ARIS-assisted wireless power supply D2D communication system and rate optimization method

Abstract

The invention discloses an ARIS-assisted wireless power supply D2D communication system and a rate optimization method, the system comprises a power station, a D2D user pair and an ARIS node, and the ARIS is used for assisting the wireless energy transmission and wireless information transmission process of the system. In order to effectively improve the system sum rate, the invention provides a joint optimization method of a time resource and an ARIS reflection coefficient, and constructs an ARIS-assisted wireless power supply D2D communication system and a rate maximization problem. Specifically, the method takes maximization of the sum rate of a D2D communication system as a target, and utilizes an alternate optimization algorithm to jointly optimize time resource allocation and an ARIS reflection coefficient. A simulation result shows that compared with a traditional passive reconfigurable intelligent surface scheme and an RIS-assistance-free scheme, the scheme provided by the invention remarkably improves the sum rate of a wireless power supply D2D communication system.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to an ARIS-assisted wireless power supply D2D communication system and a rate optimization method. Background Art

[0002] With the growth in the number of mobile devices and the widespread adoption of wireless communication applications, wireless communication networks are facing increasing pressure on data transmission. Device-to-device (D2D) communication allows devices to exchange information directly without forwarding through a base station, effectively reducing the base station's communication load and significantly reducing transmission latency. Despite these advantages, D2D devices have limited battery capacity and require wired charging or battery replacement to maintain operation. Wired charging is susceptible to environmental constraints, and frequent battery replacements lead to high operational and maintenance costs. To address the recharging problem of energy-limited devices, the concept of wireless power communication has been proposed. Leveraging wireless energy transmission technology, it can provide continuous and stable wireless charging for passive devices in various complex scenarios, thereby extending the operating life of wireless communication networks. However, wireless energy transmission is inefficient, susceptible to obstruction by obstacles, and currently only applicable to short-range scenarios, limiting its potential for wide-area communication.

[0003] Emerging reconfigurable intelligent surfaces (RIS) technology promises to address these challenges by enabling passive beamforming to compensate for severe distance-based path loss. However, currently studied passive RIS suffer from double-path fading, caused by the cascade of the incident and reflected links. This prevents them from fully utilizing their performance. To address this, active reconfigurable intelligent surfaces (ARIS) have emerged. ARIS integrates active power amplifiers into reflective elements, significantly mitigating the performance loss associated with double-path fading in passive RIS by simultaneously manipulating the phase and amplitude of the reflected signal. In this context, combining ARIS technology with wireless powered D2D communications is expected to significantly improve system performance, including energy efficiency and spectrum utilization, and is crucial for the long-term sustainable development of wireless powered communication networks. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes an ARIS-assisted wireless power supply D2D communication system and rate optimization method, aiming to maximize the system and rate.

[0005] The technical solution adopted by the present invention comprises the following steps:

[0006] Obtain channel state information between the power station (PS), D2D user pairs, and each node in the ARIS system;

[0007] Based on the "collect first, then transmit" protocol, during the Wireless Energy Transfer (WET) phase, ARIS assists the PS in broadcasting energy signals to passive D2D user pairs. Each D2D user calculates the energy it has collected based on the received energy signals.

[0008] During the Wireless Information Transfer (WIT) phase, D2D user pairs communicate bidirectionally using the Time Division Multiple Access (TDMA) protocol, and ARIS assists in this information transfer process. It calculates the individual transmission rate of each D2D user.

[0009] Taking the total transmission time of the system, the ARIS amplification power budget and the maximum ARIS amplitude as constraints, a system sum rate maximization problem of jointly optimizing the time resource and the ARIS reflection coefficient is constructed;

[0010] The constructed optimization problem is decomposed into three sub-problems: time resource allocation optimization, ARIS reflection coefficient optimization in the WIT stage, and ARIS reflection coefficient optimization in the WET stage. The three sub-problems are iteratively solved by the alternating optimization algorithm until convergence, and the optimized system and rate are obtained.

[0011] Assume that the system consists of a single-antenna PS, a pair of single-antenna passive D2D users, and an ARIS equipped with N reflection units, where the ARIS is used to adjust the phase and amplitude of the reflected signal to reduce the path loss.

[0012] Consider a block fading channel model, where the channel state information remains constant within the channel coherence time T. Based on the "acquisition before transmission" protocol, a time block T is divided into a WET phase (t0) and a WIT phase (t1 + t2). Here, t1 represents the time when D2D user 1 (DU1) transmits information to D2D user 2 (DU2), and t2 represents the time when DU2 transmits information to DU1. For ease of processing, the time block T is normalized to T = 1.

[0013] In the WET phase, ARIS assists PS to broadcast energy signals to passive D2D user pairs. i The energy signal received by (i∈{1,2}) is expressed as:

[0014]

[0015] where d i (i∈{1,2}) represents D2D users DU1 and DU2; P p Indicates the transmit power of PS; x p Represents the transmitted signal of PS, satisfying E{|x p | 2}=1, where E{·} represents the mathematical expectation, and |·| represents the modulus of the complex number; Indicates PS to DU i The channel state information of the link, where represents the space of complex-valued matrices of x×y dimensions; Indicates ARIS to DU i The channel state information vector of the link, where (·) H represents conjugate transpose; represents the channel state information vector of the PS to ARIS link; Θ0 = diag(φ 0,1 ,…,φ0,n,···,φ 0,N ) represents the reflection coefficient matrix of ARIS in the WET stage, Where a 0,n and Represent the amplitude and phase of the nth reflection unit in ARIS, and need to satisfy a 0,n ≥0 and j is the imaginary unit, and diag(·) represents the diagonalization operation. In addition, n r is the thermal noise generated by the active amplifier integrated on the ARIS reflector element, which has zero mean and variance of The complex Gaussian distribution of Where 0 N×1 Represents an N×1-dimensional zero vector, I N represents the N-dimensional identity matrix; For DU i The additive white Gaussian noise (AWGN) at , which has zero mean and variance σ 2 The complex Gaussian distribution of

[0016] Since the AWGN energy received by the D2D user is small and can be ignored, the D2D user energy reception signal given by formula (1) is: i The energy collected by (i∈{1,2}) is:

[0017]

[0018] Where t0 is the energy collection time, η∈(0,1) is the energy conversion efficiency. The D2D user divides the collected energy into two parts: one part is used for the circuit power consumption required for energy signal reception and information signal transmission and reception, and the other part is used to send information to the other D2D user. Therefore, considering the circuit power DU i The transmission power of (i∈{1,2}) is expressed as:

[0019]

[0020] In the WIT phase, D2D user pairs use the TDMA protocol to transmit information to each other. During the t1 period, DU1 sends information to DU2, and the received signal of DU2 is expressed as:

[0021]

[0022] During the t2 period, DU2 sends information to DU1, and the received signal of DU1 is expressed as:

[0023]

[0024] in Calculated by formula (3), Indicates DU i The sending signal and satisfy Indicates the channel state information between D2D user pairs; Indicates DU i Channel state information vector to the ARIS link; Θ1 = diag(φ 1,1 ,…,φ 1,n ,···,φ 1,N ) represents the reflection coefficient matrix of ARIS at time t1, where a 1,n and represent the amplitude and phase of the nth reflection unit in ARIS, respectively, and satisfy a 1,n ≥0 and Θ2=diag(φ 2,1 ,…,φ 2,n ,···,φ 2,N ) represents the reflection coefficient matrix of ARIS at time t2, where a 2,n and represent the amplitude and phase of the nth reflection unit in ARIS, respectively, and satisfy a 2,n ≥0 and

[0025] According to the D2D user received signal expressions given in equations (4) and (5), the communication rates from DU1 to DU2 and from DU2 to DU1 under unit bandwidth can be calculated as follows:

[0026]

[0027] Taking the total system transmission time, ARIS amplification power budget and ARIS maximum amplitude as constraints, a system sum rate maximization problem of jointly optimizing time resources {t0, t1, t2} and ARIS reflection coefficients {Θ0, Θ1, Θ2} is constructed:

[0028]

[0029] t0≥0,t1≥0,t2≥0(8e)

[0030] t0+t1+t2≤1(8f)

[0031] in a max and They represent the maximum amplitude and amplification power budget of ARIS respectively. Equations (8a), (8b) and (8c) represent the amplification power constraints of ARIS in each time period respectively. Equation (8d) represents the amplitude constraint of ARIS components. Equations (8e) and (8f) represent the time constraints.

[0032] Since problem (P1) is a non-convex optimization problem, this paper proposes an effective algorithm to obtain its optimal solution. First, it can be proved that when t0+t1+t2=1, the system sum rate can reach the maximum value, so we get t0=1-t1-t2, so the time allocation scheme of the system only needs to optimize the two variables {t1, t2}. Then, let v m =[φ m,1 ,…,φ m,N ] T , Equations (2) and (3) are transformed into:

[0033]

[0034] in Where Tr(·) represents the trace of the matrix, At the same time, Equations (6) and (7) are transformed into:

[0035]

[0036] According to equations (10), (11) and (12), the original optimization problem (P1) is transformed into problem (P2):

[0037]

[0038]

[0039] rank(V m )=1,m∈{0,1,2}(13f)

[0040] t1≥0,t2≥0(13g)

[0041] in rank(·) represents the rank of the matrix, Since the rank-one constraint in formula (13f) is non-convex, problem (P2) is difficult to solve. Therefore, the present invention uses the semidefinite relaxation (SDR) method to relax the constraint. After relaxing the rank-one constraint, the specific solution process of the optimization problem is as follows:

[0042] Since the optimization variables are strongly coupled in the objective function and the constraints, the original optimization problem is difficult to solve directly. To solve problem (P2), the present invention decomposes the original optimization problem into three sub-problems: time resource allocation optimization, ARIS reflection coefficient optimization in the WIT phase, and ARIS reflection coefficient optimization in the WET phase. These three sub-problems are solved alternately using an alternating optimization algorithm to obtain the optimized system sum rate and the corresponding optimized solution.

[0043] First, given the reflection coefficients {V0, V1, V2} of ARIS, optimize the time resource allocation of the system {t1, t2} and construct the corresponding sub-problem (P2-1) as follows:

[0044]

[0045] st(13g)

[0046] By calculating the Hessian matrix of the objective function, it can be proved that the objective function is a concave function with respect to variables t1 and t2. Therefore, the subproblem (P2-1) is a standard convex optimization problem and can be solved directly using the CVX tool.

[0047] Secondly, given the time resource allocation {t1, t2} and the reflection coefficients {V1, V2} of ARIS in the WIT phase, we optimize the reflection coefficient V0 of ARIS in the WET phase and construct the corresponding sub-problem (P2-2) as follows:

[0048]

[0049] st(13a)

[0050]

[0051] Problem (P2-2) is a convex semidefinite programming problem, and its optimal solution can be found using the CVX tool.

[0052] Finally, given the time resource allocation {t1, t2} and the reflection coefficient {V0} of ARIS in the WET phase, optimize the reflection coefficient {V1, V2} of ARIS in the WIT phase. The corresponding sub-problem (P2-3) is constructed as follows:

[0053]

[0054] st(13b),(13c)

[0055]

[0056] Since the objective function is non-convex with respect to variables V1 and V2, the subproblem (P2-3) is difficult to solve. The present invention applies the following theorem to transform the non-convex objective function into a convex function that is easy to handle.

[0057] Theorem: For any x>0, let The following formula is obtained:

[0058]

[0059] And the function The optimal solution is:

[0060]

[0061] Based on the above theorem, firstly transform the The logarithmic fraction is converted into the difference of concave functions as shown below:

[0062]

[0063] Then use the function For the second term of formula (19) Perform the conversion, namely:

[0064]

[0065] So we can get:

[0066]

[0067] Similarly, the formula (12) Convert it into the form of difference of concave functions, and for the second term By converting, you can get:

[0068]

[0069] Based on equations (21) and (22), subproblem (P2-3) is reformulated as:

[0070]

[0071] stz1>0,z2>0(23a)

[0072] (13b),(13c),(16a),(16b)

[0073] Since the constant ln2 does not affect the optimality of the objective function, it can usually be omitted. After the above transformation, problem (P2-4) is a convex optimization problem with respect to the variables {V1, V2} and {z1, z2}, respectively. By alternately optimizing {V1, V2} and {z1, z2}, their optimal values ​​can be obtained. The alternating solution process of problem (P2-4) is described in detail below:

[0074] Given {V1, V2}, according to formula (18), the optimal closed-form solution is They are:

[0075]

[0076] In getting Afterwards, the problem (P2-4) can be transformed into the following form:

[0077]

[0078] st(13b),(13c),(16a),(16b)

[0079] Problem (P2-5) is a convex semidefinite programming problem, and its optimal solution can be found using the CVX tool. The alternating optimization algorithm for solving problem (P2-4) is shown in Table 1. Here, the objective function value of problem (P2-4) is defined as L = f(z1, z2, V1, V2), and ε is the convergence threshold of the algorithm.

[0080] Table 1 Iterative algorithm for solving problem (P2-4)

[0081]

[0082] The subproblems (P2-1), (P2-2) and (P2-4) are optimized alternately through an iterative algorithm until the objective function converges. Since the rank-one constraint is relaxed, the solution obtained is It is not a feasible solution to the original optimization problem (P2). Recover the optimized solution to the original problem The Gaussian randomization method is used to process the obtained solution. Specifically, For example, we can get in is a diagonal matrix of eigenvalues, Each column element is the eigenvector corresponding to each eigenvalue in ∑0. By generating a mean of zero and a covariance matrix of I N+1 Circular symmetric complex Gaussian random vector An optimal solution can be constructed Among the random vectors generated in a large enough number, select the solution that maximizes the objective function value. So the optimal solution of the original optimization problem is Similarly, recover the optimized solution The process is the same as above recovery The steps are consistent.

[0083] Table 2 gives the detailed steps of the proposed algorithm.

[0084] Table 2 Detailed steps of the algorithm used

[0085]

[0086] Compared to existing technologies, the present invention offers the following advantages: the proposed ARIS-assisted joint optimization solution can further improve system summation and speed. This is because ARIS integrates a power amplifier that amplifies reflected signals, allowing power stations to meet quality of service requirements with shorter energy transmission times and lower transmission power. Furthermore, ARIS can further mitigate the double path loss caused by the cascade of the incident and reflected links, enhancing signal transmission strength. Furthermore, as the number of passive RIS and ARIS reflector units increases, both system summation and speed show an upward trend, demonstrating that deploying more low-cost reflector units can effectively improve system summation and speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 This is a flowchart of the steps of an active reconfigurable smart surface-assisted wireless power D2D communication system and rate optimization method in the present invention;

[0088] Figure 2 Schematic diagram of a wireless power supply D2D communication system assisted by an active reconfigurable smart surface in an embodiment of the present invention;

[0089] Figure 3 This is a simulation diagram of the relationship between the system sum rate and the number of ARIS reflection units in an embodiment of the present invention. DETAILED DESCRIPTION

[0090] The technical solution of the present invention will be further described below with reference to the accompanying drawings:

[0091] This paper proposes an Active Reconfigurable Intelligent Surface (ARIS)-assisted wireless powered device-to-device (D2D) communication system and rate optimization method. The system consists of a single-antenna power station (PS), a pair of single-antenna passive D2D users, and an ARIS equipped with N reflectors. The ARIS adjusts the phase and amplitude of the reflected signal to reduce path loss. The method includes the following steps:

[0092] Step A: Obtain channel state information between PS, D2D user pairs and nodes of the ARIS system;

[0093] Step B: Based on the "collect first, then transmit" protocol, during the Wireless Energy Transfer (WET) phase, ARIS assists the PS in broadcasting energy signals to passive D2D user pairs. Each D2D user calculates the energy it has collected based on the received energy signals.

[0094] Step C: During the Wireless Information Transfer (WIT) phase, D2D user pairs communicate bidirectionally using the Time Division Multiple Access (TDMA) protocol, with ARIS assisting in this information transfer process. The individual transmission rate of each D2D user is calculated.

[0095] Step D: Taking the total system transmission time, ARIS amplification power budget and ARIS maximum amplitude as constraints, a system sum rate maximization problem is constructed to jointly optimize the time resource and ARIS reflection coefficient;

[0096] Step E: Decompose the constructed optimization problem into three sub-problems: time resource allocation optimization, ARIS reflection coefficient optimization in the WIT stage, and ARIS reflection coefficient optimization in the WET stage. The three sub-problems are iteratively solved by the alternating optimization algorithm until convergence to obtain the optimized system and rate.

[0097] Step A specifically includes the following:

[0098] Consider a block fading channel model, where the channel state information remains constant within the channel coherence time T. Based on the "acquisition before transmission" protocol, a time block T is divided into a WET phase (t0) and a WIT phase (t1 + t2). Here, t1 represents the time when D2D user 1 (DU1) transmits information to D2D user 2 (DU2), and t2 represents the time when DU2 transmits information to DU1. For ease of processing, the time block T is normalized to T = 1.

[0099] Step B specifically includes the following:

[0100] In the WET phase, ARIS assists PS in broadcasting energy signals to passive D2D user pairs. i The energy signal received by (i∈{1,2}) is expressed as:

[0101]

[0102] where d i (i∈{1,2}) represents D2D users DU1 and DU2; P p Indicates the transmit power of PS; x p Represents the transmitted signal of PS, satisfying E{|x p | 2}=1, where E{·} represents the mathematical expectation, and |·| represents the modulus of the complex number; Indicates PS to DU i The channel state information of the link, where represents the space of complex-valued matrices of dimension x×y; Indicates ARIS to DU i The channel state information vector of the link, where (·) H represents the conjugate transpose; represents the channel state information vector of the PS to ARIS link; Θ0 = diag(φ 0,1 ,…,φ 0,n ,···,φ 0,N ) represents the reflection coefficient matrix of ARIS in the WET stage, Where a 0,n and Represent the amplitude and phase of the nth reflection unit in ARIS, and need to satisfy a 0,n ≥0 and j is the imaginary unit, and diag(·) represents the diagonalization operation. In addition, n r is the thermal noise generated by the active amplifier integrated on the ARIS reflector element, which has zero mean and variance of The complex Gaussian distribution of Where 0 N×1Represents an N×1-dimensional zero vector, I N represents the N-dimensional identity matrix; For DU i The additive white Gaussian noise (AWGN) at , which has zero mean and variance σ 2 The complex Gaussian distribution of

[0103] Since the AWGN energy received by the D2D user is small and can be ignored, the D2D user energy reception signal given by formula (1) is: i The energy collected by (i∈{1,2}) is:

[0104]

[0105] Where t0 is the energy collection time, η∈(0,1) is the energy conversion efficiency. The D2D user divides the collected energy into two parts: one part is used for the circuit power consumption required for energy signal reception and information signal transmission and reception, and the other part is used to send information to the other D2D user. Therefore, considering the circuit power DU i The transmission power of (i∈{1,2}) is expressed as:

[0106]

[0107] Step C specifically includes the following:

[0108] In the WIT phase, D2D user pairs use the TDMA protocol to transmit information to each other. During the t1 period, DU1 sends information to DU2, and the received signal of DU2 is expressed as:

[0109]

[0110] During the t2 period, DU2 sends information to DU1, and the received signal of DU1 is expressed as:

[0111]

[0112] in Calculated by formula (3), Indicates DU i The sending signal and satisfy Indicates the channel state information between D2D user pairs; Indicates DU i Channel state information vector to the ARIS link; Θ1 = diag(φ 1,1 ,…,φ 1,n,···,φ 1,N ) represents the reflection coefficient matrix of ARIS at time t1, where a 1,n and represent the amplitude and phase of the nth reflection unit in ARIS, respectively, and satisfy a 1,n ≥0 and Θ2=diag(φ 2,1 ,…,φ 2,n ,···,φ 2,N ) represents the reflection coefficient matrix of ARIS at time t2, where a 2,n and represent the amplitude and phase of the nth reflection unit in ARIS, respectively, and satisfy a 2,n ≥0 and

[0113] According to the D2D user received signal expressions given in equations (4) and (5), the communication rates from DU1 to DU2 and from DU2 to DU1 under unit bandwidth can be calculated as follows:

[0114]

[0115] Step D specifically includes the following:

[0116] Taking the total system transmission time, ARIS amplification power budget and ARIS maximum amplitude as constraints, a system sum rate maximization problem of jointly optimizing time resources {t0, t1, t2} and ARIS reflection coefficients {Θ0, Θ1, Θ2} is constructed:

[0117]

[0118] t0≥0,t1≥0,t2≥0(8e)

[0119] t0+t1+t2≤1(8f)

[0120] in a max and They represent the maximum amplitude and amplification power budget of ARIS respectively. Equations (8a), (8b) and (8c) represent the amplification power constraints of ARIS in each time period respectively. Equation (8d) represents the amplitude constraint of ARIS components. Equations (8e) and (8f) represent the time constraints.

[0121] Step E specifically includes the following:

[0122] Since problem (P1) is a non-convex optimization problem, this paper proposes an effective algorithm to obtain its optimal solution. First, it can be proved that when t0+t1+t2=1, the system sum rate can reach the maximum value, so we get t0=1-t1-t2, so the time allocation scheme of the system only needs to optimize the two variables {t1, t2}. Then, let v m =[φ m,1 ,…,φ m,N ] T , Equations (2) and (3) are transformed into:

[0123]

[0124] in Where Tr(·) represents the trace of the matrix, At the same time, Equations (6) and (7) are transformed into:

[0125]

[0126] According to equations (10), (11) and (12), the original optimization problem (P1) is transformed into problem (P2):

[0127]

[0128] rank(V m )=1,m∈{0,1,2}(13f)

[0129] t1≥0,t2≥0(13g)

[0130] in rank(·) represents the rank of the matrix, Since the rank-one constraint in formula (13f) is non-convex, problem (P2) is difficult to solve. Therefore, the present invention uses the semidefinite relaxation (SDR) method to relax the constraint. After relaxing the rank-one constraint, the specific solution process of the optimization problem is as follows:

[0131] Since the optimization variables are strongly coupled in the objective function and the constraints, the original optimization problem is difficult to solve directly. To solve problem (P2), the present invention decomposes the original optimization problem into three sub-problems: time resource allocation optimization, ARIS reflection coefficient optimization in the WIT phase, and ARIS reflection coefficient optimization in the WET phase. These three sub-problems are solved alternately using an alternating optimization algorithm to obtain the optimized system sum rate and the corresponding optimized solution.

[0132] First, given the reflection coefficients {V0, V1, V2} of ARIS, optimize the time resource allocation of the system {t1, t2} and construct the corresponding sub-problem (P2-1) as follows:

[0133]

[0134] st(13g)

[0135] By calculating the Hessian matrix of the objective function, it can be proved that the objective function is a concave function with respect to variables t1 and t2. Therefore, the subproblem (P2-1) is a standard convex optimization problem and can be solved directly using the CVX tool.

[0136] Secondly, given the time resource allocation {t1, t2} and the reflection coefficients {V1, V2} of ARIS in the WIT phase, we optimize the reflection coefficient V0 of ARIS in the WET phase and construct the corresponding sub-problem (P2-2) as follows:

[0137]

[0138] st(13a)

[0139]

[0140] Problem (P2-2) is a convex semidefinite programming problem, and its optimal solution can be found using the CVX tool.

[0141] Finally, given the time resource allocation {t1, t2} and the reflection coefficient {V0} of ARIS in the WET phase, optimize the reflection coefficient {V1, V2} of ARIS in the WIT phase. The corresponding sub-problem (P2-3) is constructed as follows:

[0142]

[0143] st(13b),(13c)

[0144]

[0145]

[0146] Since the objective function is non-convex with respect to variables V1 and V2, the subproblem (P2-3) is difficult to solve. The present invention applies the following theorem to transform the non-convex objective function into a convex function that is easy to handle.

[0147] Theorem: For any x>0, let The following formula is obtained:

[0148]

[0149] And the function The optimal solution is:

[0150]

[0151] Based on the above theorem, firstly transform the The logarithmic fraction is converted into the difference of concave functions as shown below:

[0152]

[0153] Then use the function For the second term of formula (19) Perform the conversion, namely:

[0154]

[0155] So we can get:

[0156]

[0157] Similarly, the formula (12) Convert it into the form of difference of concave functions, and for the second term By converting, you can get:

[0158]

[0159] Based on equations (21) and (22), subproblem (P2-3) is reformulated as:

[0160]

[0161] stz1>0,z2>0(23a)

[0162] (13b),(13c),(16a),(16b)

[0163] Since the constant ln2 does not affect the optimality of the objective function, it can usually be omitted. After the above transformation, problem (P2-4) is a convex optimization problem with respect to the variables {V1, V2} and {z1, z2}, respectively. By alternately optimizing {V1, V2} and {z1, z2}, their optimal values ​​can be obtained. The alternating solution process of problem (P2-4) is described in detail below:

[0164] Given {V1, V2}, according to formula (18), the optimal closed-form solution is They are:

[0165]

[0166] In getting Afterwards, the problem (P2-4) can be transformed into the following form:

[0167]

[0168] st(13b),(13c),(16a),(16b)

[0169] Problem (P2-5) is a convex semidefinite programming problem, and its optimal solution can be found using the CVX tool. The alternating optimization algorithm for solving problem (P2-4) is shown in Table 1. Here, the objective function value of problem (P2-4) is defined as L = f(z1, z2, V1, V2), and ε is the convergence threshold of the algorithm.

[0170] Table 1 Iterative algorithm for solving problem (P2-4)

[0171]

[0172]

[0173] The subproblems (P2-1), (P2-2) and (P2-4) are optimized alternately through an iterative algorithm until the objective function converges. Since the rank-one constraint is relaxed, the solution obtained is It is not a feasible solution to the original optimization problem (P2). Recover the optimized solution to the original problem The Gaussian randomization method is used to process the obtained solution. Specifically, For example, we can get in is a diagonal matrix of eigenvalues, Each column element is the eigenvector corresponding to each eigenvalue in ∑0. By generating a mean of zero and a covariance matrix of I N+1 Circular symmetric complex Gaussian random vector An optimal solution can be constructed Among the random vectors generated in a large enough number, select the solution that maximizes the objective function value. So the optimal solution of the original optimization problem is Similarly, recover the optimized solution The process is the same as above recovery Table 2 gives the detailed steps of the proposed algorithm.

[0174] Table 2 Detailed steps of the algorithm used

[0175]

[0176]

[0177] The embodiment of the present invention provides a simulation experiment to verify the effect of the method of the present invention.

[0178] Utilize MATLAB software simulation to realize an example of the present invention, system model is as follows Figure 2 As shown in Figure 2, in the simulation experiment, the wireless channels are assumed to be independent of each other, and the channel state information remains constant in the coherent time block T. Consider a three-dimensional coordinate system, where the coordinates of the power station, ARIS, user DU1, and user DU2 are (0,0,0)m, (6,2,5)m, (4,0,0)m, and (8,0,0)m, respectively. The channel state information h between the power station and ARIS is p,r , channel status information between DU1 and ARIS Channel status information between DU2 and ARIS Channel status information between ARIS and DU1 And the channel status information between ARIS and DU2 All obey Rice fading; the direct link is the channel state information between the power station and DU1 Channel status information between the power station and DU2 Channel status information between DU1 and DU2 And the channel status information between DU2 and DU1 All of them obey Rayleigh fading. Set the number of reflection units N = 16, the energy collection efficiency η = 0.9, and the circuit power of D2D users The maximum amplitude of ARIS a max =8dB, ARIS's amplifier power budget Convergence threshold ε=0.001, noise power The Gaussian random number is set to 1000.

[0179] Figure 3 This is a simulation diagram of the relationship between the system sum rate and the number of ARIS reflection units in an embodiment of the present invention.

[0180] As can be seen from the figure, compared to the solution without RIS assistance, the joint optimization solution using passive RIS assistance can effectively improve the system sum rate, because the passive beamforming of passive RIS can improve channel quality, thereby increasing energy efficiency and reducing energy loss caused by path loss. However, compared to passive RIS, the ARIS-assisted joint optimization solution proposed in this invention can further improve the system sum rate. This is because ARIS integrates a power amplifier that can amplify the reflected signal, so the power station only needs a shorter energy transmission time and lower transmission power to meet the quality of service requirements. At the same time, ARIS can also further alleviate the double path loss caused by the cascade of the incident link and the reflection link, enhancing signal transmission strength. In addition, as the number of passive RIS and ARIS reflection units increases, the system sum rate shows an upward trend, indicating that deploying more low-cost reflection units can effectively improve the system sum rate.

[0181] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An ARIS-assisted wireless power supply D2D communication system and rate optimization method, characterized in that: The system includes a single-antenna power station, a pair of single-antenna passive D2D users, and an ARIS equipped with N reflective units. The method includes the following steps: Obtain channel status information between power stations, D2D user pairs, and nodes in the ARIS system; Based on the collect-first-then-transmit protocol, during the wireless energy transmission phase, the ARIS auxiliary power station broadcasts energy signals to passive D2D user pairs. Each D2D user calculates the energy it collects based on the received energy signals. During the wireless information transmission phase, D2D user pairs conduct bidirectional communication using a time division multiple access protocol. ARIS assists this information transmission process and calculates the individual transmission rate of each D2D user. Taking the total transmission time of the system, the ARIS amplification power budget and the maximum ARIS amplitude as constraints, a system sum rate maximization problem of jointly optimizing the time resource and the ARIS reflection coefficient is constructed; The constructed optimization problem is decomposed into three sub-problems: time resource allocation optimization, ARIS reflection coefficient optimization in the WIT stage, and ARIS reflection coefficient optimization in the WET stage. The three sub-problems are iteratively solved by the alternating optimization algorithm until convergence, and the optimized system and rate are obtained.

2. The ARIS-assisted wireless power supply D2D communication system and rate optimization method according to claim 1, characterized in that: Consider a block fading channel model, in which the channel state information remains unchanged within the channel coherence time T. Based on the acquisition-before-transmission protocol, a time block T is divided into the WET phase duration t0 and the WIT phase duration t1+t2, where t1 represents the time when D2D user DU1 sends information to D2D user DU2, and t2 represents the time when user DU2 sends information to user DU1. The time block T is normalized, that is, T=1.

3. The ARIS-assisted wireless power supply D2D communication system and rate optimization method according to claim 1, characterized in that: In the WET phase, ARIS assists PS to broadcast energy signals to passive D2D user pairs. i The energy signal received by (i∈{1,2}) is expressed as: where d i (i∈{1,2}) represents D2D users DU1 and DU2; P p Indicates the transmission power of the power station; x p Represents the transmission signal of the power station, satisfying E{|x p | 2 }=1, where E{·} represents the mathematical expectation, and |·| represents the modulus of the complex number; Indicates the power station to DU i The channel state information of the link, where represents the space of complex-valued matrices of dimension x×y; Indicates ARIS to DU i The channel state information vector of the link, where (·) H represents conjugate transpose; represents the channel state information vector of the power station to ARIS link; Θ0 = diag(φ 0,1 ,…,φ 0,n ,···,φ 0,N ) represents the reflection coefficient matrix of ARIS in the WET stage, Where a 0,n and Represent the amplitude and phase of the nth reflection unit in ARIS, and need to satisfy a 0,n ≥0 and j is the imaginary unit, diag(·) represents the diagonalization operation, and n r is the thermal noise generated by the active amplifier integrated on the ARIS reflector element, which has zero mean and variance of The complex Gaussian distribution of Where 0 N×1 Represents an N×1-dimensional zero vector, I N represents the N-dimensional identity matrix; For DU i The additive Gaussian white noise at , which has zero mean and variance σ 2 The complex Gaussian distribution of According to the D2D user energy reception signal given by formula (1), DU i The energy collected by (i∈{1,2}) is: Where t0 is the energy collection time, η∈(0,1) is the energy conversion efficiency, and the user D2D divides the collected energy into two parts: one part is used for the circuit power consumption required for energy signal reception and information signal transmission and reception, and the other part is used to send information to the other end D2D user. Therefore, considering the circuit power DU i The transmission power of (i∈{1,2}) is expressed as:

4. The ARIS-assisted wireless power supply D2D communication system and rate optimization method according to claim 1, characterized in that: In the WIT phase, user DU1 and user DU2 use the TDMA protocol to transmit information to each other. During the t1 period, user DU1 sends information to user DU2. The received signal of user DU2 is expressed as: During the time period t2, user DU2 sends information to user DU1. The received signal of user DU1 is expressed as: in Calculated by formula (3), Indicates DU i The sending signal and satisfy Indicates the channel state information between D2D user pairs; Indicates DU i Channel state information vector to the ARIS link; Θ1 = diag(φ 1,1 ,…,φ 1,n ,···,φ 1,N ) represents the reflection coefficient matrix of ARIS at time t1, where a 1,n and represent the amplitude and phase of the nth reflection unit in ARIS, respectively, and satisfy a 1,n ≥0 and Θ2=diag(φ 2,1 ,…,φ 2,n ,···,φ 2,N ) represents the reflection coefficient matrix of ARIS at time t2, where a 2,n and represent the amplitude and phase of the nth reflection unit in ARIS, respectively, and satisfy a 2,n ≥0 and According to the D2D user received signal expressions given by equations (4) and (5), the communication rates from user DU1 to user DU2 and from user DU2 to user DU1 under unit bandwidth can be calculated as follows:

5. The ARIS-assisted wireless power supply D2D communication system and rate optimization method according to claim 1, characterized in that: Taking the total system transmission time, ARIS amplification power budget and ARIS maximum amplitude as constraints, a system sum rate maximization problem of jointly optimizing time resources {t0, t1, t2} and ARIS reflection coefficients {Θ0, Θ1, Θ2} is constructed: t0≥0,t1≥0,t2≥0(8e) t0+t1+t2≤1(8f) where R sum (t0, t1, t2, Θ0, Θ1, Θ2) = R d1 +R d2 , a max and They represent the maximum amplitude and amplification power budget of ARIS respectively. Equations (8a), (8b) and (8c) represent the amplification power constraints of ARIS in each time period respectively. Equation (8d) represents the amplitude constraint of ARIS components. Equations (8e) and (8f) represent the time constraints.

6. The ARIS-assisted wireless power supply D2D communication system and rate optimization method according to claim 1, characterized in that: The original optimization problem is decomposed into three sub-problems: time resource allocation optimization, ARIS reflection coefficient optimization in the WIT stage, and ARIS reflection coefficient optimization in the WET stage. The three sub-problems are iteratively solved by the alternating optimization algorithm until convergence, and the optimized system and rate and the corresponding optimization solution are obtained.

7. The ARIS-assisted wireless power supply D2D communication system and rate optimization method according to claim 6, characterized in that: The detailed steps of the iterative algorithm can be described as: First, given the reflection coefficients {Θ0, Θ1, Θ2} of ARIS, optimize the time resource allocation of the system {t0, t1, t2}; Secondly, given the time resource allocation {t0, t1, t2} and the ARIS reflection coefficient {Θ1, Θ2} of the WIT phase, optimize the ARIS reflection coefficient Θ0 of the WET phase; Finally, given the time resource allocation {t0, t1, t2} and the reflection coefficient Θ0 of ARIS in the WET phase, optimize the reflection coefficient {Θ1, Θ2} of ARIS in the WIT phase; The above three steps are iterated repeatedly until the algorithm converges, at which point the optimized system and rate and the corresponding optimized solution are obtained.

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