Communication and rate optimization method and system for ARIS-assisted ISAC-NOMA system

Abstract

The invention relates to the technical field of wireless communication, in particular to a communication and rate optimization method and system of an ARIS-assisted ISAC-NOMA system. Channel fading gains from a user terminal to an ARIS node and from the ARIS node to a DFRC-BS node, a steering vector when a DFRC-BS node antenna array sends a sensing signal and a complex path loss coefficient of the sensing signal are obtained, the DFRC-BS node sends the sensing signal, the user terminal sends an uplink communication signal, and the DFRC-BS node receives a sensing target echo signal and a communication signal at the same time; a continuous interference elimination technology is adopted for the communication sensing mixed signals, and the communication sum rate and the sensing signal-to-noise ratio are calculated; and iteratively solving an optimization problem by using an alternating optimization algorithm to obtain the maximum communication and rate of the system. Compared with the traditional method, the method provided by the invention can realize higher communication rate and better sensing performance in different scenes, and has higher practical application value.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular to a communication and rate optimization method and system for an ARIS-assisted ISAC-NOMA system. Background Art

[0002] With the rapid development of global communication network technology, the current wireless communication field is facing the problem of shortage of frequency band resources. NOMA technology can use power multiplexing to provide communication services to multiple users at the same time using the same time domain or frequency domain resources. To a certain extent, it overcomes the shortage of spectrum resources caused by traditional orthogonal access technology and effectively improves resource utilization.

[0003] At the same time, ISAC technology has gradually become a research hotspot in the field of wireless communications. It can integrate radar perception functions and wireless communication functions on the same hardware platform, achieving the purpose of environmental perception and information transmission at the same time. This technology reduces the hardware modification cost of existing networks, significantly enhances the perception capability of wireless networks, and improves the efficiency of spectrum and energy.

[0004] However, when the wireless electromagnetic environment is harsh, the above two communication technologies are difficult to implement effectively. For this reason, the reconfigurable intelligent surface (RIS) technology that has emerged in recent years can be introduced. RIS is a surface composed of microelectronic controllable components that can dynamically adjust the amplitude and phase of the incident signal, thereby changing the wireless electromagnetic environment according to different communication needs. The traditional passive reconfigurable intelligent surface (PRIS) brings about the "multiplicative attenuation" effect, for which ARIS came into being. Compared with the traditional PRIS, each reflection unit on ARIS is equipped with a signal amplifier, which effectively enhances the strength of the reflected signal while achieving amplitude and phase regulation, thereby reducing the impact of "multiplicative attenuation" and significantly improving the capacity of the communication system. Summary of the invention

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

[0006] Therefore, the present invention provides a communication and rate optimization method for an ARIS-assisted ISAC-NOMA system, which can solve the problem that traditional orthogonal access technology is difficult to meet the growing wireless communication needs due to the limited frequency band resources. Existing wireless networks usually need to implement radar perception and wireless communication functions separately on different hardware platforms, resulting in high hardware costs and lack of effective perception functions, and cannot fully perceive the surrounding environment.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a communication and rate optimization method for an ARIS-assisted ISAC-NOMA system, comprising: obtaining channel fading gains from a user terminal to an ARIS node and from an ARIS node to a DFRC-BS node, a steering vector when the DFRC-BS node antenna array sends a perception signal, and a complex path loss coefficient of the perception signal; the DFRC-BS node sends a perception signal, the user terminal uses the NOMA technology to send an uplink communication signal, and the DFRC-BS node simultaneously receives a perception target echo signal and a communication signal; for the communication perception mixed signal received by the DFRC-BS node, a continuous interference elimination technology is used to calculate the communication and rate and the perception 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 to iteratively solve the problem to obtain the maximum communication and rate of the system.

[0008] As a preferred solution of the communication and rate optimization method of the ARIS-assisted ISAC-NOMA system of the present invention, the communication perception mixed signal formula 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 ) H (θ t ) t x r

[0012] y rad =ηa(θ t ) H (θ t ) t x r

[0013]

[0014] Among them, y is the communication perception mixed signal vector, y c is the received communication signal vector, y rad is the received sensing target echo signal vector, G is the channel fading gain matrix from the ARIS node to the DFRC-BS node, Θ is the reflection phase shift matrix of the ARIS node, diag is the diagonalization operation, j is the imaginary unit, β n ,θ n are the amplitude and phase of the nth reflection unit, N is the total number of reflection units, and pk is the transmission power of the kth user terminal, K is the total number of user terminals, k is the user terminal index value, and h k is the channel fading gain vector from the kth user terminal to the ARIS node, x u,k is the information sent by the kth user terminal to the DFRC-BS node, n a The noise power of ARIS node is Complex additive Gaussian white noise vector, n b The noise power of DFRC-BS node is The complex additive Gaussian white noise vector, σ 2 is the noise power, η is the complex path loss coefficient of the target echo signal, a(θ t ) is the steering vector of the antenna array sending the sensing signal, θ t is the azimuth of the perceived target relative to the antenna array, d is the wavelength-normalized spacing between two adjacent antennas, and w t is the transmit beamforming vector of the DFRC-BS node, x r is the sensing signal sent by the DFRC-BS node, 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.

[0015] As a preferred solution of the communication and rate optimization method of the ARIS-assisted ISAC-NOMA system of the present invention, wherein: the DFRC-BS node decodes the received signal using a linear receive beamforming vector, and the recovered signal scalar The formula is expressed as,

[0016]

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

[0018] As a preferred solution of the communication and rate optimization method of the ARIS-assisted ISAC-NOMA system described in the present invention, the continuous interference elimination technology includes the user terminal using the NOMA technology for uplink communication, the DFRC-BS node using the SIC technology to decode the communication signal, the reflection phase shift matrix of the ARIS node is a unit matrix, the equivalent channel gain from the user terminal to the DFRC-BS node is arranged in descending order, and the signal to noise ratio γ of the kth user terminal signal is u,k , the formula is expressed as,

[0019]

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

[0021] Get the information transmission rate R of the kth user terminal u,k , the formula is expressed as,

[0022] R u,k =log 2 (1+γ u,k )

[0023] Wherein, log is the logarithmic function.

[0024] As a preferred solution of the communication and rate optimization method of the ARIS-assisted ISAC-NOMA system of the present invention, wherein: the perceived signal-to-noise ratio includes considering the perceived target echo signal y rad After all user communication signals are decoded, detection is performed. The SIC process has eliminated the interference caused by the communication signals. The link between the sensing target and the DFRC-BS node is a line-of-sight link. The signal-to-noise ratio formula of the sensing target echo signal is expressed as:

[0025]

[0026] Among them, γ rad is the signal-to-noise ratio of the perceived target echo signal.

[0027] As a preferred solution of the communication and rate optimization method of the ARIS-assisted ISAC-NOMA system described in the present invention, wherein: the construction of the optimization problem includes jointly optimizing the reflection phase shift matrix of the ARIS node, the transmit power of the user terminal, the transmit beamforming vector and the receive beamforming vector of the DFRC-BS node, maximizing the communication and rate of the user terminal, and the objective function formula of the optimization problem is expressed as,

[0028]

[0029] Among them, max is the maximization function;

[0030] The amplitude constraint formula of the ARIS node reflection unit is expressed as:

[0031] β n ≤β max

[0032] Among them, β max is the maximum amplitude of the reflection unit;

[0033] The power constraint formula of ARIS node is expressed as:

[0034]

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

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

[0037]

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

[0039] The normalization formula of the receiving beamforming vector of the DFRC-BS node is expressed as:

[0040] ||w r ||=1

[0041] The perceptual signal-to-noise ratio constraint formula is expressed as:

[0042] γ rad ≥α

[0043] Among them, α is the minimum perceptual signal-to-noise ratio that the system needs to meet.

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

[0045]

[0046] 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 the alternating optimization algorithm;

[0047] Fix the reflection phase shift matrix of the ARIS node, the transmit power of the K user terminals and the receive beamforming vector of the DFRC-BS node, optimize the transmit beamforming vector of the DFRC-BS node, and construct the first sub-problem. The formula is expressed as:

[0048]

[0049] γ rad ≥α

[0050] After transforming the first subproblem, the semi-positive definite relaxation method is used to relax the non-convex rank-one constraint, which becomes a convex optimization problem. The convex optimization toolkit CVX is used to solve it, and the solution is subjected to singular value decomposition to recover the optimized transmit beamforming vector.

[0051] Fix the reflection phase shift matrix of the ARIS node, the transmit power of the K user terminals and the transmit beamforming vector of the DFRC-BS node, optimize the receive beamforming vector of the DFRC-BS node, and construct the second sub-problem. The formula is expressed as:

[0052]

[0053] ||w r ||=1

[0054] γ rad ≥α

[0055] After transforming the second sub-problem, the SDR method is used to relax the non-convex rank-one constraint. The Dinkelbach method is used to transform the objective function from fractional form to linear form. The convex optimization toolkit CVX is used to solve it, and the singular value decomposition is performed on the solution to restore the optimized receive beamforming vector.

[0056] Fix the reflection phase shift matrix of the ARIS node, the receiving beamforming vector of the DFRC-BS node, and the transmitting beamforming vector of the DFRC-BS node, optimize the transmit power of K user terminals, and construct the third sub-problem, which is expressed as follows:

[0057]

[0058] The third sub-problem is a convex optimization problem. The convex optimization toolkit CVX is used to solve the optimized transmission power p of K user terminals. * ;

[0059] Fix 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, optimize the reflection phase shift matrix of the ARIS node, and construct the fourth sub-problem. The formula is expressed as:

[0060]

[0061] β n ≤β max

[0062]

[0063] γ rad ≥α

[0064] The fourth subproblem is transformed by using the SDR method to relax the non-convex rank-one constraint, and the Dinkelbach method is used to transform the objective function from fractional form to linear form. The convex optimization toolkit CVX is used to solve the problem, and the solution is subjected to singular value decomposition to obtain the optimized reflection phase shift matrix Θ* ;

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

[0066] Another object of the present invention is to provide a communication and rate optimization system for an ARIS-assisted ISAC-NOMA system. In order to improve the uplink communication rate of a user terminal while ensuring the perception performance, the present invention constructs a communication and rate maximization model, and proposes an alternating optimization algorithm for jointly optimizing the ARIS node reflection phase shift matrix, the user terminal transmission power, and the DFRC-BS node transmission and reception beamforming vectors. The method of the present invention can significantly improve the communication performance and rate of the system in a complex wireless electromagnetic environment.

[0067] As a preferred solution of the communication and rate optimization system of the ARIS-assisted ISAC-NOMA system of the present invention, it includes:

[0068] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein when the processor executes the computer program, the steps of any one of the methods for communication and rate optimization of an ARIS-assisted ISAC-NOMA system are implemented.

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

[0070] Beneficial effects of the present invention: The present invention discloses a communication and rate optimization method for an active reconfigurable intelligent surface (ARIS) assisted ISAC (Integrated Sensing and Communication) non-orthogonal multiple access (NOMA) system, which 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, wherein multiple user terminals use NOMA technology for uplink communication. In order to ensure the perception performance while improving the uplink communication and rate of the user terminal, the present invention constructs a communication and rate maximization model for an ARIS assisted ISAC-NOMA system, and proposes an alternating optimization algorithm for jointly optimizing the ARIS node reflection phase shift matrix, the user terminal transmission power, the DFRC-BS node transmission beamforming vector and the receiving beamforming vector. Compared with the traditional passive reconfigurable intelligent surface (PRIS) assisted solution, the proposed scheme of the present invention achieves higher communication and rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0072] Figure 1 A flow chart of a method for communication and rate optimization of an ARIS-assisted ISAC-NOMA system provided in accordance with an embodiment of the present invention.

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

[0074] Figure 3 A relationship diagram between the number of reflection units and the communication and rate of the ISAC-NOMA system provided by an ARIS-assisted ISAC-NOMA system communication and rate optimization method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0075] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

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

[0077] like Figure 2 As shown in the figure, 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 reflection units. 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 comprises the steps of:

[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 DFRC-BS node antenna array sends the sensing signal, and the complex path loss coefficient of the sensing signal, where the corresponding communication link between the user terminal and the DFRC-BS node is negligible 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 a perception signal. In the second stage, the user terminal uses NOMA technology to send an uplink communication signal. The DFRC-BS node receives the perception target echo signal and the communication signal at the same time.

[0081] Step 3: For the received communication perception mixed signal, the successive interference cancellation (SIC) technology is used to calculate the communication rate and the perception signal-to-noise ratio respectively;

[0082] Step 4: To maximize the uplink communication and rate of the user terminal, a model is constructed to jointly optimize the user terminal transmit power, the ARIS node reflection phase shift matrix, the DFRC-BS node transmit beamforming vector, and the receive beamforming vector.

[0083] Step 5: Use the alternating optimization algorithm to divide the optimization problem into four sub-problems and solve them iteratively to obtain the maximum communication and rate of the system.

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

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

[0086]

[0087] y rad =ηa(θ t ) H (θ t ) t x r

[0088] y rad =ηa(θ t ) H (θ t ) t x r

[0089]

[0090] Among them, y c is the received communication signal vector, y rad is the received sensing target echo signal vector, G is the channel fading gain matrix from the ARIS node to the DFRC-BS node, Θ is the reflection phase shift matrix of the ARIS node, diag is the diagonalization operation, j is the imaginary unit, β n ,θ n represent the amplitude and phase of the nth reflection unit respectively, and satisfy β n >0,θ n ∈(0,2π),p k is the transmission power of the kth user terminal, and satisfies p k > 0, K is the total number of user terminals, k is the user terminal index value, h k is the channel fading gain vector from the kth user terminal to the ARIS node, x u,k is the information sent by the kth user terminal to the DFRC-BS node, and satisfies E{|x u,k ∣ 2}=1, E is the mathematical expectation operation, n a The noise power of ARIS node is Complex additive Gaussian white noise vector, n b The noise power of DFRC-BS node is The complex additive Gaussian white noise vector, σ 2is the noise power, I N ,I M are different unit matrices, CN is the normal distribution in the complex domain, η is the complex path loss coefficient of the perceived target echo signal, a(θ t ) is the steering vector of the antenna array sending the sensing signal, θ t is the azimuth of the perceived target relative to the antenna array, d is the wavelength-normalized spacing between two adjacent antennas, and w t is the transmit beamforming vector of the DFRC-BS node, x r is the sensing signal sent by the DFRC-BS node and 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 that the DFRC-BS node uses a linear receive beamforming vector to decode the received signal, the recovered signal scalar can be expressed as,

[0092]

[0093] Among them, w r is the receiving beamforming vector of the DFRC-BS node, and satisfies ||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 the interference between different signals. Here, we consider giving priority to decoding the communication signal y c To simplify the analysis, the reflection phase shift matrix of the ARIS node can be set as the unit matrix, and it is assumed that the equivalent channel gain from the user terminal to the DFRC-BS node is arranged in descending order, that is, ∥Gh 1 ∥≥∥Gh 2 ∥≥...∥Gh K ∥, where Gh k is the equivalent channel gain from the user terminal to the DFRC-BS node, and k∈(1,K). Therefore, the signal-to-interference-noise ratio of the kth user terminal signal can be expressed as,

[0095]

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

[0097] Get the information transmission rate R of the kth user terminal u,k , the formula is expressed as,

[0098] R u,k =log 2 (1+γ u,k )

[0099] Wherein, log is the logarithmic function.

[0100] Furthermore, consider that the sensing target echo signal is detected after all user communication signals are decoded. At this time, the SIC process has eliminated the interference caused by the communication signal, and there is a line of sight link (LoS) between the sensing target and the DFRC-BS node, and there is no other reflected clutter to interfere with the received sensing target echo signal. Therefore, the signal-to-noise ratio formula of the sensing target echo signal is expressed as,

[0101]

[0102] Among them, γ rad is the signal-to-noise ratio of the perceived target echo signal.

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

[0104] (P0):

[0105] Among them, max is the maximization function;

[0106] The amplitude constraint formula of the ARIS node reflection unit is expressed as:

[0107] β n ≤β max

[0108] Among them, β max is the maximum amplitude of the reflection unit;

[0109] The power constraint formula of ARIS node is expressed as:

[0110]

[0111] in, is the maximum power of the ARIS node, F is the Frobenius norm, ||Θ|| F Represents the square root of the sum of the squares of all elements in the matrix.

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

[0113]

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

[0115] The normalization formula of the receiving beamforming vector of the DFRC-BS node is expressed as:

[0116] ||w r ||=1

[0117] The perceptual signal-to-noise ratio constraint formula is expressed as:

[0118] γ rad ≥α

[0119] Among them, α is the minimum perceptual signal-to-noise ratio that the system needs to meet.

[0120] Furthermore, to facilitate the solution of 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 the alternating optimization algorithm;

[0125] Fix the reflection phase shift matrix of the ARIS node, the transmit power of the K user terminals and the receive beamforming vector of the DFRC-BS node, optimize the transmit beamforming vector of the DFRC-BS node, and construct the first sub-problem. The formula is expressed as:

[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] Among them, min is the minimum function, Tr is the trace of the matrix, rank is the rank of the matrix, W T , B are different construction matrices used in the first sub-problem;

[0138] Due to the use of transformation (P2-2) Two new constraints are added, W T ±0 constraint ensures W T is a semi-positive definite matrix, rank(W T )=1 constraint guarantees W T The rank of is one, and the constraint is non-convex. It can be processed by using the semidefinite relaxation (SDR) method to relax the rank one constraint. At this point, (P2-2) is transformed into a convex problem and can be solved using the convex optimization toolkit CVX. For the obtained optimization solution Through singular value decomposition, the optimized transmit beamforming vector is restored

[0139] Fix the reflection phase shift matrix of the ARIS node, the transmit power of the K user terminals and the transmit beamforming vector of the DFRC-BS node, optimize the receive beamforming vector of the DFRC-BS node, and construct the second sub-problem. The formula is expressed as:

[0140] (P3):

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

[0142] γ rad ≥α

[0143] make C=|η| 2 a(θ t ) H (θ t ) t (a(θ t ) H(θ t ) 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 sub-problem;

[0151] Due to the use of transformation (P3-1) Two new bundles are added, W R is a semi-positive definite matrix, ensuring that W R The rank of is one, where rank(W R )=1 constraint is a non-convex constraint, which can be processed by SDR to relax the rank-one constraint. For the objective function in fractional form (P3-1), the Dinkelbach method is used to introduce auxiliary variables q, transform it into a linear form, and solve it iteratively. 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 )=0, the update rule of q is is the optimal solution obtained using the convex optimization toolkit CVX in the lth iteration.

[0158] The algorithm is terminated when the maximum number of iterations is reached or the (P3-2) objective function is less than the set threshold. Through singular value decomposition, the optimized receive beamforming vector is restored

[0159] Fix the reflection phase shift matrix of the ARIS node, the receiving beamforming vector of the DFRC-BS node, and the transmitting beamforming vector of the DFRC-BS node, optimize the transmit power of K user terminals, and construct the third sub-problem, which is expressed as follows:

[0160] (P4):

[0161]

[0162] Further, the problem is transformed into,

[0163] (P4-1):

[0164]

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

[0166] Finally, 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 are fixed, the reflection phase shift matrix of the ARIS node is optimized, and the fourth sub-problem is constructed. The formula is expressed as:

[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, Q sum , R and U sum The different construction matrices used for the fourth sub-problem respectively;

[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] Since the transformation V = v is used H v, (P5-1) adds two new constraints. The constraint V±0 ensures that V is a semi-positive definite matrix. The rank(V)=1 constraint ensures that the rank of V is one. The constraint is a non-convex constraint and can be processed using SDR to relax the rank-one constraint. For the objective function in fractional form of (P5-1), the Dinkelbach method is used to introduce an auxiliary variable r different from q, transform it into a linear form, and iterate the solution. 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, that is, r (0) = 0, the update rule of r is,

[0186]

[0187] The optimal solution obtained by solving (P5-2) in the lth iteration using the convex optimization toolkit CVX. The algorithm is terminated when the maximum number of iterations is reached or the objective function of (P5-2) is less than the set threshold. For the final V * , through singular value decomposition, restore the optimized v * , and then get the optimized reflection phase shift matrix Θ * .

[0188] The above is a schematic scheme of the communication and rate optimization method of the ARIS-assisted ISAC-NOMA system of this embodiment. It should be noted that the technical scheme of the communication and rate optimization system of the ARIS-assisted ISAC-NOMA system and the technical scheme of the communication and rate optimization method of the ARIS-assisted ISAC-NOMA system mentioned above belong to the same concept. For the details not described in detail in the technical scheme of the communication and rate optimization system of the ARIS-assisted ISAC-NOMA system in this embodiment, please refer to the description of the technical scheme of the communication and rate optimization method of the ARIS-assisted ISAC-NOMA system mentioned above.

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

[0190] In the information acquisition and signal transmission module, the DFRC-BS node sends a perception signal, the user terminal uses the NOMA technology to send an uplink communication signal, and the DFRC-BS node simultaneously receives the perception target echo signal and the communication signal, and obtains the channel fading gain from the user terminal to the ARIS node and the ARIS node to the DFRC-BS node, the steering vector when the DFRC-BS node antenna array sends the perception signal, and the complex path loss coefficient of the perception signal;

[0191] The signal processing module uses continuous interference elimination technology to calculate the communication rate and perception signal-to-noise ratio respectively for the communication perception mixed signal received by the DFRC-BS node, so as to ensure effective separation and identification of the signal;

[0192] The optimization problem solving module considers maximizing the uplink communication and rate of the user terminal, constructs an optimization problem, and uses an alternating optimization algorithm to perform iterative solutions to obtain the maximum communication and rate of the system.

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

[0194] This embodiment also provides a computing device suitable for the case where ARIS assists the communication and rate optimization of the ISAC-NOMA system, including: the present invention can be implemented with the help of software and necessary general-purpose hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (Random Access Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

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

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

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

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

[0199] like Figure 3 As shown in the figure, α is the minimum perceptual signal-to-noise ratio that the ISAC-NOMA system needs to meet, reflecting the perceptual performance of the system. As can be seen from the figure, the communication and rate of the scheme proposed in the present invention are significantly improved compared with the scheme of the traditional PRIS-assisted ISAC-NOMA system. Under the same RIS assistance, the higher the α, the lower the communication and rate of the system. This is because the higher the perceptual performance requirements of the system, the greater the power of the transmitted signal used for perception, the stronger the interference to the communication signal, and the lower the communication performance of the system.

[0200] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. ARIS-assisted communication and rate optimization method for ISAC-NOMA system, characterized by: include, The channel fading gain from the user terminal to the ARIS node and the ARIS node to the DFRC-BS node, the steering vector when the DFRC-BS node antenna array sends the perception signal, and the complex path loss coefficient of the perception signal are obtained. The DFRC-BS node sends the perception signal, the user terminal uses the NOMA technology to send the uplink communication signal, and the DFRC-BS node receives the perception target echo signal and the communication signal at the same time; For the communication perception mixed signal received by the DFRC-BS node, the continuous interference cancellation technique is used to calculate the communication sum rate and perception signal-to-noise ratio respectively; Considering maximizing the uplink communication and rate of the user terminal, an optimization problem is constructed, and the alternating optimization algorithm is used to iteratively solve the problem to obtain the maximum communication and rate of the system.

2. The communication and rate optimization method of the ARIS-assisted ISAC-NOMA system according to claim 1, characterized in that: The communication perception mixed signal formula received by the DFRC-BS node is expressed as: y=y c +y 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 Among them, y is the communication perception mixed signal vector, y c is the received communication signal vector, y rad is the received sensing target echo signal vector, G is the channel fading gain matrix from the ARIS node to the DFRC-BS node, Θ is the reflection phase shift matrix of the ARIS node, diag is the diagonalization operation, j is the imaginary unit, β n ,θ n are the amplitude and phase of the nth reflection unit, N is the total number of reflection units, and p k is the transmission power of the kth user terminal, K is the total number of user terminals, k is the user terminal index value, and h k is the channel fading gain vector from the kth user terminal to the ARIS node, x u,k is the information sent by the kth user terminal to the DFRC-BS node, n a The noise power of ARIS node is Complex additive Gaussian white noise vector, n b The noise power of DFRC-BS node is The complex additive Gaussian white noise vector, σ 2 is the noise power, η is the complex path loss coefficient of the target echo signal, a(θ t ) is the steering vector of the antenna array sending the sensing signal, θ t is the azimuth of the perceived target relative to the antenna array, d is the wavelength-normalized spacing between two adjacent antennas, and w t is the transmit beamforming vector of the DFRC-BS node, x r is the sensing signal sent by the DFRC-BS node, 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.

3. The communication and rate optimization method of the ARIS-assisted ISAC-NOMA system as claimed in claim 2, characterized in that: The DFRC-BS node decodes the received signal using a linear receive beamforming vector and recovers the signal scalar The formula is expressed as, Among them, w r is the receive beamforming vector of the DFRC-BS node.

4. The communication and rate optimization method of the ARIS-assisted ISAC-NOMA system as claimed in claim 3, characterized in that: The continuous interference elimination technology includes the user terminal using NOMA technology for uplink communication, the DFRC-BS node using SIC technology to decode the communication signal, the reflection phase shift matrix of the ARIS node is a unit matrix, the equivalent channel gain from the user terminal to the DFRC-BS node is arranged in descending order, and the signal to noise ratio γ of the kth user terminal signal u,k , the formula is expressed as, Where i is the index of the k+1th user terminal, h i is the channel fading gain vector from the i-th user terminal to the ARIS node, p i is the transmit power of the i-th user terminal; Get the information transmission rate R of the kth user terminal u,k , the formula is expressed as, R u,k =log2(1+γ u,k ) Wherein, log is the logarithmic function.

5. The communication and rate optimization method of the ARIS-assisted ISAC-NOMA system as claimed in claim 4, characterized in that: The perceived signal-to-noise ratio includes the consideration that the perceived target echo signal is detected after all user communication signals are decoded, the SIC process has eliminated the interference caused by the communication signal, and there is a line-of-sight link between the perceived target and the DFRC-BS node. The signal-to-noise ratio formula of the perceived target echo signal is expressed as: Among them, γ rad is the signal-to-noise ratio of the perceived target echo signal.

6. The communication and rate optimization method of the ARIS-assisted ISAC-NOMA system as claimed in claim 5, characterized in that: The optimization problem includes jointly optimizing the reflection phase shift matrix of the ARIS node, the transmit power of the user terminal, the transmit beamforming vector and the receive beamforming vector of the DFRC-BS node, maximizing the communication and rate of the user terminal, and the objective function formula of the optimization problem is expressed as: Among them, max is the maximization function; The amplitude constraint formula of the ARIS node reflection unit is expressed as: β n ≤β max Among them, β max is the maximum amplitude of the reflection unit; The power constraint formula of ARIS node is expressed as: in, is the maximum power of the ARIS node, F is the Frobenius norm; The transmit power constraint formula of the user terminal is expressed as: in, is the maximum transmit power of the user terminal; The normalization formula of the receiving beamforming vector of the DFRC-BS node is expressed as: ||w r ||＝1 The perceptual signal-to-noise ratio constraint formula is expressed as: c rad ≥a Among them, α is the minimum perceptual signal-to-noise ratio that the system needs to meet.

7. The communication and rate optimization method of the ARIS-assisted ISAC-NOMA system as claimed in claim 6, characterized in that: The iterative solution using the alternating optimization algorithm includes transforming the objective function, which is equivalent to: Among them, max is the maximum value function; 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 the alternating optimization algorithm; Fix the reflection phase shift matrix of the ARIS node, the transmit power of the K user terminals and the receive beamforming vector of the DFRC-BS node, optimize the transmit beamforming vector of the DFRC-BS node, and construct the first sub-problem. The formula is expressed as: c rad ≥a After transforming the first subproblem, the semi-positive relaxation method is used to relax the non-convex rank-one constraint, which becomes a convex optimization problem. The convex optimization toolkit CVX is used to solve it, and the solution is subjected to singular value decomposition to recover the optimized transmit beamforming vector. Fix the reflection phase shift matrix of the ARIS node, the transmit power of the K user terminals and the transmit beamforming vector of the DFRC-BS node, optimize the receive beamforming vector of the DFRC-BS node, and construct the second sub-problem. The formula is expressed as: ||w r ||＝1 c rad ≥a After transforming the second sub-problem, the SDR method is used to relax the non-convex rank-one constraint. The Dinkelbach method is used to transform the objective function from fractional form to linear form. The convex optimization toolkit CVX is used to solve it, and the singular value decomposition is performed on the solution to restore the optimized receive beamforming vector. Fix the reflection phase shift matrix of the ARIS node, the receiving beamforming vector of the DFRC-BS node, and the transmitting beamforming vector of the DFRC-BS node, optimize the transmit power of K user terminals, and construct the third sub-problem, which is expressed as follows: The third sub-problem is a convex optimization problem. The convex optimization toolkit CVX is used to solve the optimized transmission power p of K user terminals. * ; Fix 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, optimize the reflection phase shift matrix of the ARIS node, and construct the fourth sub-problem. The formula is expressed as: β n ≤β max c rad ≥a The fourth subproblem is transformed by using the SDR method to relax the non-convex rank-one constraint, and the Dinkelbach method is used to transform the objective function from fractional form to linear form. The convex optimization toolkit CVX is used to solve the problem, and the solution is subjected to singular value decomposition to obtain the optimized reflection phase shift matrix Θ * ; The four sub-problems are optimized alternately and solved iteratively until the objective function converges.

8. A system based on the communication and rate optimization method of the ARIS-assisted ISAC-NOMA system according to any one of claims 1 to 7, characterized in that: It includes information acquisition and signal transmission module, signal processing module and optimization problem solving module; In the information acquisition and signal transmission module, the DFRC-BS node sends a perception signal, the user terminal uses the NOMA technology to send an uplink communication signal, and the DFRC-BS node simultaneously receives the perception target echo signal and the communication signal, and obtains the channel fading gain from the user terminal to the ARIS node and the ARIS node to the DFRC-BS node, the steering vector when the DFRC-BS node antenna array sends the perception signal, and the complex path loss coefficient of the perception signal; The signal processing module uses continuous interference elimination technology to calculate the communication rate and perception signal-to-noise ratio respectively for the communication perception mixed signal received by the DFRC-BS node, so as to ensure effective separation and identification of the signal; The optimization problem solving module considers maximizing the uplink communication and rate of the user terminal, constructs an optimization problem, and uses an alternating optimization algorithm to perform iterative solutions to obtain the maximum communication and rate of the system.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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