Resource allocation method for symbiotic radio system based on full-duplex-noma under α-fairness principle

Abstract

The application relates to a resource allocation method for a full-duplex-non-orthogonal multiple access (NOMA) based symbiotic radio system under an alpha-fairness principle, and belongs to the field of Internet of Things. The method comprises the following steps: establishing a full-duplex-NOMA based symbiotic radio system, wherein full-duplex mode is adopted at both the transmitting end and the receiving end, and the residual self-interference problem caused by full-duplex is considered; combining non-linear energy collection and non-perfect channel state information conditions, and constructing a resource allocation model for maximizing the backscatter device (BD) throughput under the alpha-fairness framework; adopting a block coordinate descent method to convert the resource allocation model for maximizing the throughput into a convex optimization problem; solving the optimization problem by using a convex optimization toolbox to obtain the time slots between NOMA clusters, the reflection coefficient of the BD and the transmitting power of full-duplex, that is, to obtain a resource allocation scheme. In the application, the BD achievable throughput is obviously improved in terms of fairness, and even under the condition of strong residual self-interference, the throughput performance is still excellent.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of Internet of Things, and relates to a resource allocation method for a full-duplex-NOMA based symbiotic radio system under an alpha-fairness principle. BACKGROUND

[0002] With the rapid penetration of the Internet of Things in various fields of social production, the number of sensor nodes in the Internet of Things shows an explosive growth. However, the scarcity of spectrum resources and the insufficient endurance of devices become key factors restricting the sustainable development of the Internet of Things. In recent years, as a new communication paradigm, symbiotic radio is considered as one of the core technologies of future Internet of Things due to its mutual sharing characteristics in the spectrum and energy domains, and high-reliable backscatter communication can be achieved through joint decoding. Symbiotic radio technology is expected to play a more important role in future communication fields.

[0003] However, the existing scheme does not fully consider the relationship between the transmission performance and fairness of BD, and sacrifices the fairness between BDs to improve the total throughput, which cannot meet the demand of green wide coverage advocated by future Internet of Things. Moreover, most of the assumptions assume that the channel state is perfectly known, the energy harvesting of BD is linear, and the residual self-interference problem of full-duplex is ignored, which may lead to some link interruption events due to unreasonable resource allocation under ideal conditions. SUMMARY

[0004] In view of this, the purpose of the present application is to provide a resource allocation method for a full-duplex-NOMA based symbiotic radio system under an alpha-fairness principle.

[0005] To achieve the above purpose, the present application provides the following technical scheme:

[0006] A resource allocation method for a full-duplex-NOMA based symbiotic radio system under an alpha-fairness principle, the method comprising the following steps:

[0007] Step 1: establishing a full-duplex-NOMA based symbiotic radio system, wherein both the transmitting end and the receiving end adopt a full-duplex mode, and the residual self-interference problem caused by full-duplex is considered;

[0008] Step 2: combining non-linear energy harvesting and non-perfect channel state information conditions, and constructing a resource allocation model for maximizing the throughput of backscatter devices (BDs) under an alpha-fairness framework;

[0009] Step 3: using a block coordinate descent method to convert the resource allocation model for maximizing the throughput into a convex optimization problem;

[0010] Step four: solve the optimization problem by using convex optimization toolbox, and obtain the time slot between NOMA clusters, the reflection coefficient of BD, and the transmit power of full-duplex, i.e., obtain the resource allocation scheme.

[0011] Further, in step one, the full-duplex-NOMA coexisting radio system includes two full-duplex access point devices, represented by FAP1 and FAP2, respectively, and the i-th FAP is represented by i, i = 1, 2, with 2K BDs, and the BDs are divided into K NOMA backscatter clusters, and each cluster contains 2 BDs. Let the reflection coefficient of the BD in the K NOMA clusters be z = [z 1,1 ,z 1,2 ,...,z K,1 ,z K,2 ], satisfying z k,1 > z k,2 , k = 1, 2,..., K, wherein the first subscript corresponds to the NOMA cluster, and the second subscript corresponds to the BD in the NOMA cluster. The residual self-interference of the full-duplex access point device at the transceiver end is represented by I1 and I2, respectively, satisfying , which is subject to a complex Gaussian distribution, 0 is the mean of the distribution, and θ 2 P i,k is the variance of the distribution, wherein θ represents the residual self-interference level, and P i,k represents the power allocation of FAPi in the time slot of the k-th NOMA cluster, and the subscript i corresponds to FAPi, and k corresponds to the NOMA cluster. For ease of analysis, BD(k, m) is used to represent the m-th BD in the k-th NOMA cluster, wherein k = 1, 2,..., K, and m = 1, 2.

[0012] Further, in step two, under the condition of non-linear energy harvesting and non-perfect channel state information, a resource allocation model for maximizing the throughput of BD is constructed under the framework of α-fairness, which is configured as:

[0013]

[0014] , wherein, is the transmit power of FAP, represents a 2K × 1-dimensional complex matrix space, is the time slot of the NOMA cluster, is the reflection coefficient of the BD; max represents the maximum value of the objective function; is the throughput of BD(k, m), and the superscript b represents the throughput of the corresponding BD, and the subscript corresponds to BD(k, m), u α is the α-fairness utility function; and is the achievable throughput and minimum throughput demand of the i-th FAPi in the time slot of the k-th NOMA cluster, wherein The superscript s denotes the throughput of the corresponding FAP, the subscript i corresponds to FAPi, and k corresponds to the NOMA cluster, The superscript min denotes the minimum value of the throughput; and The energy collected by BD(k, m) and the energy consumed, where The superscript corresponds to the collected energy, and the subscript corresponds to BD(k, m), The superscript corresponds to the consumed energy, and the subscript corresponds to BD(k, m); P i,k P(k) is the power allocation of FAPi in the kth NOMA cluster time slot, P i m P(k) is the power allocation of FAPi in the kth NOMA cluster time slot, P k T(k) is the time slot of the kth NOMA cluster, and T is the total time slot of the kth NOMA cluster; z k,1 and z k,2 respectively, are the reflection coefficients of the BDs in the same NOMA cluster; C1 ensures the minimum throughput required by 2 FAPs; C2 indicates that the energy collected by each BD is greater than the energy consumed; C3 indicates the total transmission power limit of the FAP; C4 indicates that the sum of each time slot of TDMA cannot be greater than the system frame length, and the non-negative requirement; C5 is the requirement for the reflection coefficient of each BD in the NOMA cluster.

[0015] Further, in step three, in the solving process of the resource allocation model for throughput maximization, the following steps are included:

[0016] a. For NOMA cluster time slot optimization, the following optimization problem is obtained, which is a linear programming problem and can be directly solved;

[0017]

[0018] and C4

[0019] where z (n) and P (n) respectively represent the reflection coefficient and transmission power given in the nth iteration.

[0020] b. For the reflection coefficient optimization of the BD, the following optimization problem is obtained:

[0021]

[0022] and C5

[0023] where τ (n) represents the NOMA cluster time slot given in the nth iteration.

[0024] c. For the transmission power optimization of the full-duplex access point, the following optimization problem is obtained:

[0025]

[0026] and C3

[0027] The present application has the beneficial effects of:

[0028] In the face of future Internet of Things spectrum resource scarcity and excessive energy consumption challenges and the demand for green wide coverage, the present application combines full duplex, symbiotic radio and NOMA technology, considers non-linear energy harvesting, and designs a resource allocation method for symbiotic radio systems based on full duplex-NOMA under the principle of α-fairness, in the presence of channel estimation errors and full duplex residual self-interference. In the framework of maximizing the total throughput of BD under the α-fair utility function, a non-convex optimization function subject to multiple conditions is constructed. By using fast coordinate descent, quadratic variation and concave-convex programming methods, it is converted into a solvable convex optimization problem; finally, based on the idea of alternating iteration, the joint optimization of NOMA cluster time slot allocation, BD reflection coefficient and FAP power allocation is realized. The results show that compared with existing methods, the proposed method has good convergence and effectiveness, and according to the actual application scene demand, the α value can be adjusted flexibly to achieve different levels of throughput fairness, ensuring that all BDs in the secondary system can achieve efficient and fair backscattering transmission. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a flowchart of the resource allocation method of the present application;

[0030] Figure 2 is a whole framework diagram of the symbiotic radio system based on full duplex-NOMA under the principle of α-fairness of the present application;

[0031] Figure 3 is a convergence characteristic curve diagram of the objective function under different fairness levels of the present application;

[0032] Figure 4 is a curve diagram of the total throughput of BD under different fairness levels of the present application and the comparison method; DETAILED DESCRIPTION

[0033] Please refer to Figures 1-4 , a resource allocation method for a symbiotic radio system based on full duplex-NOMA under the principle of α-fairness.

[0034] The present application provides a resource allocation method for a symbiotic radio system based on full duplex-NOMA under the principle of α-fairness, as shown in Figure 1 , the method comprises the following steps:

[0035] Step one: Establishing the coexisting radio system of full-duplex-NOMA, in which both transceiver ends adopt full-duplex mode, and the residual self-interference problem caused by full-duplex is considered;

[0036] Step two: In the framework of α-fairness, a resource allocation model is constructed to maximize the BD throughput, combined with non-linear energy harvesting and imperfect channel state information conditions;

[0037] Step three: The block coordinate descent method is adopted to transform the resource allocation model for maximizing the throughput into a convex optimization problem;

[0038] Step four: The optimization problem is solved by using the convex optimization toolbox, and the time slots between NOMA clusters, the reflection coefficients of BD, and the transmit power of full-duplex are obtained, i.e., the resource allocation scheme is obtained.

[0039] Further, in step one, as Figure 2 , the main system transceiver of the system adopts a full-duplex access point device with one transceiver antenna, and the secondary system includes two full-duplex access point devices represented by FAP1 and FAP2, with 2K BDs, which are divided into K NOMA backscatter clusters, each containing 2 BDs. Let the BD reflection coefficients in the K NOMA clusters be z = [z 1,1 ,z 1,2 ,...,z K,1 ,z K,2 ], satisfying z k,1 > z k,2 , k = 1, 2,..., K. The residual self-interference of the full-duplex access point devices at both transceiver ends is represented by I1 and I2, respectively, satisfying where θ represents the residual self-interference level, i = 1, 2. For ease of analysis, the mth BD in the kth NOMA cluster is represented by BD(k, m), where k = 1, 2,..., K, and m = 1, 2.

[0040] Further, in step two, the process of constructing the resource allocation model for maximizing the BD throughput includes:

[0041] a. Based on the minimum throughput constraint of the primary user, the energy harvesting constraint of the BD, the total transmit power limit of the full-duplex access point, etc., a resource allocation model is established to maximize the throughput of the secondary system in the framework of α-fairness;

[0042] b. Combined with non-linear energy harvesting and imperfect channel state information conditions, a more practical resource allocation model is established.

[0043] Assuming that the channel coefficient is estimated based on the minimum mean square error criterion, the true channel coefficient h and the estimated value satisfy where e hdenotes the channel estimation error, subscript h corresponds to channel h, which is a Gaussian random variable independent of h, i.e. Thus, we have where d 12 is the distance between FAP1 and FAP2, and superscript β1 denotes the path loss exponent between FAP1 and FAP2. Similarly, f k,m and g k,m are the channel coefficients between FAP1 and BD(k, m), and between FAP2 and BD(k, m), respectively. and satisfy the following relationship: where subscript h corresponds to BD(k, m), and denote the estimation errors of the corresponding channel coefficients, and the variances of all channel estimation errors are set to where h is the channel coefficient between FAP1 and FAP2, f k,m is the channel coefficient between FAP1 and BD(k, m), and g k,m is the channel coefficient between FAP2 and BD(k, m).

[0044] Assume that the symbols transmitted by FAP1 and FAP2 in the communication time slot T are denoted by s1 and s2, respectively, which are assumed to follow a standard circularly symmetric complex Gaussian distribution, i.e. i = 1, 2. Let c k,m be the symbols transmitted by BD(k, m) to FAP1 and FAP2, subscript h corresponds to BD(k, m), and c k,m ~ CN(0, 1), k = 1, 2,..., K, m = 1, 2. Thus, the backscattered signal of BD(k, m) can be expressed as

[0045]

[0046] where P i,k satisfies P i m is the total transmit power of FAPi, i = 1, 2.

[0047] Considering the limited energy harvesting capability of BD, the energy harvesting per unit time can be modeled as:

[0048]

[0049] where Φ(p k,m ) denotes a function of variable p k,m , η k,m ∈ [0, 1] represents the energy harvesting efficiency of BD(k, m), subscript h corresponds to BD(k, m), denotes the saturation state, and the subscript corresponds to BD(k, m). Thus, the energy collected by BD(k, m) can be expressed as:

[0050]

[0051] Meanwhile, when BD is in the energy collection state, the circuit energy is also needed to be consumed. The circuit energy consumption of BD(k, m) is modeled as:

[0052]

[0053] where, and denote the power consumption of BD(k, m) in the backscattering state and in the energy collection state, respectively, and the superscripts BC and EC correspond to the backscattering state and the energy collection state, respectively, for distinction.

[0054] Under the above settings, in the time slot τ k , the signal received by FAP1 is:

[0055]

[0056] where w 1,k denotes the noise at FAP1 in the time slot τ k , the subscript 1FAP1, and k corresponds to the time slot of the NOMA cluster, which is assumed to be an additive white Gaussian noise with a mean of 0 and a variance of . Similarly, in the time slot τ k , the signal received by FAP2 is:

[0057]

[0058] where w 2,k denotes the noise at FAP2 in the time slot τ k , which is assumed to be an additive white Gaussian noise with a mean of 0 and a variance of . It is assumed that k = 1, 2, …, K.

[0059] FAP1 and FAP2 adopt the same decoding mode. Therefore, first, the signal at FAP1 is analyzed. In the time slot τ k , FAP1 first decodes the main signal s2 from FAP2, and the corresponding signal-to-interference-and-noise ratio (SINR) is:

[0060]

[0061] where, the superscript corresponds to the FAP in the main system, and the subscript corresponds to the kth time slot of the NOMA cluster, The subscript corresponds to FAP1, and is related to the interference caused by the backscattered sign of BD(k,m) to FAP1, m=1,2. Therefore, for the primary system, FAP1 in time slot τ k The throughput is:

[0062]

[0063] Where log2(·) represents the logarithmic function with base 2.

[0064] After decoding the main signal s2, FAP1 decodes the backscattered signals from the BDs of the k-th NOMA cluster. According to the decoding rule of uplink NOMA, BD(k,1) with higher reflection coefficient and better channel condition is decoded first, and then the backscattered signal of BD(k,2) is decoded. Therefore, FAP1 decodes c k,1 and c k,2 The SINRs are:

[0065]

[0066] in The superscript corresponds to BD in the subsystem, and the subscript corresponds to FAP1 decoding c k,1 ,In 1,m Indicates the interference caused by channel estimation error, subscript 1 corresponds to FAP1, m corresponds to the mth BD. For BD(k,m), In 1,m Further expressed as

[0067]

[0068] In the process of BD(k,m) backscattering to FAP1, BD(k,m) is in the time slot τ k The throughput is:

[0069]

[0070] in, The superscripts correspond to BD in the subsystem, and the subscripts correspond to FAP1 and BD(k,m).

[0071] Similarly, the SINR of the main signal s1 decoded by FAP2 from FAP1 is:

[0072]

[0073] in, Related to the interference caused by BD(k,m) backscatter symbol to FAP2, m=1,2. Therefore, for the primary system, FAP2 in time slot τ k The throughput is:

[0074]

[0075] Similarly, FAP2 decodes c k,1 and the SINR of c k,2 are respectively:

[0076]

[0077] where m = 1, 2. Thus, the throughput of BD(k, m) during its reverse scattering to FAP2 at time slot τ k is:

[0078]

[0079] In this system, the total throughput of BD(k, m) during its reverse scattering to FAP1 and FAP2 at time slot τ

[0080]

[0081] The present application will employ an a-fair utility function to analyze the achievable throughput of BDs under different fairness levels. The general form of the a-fair utility function is:

[0082]

[0083] where ln(·) denotes the logarithm function with base e.

[0084] The objective of the present application is to jointly optimize the power allocation P = [P 1,1 ,..., P 1,K ,..., P 2,1 ,..., P 2,K ] of the full-duplex access points, the TDMA time slot allocation τ = [τ1, τ2,..., τ K ] of each NOMA cluster, and the reflection coefficient z of the BDs. This resource optimization problem can be described as:

[0085]

[0086] where constraint condition C1 ensures the minimum throughput required by FAP1 and FAP2; constraint condition C2 indicates that the energy collected by each BD is greater than the energy consumed by it; constraint condition C3 indicates the total transmission power limit of FAPs; constraint condition C4 indicates that the sum of each TDMA time slot cannot be greater than the system frame length, and the non-negativity requirement; and constraint condition C5 is the requirement for the reflection coefficient of each BD in the NOMA cluster.

[0087] For P1, the block coordinate descent method is first used to transform it into the following five sub-optimization problems:

[0088] a. The TDMA slot optimization of NOMA cluster, P1 is transformed into the following optimization problem, which can be solved directly.

[0089]

[0090] and C4

[0091] b. The reflection coefficient optimization of BD, the transformed optimization problem of P1 is as follows:

[0092]

[0093] and C5

[0094] For z, the objective function is a non-convex function, and the constraint conditions C1 and C2 are non-convex constraints.

[0095] In order to solve P1-2, according to the quadratic transformation method, the objective function is rewritten as:

[0096]

[0097] Wherein, f k,m,i (·) is a function about z and auxiliary variable y k,m,i , 1≤k≤K, m∈[1,2], i∈[1,2] respectively correspond to FAP1 and FAP2, and y k,m =[y k,m,1 ,y k,m,2 ]. When m=1, f k,1,1 (z,y k,1,1 ) and f k,1,2 (z,y k,1,2 ) can be respectively expressed as:

[0098]

[0099] When m=2, f k,2,1 (z,y k,2,1 ) and f k,2,2 (z,y k,2,2 ) are respectively expressed as the formula:

[0100]

[0101] Through analysis, the optimal value of y k,1,1 is:

[0102]

[0103] Wherein, the superscript of indicates the optimal value, and the superscript (n) indicates the nth iteration value. The form of the optimal value of other auxiliary variables is similar.

[0104] After the quadratic transformation, further consider using the a-fair utility function. When a ≥ 0, the objective function after quadratic transformation is concave with respect to variable z.

[0105] The non-convex constraint condition of P1-2 is processed. First, for C1, the concave-convex programming method is used to transform the non-convex function into an approximate convex function by using Taylor's theorem. Specifically, first, the non-convex function and are written in the convex difference form, respectively as:

[0106]

[0107]

[0108] where, for the convenience of analysis, f si represents the corresponding bracketed part.

[0109] Next, the first-order Taylor series expansion is performed at the local point z (n) , and the lower bound of the main system throughput of FAP1 and FAP2, respectively, FAP1 and FAP2, can be obtained as: and respectively:

[0110]

[0111] where, The upper index L1 of indicates the lower bound of the throughput value when FAP1.

[0112] For C2, by introducing a relaxation variable r k,m , it is converted into a convex constraint, where 1≤k≤K, m∈[1,2]. Through the above conversion, P1-2 becomes P1-2', which is obviously a convex optimization problem, and therefore, can be effectively solved by using the convex optimization (CVX) tool.

[0113] d. The transmission power optimization of the full-duplex access point, the optimization problem of P1 transformation is as follows:

[0114]

[0115] and C3

[0116] Since the objective function, and the constraints C1 and C2 are all non-convex, similar to the foregoing, the quadratic transformation and Taylor expansion are used to process the objective function and the constraint condition C1, respectively. After such processing, the following are obtained:

[0117]

[0118] where where the subscript corresponds to BD(k,m).

[0119] C2, Introducing a relaxation variable γ k,m ; By doing so, the problem P1-3 is approximated as P1-3', which is a convex optimization problem and thus can be solved by CVX tool. Introducing a relaxation variable β j,m ; By doing so, the problem P1-3 is approximated as P1-3', which is a convex optimization problem and thus can be solved by CVX tool.

[0120] Embodiment

[0121] The application effect of the present application is described in detail in combination with simulation.

[0122] The convergence and effectiveness of the method are verified by simulation results. It is assumed that the number of NOMA clusters is 2, i.e. the system contains 4 BDs; the distance between FAP1 and FAP2 is 12 m; the distances from FAP1 to the 4 BDs are 7 m, 9 m, 11 m and 13 m respectively; the distances from FAP2 to the 4 BDs are 8 m, 10 m, 12 m and 13 m respectively; other parameters are set as follows: β1=3, β2=2, β3=2, σ e =10 -3 , θ=0.01, T=2, η k,m =0.6,

[0123] In this embodiment, the proposed resource allocation method for symbiotic wireless radio system based on full-duplex-NOMA under the α-fairness principle is compared with the traditional method. Figure 3 It can be seen that the change characteristics of the objective function of the method with the number of iterations. In the figure, the vertical coordinate represents the specific value of the objective function, that is, the value after the BD throughput is brought into the α-fair utility function. It can be known from the figure that when 0≤α≤1, the value of the objective function is positive; when α>1, the value of the objective function is negative, which corresponds to the form of the utility function. In particular, the objective function tends to be stable after about 5 iterations, that is, convergence is achieved, which indicates that the BD can obtain stable throughput under different fairness level requirements after 5 iterations.

[0124] In comparison with the traditional method, it can be seen from Figure 4It can be seen that the total throughput of all schemes decreases with the increase of α, which confirms that maximizing the total throughput usually at the cost of reducing the fairness among BDs. However, considering the fairness among BDs, it is more realistic to enable the receiving end to comprehensively collect information from BDs. Therefore, maximizing the total throughput of BDs under the α-fair utility function can control the balance between throughput performance and fairness, and obtain good performance in overall system performance. Meanwhile, compared with the full-duplex-TDMA scheme, the resource allocation scheme of the full-duplex-NOMA (the present scheme) has higher throughput performance under the same residual self-interference level; compared with the half-duplex-NOMA scheme, there is no self-interference problem in the half-duplex, but the present scheme can obtain better throughput performance.

[0125] Finally, it should be pointed out that the above preferred embodiments are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application has been described in detail through the above preferred embodiments, those skilled in the art should understand that various modifications can be made in form and details without departing from the scope defined by the claims of the present application.

Claims

1. A resource allocation method for coexisting radio systems based on full-duplex - non-orthogonal multiple access (NOMA) under the principle of α-fairness, characterized in that, Comprising the following steps: Step one: Establishing the full duplex-NOMA coexistence radio system, in which both the transmitting and receiving ends adopt full duplex mode, and considering the residual self-interference problem caused by full duplex; Step two: Under the framework of α-fairness, a resource allocation model is constructed to maximize the backscatter device (BD) throughput by combining non-linear energy harvesting and non-perfect channel state information conditions; wherein, is the transmit power of FAP, denotes a 2Kx1 dimensional complex matrix space, is the time slot of NOMA cluster, is the reflection coefficient of BD; max denotes the maximum value of the objective function; is the throughput of BD(k, m), the superscript b denotes the throughput of the corresponding BD, the subscript corresponds to BD(k, m), u α is the α-fairness utility function; and is the achievable throughput and minimum throughput requirement of the FAPi in the kth NOMA cluster time slot, wherein the superscript s denotes the throughput of the corresponding FAP, the subscript i corresponds to FAPi, and k corresponds to the NOMA cluster, the superscript min denotes the minimum value of the throughput; and is the energy harvested and consumed by BD(k, m), wherein the superscript corresponds to the harvested energy, and the subscript corresponds to BD(k, m), the superscript corresponds to the consumed energy, and the subscript corresponds to BD(k, m); P i,k is the power allocation of FAPi in the kth NOMA cluster time slot, P i m is the required consumed power of the FAPi, the superscript denotes the total required consumed power of FAPi, and the subscript corresponds to FAPi; τ k is the time slot of the kth NOMA cluster, T is the time slot of the kth NOMA cluster and the total time slot; z k,1 and z k,2 are the reflection coefficients of BDs in the same NOMA cluster; C1 ensures the minimum throughput required by 2 FAPs; C2 indicates that the energy harvested by each BD is greater than the energy consumed by it; C3 indicates the total transmit power limit of FAP; C4 indicates that the sum of each time slot of TDMA cannot be greater than the system frame length, and the non-negative requirement; C5 is the reflection coefficient requirement of each BD in the NOMA cluster; Step three: Using the block coordinate descent method, the resource allocation model for maximizing the throughput is converted into a convex optimization problem; Step four: Using the convex optimization toolbox to solve the optimization problem, the time slots between NOMA clusters, the reflection coefficient of BD and the transmit power of full duplex are obtained; the solving process includes the following steps: a. For NOMA cluster time slot optimization, the following optimization problem is obtained, which can be directly solved; and C4 where z (n) and P (n) denote the given reflection coefficient and the transmit power at the nth iteration, respectively. b. For the optimization of the reflection coefficient of BD, the following optimization problem is obtained: and C5 where τ (n) The objective function is non-convex for z, and the constraints C1 and C2 are non-convex constraints. According to the method of second variation, the optimization of the objective function can be completed. For C1, the concave-convex programming method is used to convert the non-convex function into an approximate convex function by using Taylor's theorem. For C2, a relaxation variable r k,m is introduced to convert it into a convex constraint, where the subscript corresponds to BD(k, m), 1≤k≤K, m∈[1, 2]; thus, the problem can be converted into a convex optimization problem. c. For the optimization of the transmit power of the full duplex access point device, the following optimization problem is obtained: and C3 Wherein, since the objective function, and the constraints C1 and C2 are all non-convex, the quadratic transformation and Taylor expansion are adopted to process the objective function and the constraint C1 respectively; the constraint C2 is processed by introducing a slack variable β, Introducing a slack variable γ k,m The subscript corresponds to BD(k,m); Introducing a slack variable β j,m The subscript corresponds to BD(j,m); the problem is converted into a convex optimization problem.

2. The method of claim 1, wherein: In step one, the coexisting radio system of full-duplex-NOMA includes two full-duplex access point devices, denoted as FAPi, subscript i = 1, 2, representing the i-th full-duplex access point; 2K BDs are divided into K NOMA backscatter clusters, each cluster containing 2 BDs; let the BD reflection coefficients in the K NOMA clusters be z = [z 1,1 ,z 1,2 ,...,z K,1 ,z K,2 ], satisfying z k,1 > z k,2 , k = 1, 2,..., K, where the first subscript corresponds to the NOMA cluster, and the second subscript corresponds to the BD in a certain NOMA cluster; the residual self-interference of the full-duplex access point device at the transceiver end is denoted as I1 and I2, respectively, satisfying , which is subject to a complex Gaussian distribution, 0 is the mean of the distribution, θ 2 P i,k is the variance of the distribution, where θ represents the residual self-interference level, P i,k represents the power allocation of FAPi in the k-th NOMA cluster time slot, k corresponds to the NOMA cluster; the m-th BD in the k-th NOMA cluster is denoted as BD(k, m), where k = 1, 2,..., K, m = 1, 2.

3. The method of claim 1, wherein: In step four, the optimization problem has been converted into a convex optimization problem, which can be directly solved using the convex optimization (CVX) tool to obtain the time slots between NOMA clusters, the reflection coefficient of BD and the transmit power of full duplex.

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