Method and system for aris assisted isac-noma system communication and rate optimization

Abstract

The present application relates to the technical field of wireless communication, and especially relates to a communication and rate optimization method and system of an ARIS assisted ISAC-NOMA system. Channel fading gains of a user terminal to an ARIS node and the ARIS node to a DFRC-BS node, a steering vector when the DFRC-BS node antenna array transmits a sensing signal, and a complex path loss coefficient of the sensing signal are acquired, the DFRC-BS node transmits the sensing signal, the user terminal transmits an uplink communication signal, and the DFRC-BS node simultaneously receives a sensing target echo signal and a communication signal; a successive interference cancellation technology is used for a communication and sensing mixed signal, a communication and rate and a sensing signal-to-noise ratio are calculated, an iterative solution optimization problem is solved by using an alternating optimization algorithm, and the maximum communication and rate of the system is obtained. The method of the present application can achieve a higher communication rate and better sensing performance than traditional methods in different scenarios, and has high practical application value.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a communication and rate optimization method and system for ARIS-assisted ISAC-NOMA systems. Background Technology

[0002] With the rapid development of global communication network technology, the current wireless communication field is facing the problem of scarce frequency band resources. NOMA technology can provide communication services to multiple users simultaneously by using the same time domain or frequency domain resources through power reuse, which to some extent overcomes the spectrum resource shortage problem caused by traditional orthogonal access technology and effectively improves resource utilization.

[0003] Meanwhile, ISAC technology has gradually become a research hotspot in the field of wireless communication. It can integrate radar sensing and wireless communication functions on the same hardware platform, achieving both environmental perception and information transmission. This technology reduces the hardware modification costs of existing networks, significantly enhances the sensing capabilities of wireless networks, and improves spectrum and energy efficiency.

[0004] However, in harsh radio electromagnetic environments, both of the aforementioned communication technologies are difficult to implement effectively. Therefore, the reconfigurable intelligent surface (RIS) technology, which has emerged in recent years, can be introduced. A RIS is a surface composed of microelectronically controllable components that can dynamically adjust the amplitude and phase of the incident signal, thereby changing the radio electromagnetic environment according to different communication needs. Traditional passive reconfigurable intelligent surfaces (PRIS) suffer from a "multiplicative fading" effect, which led to the development of ARIS. Compared to traditional PRIS, each reflective unit on an ARIS is equipped with a signal amplifier, effectively strengthening the reflected signal while achieving amplitude and phase modulation, thus reducing the impact of "multiplicative fading" and significantly improving the capacity of the communication system. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention is proposed.

[0006] Therefore, this invention provides a communication and rate optimization method for ARIS-assisted ISAC-NOMA systems, which can solve the problem that traditional orthogonal access technology is difficult to meet the ever-increasing wireless communication demand due to the limited frequency band resources. Existing wireless networks usually need to implement radar sensing and wireless communication functions on different hardware platforms, resulting in high hardware costs and a lack of effective sensing functions, making it impossible to fully perceive the surrounding environment.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a communication and rate optimization method for an ARIS-assisted ISAC-NOMA system, comprising: obtaining the channel fading gain from the user terminal to the ARIS node and from the ARIS node to the DFRC-BS node, the steering vector of the DFRC-BS node antenna array transmitting sensing signals, and the complex path loss coefficient of the sensing signals; the DFRC-BS node transmitting sensing signals; the user terminal transmitting uplink communication signals using NOMA technology; and the DFRC-BS node simultaneously receiving sensing target echo signals and communication signals; for the mixed communication and sensing signals received by the DFRC-BS node, continuous interference cancellation technology is used to calculate the communication and rate and the sensing signal-to-noise ratio respectively; considering maximizing the uplink communication and rate of the user terminal, an optimization problem is constructed, and an alternating optimization algorithm is used for iterative solution to obtain the maximum communication and rate of the system.

[0008] As a preferred embodiment of the communication and rate optimization method for the ARIS-assisted ISAC-NOMA system described in this invention, the communication-aware hybrid signal received by the DFRC-BS node is expressed as follows:

[0009] y = y c +y rad +n b

[0010]

[0011] y rad =ηa(θ) t )a H (θ t )w t x r

[0012] y rad =ηa(θ) t )a H (θ t )w t x r

[0013]

[0014] Where y is the communication sensing hybrid signal vector, y c Let y be the received communication signal vector. rad Let G be the received target echo signal vector, G be the channel fading gain matrix from the ARIS node to the DFRC-BS node, Θ be the reflection phase shift matrix of the ARIS node, diag be the diagonalization operation, j be the imaginary unit, and β be the received target echo signal vector. n θ n These represent the amplitude and phase of the nth reflecting element, respectively, where N is the total number of reflecting elements, and pk Let h be the transmit power of the k-th user terminal, K be the total number of user terminals, k be the user terminal index value, and h be the transmit power of the k-th user terminal. k Let x be the channel fading gain vector from the k-th user terminal to the ARIS node. u,k For the information sent by the k-th user terminal to the DFRC-BS node, n a The noise power of the ARIS node is The additive white Gaussian noise vector, n b The noise power of the DFRC-BS node is The additive white Gaussian noise vector, σ 2 Let η be the noise power, η be the complex path loss coefficient of the perceived target echo signal, and a(θ) be the noise power. t θ is the steering vector for the sensing signal transmitted by the antenna array. t To sense the azimuth angle of the target relative to the antenna array, d is the wavelength-normalized spacing between two adjacent antennas, and w t x is the transmit beamforming vector of the DFRC-BS node. r The sensing signal transmitted by the DFRC-BS node is sin, T is the transpose of the matrix, H is the conjugate transpose, M is the order, and e is the Euler number.

[0015] As a preferred embodiment of the communication and rate optimization method for the ARIS-assisted ISAC-NOMA system described in this invention, the DFRC-BS node uses a linear receive beamforming vector to decode the received signal and recover the signal scalar. The formula is expressed as follows:

[0016]

[0017] Among them, w r This is the receive beamforming vector for the DFRC-BS node.

[0018] As a preferred embodiment of the communication and rate optimization method for the ARIS-assisted ISAC-NOMA system described in this invention, the continuous interference cancellation technique includes user terminals using NOMA technology for uplink communication, DFRC-BS nodes using SIC technology to decode communication signals, the reflection phase shift matrix of the ARIS node being an identity matrix, the equivalent channel gains from the user terminal to the DFRC-BS node being arranged in descending order, and the signal-to-interference-plus-noise ratio γ of the k-th user terminal signal being... u,k The formula is expressed as follows:

[0019]

[0020] Where i is the index of the (k+1)th user terminal, h iLet p be the channel fading gain vector from the i-th user terminal to the ARIS node. i Let be the transmit power of the i-th user terminal;

[0021] Obtain the information transmission rate R of the k-th user terminal u,k The formula is expressed as follows:

[0022] R u,k =log2(1+γ) u,k )

[0023] Where log is the logarithmic function.

[0024] As a preferred embodiment of the communication and rate optimization method for the ARIS-assisted ISAC-NOMA system described in this invention, the sensing signal-to-noise ratio includes consideration of the sensing target echo signal y. rad After all user communication signals are decoded, detection is performed. The SIC process eliminates interference caused by communication signals. The sensing target and the DFRC-BS node have a line-of-sight link. The signal-to-noise ratio formula for the sensing target echo signal is expressed as follows:

[0025]

[0026] Where, γ rad To sense the signal-to-noise ratio of the target echo signal.

[0027] As a preferred embodiment of the communication and rate optimization method for the ARIS-assisted ISAC-NOMA system described in this invention, the optimization problem involves jointly optimizing the reflection phase shift matrix of the ARIS node, the transmit power of the user terminal, and the transmit beamforming vector and receive beamforming vector of the DFRC-BS node to maximize the communication and rate of the user terminal. The objective function of the optimization problem is expressed as follows:

[0028]

[0029] Where max is the maximization function;

[0030] The amplitude constraint formula for the ARIS node reflection element is expressed as follows:

[0031] β n ≤β max

[0032] Where, β max This represents the maximum amplitude of the reflecting unit;

[0033] The power constraint formula for ARIS nodes is expressed as follows:

[0034]

[0035] in, is the maximum power of the ARIS node, and F is the Frobenius norm;

[0036] The formula for the transmit power constraint of the user terminal is expressed as follows:

[0037]

[0038] in, This is the maximum transmit power of the user terminal;

[0039] The formula for normalizing the received beamforming vector at the DFRC-BS node is expressed as follows:

[0040] ||w r ||=1

[0041] The formula for the perceived signal-to-noise ratio constraint is expressed as follows.

[0042] γ rad ≥α

[0043] Where α is the minimum perceived signal-to-noise ratio that the system must satisfy.

[0044] As a preferred embodiment of the communication and rate optimization method for the ARIS-assisted ISAC-NOMA system described in this invention, the iterative solution using the alternating optimization algorithm includes transforming the objective function, which is equivalent to:

[0045]

[0046] Considering the mutual coupling of variables in the objective function, which cannot be solved directly, the objective function is divided into four sub-problems using an alternating optimization algorithm.

[0047] Given a fixed ARIS node reflection phase shift matrix, K user terminal transmit powers, and DFRC-BS node receive beamforming vector, optimize the DFRC-BS node transmit beamforming vector to construct the first sub-problem, expressed by the formula:

[0048]

[0049] γ rad ≥α

[0050] After transforming the first subproblem, a positive semidefinite relaxation method is used to relax the non-convex rank-one constraints, resulting in a convex optimization problem. This problem is solved using the convex optimization toolkit CVX, and singular value decomposition is performed on the solution to recover the optimized transmit beamforming vector.

[0051] Given a fixed ARIS node reflection phase shift matrix, K user terminal transmit powers, and DFRC-BS node transmit beamforming vector, optimize the DFRC-BS node receive beamforming vector to construct a second sub-problem, expressed by the formula:

[0052]

[0053] ||w r ||=1

[0054] γ rad ≥α

[0055] After transforming the second subproblem, the SDR method is used to relax the non-convex rank-one constraints. The Dinkelbach method is then used to transform the objective function from a fractional form to a linear form. The solution is obtained using the convex optimization toolkit CVX, and singular value decomposition is performed on the solution to recover the optimized receive beamforming vector.

[0056] By fixing the reflection phase shift matrix of the ARIS node, the receive beamforming vector of the DFRC-BS node, and the transmit beamforming vector of the DFRC-BS node, the transmit power of K user terminals is optimized, thus constructing a third sub-problem, expressed by the formula:

[0057]

[0058] The third subproblem is a convex optimization problem, which is solved using the convex optimization toolkit CVX to obtain the optimized transmit power p for the K user terminals. * ;

[0059] With fixed transmit power of K user terminals, receive beamforming vector of DFRC-BS node, and transmit beamforming vector of DFRC-BS node, optimize the reflection phase shift matrix of ARIS node to construct the fourth sub-problem, expressed by the formula:

[0060]

[0061] β n ≤β max

[0062]

[0063] γ rad ≥α

[0064] The fourth subproblem is transformed by applying the SDR method to relax the non-convex rank-one constraints, and then using the Dinkelbach method to transform the objective function from a fractional form to a linear form. The solution is obtained using the convex optimization toolkit CVX, and singular value decomposition is performed on the solution to obtain the optimized reflection phase shift matrix Θ.* ;

[0065] The four subproblems are alternately optimized and iteratively solved until the objective function converges.

[0066] Another objective of this invention is to provide a communication and rate optimization system for ARIS-assisted ISAC-NOMA systems. To improve the uplink communication rate of user terminals while maintaining sensing performance, this invention constructs a communication and rate maximization model and proposes an alternating optimization algorithm that jointly optimizes the ARIS node reflection phase shift matrix, user terminal transmit power, and DFRC-BS node transmit and receive beamforming vectors. The method of this invention can significantly improve the communication performance and rate of the system in complex radio electromagnetic environments.

[0067] As a preferred embodiment of the communication and rate optimization system for the ARIS-assisted ISAC-NOMA system described in this invention, it includes:

[0068] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of any one of the communication and rate optimization methods for an ARIS-assisted ISAC-NOMA system.

[0069] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of any one of the communication and rate optimization methods for an ARIS-assisted ISAC-NOMA system.

[0070] The beneficial effects of this invention: This invention discloses a communication and rate optimization method for an Active Reconfigurable Intelligent Surface (ARIS)-assisted Integrated Sensing and Communication (ISAC) Non-Orthogonal Multiple Access (NOMA) system. It is applicable to a system consisting of a Dual Function Radar Communication Base Station (DFRC-BS) node, an ARIS node, multiple user terminals, and a sensing target, where the multiple user terminals use NOMA technology for uplink communication. To improve uplink communication and rate of user terminals while ensuring sensing performance, this invention constructs a communication and rate maximization model for an ARIS-assisted ISAC-NOMA system and proposes an alternating optimization algorithm that jointly optimizes the ARIS node's reflection phase shift matrix, user terminal transmit power, and DFRC-BS node's transmit and receive beamforming vectors. Compared to traditional passive reconfigurable intelligent surface (PRIS)-assisted schemes, the proposed scheme achieves higher communication and rate. Attached Figure Description

[0071] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 This is a schematic flowchart of a communication and rate optimization method for an ARIS-assisted ISAC-NOMA system provided in one embodiment of the present invention.

[0073] Figure 2 This is a system model diagram of communication and rate optimization for an ARIS-assisted ISAC-NOMA system provided in one embodiment of the present invention.

[0074] Figure 3 The graph shows the relationship between the number of reflection units and the communication and rate of the ISAC-NOMA system in the communication and rate optimization method for the ARIS-assisted ISAC-NOMA system provided in one embodiment of the present invention. Detailed Implementation

[0075] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0076] Example 1, referring to Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a communication and rate optimization method for an ARIS-assisted ISAC-NOMA system, comprising:

[0077] like Figure 2 As shown, there is a DFRC-BS node equipped with M antennas, multiple single-antenna user terminals, a sensing target, and an ARIS node equipped with N reflector elements. The DFRC-BS node integrates sensing and communication functions, and the ARIS node can assist multiple user terminals in uplink communication by adjusting its phase and amplitude.

[0078] The method of the present invention includes the following steps:

[0079] Step 1: Obtain the channel fading gain from the user terminal to the ARIS node and from the ARIS node to the DFRC-BS node, the steering vector when the antenna array of the DFRC-BS node transmits the sensing signal, and the complex path loss coefficient of the sensing signal. Consider that the communication link between the user terminal and the DFRC-BS node is ignored due to the presence of obstacles.

[0080] Step 2: The system is divided into two stages. In the first stage, the DFRC-BS node sends sensing signals, and in the second stage, the user terminal uses NOMA technology to send uplink communication signals. The DFRC-BS node simultaneously receives the sensing target echo signal and the communication signal.

[0081] Step 3: For the received communication-sensing mixed signal, use Successive Interference Cancellation (SIC) technology to calculate the communication and rate signal-to-noise ratio and the sensing signal-to-noise ratio respectively.

[0082] Step 4: With the goal of maximizing the uplink communication and rate of the user terminal, construct a model that jointly optimizes the user terminal transmit power, the ARIS node reflection phase shift matrix, and the DFRC-BS node transmit beamforming vector and receive beamforming vector.

[0083] Step 5: Divide the optimization problem into four subproblems using the alternating optimization algorithm and solve them iteratively to obtain the maximum communication and speed of the system.

[0084] It should be noted that the vector formula for a DFRC-BS node to simultaneously receive both the echo signal from the sensed target and the communication signal is expressed as follows:

[0085] y = y c +y rad +n b

[0086]

[0087] y rad =ηa(θ) t )a H (θ t )w t x r

[0088] y rad =ηa(θ) t )a H (θ t )w t x r

[0089]

[0090] Among them, y c Let y be the received communication signal vector. rad Let G be the received target echo signal vector, G be the channel fading gain matrix from the ARIS node to the DFRC-BS node, Θ be the reflection phase shift matrix of the ARIS node, diag be the diagonalization operation, j be the imaginary unit, and β be the received target echo signal vector. n θ n Let represent the amplitude and phase of the nth reflecting element, respectively, and satisfy β. n >0, θ n ∈(0,2π), p k Let p be the transmit power of the k-th user terminal, and satisfy p k >0, K is the total number of user terminals, k is the user terminal index value, h k Let x be the channel fading gain vector from the k-th user terminal to the ARIS node. u,k Let $\frac{ ... u,k | 2} = 1, E is the expected value operation, n a The noise power of the ARIS node is The additive white Gaussian noise vector, n b The noise power of the DFRC-BS node is The additive white Gaussian noise vector, σ 2For noise power, I N I M For different identity matrices, CN represents the normal distribution in the complex domain, η is the complex path loss coefficient of the perceived target echo signal, and a(θ) t θ is the steering vector for the sensing signal transmitted by the antenna array. t To sense the azimuth angle of the target relative to the antenna array, d is the wavelength-normalized spacing between two adjacent antennas, and w t x is the transmit beamforming vector of the DFRC-BS node. r The sensing signal sent by the DFRC-BS node satisfies E{|x r | 2} = 1, sin is the sine function, T is the transpose of the matrix, H is the conjugate transpose, M is the order, and e is the Euler number.

[0091] It should be noted that, assuming the DFRC-BS node uses a linear receive beamforming vector to decode the received signal, the recovered signal scalar can be expressed as follows:

[0092]

[0093] Among them, w r Let w be the receive beamforming vector of the DFRC-BS node, and satisfy ||w r ||=1.

[0094] Furthermore, multiple user terminals in the system use NOMA technology for uplink communication, and the DFRC-BS node uses SIC technology to first decode the signal with better channel conditions to reduce interference between different signals. Here, priority is given to decoding the communication signal y. c To simplify the analysis, the reflection phase shift matrix of the ARIS node can be set as an identity matrix, and it can be assumed that the equivalent channel gains from the user terminal to the DFRC-BS node are arranged in descending order, i.e., ∥Gh1∥≥∥Gh2∥≥...∥Gh K ∥, where Gh k Let be the equivalent channel gain from the user terminal to the DFRC-BS node, and k∈(1,K). Therefore, the signal-to-interference-plus-noise ratio (SIR) of the k-th user terminal signal can be expressed as:

[0095]

[0096] Where i is the index of the (k+1)th user terminal, h i Let p be the channel fading gain vector from the i-th user terminal to the ARIS node. i Let be the transmit power of the i-th user terminal;

[0097] Obtain the information transmission rate R of the k-th user terminal u,kThe formula is expressed as follows:

[0098] R u,k =log2(1+γ) u,k )

[0099] Where log is the logarithmic function.

[0100] Furthermore, considering that the target echo signal is detected after all user communication signals have been decoded, the SIC process has eliminated interference from the communication signals. Also, the target and the DFRC-BS node are on a line-of-sight (LoS) link, and there are no other reflected clutter interfering with the received target echo signal. Therefore, the signal-to-noise ratio (SNR) of the target echo signal is expressed as:

[0101]

[0102] Where, γ rad To sense the signal-to-noise ratio of the target echo signal.

[0103] It should be noted that the optimization problem involves jointly optimizing the reflection phase shift matrix of the ARIS node, the transmit power of the user terminal, and the transmit beamforming vector and receive beamforming vector of the DFRC-BS node to maximize the communication and data rate of the user terminal. The objective function of the optimization problem is expressed as follows:

[0104] (P0):

[0105] Where max is the maximization function;

[0106] The amplitude constraint formula for the ARIS node reflection element is expressed as follows:

[0107] β n ≤β max

[0108] Where, β max This represents the maximum amplitude of the reflecting unit;

[0109] The power constraint formula for ARIS nodes is expressed as follows:

[0110]

[0111] in, Let F be the maximum power of the ARIS node, and F be the Frobenius norm, ||Θ|| F This represents the square root of the sum of the squares of all elements in the matrix.

[0112] The formula for the transmit power constraint of the user terminal is expressed as follows:

[0113]

[0114] in, This is the maximum transmit power of the user terminal;

[0115] The formula for normalizing the received beamforming vector at the DFRC-BS node is expressed as follows:

[0116] ||w r ||=1

[0117] The formula for the perceived signal-to-noise ratio constraint is expressed as follows.

[0118] γ rad ≥α

[0119] Where α is the minimum perceived signal-to-noise ratio that the system must satisfy.

[0120] Furthermore, to facilitate solving the objective function, the following transformation is performed:

[0121]

[0122] The original problem is equivalent to,

[0123] (P1):

[0124] Furthermore, considering that the variables in the objective function are coupled with each other and cannot be solved directly, the objective function is divided into four sub-problems using an alternating optimization algorithm;

[0125] Given a fixed ARIS node reflection phase shift matrix, K user terminal transmit powers, and DFRC-BS node receive beamforming vector, optimize the DFRC-BS node transmit beamforming vector to construct the first sub-problem, expressed by the formula:

[0126] (P2):

[0127] s.tγ rad ≥α

[0128] In optimization problems, the symbol "st" stands for "subject to" and is used to guide the constraints, indicating that the objective function is optimized under these constraints.

[0129] (P2) can be transformed into,

[0130] (P2-1):

[0131] s.tγ rad ≥α

[0132] make (P2-1) is further transformed into,

[0133] (P2-2):minTr(W T B)

[0134]

[0135] W T ±0

[0136] rank(W T ) = 1

[0137] Where min is the minimum value function, Tr is the trace of the matrix, rank is the rank of the matrix, and W T B and B are different construction matrices used in the first subproblem;

[0138] Because of the use of transformation (P2-2) Two new constraints have been added, W T ±0 constraint guarantees W T Let W be a positive semi-definite matrix. T ) = 1 constraint guarantees W T Since the rank is one and the constraint is non-convex, it can be handled using the semidefinite relaxation (SDR) method to relax the rank-one constraint. At this point, (P2-2) is transformed into a convex problem, which can be solved using the convex optimization toolkit CVX. For the obtained optimization solution... The optimized transmit beamforming vector is recovered through singular value decomposition.

[0139] Given a fixed ARIS node reflection phase shift matrix, K user terminal transmit powers, and DFRC-BS node transmit beamforming vector, optimize the DFRC-BS node receive beamforming vector to construct a second sub-problem, expressed by the formula:

[0140] (P3):

[0141] st||w r ||=1

[0142] γ rad ≥α

[0143] make C=∣η∣ 2 a(θ t )a H (θ t )w t (a(θ t )a H (θ t )w t )H ,

[0144] P3) is further transformed into,

[0145] (P3-1):

[0146] Tr(W R ) = 1

[0147]

[0148] W R ±0

[0149] rank(W R ) = 1

[0150] Among them, W R A sum D, C, and X are different construction matrices used in the second subproblem;

[0151] Because of the use of transformation (P3-1) Two new bundles have been added, W R It is a positive semi-definite matrix, guaranteeing that W R The rank of is one, where rank(W) R The constraint ) = 1 is a non-convex constraint, which can be handled using SDR to relax the rank-one constraint. For the objective function in fractional form (P3-1), the Dinkelbach method is used to introduce an auxiliary variable q, transforming it into a linear form, and then iteratively solving it. Therefore, the problem is further transformed into:

[0152] (P3-2):

[0153] Tr(W R ) = 1

[0154]

[0155] W R ±0

[0156] (P3-2) is a convex optimization problem, which can be solved using the convex optimization toolkit CVX.

[0157] The auxiliary variable q is initialized to 0, that is, q( 0 If ) = 0, the update rule for q is: This is the optimized solution obtained in the l-th iteration using the convex optimization toolkit CVX.

[0158] The algorithm terminates when it reaches the maximum number of iterations or when the objective function (P3-2) is less than a set threshold. Finally, for the obtained... The optimized receive beamforming vector is recovered through singular value decomposition.

[0159] By fixing the reflection phase shift matrix of the ARIS node, the receive beamforming vector of the DFRC-BS node, and the transmit beamforming vector of the DFRC-BS node, the transmit power of K user terminals is optimized, thus constructing a third sub-problem, expressed by the formula:

[0160] (P4):

[0161]

[0162] Furthermore, the problem becomes,

[0163] (P4-1):

[0164]

[0165] (P4-1) is a convex optimization problem. The optimized transmit power p of the K user terminals can be obtained by using the convex optimization toolkit CVX. * ;

[0166] Finally, by fixing the transmit power of K user terminals, the receive beamforming vector of the DFRC-BS node, and the transmit beamforming vector of the DFRC-BS node, the reflection phase shift matrix of the ARIS node is optimized, thus constructing the fourth sub-problem, expressed by the formula:

[0167] (P5):

[0168] β n ≤β max

[0169]

[0170] γ rad ≥α

[0171] Let Θ = diag(v), V = v H v,

[0172]

[0173] Among them, V and Q sum R and U sum These are the different construction matrices used for the fourth subproblem;

[0174] (P5) can be further transformed into,

[0175] (P5-1):

[0176] s.tV n,n ≤β max

[0177]

[0178] V±0

[0179] rank(V) = 1

[0180] Because the transformation V = v was used H In (P5-1), two new constraints are added: the constraint V±0 ensures that V is a positive semi-definite matrix. The constraint rank(V)=1 ensures that the rank of V is one. Since this constraint is non-convex, it can be handled using SDR (Self-Reliance and Decomposition), relaxing the rank-one constraint. For the objective function in fractional form (P5-1), the Dinkelbach method is used, introducing an auxiliary variable r different from q, to ​​transform it into a linear form, and then iteratively solving it. Therefore, the problem is further transformed into...

[0181] (P5-2):

[0182] s.tV n,n ≤β max n = 1, 2, ..., N

[0183]

[0184] v±0

[0185] (P5-2) is a convex optimization problem, which can be solved using the convex optimization toolkit CVX. The auxiliary variable r is initialized to 0, i.e., r0 = r0. (0) =0, the update rule for r is,

[0186]

[0187] This is the optimized solution obtained in the l-th iteration using the convex optimization toolkit CVX (P5-2). The algorithm terminates when it reaches the maximum number of iterations or when the objective function (P5-2) is less than a set threshold. The final V is then calculated. * By using singular value decomposition, the optimized v is recovered. * This leads to the optimized reflection phase shift matrix Θ. * .

[0188] The above is an illustrative scheme of the communication and rate optimization method for the ARIS-assisted ISAC-NOMA system according to this embodiment. It should be noted that the technical solution of this ARIS-assisted ISAC-NOMA system communication and rate optimization system belongs to the same concept as the technical solution of the ARIS-assisted ISAC-NOMA system communication and rate optimization method described above. Details not described in detail in this embodiment can be found in the description of the ARIS-assisted ISAC-NOMA system communication and rate optimization method described above.

[0189] In this embodiment, the communication and rate optimization system of the ARIS-assisted ISAC-NOMA system includes: an information acquisition and signal transmission module, a signal processing module, and an optimization problem solving module;

[0190] The information acquisition and signal transmission module includes the DFRC-BS node transmitting sensing signals, the user terminal transmitting uplink communication signals using NOMA technology, and the DFRC-BS node simultaneously receiving the sensing target echo signal and the communication signal. It also acquires the channel fading gain from the user terminal to the ARIS node and from the ARIS node to the DFRC-BS node, the steering vector when the DFRC-BS node antenna array transmits sensing signals, and the complex path loss coefficient of the sensing signals.

[0191] The signal processing module uses continuous interference cancellation technology to calculate the communication and rate-sensing signal-to-noise ratios of the mixed communication and sensing signals received by the DFRC-BS node, ensuring effective signal separation and identification.

[0192] The optimization problem-solving module considers maximizing the uplink communication and speed of the user terminal, constructs an optimization problem, and uses an alternating optimization algorithm to iteratively solve it, thereby obtaining the maximum communication and speed of the system.

[0193] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or independent of it, or they can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0194] This embodiment also provides a computing device suitable for communication and rate optimization of ARIS-assisted ISAC-NOMA systems. The invention can be implemented using software and necessary general-purpose hardware, and can also be implemented using hardware, but in many cases the former is a preferred implementation. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of this invention.

[0195] Example 2, Reference Figure 3 This is the second embodiment of the present invention, which provides a communication and rate optimization method for an ARIS-assisted ISAC-NOMA system. To verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0196] The following is an example of an invention being simulated on a computer using the MATLAB language. For example... Figure 3 As shown, consider a two-dimensional Cartesian coordinate system, with the number of user terminals K set to 2. The positions of the DFRC-BS node, ARIS node, sensing target, first user terminal, and second user terminal are set to (0,0), (20,20), (0,50), (50,0), and (55,0), respectively, in meters.

[0197] The target being sensed is the antenna array facing the DFRC-BS node, with an azimuth angle θ. t The value is 0°. The channel h from user terminal 1 to the ARIS node... i The channel h2 from user terminal 2 to the ARIS node and the channel G from the ARIS node to the DFRC-BS node both follow a Rice distribution, and it is assumed that the channel state information remains unchanged within the relevant time block, with a path fading of -30dB at a reference distance of 1 meter.

[0198] The DFRC-BS node has 4 antennas (M), and each user terminal has a single antenna. The maximum transmit power of the user terminal is... For 1W, the maximum amplitude β of each reflection unit in the ARIS node max The maximum amplification power of the ARIS node is 100. The noise power of the DFRC-BS node is 1W. The noise power of the ARIS node is -90 dBm. It is -80dBm.

[0199] like Figure 3 As shown in the figure, α represents the minimum sensing signal-to-noise ratio that the ISAC-NOMA system must meet, reflecting the system's sensing performance. It can be seen from the figure that the proposed solution significantly improves communication and speed compared to the traditional PRIS-assisted ISAC-NOMA system. Under the same PRIS assistance, the higher the α, the lower the system's communication and speed. This is because higher sensing performance requirements lead to higher transmitted signal power for sensing, resulting in stronger interference with the communication signal and consequently lower system communication performance.

[0200] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing communication and rate in an ARIS-assisted ISAC-NOMA system, characterized by: include, The channel fading gain from the user terminal to the ARIS node and from the ARIS node to the DFRC-BS node is obtained, as well as the steering vector and complex path loss coefficient of the sensing signal when the antenna array of the DFRC-BS node transmits the sensing signal. The DFRC-BS node transmits the sensing signal, and the user terminal transmits the uplink communication signal using NOMA technology. The DFRC-BS node simultaneously receives the sensing target echo signal and the communication signal. For the communication-sensing mixed signal received by the DFRC-BS node, continuous interference cancellation technology is used to calculate the communication and rate and the sensing signal-to-noise ratio respectively; To maximize the uplink communication and speed of user terminals, an optimization problem is constructed and iteratively solved using an alternating optimization algorithm to obtain the maximum communication and speed of the system. The continuous interference cancellation technique includes user terminals using NOMA technology for uplink communication, DFRC-BS nodes using SIC technology to decode communication signals, the ARIS node's reflection phase shift matrix being an identity matrix, the equivalent channel gains from user terminals to DFRC-BS nodes being arranged in descending order, and the signal-to-interference-plus-noise ratio γ of the k-th user terminal signal being... u,k The formula is expressed as follows: Among them, w r Let H be the receiving beamforming vector of the DFRC-BS node, H be the conjugate transpose, Θ be the reflection phase shift matrix of the ARIS node, and h be the receiving beamforming vector of the DFRC-BS node. k Let p be the channel fading gain vector from the k-th user terminal to the ARIS node. k Let p be the transmit power of the k-th user terminal, and satisfy p k >0, where K is the total number of user terminals, i is the index of the (k+1)th user terminal, and h i Let p be the channel fading gain vector from the i-th user terminal to the ARIS node. i Let η be the transmit power of the i-th user terminal, η be the complex path loss coefficient of the perceived target echo signal, and a(θ) be the transmit power of the i-th user terminal. t ) represents the steering vector for the antenna array to transmit sensing signals, w t For the transmit beamforming vector of the DFRC-BS node, Noise power at the DFRC-BS node; Obtain the information transmission rate R of the k-th user terminal u,k The formula is expressed as follows: R u,k =log2(1+γ u,k ) Where log is the logarithmic function, γ u,k Let be the signal-to-interference-plus-noise ratio (SINR) of the k-th user terminal signal; The perceived signal-to-noise ratio (SNR) takes into account that the target echo signal is detected after all user communication signals have been decoded. The SIC process has eliminated interference caused by communication signals. The link between the target and the DFRC-BS node is a line-of-sight link. The SNR formula for the target echo signal is expressed as follows: Where, γ rad To detect the signal-to-noise ratio of the target echo signal; The optimization problem involves jointly optimizing the reflection phase shift matrix of the ARIS node, the transmit power of the user terminal, and the transmit and receive beamforming vectors of the DFRC-BS node to maximize the communication and data rate of the user terminal. The objective function of the optimization problem is expressed as follows: Where max is the maximization function and p is the transmit power of the user terminal; The amplitude constraint formula for the ARIS node reflection element is expressed as follows: β n ≤β max Where, β max β is the maximum amplitude of the reflecting unit. n To represent the amplitude of the nth reflecting unit; The power constraint formula for ARIS nodes is expressed as follows: in, is the maximum power of the ARIS node, and F is the Frobenius norm; The formula for the transmit power constraint of the user terminal is expressed as follows: in, This is the maximum transmit power of the user terminal; The formula for normalizing the received beamforming vector at the DFRC-BS node is expressed as follows: ||w r ||＝1 The formula for the perceived signal-to-noise ratio constraint is expressed as follows. c rad ≥a Where α is the minimum perceived signal-to-noise ratio that the system must satisfy.

2. The communication and rate optimization method for an ARIS-assisted ISAC-NOMA system as described in claim 1, characterized in that: The formula for the communication-aware hybrid signal received by the DFRC-BS node is expressed as follows: y=y c +and rad +n b y rad =ηa(θ t )a H (i t )w t x r y rad =ηa(θ t )a H (i t )w t x r Where y is the communication sensing hybrid signal vector, y c Let y be the received communication signal vector. rad Let G be the received target echo signal vector, G be the channel fading gain matrix from the ARIS node to the DFRC-BS node, Θ be the reflection phase shift matrix of the ARIS node, diag be the diagonalization operation, j be the imaginary unit, and β be the received target echo signal vector. n θ n These represent the amplitude and phase of the nth reflecting element, respectively, where N is the total number of reflecting elements, and p k Let h be the transmit power of the k-th user terminal, K be the total number of user terminals, k be the user terminal index value, and h be the transmit power of the k-th user terminal. k Let x be the channel fading gain vector from the k-th user terminal to the ARIS node. u,k For the information sent by the k-th user terminal to the DFRC-BS node, n a The noise power of the ARIS node is The additive white Gaussian noise vector, n b The noise power of the DFRC-BS node is The additive white Gaussian noise vector, σ 2 Let η be the noise power, η be the complex path loss coefficient of the perceived target echo signal, and a(θ) be the noise power. t θ is the steering vector for the sensing signal transmitted by the antenna array. t To sense the azimuth angle of the target relative to the antenna array, d is the wavelength-normalized spacing between two adjacent antennas, and w t Let x be the transmit beamforming vector of the DFRC-BS node. r The sensing signal transmitted by the DFRC-BS node is sin, T is the transpose of the matrix, H is the conjugate transpose, M is the order, and e is the Euler number.

3. The communication and rate optimization method for an ARIS-assisted ISAC-NOMA system as described in claim 2, characterized in that: The DFRC-BS node uses a linear receive beamforming vector to decode the received signal and recover the signal scalar. The formula is expressed as follows: Among them, w r This is the receive beamforming vector for the DFRC-BS node.

4. The communication and rate optimization method for an ARIS-assisted ISAC-NOMA system as described in claim 3, characterized in that: The iterative solution using the alternating optimization algorithm includes transforming the objective function, which is equivalent to: Where max is the maximum value function; Considering the mutual coupling of variables in the objective function, which cannot be solved directly, the objective function is divided into four sub-problems using an alternating optimization algorithm. Given a fixed ARIS node reflection phase shift matrix, K user terminal transmit powers, and DFRC-BS node receive beamforming vector, optimize the DFRC-BS node transmit beamforming vector to construct the first sub-problem, expressed by the formula: c rad ≥a After transforming the first subproblem, a positive semidefinite relaxation method is used to relax the non-convex rank-one constraints, resulting in a convex optimization problem. This problem is solved using the convex optimization toolkit CVX, and singular value decomposition is performed on the solution to recover the optimized transmit beamforming vector. Given a fixed ARIS node reflection phase shift matrix, K user terminal transmit powers, and DFRC-BS node transmit beamforming vector, optimize the DFRC-BS node receive beamforming vector to construct a second sub-problem, expressed by the formula: ||w r ||＝1 c rad ≥a After transforming the second subproblem, the SDR method is used to relax the non-convex rank-one constraints. The Dinkelbach method is then used to transform the objective function from a fractional form to a linear form. The solution is obtained using the convex optimization toolkit CVX, and singular value decomposition is performed on the solution to recover the optimized receive beamforming vector. By fixing the reflection phase shift matrix of the ARIS node, the receive beamforming vector of the DFRC-BS node, and the transmit beamforming vector of the DFRC-BS node, the transmit power of K user terminals is optimized, thus constructing a third sub-problem, expressed by the formula: The third subproblem is a convex optimization problem, which is solved using the convex optimization toolkit CVX to obtain the optimized transmit power p for the K user terminals. * ; With fixed transmit power of K user terminals, receive beamforming vector of DFRC-BS node, and transmit beamforming vector of DFRC-BS node, optimize the reflection phase shift matrix of ARIS node to construct the fourth sub-problem, expressed by the formula: β n ≤β max c rad ≥a The fourth subproblem is transformed by applying the SDR method to relax the non-convex rank-one constraints, and then using the Dinkelbach method to transform the objective function from a fractional form to a linear form. The solution is obtained using the convex optimization toolkit CVX, and singular value decomposition is performed on the solution to obtain the optimized reflection phase shift matrix Θ. * ; The four subproblems are alternately optimized and iteratively solved until the objective function converges.

5. A system based on the communication and rate optimization method of the ARIS-assisted ISAC-NOMA system according to any one of claims 1-4, characterized in that: It includes an information acquisition and signal transmission module, a signal processing module, and an optimization problem solving module; The information acquisition and signal transmission module includes the DFRC-BS node transmitting sensing signals, the user terminal transmitting uplink communication signals using NOMA technology, and the DFRC-BS node simultaneously receiving the sensing target echo signal and the communication signal. It also acquires the channel fading gain from the user terminal to the ARIS node and from the ARIS node to the DFRC-BS node, the steering vector when the DFRC-BS node antenna array transmits sensing signals, and the complex path loss coefficient of the sensing signals. The signal processing module uses continuous interference cancellation technology to calculate the communication and rate-sensing signal-to-noise ratios of the mixed communication and sensing signals received by the DFRC-BS node, ensuring effective signal separation and identification. The optimization problem-solving module considers maximizing the uplink communication and speed of the user terminal, constructs an optimization problem, and uses an alternating optimization algorithm to iteratively solve it, thereby obtaining the maximum communication and speed of the system.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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