Ris-based full-duplex cooperative noma integrated sensing communication system and optimization method

Abstract

The application discloses a full-duplex cooperative NOMA integrated sensing communication system and an optimization method based on RIS, and relates to the technical field of wireless communication. The existing sensing communication system combined with RIS does not combine the full-duplex cooperation mechanism, and the base station beamforming matrix, the power allocation factor and the RIS phase shift matrix are jointly optimized to solve the problems of large user-to-user interference, poor communication quality of far users and low resource allocation efficiency in the sensing communication system combined with RIS. With the help of RIS and the optimization of the algorithm, the communication performance of the user is improved; the beamforming gain is enhanced and the target detection accuracy is improved through algorithm optimization; the cost of hardware is reduced through the integration of communication and sensing; the communication quality of the far user is guaranteed through the decoding and retransmission of the near user and the cooperation of RIS; and the energy efficiency is greatly improved through reasonable allocation of resources.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless communication, and particularly relates to a full-duplex cooperative NOMA integrated sensing communication system based on RIS and an optimization method. BACKGROUND

[0002] With the increasing demand for effective utilization of spectrum and energy in interconnected devices, integrated sensing and communication (ISAC) has become a cornerstone technology for the next generation of wireless networks. ISAC realizes the dual functions of environmental sensing and data transmission by sharing the frequency band, eliminates hardware costs, and significantly improves spectrum efficiency.

[0003] With the advancement of 6G communication technology, non-orthogonal multiple access (NOMA), cooperative NOMA, and reconfigurable intelligent surface (RIS) technology have gradually become key technologies for improving the performance of sensing communication systems. NOMA improves system capacity by allowing multiple users to share channels on the same spectrum resources, but traditional NOMA has significant inter-user interference and far user path loss problems. Cooperative NOMA improves the capacity of far users through user cooperation, but still faces self-interference and cross-cluster interference problems. RIS, as a new type of signal reflection device, can enhance the quality and transmission efficiency of signals by dynamically adjusting the phase of the reflection surface, but existing sensing communication systems combined with RIS do not combine full-duplex cooperation mechanisms, resulting in insufficient resource allocation and beamforming efficiency. The hybrid NOMA scheme alleviates interference, but does not solve the power allocation and phase optimization coupling problem in the multi-user scenario. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a full-duplex cooperative NOMA integrated sensing communication system based on RIS and an optimization method, which solves the problem that existing sensing communication systems combined with RIS do not combine full-duplex cooperation mechanisms, and simultaneously optimizes the base station beamforming matrix, power allocation factor, and RIS phase shift matrix to solve the problems of large inter-user interference, poor far user communication quality, and low resource allocation efficiency in sensing communication systems combined with RIS.

[0005] The first aspect of the present application provides a full-duplex cooperative NOMA integrated sensing communication system based on RIS, comprising:

[0006] Base station: generating signals and transmitting signals to users in G' clusters of reconfigurable intelligent surfaces using M equipped antennas to provide services; receiving echo signals reflected by radar targets to realize sensing I radar targets; the signals generated by the base station can be used for communication and sensing at the same time; the users include near users and far users;

[0007] Reconfigurable intelligent surface: adjusting the phase of the incident signal using N equipped reflection elements, and reflecting the adjusted signal;

[0008] G clusters: each cluster includes a near user U(g,n) and a far user U(g,f), wherein g is the number of the cluster, the near user in the same cluster represents a user closer to the base station, the far user represents a user farther away from the base station, and each cluster is allocated a sub-resource block, and the far user and the near user in each cluster adopt non-orthogonal multiple access (NOMA);

[0009] I radar targets: I targets to be perceived within the user setting range.

[0010] The RIS-based full-duplex cooperative NOMA integrated perception communication system includes two communication modes of direct transmission and cooperative transmission, and the two communication modes are simultaneously performed in the communication process;

[0011] In the direct transmission, the base station sends signals to the users of each cluster, then the near user U(g,n) decodes the signal of the far user U(g,f) through serial interference cancellation, and then subtracts the decoded signal of the far user from the received signal to obtain the signal of the near user itself; on the other hand, the far user directly decodes the signal of itself;

[0012] In the cooperative transmission, the near user U(g,n) receives the signal and retransmits it to the far user U(g,f), and the near user retransmits the signal to the far user in a full-duplex mode; the far user U(g,f) combines the signal retransmitted by the near user and the signal sent by the base station, and decodes it.

[0013] The second aspect of the present application provides an optimization method of an RIS-based full-duplex cooperative NOMA integrated perception communication system, which is used for optimizing an RIS-based full-duplex cooperative NOMA integrated perception communication system, and includes the following steps:

[0014] establishing an objective function of maximizing the total data rate and the total perception beam gain of the users and constraints thereof;

[0015] transforming the established objective function of maximizing the total data rate and the total perception beam gain of the communication users into three sub-objective functions; the three sub-objective functions are respectively a power allocation objective function in the cluster, a beamforming objective function and an RIS phase shift objective function;

[0016] optimizing and solving the established three sub-objective functions by using an AO algorithm to obtain an optimized beamforming matrix, an RIS phase shift matrix and a power allocation parameter; the power allocation parameter includes a power allocation factor of the base station and a power allocation factor of the near user.

[0017] Further, the process of establishing the objective function of maximizing the total data rate and the total perception beam gain of the users and the constraints thereof is specifically:

[0018] The signal received by the near user is denoted as:

[0019]

[0020] where y g,n denotes the signal received by the near user, denotes the channel from the reconfigurable intelligent surface to the near user, H denotes the conjugate transpose of a matrix, denotes a set of complex numbers; Φ = diag(v) is the RIS phase shift matrix, and is a reflection vector, j denotes an imaginary unit, φ l denotes the phase shift angle of the l-th reflecting element and obeys φ l ∈ [0, 2π), l denotes the number of reflecting elements, denotes the channel from the base station to the reconfigurable intelligent surface, is a beamforming vector; x g,n and denote the required signals of the near user U(g, n) and the far user U(g, f), respectively, α g,n and α g,f denote the power allocation factors corresponding to the near user U(g, n) and the far user U(g, f), respectively, n g,n is a Gaussian white noise, obeying zero mean and a standard deviation of P g,n denotes the energy of the near user retransmission, denotes a self-interference coefficient, satisfies where i denotes the i-th time slot, and τ is the decoding time;

[0021] The signal received by the far user is denoted as:

[0022]

[0023] where y g,f denotes the signal received by the far user, is the channel from the reconfigurable intelligent surface to the far user, and denote the channel from the near user U(g, n) to the reconfigurable intelligent surface, the channel from the reconfigurable intelligent surface to the far user U(g, f), and the channel from the near user U(g, n) to the far user U(g, f) in cooperative transmission, respectively; n g,f is a Gaussian white noise, obeying zero mean and a standard deviation of

[0024] The signal-to-noise ratio of the near user U(g, n) decoding the signal of the far user U(g, f) is:

[0025]

[0026] where S g,n→f denotes the signal-to-noise ratio of the near user U(g, n) decoding the signal of the far user U(g, f);

[0027] The signal-to-noise ratio of the near user decoding its own signal is:

[0028]

[0029] where S g,n denotes the signal-to-noise ratio of the near user decoding its own signal;

[0030] The data rate of the near user is:

[0031] R g,n = log2(1 + S g,n ) (5)

[0032] where R g,n denotes the data rate of the near user;

[0033] The signal-to-noise ratio of the far user is:

[0034]

[0035] where denotes the signal-to-noise ratio of the far user;

[0036] The actual data rate of the far user is:

[0037]

[0038] where R g,f denotes the actual data rate of the far user;

[0039] The total data rate of the users is:

[0040]

[0041] where R sum denotes the total data rate of the users;

[0042] The target detection angle is θ i The perception beam gain of the target detection angle θ

[0043]

[0044] where θ i denotes the target detection angle, i denotes the number of radar targets, and P(θ i ) is the perception beam gain of the target detection angle θ i ; It is the steering vector of the reconfigurable smart surface, where λ is the carrier wavelength. Indicates the distance between adjacent reflective elements;

[0045] The total sensing beam gain for I radar targets is:

[0046]

[0047] Where P(θ) represents the total sensing beam gain;

[0048] The objective function for maximizing the total data rate and total sensed beam gain for communication users is:

[0049]

[0050] The constraints on the objective function that maximizes the total data rate and total perceived beam gain for communication users are:

[0051]

[0052] Where W = [w1,...,w g ,...,w G' ] is the beamforming matrix, α = [α 1,f ,...,α g,f ,...,α G',f [P] represents the power allocation factor for the base station. N =[P 1,n ,...,P g,n ,...,P G',n [Energy for all near-user retransmissions; β] c and β s For a given normalization parameter, P b It is the maximum energy threshold of the base station. It is the minimum communication rate threshold for remote users. It is the minimum communication rate threshold for near-users, P min (θ i Minimum sensing beam gain threshold It is the maximum retransmission power threshold.

[0053] Furthermore, the objective function for power allocation within the cluster is:

[0054]

[0055] The constraints of the power allocation objective function within the cluster are:

[0056]

[0057]

[0058] where S n→f = [S 1,n→f ,..., S G',n→f ], S n = [S 1,n ,..., S G',n ], X = [X1,..., X g ,..., X G' ], X g are the slack variables; where S n→f = [S 1,n→f ,..., S G',n→f ], S n = [S 1,n ,..., S G',n ], X = [X1,..., X g ,..., X G' ], X g are the slack variables; t represents the number of iterations, represents the real result of the power allocation factor of the near user U(g, n) obtained in the last iteration, represents the real result of the energy of the near user retransmission obtained in the last iteration.

[0059] Further, the beamforming objective function is:

[0060]

[0061] The constraint of the beamforming objective function is:

[0062]

[0063]

[0064] where p is the slack variable, I n→f = [I 1,n→f ,..., I g,n→f ,..., I G',n→f ], I g,n→f are the slack variables, are the slack variables, diag represents a diagonal matrix; represents taking the real part, v (t) represents the real result of the reflection vector obtained in the last iteration, and represent the real result of the slack variable obtained in the last iteration.

[0065] Further, the RIS phase shift objective function is:

[0066]

[0067] The constraint of the RIS phase shift target function is:

[0068]

[0069] where Tr denotes the trace, rank denotes the rank, V = vv H , K i = diag{a H (θ i )G} ;||| * is the nuclear norm, ||| 2 is the spectral norm, ρ > 0 is a penalty factor, is the real result of W g obtained in the last iteration, is the eigenvector corresponding to the maximum eigenvalue.

[0070] Further, the three sub-target functions established based on the are used to optimize the beamforming matrix, the RIS phase shift matrix and the power allocation parameter by using the AO algorithm, to obtain the optimized beamforming matrix, the RIS phase shift matrix and the power allocation parameter; the power allocation parameter includes a power allocation factor of the base station and a power allocation factor of the near user, and specifically includes:

[0071] S1: initializing the BS beamforming matrix, the RIS phase shift matrix and the power allocation parameter;

[0072] S2: solving the power allocation target function by multiple iterations of CVX, updating the power allocation factor of the base station and the power allocation factor of the near user;

[0073] S3: solving the RIS phase shift target function by multiple iterations of CVX, updating the RIS phase shift matrix;

[0074] S4: solving the beamforming target function by multiple iterations of CVX, updating the beamforming matrix, and increasing the penalty factor in each iteration until the target function converges;

[0075] S5: judging whether the target function of maximizing the total data rate and the total perceived beam gain of the communication user converges, if yes, obtaining the optimized beamforming matrix, the RIS phase shift matrix and the power allocation parameter, otherwise returning to S2.

[0076] The third aspect of the present application provides a RIS-based full-duplex cooperative NOMA integrated sensing communication system optimization system for realizing the RIS-based full-duplex cooperative NOMA integrated sensing communication system optimization method, comprising:

[0077] A target function construction module is configured to establish a target function for maximizing the total data rate and the total sensing beam gain of users and constraints thereof;

[0078] A target function decomposition module is configured to convert the established target function for maximizing the total data rate and the total sensing beam gain of users into three sub-target functions; the three sub-target functions are respectively a power allocation target function within a cluster, a beamforming target function and an RIS phase shift target function;

[0079] A solving module is configured to optimize a BS beamforming matrix, an RIS phase shift matrix and a power allocation parameter based on the established three sub-target functions by using an AO algorithm to obtain an optimized beamforming matrix, an RIS phase shift matrix and a power allocation parameter; the power allocation parameter comprises a power allocation factor of a base station and a power allocation factor of a near user.

[0080] The fourth aspect of the present application provides an electronic device comprising a processor, a memory and a bus, the memory stores machine readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, and the machine readable instructions are executed by the processor to perform the steps of the RIS-based full-duplex cooperative NOMA integrated sensing communication system optimization method.

[0081] The fifth aspect of the present application provides a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program is executed by a processor to perform the steps of the RIS-based full-duplex cooperative NOMA integrated sensing communication system optimization method.

[0082] Compared with the prior art, the present application has the following advantages:

[0083] 1. Communication performance: the communication performance of users is improved through the help of RIS and the optimization of the algorithm;

[0084] 2. Sensing performance: the beamforming gain is enhanced and the target detection accuracy is improved through algorithm optimization;

[0085] 3. Spectrum utilization rate: because the communication signal and the sensing signal share one spectrum and NOMA is used between users, the spectrum utilization rate is greatly improved;

[0086] 4. Hardware cost: the hardware cost is reduced through the integration of communication and sensing;

[0087] 5. Communication guarantee of far user: through the cooperation of the decoding retransmission of near user and RIS, the communication quality of far user is guaranteed;

[0088] 6. Energy efficiency: through the reasonable allocation of resources, the energy efficiency is greatly improved. BRIEF DESCRIPTION OF DRAWINGS

[0089] Figure 1 It is a schematic diagram of the RIS-based full-duplex cooperative NOMA integrated sensing communication system in the embodiment of the application.

[0090] Figure 2 It is a convergence performance diagram of the RIS-based full-duplex cooperative NOMA integrated sensing communication system optimization method under different numbers of base station antennas and RIS reflecting elements.

[0091] Figure 3 It is a schematic diagram of the total data rate of communication users under different numbers of RIS reflecting elements.

[0092] Figure 4 It is a schematic diagram of the total data rate of communication users under different numbers of BS antennas.

[0093] Figure 5 It is a schematic diagram of the total data rate of communication users under different maximum energy thresholds of base stations.

[0094] Figure 6 It is a schematic diagram of the total data rate of communication users under different communication rate thresholds.

[0095] Figure 7 It is a schematic diagram of the sensing beam gain of three schemes under different target detection angles. DETAILED DESCRIPTION

[0096] The application will be described in detail below with reference to the drawings and embodiments.

[0097] As shown in the figure, the embodiment provides a RIS-based full-duplex cooperative NOMA integrated sensing communication system, which comprises: Figure 1 Base station (BS): generates a signal and transmits the signal to a reconstructed intelligent surface (RIS) using equipped M antennas to provide services to users in G' clusters; receives echo signals reflected by radar targets to realize sensing I radar targets; the signal can be used for communication and sensing at the same time; the users include near users and far users;

[0098]

[0099] ​Reconfigurable intelligent surface (RIS): due to the direct link from the base station to the user and the radar target being blocked, only the link from the base station to the RIS and then to the user and the radar target exists, so as to optimize the signal transmission by using the N equipped reflecting elements, adjusting the phase of the incident signal, and reflecting the adjusted signal, i.e. the echo signal;

[0100] G clusters: each cluster includes one near user U(g,n) and one far user U(g,f), where g is the number of the cluster, the near user in the same cluster represents a user closer to the base station, and the far user represents a user farther from the base station, and each cluster is allocated a sub-resource block (RB), and the near and far users in each cluster use non-orthogonal multiple access (NOMA);

[0101] I radar targets: I targets to be perceived near the user;

[0102] In this embodiment, the base station (BS) equipped with an antenna is located at the origin, and the reconfigurable intelligent surface (RIS) equipped with reflecting elements is located at (20, 20). Three clusters are set, and the users in each cluster are randomly distributed on a circle with a radius of 3 m centered at (10, 5), (26, 5) and (15, 11). The angles of the two radar targets are set to 30° and 20°, respectively. The power budget of the base station BS and the near user is 35 dBm and 25 dBm, respectively. The noise power is set to -90 dBm. In addition, all channels follow the Rician fading distribution, and the Rician factor is 3. The path loss exponent is set to 2.2, and the path loss at a reference distance of 1 meter is -30 dB.

[0103] The communication mode of the RIS-based full-duplex cooperative NOMA integrated perception communication system includes two communication modes: direct transmission (DT) and cooperative transmission (CT), and the two communication modes are performed simultaneously in the communication process;

[0104] In direct transmission (DT), the base station (BS) transmits signals to the users in each cluster, then the near user U(g,n) decodes the signals of the far user U(g,f) through serial interference cancellation (SIC), and then subtracts the decoded signals of the far user from the received signals to obtain the signals of the near user itself; on the other hand, the far user directly decodes its own signals;

[0105] In cooperative transmission (CT), the near user U(g,n) receives signals and decodes and retransmits them to the far user U(g,f), and the near user retransmits signals to the far user in full-duplex mode; the far user U(g,f) combines the signals retransmitted by the near user with the signals transmitted by the base station and decodes them;

[0106] The embodiment provides a full-duplex cooperative NOMA integrated sensing communication system optimization method based on RIS, and comprises the following steps:

[0107] Step 1: establishing an objective function maximizing the total data rate and total sensing beam gain of users and constraints thereof;

[0108] In the embodiment, the full-duplex mode is considered, which causes the self-interference (SI) of the near user. Let SI represent the channel coefficient, which follows a complex symmetric Gaussian random variable with a mean of zero and a standard deviation of σ SI Therefore, the signal received by the near user is represented as:

[0109]

[0110] where y g,n represents the signal received by the near user, represents the channel from the reconfigurable intelligent surface (RIS) to the near user, H represents the conjugate transpose of the matrix, represents a complex number set; Φ = diag(v) is the RIS phase shift matrix, and is a reflection vector, j represents an imaginary unit, φ l represents the phase shift angle of the lth reflection element and follows φ l ∈ [0, 2π), l represents the number of reflection elements, represents the channel from the base station (BS) to the reconfigurable intelligent surface (RIS), is a beamforming vector; x g,n and x g,f represent the required signals of the near user U(g, n) and the far user U(g, f) respectively, α g,n and α g,f represent the power allocation factors corresponding to the near user U(g, n) and the far user U(g, f) respectively, n g,n is a Gaussian white noise, which obeys zero mean and a standard deviation of P g,n represents the energy of the near user retransmission, represents the self-interference coefficient, and satisfies where i represents the ith time slot, and τ is the decoding time, so as to cause the interference of the near user when decoding the far user;

[0111] For the far user U(g, f), it simultaneously receives the signals from the base station (BS) and the near user U(g, n), and therefore, the signal received by the far user is represented as:

[0112]

[0113] where yg,f denotes the signal received by the far user, is the channel from the reconfigurable intelligent surface (RIS) to the far user, and are the channel from the near user U(g,n) to the reconfigurable intelligent surface (RIS), the channel from the reconfigurable intelligent surface (RIS) to the far user U(g,f) and the channel from the near user U(g,n) to the far user U(g,f) in cooperative transmission (CT), respectively; n g,f is the Gaussian white noise, obeying zero mean and standard deviation

[0114] Therefore, the signal-to-noise ratio (SINR) of the near user U(g,n) decoding the signal of the far user U(g,f) is:

[0115]

[0116] where S g,n→f denotes the signal-to-noise ratio of the near user U(g,n) decoding the signal of the far user U(g,f);

[0117] After successfully using the serial interference cancellation (SIC), the signal of the far user can be removed, and therefore, the signal-to-noise ratio (SINR) of the near user decoding its own signal is:

[0118]

[0119] where S g,n denotes the signal-to-noise ratio of the near user decoding its own signal;

[0120] Therefore, the data rate of the near user is:

[0121] R g,n = log2(1+S g,n ) (5)

[0122] where R g,n denotes the data rate of the near user;

[0123] The far user can receive its own signal from the DT and the CT, and use the maximum ratio combining (MRC) technique, and since the signals from the base station and the near user are completely separable at the far user, the signal-to-noise ratio (SINR) of the far user is:

[0124]

[0125] where denotes the signal-to-noise ratio of the far user;

[0126] Due to the application of NOMA, the actual data rate of the far user is:

[0127]

[0128] where R g,f denotes the actual data rate of the far user;

[0129] Thus, the total data rate of the users is:

[0130]

[0131] where R sum denotes the total data rate of the users;

[0132] Since the signal is dual-functional (communication and sensing), the target detection angle is θ i The sensing beam gain at θ

[0133]

[0134] where θ i denotes the target detection angle, i denotes the index of the radar target, and P(θ i ) is the sensing beam gain at θ i ; is the steering vector of the reconfigurable intelligent surface (RIS), λ is the carrier wavelength, denotes the distance between adjacent reflecting elements;

[0135] Thus, the total sensing beam gain of I radar targets is:

[0136]

[0137] where P(θ) denotes the total sensing beam gain;

[0138] To effectively utilize the proposed system, the objective of the present application is to simultaneously optimize the communication and sensing performance, i.e., to maximize the total rate of the communication users and the total sensing beam gain under the given normalized parameters β c and β s , by jointly optimizing the beamforming matrix at the base station (BS) and the RIS phase shift matrix and the transmission power of the near users:

[0139] Thus, the objective function to maximize the total data rate of the communication users and the total sensing beam gain is:

[0140]

[0141] The constraints of the objective function to maximize the total data rate of the communication users and the total sensing beam gain are:

[0142]

[0143]

[0144] Where W = [w1,...,w g ,...,w G' ] is the beamforming matrix, α = [α 1,f ,...,α g,f ,...,α G',f [P] represents the power allocation factor for the base station. N =[P 1,n ,...,P g,n ,...,P G',n [Energy for all near-user retransmissions; β] c and β s For a given normalization parameter, P b It is the maximum energy threshold of the base station. It is the minimum communication rate threshold for remote users. It is the minimum communication rate threshold for near-users, P min (θ i Minimum sensing beam gain threshold (12) is the maximum retransmission power threshold; (13) and (14) guarantee the quality of service (QoS); (15) ensures the minimum perceived beam gain for each target; (16) is the power limit for near users; (17) and (18) represent the constraints of the power allocation factor; and (19) is the modulus constraint for each RIS reflector element.

[0145] Step 2: The established objective function of maximizing the total data rate and total perceived beam gain of communication users is transformed into three sub-objective functions; the three sub-objective functions are the power allocation objective function within the cluster, the beamforming objective function, and the RIS phase shift objective function.

[0146] The problem (11) is highly coupled and non-convex. To address this problem, it is divided into three solvable subproblems;

[0147] To address the nonconvexity of the objective function, a slack variable is introduced. However, expressions (3), (4), and (6) in problem (11) still exhibit nonconvexity. Therefore, by relaxing expressions (3), (4), and (6), problem (11) can be restated to obtain the power allocation objective function within the cluster as follows:

[0148]

[0149] The constraints of the power allocation objective function within the cluster are:

[0150]

[0151]

[0152] where S n→f = [S 1,n→f ,....,S G',n→f ], S n = [S 1,n ,....,S G',n ], X = [X1,...,X g ,...,X G' ], X g is the slack variable;

[0153] However, the constraints (21), (22) and (23) are not convex; to solve this problem, the SCA method is adopted to transform the non-convex functions (21), (22) and (23) into affine form by using the first-order Taylor expansion (FTE) approximation, and the reformulated form is:

[0154]

[0155] where t denotes the iteration number, denotes the real result of the power allocation factor of the near user U(g, n) obtained in the last iteration, denotes the real result of the energy of the near user retransmission obtained in the last iteration, and without the superscript t is the parameter solved in this iteration;

[0156] Therefore, this problem (20) is further reformulated as:

[0157]

[0158] s.t. (14), (16), (17), (18), (24)-(27), (29)-(31) (33)

[0159] This problem (32) is now convex and can be easily solved by standard optimization tools such as CVX;

[0160] Similar to the design of the power allocation, auxiliary variables are added, and the expressions of (3), (4) and (6) are relaxed, and the slack variable I g,n→f and are introduced to make the target problem convex, so the problem (11) can be reformulated as the RIS phase shift target function:

[0161]

[0162] The constraints of the RIS phase shift target function are:

[0163]

[0164] (13), (14), (15), (19) (43)

[0165] where p is the relaxation variable, I n→f = [I 1,n→f ,...,I g,n→f ,...,I G',n→f ], I g,n→f is the relaxation variable, is the relaxation variable, diag denotes a diagonal matrix;

[0166] The constraints (35), (36), and (37) are non-convex, where we apply SCA to convert them into affine form:

[0167]

[0168]

[0169] where, denotes taking the real part, v (t) denotes the real result of the reflection vector from the last iteration, and denotes the real result of the relaxation variable from the last iteration;

[0170] Similarly, the non-convex constraint (42) can also be rewritten as:

[0171]

[0172] Therefore, problem (34) can be restated as:

[0173]

[0174] s.t. (13), (14), (15), (19), (38)-(41), (44)-(47) (49)

[0175] This problem (48) is presented as convex, which can be solved with CVX;

[0176] Problem (11) can be reshaped as a beamforming objective function:

[0177]

[0178] The constraints for the beamforming objective function are:

[0179]

[0180]

[0181] Where Tr represents finding the trace, and rank represents taking the rank. V = vv H , K i =diag{a H (θ i )G};

[0182] Constraints (52) and (53) are not convex; they can be restated as follows:

[0183]

[0184] The nonconvexity of these two constraints (59) and (60) is caused by the first term on the right, which has an underscore. Therefore, SCA is called to transform these terms via FTE, and these terms are:

[0185]

[0186] Then, define:

[0187]

[0188] For the rank-1 constraint (58), it is transformed into an equivalent equality constraint:

[0189]

[0190] Among them || || * It is the nuclear norm, |||2 is the spectral norm, considering W g It is positive semidefinite, therefore ||W g || * -||W g ||2>0 always holds true. Then, we introduce (65) as a penalty term to obtain a rank-1 matrix. The original problem (50) can be restated as:

[0191]

[0192] st(58),(54)-(57),(63),(64) (67)

[0193] Where ρ > 0 is a penalty factor; however, the penalty term makes the objective non-convex. Therefore, the FTE with penalty clauses is adopted, and the problem (66) can be transformed by the following problem:

[0194]

[0195] s.t. (58), (54)-(57), (63), (64) (69)

[0196] where, W is the W obtained from the last iteration g the real result of, is the eigenvector corresponding to the largest eigenvalue, this problem (68) is convex at present, which can be effectively solved by CVX;

[0197] Step 3: use the AO algorithm to optimize and solve the three sub-objective functions established, and obtain the optimized beamforming matrix, RIS phase shift matrix and power allocation parameter; the power allocation parameter includes the power allocation factor of the base station and the power allocation factor of the near user;

[0198] Step 3.1: initialize the BS beamforming matrix, RIS phase shift matrix and power allocation parameter;

[0199] Step 3.2: solve the power allocation objective function shown in formula (32) by CVX multiple iterations, update the power allocation factor of the base station (BS) and the power allocation factor of the near user;

[0200] Step 3.3: solve the RIS phase shift objective function shown in formula (48) by CVX multiple iterations, update the RIS phase shift matrix;

[0201] Step 3.4: solve the beamforming objective function shown in formula (69) by CVX multiple iterations, update the beamforming matrix, it should be noted that the penalty factor needs to be increased every time the iteration is performed until the objective function converges;

[0202] Step 3.5: determine whether the objective function shown in formula (11) of maximizing the total data rate and the total perceived beam gain of the communication user converges, if so, the optimized beamforming matrix, RIS phase shift matrix and power allocation parameter are obtained, otherwise return to step 3.2.

[0203] As Figure 2 shown, the convergence behavior of the algorithm under different numbers of BS antennas and RIS reflecting elements is shown, it can be seen that the target value converges to a stable value in a few external iterations. In addition, with the increase of the number of BS antennas and RIS reflecting elements, the target value also increases. In addition, it is worth noting that the increase of the number of antennas and RIS reflecting elements has no significant effect on the number of iterations before the algorithm converges;

[0204] As Figure 3The figure shows the impact of the number of RIS reflectors on the total data rate of communication users. It is clear that the total data rate of communication users increases with the increase of the number of RIS reflectors. This is because the increase in the number of RIS reflectors leads to an enhancement in beamforming gain, resulting in a higher SINR for the communication users. Furthermore, it can be noted that the figure compares the optimal phase and random phase schemes of the RIS. It can be seen that the optimal phase scenario is superior to the random phase scenario, and the increase in the total data rate of communication users caused by the increase in the number of RIS reflectors is not significant. Moreover, this scheme performs better than the NOMA and OMA schemes.

[0205] like Figure 4 As shown, it is clear that more BS antennas lead to an increase in the overall data rate for communication users, due to the higher beamforming gain resulting from more active beams. Furthermore, the figure compares schemes with a fixed power allocation factor and an optimal power allocation factor. The results show that the scheme with the optimal power allocation factor outperforms the scheme with the fixed power allocation factor. Moreover, the optimal power allocation scheme significantly outperforms other baseline schemes.

[0206] like Figure 5 As shown, the total data rate of communication users increases with the increase of the maximum energy threshold of the base station. This is because signal strength increases with the increase of transmission power. Furthermore, it was found that by increasing the maximum energy threshold of the base station in the CNOMA scheme, the total data rate of communication users is significantly improved. As expected, under the same maximum energy threshold of the base station, the scheme proposed in this invention outperforms the NOMA and OMA methods.

[0207] like Figure 6 As shown, it can be clearly observed that increasing the communication rate threshold reduces the total data rate for communication users. Furthermore, it can be observed that increasing the sensing power threshold at each radar target leads to a decrease in the target value. This can be attributed to more sensing capability and less communication capability. Although the total data rate for communication users decreases with increasing the communication rate threshold at the same communication rate and sensing power, this scheme outperforms the NOMA and OMA schemes.

[0208] like Figure 7 As shown, the sensing beam gain of the proposed scheme and two baseline schemes at target detection angles of 20 degrees and -30 degrees is illustrated. It is clearly noticeable that the proposed scheme achieves a dominant peak in the direction of interest, which is sharper compared to other schemes. This allows for greater sensing capabilities for the BS while also facilitating collaboration among communicating users.

[0209] The embodiment provides a RIS-based full-duplex cooperative NOMA integrated sensing communication system optimization system, which is used for realizing the RIS-based full-duplex cooperative NOMA integrated sensing communication system optimization method, and comprises the following steps:

[0210] A target function construction module is configured to establish a target function for maximizing total data rates and total sensing beam gains of users and constraints of the target function.

[0211] A target function decomposition module is configured to convert the established target function for maximizing total data rates and total sensing beam gains of communication users into three sub-target functions; the three sub-target functions are respectively a power allocation target function in a cluster, a beamforming target function and an RIS phase shift target function.

[0212] A solving module is configured to optimize a BS beamforming matrix, an RIS phase shift matrix and a power allocation parameter based on the established three sub-target functions by using an AO algorithm, so as to obtain an optimized beamforming matrix, an RIS phase shift matrix and a power allocation parameter; the power allocation parameter comprises a power allocation factor of a base station (BS) and a power allocation factor of a near user.

[0213] The embodiment provides an electronic device, comprising a processor, a memory and a bus, the memory stores machine readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, and the machine readable instructions are executed by the processor to perform the steps of the RIS-based full-duplex cooperative NOMA integrated sensing communication system optimization method.

[0214] The embodiment provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to perform the steps of the RIS-based full-duplex cooperative NOMA integrated sensing communication system optimization method.

Claims

1. A RIS-based full-duplex cooperative NOMA integrated sensing communication system, characterized in that, include: Base station: generates signals and uses the equipped M antennas to transmit the signals to the reconfigured smart surface to provide services to users in the G' clusters; The system receives echo signals reflected from radar targets to sense I radar targets; the signals generated by the base station can be used for both communication and sensing simultaneously; the users include near users and far users. Reconfigurable smart surface: It uses N reflective elements to adjust the phase of the incident signal and reflect the adjusted signal. G' clusters: Each cluster includes a near user U(g,n) and a far user U(g,f), where g is the cluster number. Near users in the same cluster refer to users closer to the base station, and far users refer to users farther from the base station. At the same time, each cluster is allocated a sub-resource block. The far users and near users in each cluster use non-orthogonal multiple access (NOMA). I radar targets: I targets to be detected within the user-defined range; The RIS-based full-duplex cooperative NOMA integrated sensing communication system includes two communication modes: direct transmission and cooperative transmission, which are carried out simultaneously during the communication process. In direct transmission, the base station sends a signal to each user in the cluster. Then, the near user U(g,n) decodes the signal of the far user U(g,f) through serial interference cancellation. Then, it subtracts the decoded signal of the far user from the received signal to obtain the signal of the near user itself. On the other hand, the far user directly decodes its own signal. In cooperative transmission, the near user U(g,n) receives the signal, decodes it, and retransmits it to the far user U(g,f). The near user retransmits the signal to the far user in full-duplex mode. The far user U(g,f) combines the signal retransmitted by the near user with the signal sent by the base station and decodes it. By jointly optimizing the beamforming matrix under the base station and the RIS phase shift matrix and transmission power near the user, the total rate and total perceived beam gain of the communication users are maximized.

2. An optimization method for a RIS-based full-duplex cooperative NOMA integrated sensing communication system, used to optimize the RIS-based full-duplex cooperative NOMA integrated sensing communication system as described in claim 1, characterized in that, Includes the following steps: Establish the objective function and its constraints that maximize the user's total data rate and total perceived beam gain; The objective function that maximizes the total data rate and total perceived beam gain of communication users is transformed into three sub-objective functions: the power allocation objective function within the cluster, the beamforming objective function, and the RIS phase shift objective function. The AO algorithm is used to optimize and solve the three established sub-objective functions to obtain the optimized beamforming matrix, RIS phase shift matrix, and power allocation parameters; the power allocation parameters include the power allocation factor of the base station and the power allocation factor of the near-user.

3. The optimization method for a RIS-based full-duplex cooperative NOMA integrated sensing communication system according to claim 2, characterized in that, The process of establishing the objective function and its constraints that maximizes the user's total data rate and total perceived beam gain is as follows: The signal received by the user is represented as follows: Among them, y g,n This indicates the signal received by the user. Let H represent the channel from the reconfigurable smart surface to the user, and let H denote the conjugate transpose of the matrix. Let represent the set of complex numbers; Φ = diag(v) is the RIS phase shift matrix, and Let φ be the reflection vector, j denote the imaginary unit, and φ be the reflection vector. l This represents the phase shift angle of the l-th reflecting element and follows φ. l ∈[0,2π), l represents the number of the reflecting element. This represents the channel from the base station to the reconfigurable smart surface. It is the beamforming vector; x g,n and Let α represent the signals required by the near user U(g,n) and the far user U(g,f), respectively. g,n and α g, f represents the power allocation factor corresponding to the near user U(g,n) and the far user U(g,f), respectively, and n g,n It is Gaussian white noise, with zero mean and a standard deviation of . P g,n This indicates the energy required for near-user retransmissions. Represents the self-interference coefficient. satisfy Where i represents the i-th time slot, and τ is the decoding time; The signal received by the remote user is represented as: Among them, y g,f This indicates the signal received by the remote user. It is a channel from a reconfigurable smart surface to a remote user. and These refer to the channels from the near user U(g,n) to the reconfigurable smart surface, the channels from the reconfigurable smart surface to the far user U(g,f), and the channels from the near user U(g,n) to the far user U(g,f) in cooperative transmission, respectively; n g,f It is Gaussian white noise, which obeys zero mean and has a standard deviation of 0. The signal-to-noise ratio of the signal decoded by the near user U(g,n) from the far user U(g,f) is: Among them, S g,n→f This represents the signal-to-noise ratio of the signal decoded by the near user U(g,n) from the distant user U(g,f); The signal-to-noise ratio of the signal decoded by the user is: Among them, S g,n This represents the signal-to-noise ratio of the signal being decoded by the user. The data rate for the nearest user is: R g,n =log2(1+S g,n ) (5) Among them, R g,n Indicates the data rate close to the user; The signal-to-noise ratio for distant users is: in, Indicates the signal-to-noise ratio for distant users; The actual data rate for remote users is: Among them, R g,f This indicates the actual data rate for the remote user; The total data rate for users is: Among them, R sum This indicates the user's total data rate; The target detection angle is θ i The sensing beam gain is: Where, θ i P(θ) represents the target detection angle, i represents the radar target number, and P(θ) represents the target detection angle. i ) is the target detection angle θ i Perceived beam gain; It is the steering vector of the reconfigurable smart surface, where λ is the carrier wavelength. Indicates the distance between adjacent reflective elements; The total sensing beam gain for I radar targets is: Where P(θ) represents the total sensing beam gain; The objective function for maximizing the total data rate and total sensed beam gain for communication users is: The constraints of the objective function that maximizes the total data rate and total perceived beam gain for communication users are: Where W = [w1,...,w g ,...,w G' ] is the beamforming matrix, α = [α 1,f ,...,α g,f ,...,α G',f [P] represents the power allocation factor for the base station. N =[P 1,n ,...,P g,n ,...,P G',n [Energy for all near-user retransmissions; β] c and β s For a given normalization parameter, P b It is the maximum energy threshold of the base station. It is the minimum communication rate threshold for remote users. It is the minimum communication rate threshold for near-users, P min (θ i Minimum sensing beam gain threshold It is the maximum retransmission power threshold.

4. The optimization method for a RIS-based full-duplex cooperative NOMA integrated sensing communication system according to claim 3, characterized in that, The objective function for power allocation within the cluster is: The constraints of the power allocation objective function within the cluster are: Among them, S n→f =[S 1,n→f ,....,S G',n→f ], S n =[S 1,n ,....,S G',n ], X = [X1,...,X] g ,...,X G' ], X g Let S be a slack variable; where S n→f =[S 1,n→f ,....,S G',n→f ], S n =[S 1,n ,....,S G',n ], X = [X1,...,X] g ,...,X G' ], X g is a slack variable; t represents the number of iterations. This represents the real-valued result of the power allocation factor for the near-user U(g,n) obtained in the previous iteration. This represents the real number result of the energy of near-user retransmission obtained in the previous iteration.

5. The optimization method for a RIS-based full-duplex cooperative NOMA integrated sensing communication system according to claim 4, characterized in that, The beamforming objective function is: The constraints on the beamforming objective function are: Where p is a slack variable, I n→f =[I 1,n→f ,...,I g,n→f ,...,I G',n→f ], I g,n→f As slack variables, As slack variables, diag represents a diagonal matrix; v represents the real part. (t) This represents the real-valued result of the reflection vector obtained in the previous iteration. and This represents the real-valued result of the slack variable obtained in the previous iteration.

6. The optimization method for a RIS-based full-duplex cooperative NOMA integrated sensing communication system according to claim 5, characterized in that, The objective function for the RIS phase shift is: The constraints of the RIS phase-shift objective function are: Where Tr represents finding the trace, and rank represents taking the rank. V = vv H , K i =diag{a H (θ i )G};|| || * It is the nuclear norm, |||2 is the spectral norm, and ρ>0 is a penalty factor. W is the value obtained from the previous iteration. g The real number result, yes The eigenvector corresponding to the largest eigenvalue.

7. The optimization method for a RIS-based full-duplex cooperative NOMA integrated sensing communication system according to claim 6, characterized in that, Based on the established three sub-objective functions, the beamforming matrix, RIS phase shift matrix, and power allocation parameters are optimized using the AO algorithm to obtain the optimized beamforming matrix, RIS phase shift matrix, and power allocation parameters. The power allocation parameters include the base station power allocation factor and the power allocation factor for near-users, specifically: S1: Initialize the BS beamforming matrix, RIS phase shift matrix, and power allocation parameters; S2: Solve the power allocation objective function through multiple iterations of CVX, and update the power allocation factor of the base station and the power allocation factor of the near user. S3: Solve the RIS phase shift objective function through multiple iterations of CVX and update the RIS phase shift matrix; S4: Solve the beamforming objective function through multiple iterations of CVX, update the beamforming matrix, and increase the penalty factor in each iteration until the objective function converges. S5: Determine whether the objective function of maximizing the total data rate and total perceived beam gain of communication users has converged. If it has, obtain the optimized beamforming matrix, RIS phase shift matrix and power allocation parameters; otherwise, return to S2.

8. A RIS-based optimization system for a full-duplex cooperative NOMA integrated sensing communication system, used to implement the RIS-based optimization method for a full-duplex cooperative NOMA integrated sensing communication system as described in any one of claims 2-7, characterized in that, include: The objective function construction module is used to establish the objective function and its constraints that maximize the user's total data rate and total perceived beam gain. The objective function decomposition module is used to transform the established objective function that maximizes the total data rate and total perceived beam gain of communication users into three sub-objective functions; the three sub-objective functions are the power allocation objective function within the cluster, the beamforming objective function, and the RIS phase shift objective function. The solution module is used to optimize the BS beamforming matrix, RIS phase shift matrix, and power allocation parameters based on the three established sub-objective functions using the AO algorithm, to obtain the optimized beamforming matrix, RIS phase shift matrix, and power allocation parameters; the power allocation parameters include the base station power allocation factor and the power allocation factor for near-users.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the RIS-based full-duplex cooperative NOMA integrated sensing communication system optimization method as described in any one of claims 2-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the RIS-based full-duplex cooperative NOMA integrated sensing communication system optimization method as described in any one of claims 2-7.

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