the steering vectors.

[0097] The baseband equivalent channel between the DFRC base station and the IRS is modeled using the Ricean channel model, expressed as:

[0098] ;

[0099] in, It is the path loss between the DFRC base station and the IRS; It is the Rice factor. This represents the line-of-sight component between the DFRC base station and the IRS. , , , Indicates the angle of arrival of the IRS. Indicates the departure angle of the DFRC base station. This represents the non-line-of-sight component between the DFRC base station and the IRS. It follows a complex normal distribution with a mean of zero and a unit variance.

[0100] S40: Construct an adaptive user grouping strategy; in this adaptive user grouping strategy, relevant communication users are divided into different user groups.

[0101] Considering that spatial correlation between two communication users can lead to increased interference between them, thereby reducing the signal-to-interference-plus-noise ratio (SINR) and degrading system communication performance, this embodiment of the invention proposes an adaptive user grouping strategy for large-scale MIMO to reduce strong interference between multiple communication users, dividing related communication users into different user groups.

[0102] The adaptive user grouping strategy includes:

[0103] (1) Calculate the channel correlation coefficient between each pair of communication users.

[0104] Here, the channel correlation coefficient is defined as:

[0105] ;

[0106] in, Represents the channel correlation coefficient. and These represent the channel parameters of the i-th and j-th communication users, respectively. This indicates the search for the L2 norm.

[0107] It is understandable that, The total number of communication users can be calculated Channel correlation coefficients.

[0108] (2) For each pair of communication users whose channel correlation coefficient is higher than the preset threshold, create two different user groups for the pair of communication users and assign the pair of communication users to the two user groups created respectively.

[0109] Specifically, the search method is used to find each pair of communication users whose channel correlation coefficient is higher than a preset threshold, and then the operation of step (2) is performed for each pair of communication users.

[0110] Understandably, after this step is completed, the number of user groups obtained is equal to twice the number of user pairs whose channel correlation coefficient is greater than the preset threshold.

[0111] (3) For each remaining ungrouped communication user, calculate the sum of the channel correlation coefficients between the communication user and all communication users in each user group, and assign the communication user to the user group with the minimum sum of channel correlation coefficients.

[0112] Specifically, for the remaining ungrouped communication users, user groups are assigned to them one by one in the manner described in step (3).

[0113] Furthermore, if no channel correlation coefficient greater than a preset threshold is found after the search, all K communication users are grouped together, and the base station provides communication services to all users simultaneously.

[0114] S50: Based on the communication achievable rate model, radar detection power model, DFRC system related channel model, and adaptive user grouping strategy, a beamforming optimization problem is constructed. This beamforming optimization problem is an optimization problem that, under the conditions of power constraints and IRS phase shift constraints, uses the active beamformer at the DFRC base station and the passive beamformer at the IRS as solution parameters, and aims to maximize the weighted sum rate of communication users and the detection power at the radar target.

[0115] Specifically, suppose that by using an adaptive grouping rule, K communication users are divided into G user groups, where the g-th group contains There are 3 communication users. Different user groups are assigned orthogonal time slots, so they will not interfere with each other. Based on the communication reachability rate model of a single communication user constructed in step S10, the reachability weighted sum rate of all communication users in the g-th user group is obtained as follows:

[0116] ;

[0117] in, This indicates the proportion of time the IRS spends configuring the IRS phase shift in each scheduling cycle. It represents the weight of the k-th communication user within the g-th user group. This represents the achievable rate at the k-th communication user within the g-th user group.

[0118] Therefore, the achievable weighted sum rate of all K communication users It can be represented as:

[0119] .

[0120] Based on this, we consider jointly optimizing the active beamformer at the DFRC base station. Passive beamformer at IRS To maximize the weighted sum rate (WSR) of communication users and the detection power at the radar target, while simultaneously satisfying power constraints and IRS phase shift constraints, the beamforming optimization problem is as follows:

[0121]

[0122] in, This is a regularization parameter used to adjust the weighting of system communication performance and radar performance. This represents the direction vector of the transmitting antenna array. This represents a function for constructing a diagonal matrix. This represents the maximum transmit power of the DFRC base station. Indicates length is A vector of all 1s; please refer to the above for an explanation of the other parameters.

[0123] In this beamforming optimization problem The calculation is a weighted sum of rates for communication users. The calculation is the radar target angle. The first constraint is the constant mode constraint applied to the radar, which ensures the optimal low peak-to-average power ratio, reduces radar channel distortion, and satisfies the total power constraint. The second constraint is the phase shift constraint for the IRS.

[0124] S60: Obtain relevant information needed to solve the beamforming optimization problem.

[0125] Here, the relevant information required to solve the beamforming optimization problem mainly includes known channel parameters and radar target angles. In addition, it also includes the communication symbols of the communication users, radar target angles, direction vectors of the transmitting antenna array, maximum transmit power of the antenna, weights of different communication users, and regularization parameters. Noise power and IRS scheduling cycle, etc.

[0126] S70: Solve the beamforming optimization problem based on the obtained relevant information to obtain the beamforming scheme.

[0127] Specifically, based on the acquired relevant information, the beamforming optimization problem is solved to obtain a beamforming scheme, including:

[0128] (1) The beamforming optimization problem is transformed into a first subproblem and a second subproblem, and the relevant information obtained is substituted into the problem.

[0129] Here, the first subproblem is an optimization problem with the active beamformer at the DFRC base station as the solution parameter, aiming to minimize the weighted mean square error of the communication users and maximize the detection power at the radar target; the second subproblem is an optimization problem with the passive beamformer at the IRS as the solution parameter, aiming to maximize the weighted sum rate of the communication users and the detection power at the radar target.

[0130] (2) For each user group, the beamforming optimization problem is iteratively optimized by alternately solving the first and second subproblems, so as to obtain the beamforming scheme for the user group when the optimization objective of the beamforming optimization problem is achieved; wherein, in each iteration, the solution parameters of the first subproblem are solved first. Then based on the solution Solving the second subproblem .

[0131] The beamforming optimization problem described above is a non-convex optimization problem, therefore a solution is needed. In this embodiment of the invention, the beamforming optimization problem is transformed into two subproblems. The first subproblem is converted into a minimum mean square error (WMMSE) framework, and the second subproblem is converted into a fractional programming (FP) problem. Then, the beamforming optimization problem is iteratively optimized by alternately solving the first and second subproblems, so that a beamforming scheme is obtained when the optimization objective of the beamforming optimization problem is achieved. The specific transformation and solution process is as follows:

[0132] To address the beamforming optimization problem, we first assume that the IRS has a passive beamformer. It is a constant, therefore it is removed from the beamforming optimization problem. Instead of considering the constraints corresponding to irrelevant variables, we optimize the active beamformer W of the DFRC base station. The beamforming optimization problem can then be rewritten as:

[0133]

[0134] At the k-th communication user, a linear decoder is used. The estimated sign is Therefore, the signal demodulated by communication user k Sending signals with base station The mean square error is expressed as:

[0135] ;

[0136] In this formula, when When fixed, it can be done by adjusting the formula. Differentiation, i.e. To obtain the optimal Through optimal The mean square error at the output can be obtained. Therefore, the objective of maximizing the weighted sum rate of communication users in the beamforming optimization problem can be transformed into minimizing the weighted mean square error.

[0137] Furthermore, in the beamforming optimization problem, the radar target angle is calculated. The detection power term can be equivalent to:

[0138] ;

[0139] Therefore, by using the WMMSE method, the problem of maximizing the weighted sum rate of communication users is transformed into an equivalent problem of minimizing the weighted mean square error, i.e., the first subproblem, expressed as:

[0140]

[0141] in, , , This represents the mean square error at the output of the receiver with the minimum mean square error for the k-th communication user. , , This represents the identity matrix; please refer to the above text for explanations of the other parameters.

[0142] The first subproblem is a solvable optimization problem. By homogenizing it, it can be transformed into a new homogeneous QCQP (quadratic constraint quadratic programming) optimization problem. By omitting the rank-1 constraint, a semidefinite relaxation (SDR) of the QCQP problem can be obtained. Then, the CVX tool can be used to solve it to obtain a suboptimal solution that maximizes the weighted sum rate of communication users' packets, thereby improving system performance.

[0143] The solution obtained from the first subproblem Based on this, we substitute it into the beamforming optimization problem to further optimize the passive beamforming matrix of the IRS. The beamforming optimization problem at this point can be expressed as:

[0144]

[0145] According to the Lagrange dual transformation, the previous optimization problem can be equivalent to:

[0146] ;

[0147] in, As an auxiliary variable. If fixed Then the above equation is an unconstrained convex optimization problem, corresponding to , optimal yes By order Calculated. If fixed. ,make ( (This is a function that vectorizes a matrix), so the above expression can be simplified to:

[0148] ;

[0149] in, , , , Please refer to the above text for explanations of the remaining parameters.

[0150] Using the quadratic transformation of fractional programming, the above equation can be further transformed into:

[0151]

[0152] in, It is an auxiliary variable. The superscript * indicates taking the conjugate. For a fixed... , It is about The unconstrained convex problem, by... The optimal solution can be obtained. .

[0153] For fixed , In The item can be restated as:

[0154] ;

[0155] Therefore, the passive beamforming matrix of the IRS The optimization problem can be transformed into the second subproblem shown below:

[0156]

[0157] The second subproblem is a quadratic programming problem, which can be solved using the CVX toolbox.

[0158] Therefore, the beamforming optimization problem can be iteratively optimized by alternately solving the first and second subproblems, thus obtaining the beamforming scheme when the optimization objective of the beamforming optimization problem is achieved. The specific solution process is shown in Figure 3, where i is the iteration number and f... all The calculation formula is as follows:

[0159] .

[0160] In summary, the beamforming method for an IRS-assisted DFRC system under correlated channels provided in this invention improves the wireless propagation environment by using an IRS-assisted DFRC system when the correlated channels cause performance loss. Based on this, users are divided into different groups using an adaptive user grouping strategy. By optimizing active and passive beamforming, a beamforming optimization problem is constructed with the objectives of maximizing the weighted sum rate of communication users and maximizing the detection power at the radar target. An alternating optimization scheme is proposed to address the unsolvability of this beamforming optimization problem. This ensures that the solved beamforming scheme improves system communication performance while minimizing radar performance loss, guaranteeing performance in both radar and communication aspects, and simultaneously satisfying power constraints and IRS phase shift reduction, effectively improving system performance.

[0161] The effectiveness of the embodiments of the present invention will be verified through simulation experiments below.

[0162] Specifically, MATLAB simulation software was used to conduct numerical simulation experiments on the scheme of this embodiment of the invention. In the simulation experiment, an IRS-assisted DFRC system was considered. The DFRC base station was located at (0m, 0m), the IRS was located at (200m, 0m), and the users were located within a circular area with a radius of 10m centered at (200m, 30m). The number of antennas at the DFRC base station M=32, the number of IRS array elements N=64, and the number of communication users K=30. The maximum transmit power at the BS is... The power of the noise level is set to 20 dBm and -117 dBm. The Ricean factor of the Ricean channel is set to 5, and the spread angle and discrete value of the semi-correlated Rayleigh channel are set to (5°, 50). A path loss model is defined based on the 3GPP propagation environment, where the path loss of the direct path is... The path loss of the reflection path is The radar target angle is set to 0°. Set to 1%.

[0163] Figure 4 illustrates the impact of spatial correlation on the performance of the radar-communication integrated system under two different scenarios. In the first uncorrelated channel scenario, the direct channel between the base station and K communication users... =( , ,… …, and the reflection channel between the IRS and K communication users. =( , ,… …, Both are assumed to be independent and identically distributed Rayleigh channels with a mean of 0 and a variance of 1; in the second correlation channel scenario, and The system is generated based on the semi-correlated non-line-of-sight Rayleigh channel model described above. As shown in Figure 4, the system performance is worse when the communication user channels are correlated compared to when they are uncorrelated. This is because the spatial correlation between the communication user channel vectors increases interference between them, leading to a decrease in system performance. Figure 4 illustrates the necessity of modeling the channels between the DFRC base station and the communication users, as well as between the DFRC base station and the IRS, as semi-correlated non-line-of-sight Rayleigh channels in this invention.

[0164] Figure 5 illustrates the impact of user grouping and IRS assistance on system communication performance. As shown in Figure 5, the WSR with IRS assistance is significantly higher than the WSR without IRS assistance. Furthermore, regardless of IRS assistance, the WSR with an adaptive grouping strategy is consistently higher than the WSR without grouping. Specifically, with IRS assistance, the WSR is maximized when the channel correlation coefficient threshold α = 0.85. Figure 5 also shows that when α is small, users are divided into different groups, each with a small number of users. The more groups created, the less time resources are available for each group. As α increases, there exists a threshold that maximizes the sum rate. When α = 1, indicating all users are in one group, interference between users increases under the influence of the correlated channel, resulting in the lowest WSR. It is evident that due to reduced intra-group interference, the sum rate initially increases with the grouping threshold, but the improvement weakens for each group due to limited available time resources.

[0165] Figure 6 illustrates the impact of IRS assistance on the system radar performance under different grouping thresholds. The radar system represented here is the system without a communication system, thus its radar performance is clearly optimal. As can be seen from Figure 6, when using the adaptive strategy, IRS assistance improves radar target detection performance and increases the detection power at the target. This demonstrates that the present invention can effectively reduce radar performance loss while improving system communication performance.

[0166] Figure 7 illustrates the impact of the number of IRS array elements on system communication performance. As shown in Figure 7, with the assistance of the IRS, the WSR improves with the increase of the number of IRS array elements. However, this improvement is not infinite, but gradually approaches an upper bound. For example, when N=128, the WSR increases by 1 bps / Hz with the assistance of the IRS; when N=224, the WSR increases by 1.2 bps / Hz.

[0167] Figure 8 illustrates the impact of the number of IRS elements on the system's radar performance. As can be seen from Figure 8, with the increase in the number of IRS elements, the radar beam pattern distribution changes, resulting in higher detection power at the target, but also an increase in sidelobes. For example, when N increases from 0 to 64, the main lobe increases by 3 dBm; when N increases from 128 to 256, the main lobe increases by 0.5 dBm, and the sidelobes also increase by 1 dBm.

[0168] Figures 7 and 8 illustrate that the use of IRS in this invention can simultaneously improve the system's performance in both communication and radar aspects. The improvement effect increases with the number of IRS array elements, but it will not be infinite; instead, it gradually approaches an upper bound.

[0169] The beamforming optimization problem and its sub-problems proposed in this embodiment of the invention can be loaded into an electronic device, thereby enabling the electronic device to perform steps S60-S70 of the beamforming method for an IRS-assisted DFRC system under any of the aforementioned related channels, and to achieve beamforming. In practical applications, this electronic device can be a computer, radar, etc.

[0170] The present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements steps S60-S70 of the beamforming method for any of the aforementioned correlated channel-assisted DFRC systems.

[0171] Optionally, the computer-readable storage medium may be non-volatile memory (NVM), such as at least one disk storage device.

[0172] Optionally, the computer-readable storage medium may also be at least one storage device located remotely from the aforementioned processor.

[0173] In another embodiment of the present invention, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute steps S60 to S70 in the beamforming method of any of the above-described related channel IRS-assisted DFRC systems.

[0174] It should be noted that, for the embodiments of the device / electronic device / storage medium / computer program product, since they are basically similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments.

[0175] It should be noted that the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure.

[0176] 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 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 or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0177] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings and the disclosure, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the description of this invention, the word "comprising" does not exclude other components or steps, "a" or "an" does not exclude a plurality, and "a plurality" means two or more, unless otherwise explicitly specified. Furthermore, while different embodiments may describe certain measures, this does not mean that these measures cannot be combined to produce good results.

[0178] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A beamforming method for an IRS-assisted DFRC system under a correlated channel, characterized in that, The system includes: a DFRC base station, a smart reflector IRS, communication users, and a radar target; wherein, the IRS is used to improve the wireless propagation environment to assist communication between the DFRC base station and the communication users; the method includes: constructing a communication reachability rate model for the communication users assisted by the IRS; constructing a radar detection power model at the target direction; constructing a DFRC system correlation channel model; constructing an adaptive user grouping strategy; wherein the adaptive user grouping strategy divides relevant communication users into different user groups; based on the communication reachability rate model, the radar detection power model, and the DFRC system correlation channel... Based on the model and the aforementioned adaptive user grouping strategy, a beamforming optimization problem is constructed. This beamforming optimization problem is an optimization problem that, under the conditions of power constraints and IRS phase shift constraints, uses the active beamformer at the DFRC base station and the passive beamformer at the IRS as solution parameters, and aims to maximize the weighted sum rate of communication users and the detection power at the radar target. Relevant information required to solve the beamforming optimization problem is obtained; this relevant information includes known channel parameters and radar target angles. Based on the obtained relevant information, the beamforming optimization problem is solved to obtain a beamforming scheme. The beamforming optimization problem is expressed as: in, This represents the beamforming matrix of the active beamformer at the DFRC base station. This represents the beamforming matrix of the passive beamformer at the IRS; all K communication users are divided into G groups; This indicates the proportion of time the IRS spends configuring the IRS phase shift in each scheduling cycle. This represents the number of communicating users in the g-th user group. For regularization parameters, It represents the weight of the k-th communication user within the g-th user group. This represents the reachable rate at the k-th communication user within the g-th user group. This represents the direction vector of the transmitting antenna array. This represents the linear precoding of the k-th communication user. The function represents the construction of a diagonal matrix. Where N is the maximum transmit power of the DFRC base station, and N is the number of reflector array elements equipped in the IRS. This represents the number of antennas in a uniform linear array of length equipped with a DFRC base station. The vector, ， It is the amplitude reflection coefficient of the array element with index n in the IRS. It is the phase-shift reflection coefficient of the array element with index n in the IRS.

2. The beamforming method for an IRS-assisted DFRC system under a correlated channel according to claim 1, characterized in that, The communication achievable rate model is expressed as follows: ， ;in, This represents the reachable rate at the k-th communication user. This represents the signal-to-interference-plus-noise ratio (SIR) of the k-th communication user. ， This represents the channel parameters from the DFRC base station to the k-th communication user, with the superscript H indicating the conjugate transpose. This represents the channel parameters from the IRS to the k-th communication user. This represents the channel parameters from the DFRC base station to the IRS; For the passive beamforming matrix on the IRS, ， It is the amplitude reflection coefficient of the array element with index n in the IRS. is the phase-shift reflection coefficient of the array element with index n in the IRS; M is the number of antennas in the uniform linear array equipped in the DFRC base station; N is the number of reflection array elements equipped in the IRS; and K is the number of communication users. Describes the linear precoder of the j-th communication user. This represents the linear precoding of the k-th communication user. Additive Gaussian noise at the k-th communication user that follows a complex Gaussian distribution The variance.

3. The beamforming method for an IRS-assisted DFRC system under a correlated channel according to claim 1, characterized in that, The radar detection power model is as follows: ;in, It is the direction vector of the transmitting antenna array, and the superscript H indicates the conjugate transpose; It is the covariance matrix of the radar waveform. For radar operating wavelength, The element spacing of the DFRC base station. This represents the radar target angle, where N is the number of reflector array elements equipped on the IRS. Indicates radar detection power. Let M represent the linear precoding of the k-th communication user, where K is the number of communication users and M is the number of antennas in the uniform linear array equipped by the DFRC base station.

4. The beamforming method for an IRS-assisted DFRC system under a correlated channel according to claim 1, characterized in that, The DFRC system related channel model includes: a semi-correlated non-line-of-sight Rayleigh channel model between the DFRC base station and the communication user, a semi-correlated non-line-of-sight Rayleigh channel model between the IRS and the communication user, and a baseband equivalent channel model between the DFRC base station and the IRS, which is a Ricean channel model; wherein, the semi-correlated non-line-of-sight Rayleigh channel model between the DFRC base station and the k-th communication user is expressed as: The semi-correlated non-line-of-sight Rayleigh channel model between the IRS and the k-th communication user is expressed as: The baseband equivalent channel between the DFRC base station and the IRS is modeled using the Ricean channel model, expressed as: ;in, This represents the channel parameters from the DFRC base station to the k-th communication user, with the superscript H indicating the conjugate transpose. This represents the channel parameters from the IRS to the k-th communication user. This represents the channel parameters between the DFRC base station and the IRS; It is the path loss between the DFRC base station and the k-th communication user. It is the path loss between the IRS and the k-th communication user. It is the path loss between the DFRC base station and the IRS; This represents the Rayleigh fading channel vector between the DFRC base station and the k-th communication user, which follows zero mean and unit variance. Represents the Rayleigh fading channel vector between the IRS and the k-th communication user, which follows zero mean and unit variance; This represents the communication between the DFRC base station and the k-th communication user, including... The steering matrix of each steering vector. This represents the communication between the IRS and the k-th user, including... The steering matrix of each steering vector; It is the Rice factor. This represents the line-of-sight component between the DFRC base station and the IRS. ， ， ， Indicates the angle of arrival of the IRS. The departure angle of the DFRC base station is represented by M, the number of antennas in the uniform linear array equipped by the DFRC base station is represented by N, and the superscript T indicates matrix transpose. This represents the non-line-of-sight component between the DFRC base station and the IRS. It follows a complex normal distribution with a mean of zero and a unit variance.

5. The beamforming method for an IRS-assisted DFRC system under a correlated channel according to claim 1, characterized in that, The adaptive user grouping strategy includes: calculating the channel correlation coefficient between every two communication users; for each pair of communication users whose channel correlation coefficient is higher than a preset threshold, creating two different user groups for that pair of communication users, and assigning the pair of communication users to the two created user groups respectively; for each remaining ungrouped communication user, calculating the sum of the channel correlation coefficients between that communication user and all communication users in each user group, and assigning that communication user to the user group with the minimum sum of channel correlation coefficients; the channel correlation coefficient is defined as: ;in, Represents the channel correlation coefficient. and These represent the channel parameters of the i-th and j-th communication users, respectively. This indicates the search for the L2 norm.

6. The beamforming method for an IRS-assisted DFRC system under a correlated channel according to claim 5, characterized in that, Under the aforementioned adaptive user grouping strategy, the reachable weighted sum rate of all communication users is expressed as: ， ;in, This represents the reachable weighted sum rate of all K communication users, which are divided into G groups. This represents the reachable weighted sum rate of all communication users in the g-th user group. It represents the weight of the k-th communication user within the g-th user group. This indicates the proportion of time the IRS spends configuring the IRS phase shift in each scheduling cycle. This represents the reachable rate at the k-th communication user within the g-th user group. This represents the number of communication users in the g-th user group.

7. The beamforming method for an IRS-assisted DFRC system under a correlated channel according to claim 1, characterized in that, Solving the beamforming optimization problem based on the acquired relevant information to obtain a beamforming scheme includes: converting the beamforming optimization problem into a first subproblem and a second subproblem, and substituting the acquired relevant information; wherein, the first subproblem is an optimization problem with the active beamformer at the DFRC base station as the solution parameter, aiming to minimize the weighted mean square error of communication users and maximize the detection power at the radar target; the second subproblem is an optimization problem with the passive beamformer at the IRS as the solution parameter, aiming to maximize the weighted sum rate of communication users and the detection power at the radar target; for each user group, the beamforming optimization problem is iteratively optimized by alternately solving the first subproblem and the second subproblem, so as to obtain the beamforming scheme for the user group when the optimization objective of the beamforming optimization problem is achieved; wherein, in each iteration, the solution parameters of the first subproblem are solved first. Then based on the solution Solve the second subproblem 。 8. The beamforming method for an IRS-assisted DFRC system under a correlated channel according to claim 7, characterized in that, The first subproblem is represented as: in, ， ， This represents the mean square error at the output of the receiver with the minimum mean square error for the k-th communication user. ， ， Represents the identity matrix. ， This represents the channel parameters from the DFRC base station to the k-th communication user, with the superscript H indicating the conjugate transpose. This represents the channel parameters from the IRS to the k-th communication user. This represents the channel parameters from the DFRC base station to the IRS; This represents the linear precoder of the k-th communication user. Describes the linear precoder of the j-th communication user. Additive Gaussian noise at the k-th communication user that follows a complex Gaussian distribution variance For functions that take the real part of complex numbers, This indicates a linear decoder.

9. The beamforming method for an IRS-assisted DFRC system under a correlated channel according to claim 7, characterized in that, The second subproblem is represented as: in, ， It is a function that vectorizes a matrix. ， ， ， and All are auxiliary variables, and the superscript * indicates taking the conjugate.

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