A method for optimizing energy efficiency of wireless power communication network based on SCMA

Abstract

The present invention discloses a method for optimizing energy efficiency of a wireless power supply communication network based on SCMA. The method can simultaneously realize the scheduling-free overload access and wireless energy replenishment (charging) of wireless terminals through SCMA multiple access technology and WPCN wireless power supply technology. The present invention aims to maximize the system energy efficiency and splits the optimization problem into two sub-optimization problems, namely, jointly optimizing the subcarrier allocation scheme and the energy collection time allocation coefficient, which can significantly improve the system energy efficiency; first, fix the energy collection time coefficient and design a subcarrier allocation scheme based on energy efficiency gain; secondly, using the obtained optimal subcarrier allocation scheme, the energy collection time coefficient is converted from non-convex optimization to convex optimization problem solution; finally, the optimal subcarrier allocation scheme and the optimal energy collection time coefficient are obtained to maximize the system energy efficiency.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method for optimizing energy efficiency of a wireless power supply communication network based on sparse code multiple access (SCMA). Background Art

[0002] The number of terminal devices in the large-scale Internet of Things is huge, and it is very difficult to replace batteries or charge them. The Wireless Powered Communication Network (WPCN) uses energy harvesting technology based on radio frequency (RF) signals to provide remote wireless charging for wireless terminal devices. WPCN can solve the problem of charging low-power devices in the Internet of Things, but the energy collected is very limited, so it is particularly important to optimize its energy efficiency to achieve a sustainable Internet of Things network. SCMA technology allocates a subcarrier to different users for sharing through appropriate codebook design, and multiple users superimpose transmission, thereby improving the system throughput and realizing overload access of the system. It is a very promising large-scale Internet of Things device access solution. The application of wireless power communication technology based on SCMA in the Internet of Things can well solve the large-scale access and power supply problems in the Internet of Things.

[0003] At present, there have been related studies on the optimization of resource allocation based on energy efficiency for SCMA systems and wireless power communication networks. Samira Jaber's paper "Subcarrier Assignment and Power Allocation for SCMA Energy Efficiency" published at the IEEE 20th International Conference on Communication Technology proposed a resource allocation method based on the uplink SCMA network with the goal of maximizing energy efficiency, but this method does not consider wireless energy harvesting and cannot solve the problem of limited energy of terminal devices. The invention patent "A method and system for resource allocation in a wireless power communication network" with publication number CN110167171A and publication date August 23, 2019, proposed a resource allocation algorithm based on the combination of TDMA and non-orthogonal multiple access (NOMA) technology with the goal of maximizing system throughput, but the invention is based on TDMA and NOMA technology, scheduling is difficult, and does not have the uplink scheduling-free access advantage of SCMA. Summary of the invention

[0004] The purpose of the present invention is to solve the above-mentioned defects in the prior art and provide a method for optimizing the energy efficiency of a wireless power supply communication network based on SCMA, which jointly optimizes subcarrier allocation and energy collection time to maximize system energy efficiency and lay the foundation for massive information transmission in future green communications.

[0005] The purpose of the present invention can be achieved by adopting the following technical solutions:

[0006] A wireless power supply communication network energy efficiency optimization method based on SCMA, wherein the communication system model used in the wireless power supply communication network energy efficiency optimization method includes M terminals U m , a hybrid access point H-AP, 1≤m≤M, the following communication system model is referred to as the system, each terminal and access point has a single antenna, the number of subcarriers is K, M terminals are multiplexed on K subcarriers, the system energy efficiency is optimized in a transmission time slot, and the time of a transmission time slot is defined as T, T is divided into two stages, in the first stage τ0T time, the hybrid access point sends a radio frequency signal to all terminals for wireless charging, in the second stage (1-τ0)T time, all terminal nodes use all the collected energy to send information to the access point through the SCMA technology, the energy collection time coefficient τ0 ranges from 0≤τ0≤1, the wireless power supply communication network energy efficiency optimization method includes the following steps:

[0007] S1. Define the optimization problem of maximizing system energy efficiency as follows:

[0008]

[0009] stC1:τ0(1-τ0)≥0

[0010] C2:

[0011] C3:f m,k ∈{0,1},1≤m≤M,1≤k≤K

[0012] C4:

[0013] Where η is the system energy efficiency, P a Indicates the transmit power of the hybrid access point H-AP. is the total system throughput, where Indicates terminal U m The throughput of m =[f m,1 ,f m,2 ,...,f m,K ] T Indicates terminal U m Subcarrier allocation method, f m,k=1 indicates that subcarrier k is allocated to terminal U m , f m,k =0 means that subcarrier k is not allocated to terminal U m ; represents the signal-to-noise ratio, where ξ m Indicates terminal U m Energy conversion efficiency, h m Indicates the downlink energy transmission process between the hybrid access point H-AP and the terminal U m The channel state information between them, L represents the number of subcarriers occupied by each terminal, σ 2 represents the noise variance of the hybrid access point H-AP as a receiver, g m =[g m,1 ,g m,2 ,…,g m,k ,…,,g m,K ] T Indicates the terminal U during uplink information transmission m The channel state information between the hybrid access point H-AP, where g m,k Indicates the uplink information transmission terminal U m Channel state information on subcarrier k; f m′ Indicates terminal U m′ The subcarrier allocation method; Constraint C1 is the constraint of energy transmission time and information transmission time; Constraint C2 represents the number of subcarriers occupied by each terminal; Constraint C3 represents f m,k Possible values; Constraint C4 indicates that the subcarrier allocations of any two terminals are different;

[0014] S2. Subcarrier allocation scheme The energy collection time coefficient τ0 is jointly optimized. First, the energy collection time coefficient τ0 is fixed and the subcarrier allocation scheme is optimized. Optimize and obtain the subcarrier allocation scheme that maximizes energy efficiency gain Then, the optimization problem of maximizing system energy efficiency is converted into a convex problem and substituted into the obtained optimal subcarrier allocation scheme Then the convex optimization toolkit CVX is used to solve the optimal solution for the energy collection time.

[0015] Furthermore, the process of step S2 is as follows:

[0016] S2.1, fixed energy collection time coefficient τ0, subcarrier allocation scheme Optimize and obtain the optimal subcarrier allocation scheme that maximizes energy efficiency gain

[0017] S2.2. Convert the optimization problem of maximizing system energy efficiency into a convex problem and substitute the optimal subcarrier allocation solution obtained in step 2.1 Then the convex optimization toolkit CVX is used to find the optimal solution for the energy collection time.

[0018] Furthermore, the process of step S2.1 is as follows:

[0019] S2.1.1, set the initial value of τ0, m = 1, and randomly generate a factor graph matrix S = (c1, c2, ..., c j ,…,c M ), where the jth column c of the factor graph matrix S is j =(c j,1 ,c j,2 ,…,c j,k ,…,c j,K ) T represents a subcarrier allocation method, where The value of is randomly generated 0 or 1;

[0020] S2.1.2. For terminal node U m ,make in R j (τ0,c j ) indicates terminal U m Using c j The throughput of the subcarriers and c J Indicates terminal U m The optimal subcarrier of terminal U m Use the optimal subcarrier allocation scheme

[0021] S2.1.3. Determine whether m≥M is true. If so, let The loop stops; otherwise, let S = S\c J , where S\c J Indicates removing c from the factor graph matrix S J , let m=m+1, and return to step S2.1.2.

[0022] Furthermore, the process of step S2.2 is as follows:

[0023] S2.2.1. Convert the optimization problem of maximizing system energy efficiency into a convex problem Where λ is an auxiliary variable, and the value of λ at the nth iteration is in is the value of the optimization variable τ0 at the n-1th iteration, express and optimal subcarrier allocation scheme The corresponding total system throughput;

[0024] S2.2.2. Set the convergence tolerance ε, the initial value of the iteration variable n = 1, and the 0th iteration value

[0025] S2.2.3. Utilization and optimal subcarrier allocation scheme Update λ (n) , use the convex optimization toolkit CVX to solve the optimization problem in step S2.2.1 and obtain the optimal solution at the nth iteration

[0026] S2.2.4. Judgment Is it true? If true, the iteration ends. Output optimal energy collection time coefficient and optimal subcarrier allocation scheme Otherwise, set n=n+1 and return to step S2.2.3.

[0027] Furthermore, in step S2.2.1, the optimization problem of maximizing system energy efficiency is converted into a convex problem using the Dinkelbach method.

[0028] Furthermore, in step S2.2.3, the continuous convex approximation method of the convex optimization toolkit CVX is used to solve the optimization problem in step S2.2.

[0029] Furthermore, in step S1, the transmission power P of the hybrid access point H-AP a The value is 10W.

[0030] Furthermore, in step S1, η is the system energy efficiency. The larger the value of η is, the higher the energy efficiency of the system is and the better the solution is.

[0031] Compared with the prior art, the present invention has the following advantages and effects:

[0032] 1. The present invention uses SCMA multiple access technology and WPCN wireless energy supply technology to simultaneously realize the scheduling-free overload access and wireless energy replenishment (charging) of wireless terminals;

[0033] 2. The present invention jointly optimizes the subcarrier allocation scheme and the energy collection time allocation coefficient, which can significantly improve the system energy efficiency. By dividing the joint optimization problem into two sub-problems, the subcarrier allocation scheme is first optimized based on the energy efficiency gain, and then the energy collection time coefficient is converted from a non-convex optimization to a convex optimization problem. The algorithm converges quickly and is simple to implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0035] Figure 1 It is a schematic diagram of an application system model of the SCMA-based wireless power supply communication network energy efficiency optimization method disclosed in the present invention;

[0036] Figure 2 It is a flow chart of the SCMA-based wireless power communication network energy efficiency optimization method disclosed in the present invention;

[0037] Figure 3 It is a flow chart of an algorithm for obtaining optimal subcarrier allocation and optimal energy collection time coefficient in the present invention;

[0038] Figure 4 This is a simulation diagram showing the change of system energy efficiency with the change of noise power before and after optimization in Example 1 of the present invention;

[0039] Figure 5 This is a simulation diagram showing how the system energy efficiency changes with the noise power before and after optimization of Example 2 of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Example 1

[0042] This embodiment discloses a method for optimizing the energy efficiency of a wireless power supply communication network based on SCMA. The communication system model includes six terminals U m , a hybrid access point H-AP, 1≤m≤6, the following communication system model is referred to as the system, each terminal and access point has a single antenna, 6 terminals are multiplexed on 4 subcarriers, the system energy efficiency is optimized in a transmission time slot, and the time of a transmission time slot is defined as T=1s, T is divided into two stages, in the first stage τ0 time, the hybrid access point sends radio frequency signals to the 6 terminals for wireless charging, in the second stage 1-τ0 time, all terminal nodes use all the collected energy to send information to the access point through the SCMA technology, the value range of the energy collection time coefficient τ0 is 0≤τ0≤1, the wireless power supply communication network energy efficiency optimization method includes the following steps:

[0043] S1. Define the optimization problem of maximizing system energy efficiency as follows:

[0044]

[0045] st.C1:τ0(1-τ0)≥0

[0046] C2:

[0047] C3:f m,k ∈{0,1},1≤m≤6,1≤k≤4

[0048] C4:

[0049] Where η is the system energy efficiency, P a =10W represents the transmission power of the hybrid access point H-AP, is the total system throughput, where Indicates terminal U m The throughput of m =[f m,1 ,f m,2 ,f m,3 ,f m,4 ] T Indicates terminal U m Subcarrier allocation method, where The value of is 0 or 1, where f m,k =1 indicates that subcarrier k is allocated to terminal U m , f m,k =0 means that subcarrier k is not allocated to terminal U m ; represents the signal-to-noise ratio, where where ξ m =0.9 indicates terminal U m Energy conversion efficiency, h m In the process of downlink energy transmission, the hybrid access point H-AP and the terminal U m The channel state information between L and 2 means that each terminal occupies 2 subcarriers, σ 2 represents the noise variance of the hybrid access point H-AP as a receiver, g m =[g m,1 ,g m,2 ,g m,3 ,g m,4 ] T Indicates the terminal U during uplink information transmission m The channel state information between the hybrid access point H-AP, where g m,k Indicates the uplink information transmission terminal U mChannel state information on subcarrier k; f m′ Indicates terminal U m ′’s subcarrier allocation method; constraint C1 is the constraint of energy transmission time and information transmission time; constraint C2 represents the number of subcarriers occupied by each terminal; constraint C3 represents f m,k Possible values; Constraint C4 indicates that the subcarrier allocations of any two terminals are different;

[0050] S2. Subcarrier allocation scheme f m =[f m,1 ,f m,2 ,f m,3 ,f m,4 ] T The energy collection time coefficient τ0 is jointly optimized, including:

[0051] S2.1, fixed energy collection time coefficient τ0, subcarrier allocation scheme f m =[f m,1 ,f m,2 ,f m,3 ,f m,4 ] T Optimize and obtain the optimal subcarrier allocation scheme that maximizes energy efficiency gain Specifically include:

[0052] S2.1.1, let τ0 = 0.5, m = 1, and randomly generate a factor graph matrix S = (c1, c2, ..., c j ,...,c6), where the j-th column c of the factor graph matrix S j =(c j,1 ,c j,2 ,c j,3 ,c j,4 ) T represents a subcarrier allocation method, where The value of is randomly generated 0 or 1;

[0053] S2.1.2. For terminal node U m ,make in R j (τ0,c j ) indicates terminal U m Using c j The throughput of the subcarriers and c J Indicates terminal U m The optimal subcarrier of terminal U m Use the optimal subcarrier allocation scheme

[0054] S2.1.3, determine whether m≥6 is true, if so, let The loop stops; otherwise, let S = S\c J , where S\c J Indicates removing c from the factor graph matrix S J , let m=m+1, and return to step S2.1.2;

[0055] S2.2. Optimal subcarrier allocation scheme obtained using step S2.1 Find the optimal solution for the energy harvesting time coefficient The specific steps are as follows:

[0056] S2.2.1, convert the optimization problem of maximizing system energy efficiency into a convex problem Where λ is an auxiliary variable, and its value at the nth iteration is in is the value of the optimization variable τ0 at the n-1th iteration, express and optimal subcarrier allocation scheme The corresponding total system throughput;

[0057] S2.2.2, set the convergence tolerance ε = 10 -3 , the initial value of the iteration variable n = 1 and the 0th iteration value

[0058] S2.2.3, Utilization and optimal subcarrier allocation scheme Update λ (n) , use the convex optimization toolkit CVX to solve the optimization problem described in step S2.2.1 and obtain the optimal solution at the nth iteration

[0059] S2.2.4, Judgment Is it true? If true, the iteration ends. Output optimal energy collection time coefficient and optimal subcarrier allocation scheme Otherwise, set n=n+1 and return to step S2.2.3.

[0060] Figure 4 This is a simulation diagram of the system energy efficiency of Example 1 that changes with the noise power. The legend represents the optimization method proposed in the present invention and the non-optimized results from top to bottom. It can be seen from the figure that as the noise power gradually increases, the system energy efficiency gradually decreases. The system energy efficiency of the optimization method proposed in the present invention is much greater than the non-optimized system energy efficiency, which shows that the method proposed in the present invention is feasible.

[0061] Example 2

[0062] This embodiment discloses a method for optimizing the energy efficiency of a wireless power supply communication network based on SCMA. The communication system model includes 9 terminals U m , a hybrid access point H-AP, 1≤m≤9, the following communication system model is referred to as the system, each terminal and access point has a single antenna, 9 terminals are multiplexed on 6 subcarriers, the system energy efficiency is optimized in a transmission time slot, and the time of a transmission time slot is defined as T=1s, T is divided into two stages, in the first stage τ0 time, the hybrid access point sends radio frequency signals to the 9 terminals for wireless charging, in the second stage 1-τ0 time, all terminal nodes use all the collected energy to send information to the access point through SCMA technology, the value range of the energy collection time coefficient τ0 is 0≤τ0≤1, the wireless power supply communication network energy efficiency optimization method includes the following steps:

[0063] S1. Define the optimization problem of maximizing system energy efficiency:

[0064]

[0065] stC1:τ0(1-τ0)≥0

[0066] C2:

[0067] C3:f m,k ∈{0,1},1≤m≤9,1≤k≤6

[0068] C4:

[0069] Where η is the system energy efficiency, P a =10W represents the transmission power of the hybrid access point H-AP, is the total system throughput, where Indicates terminal U m The throughput of m =[f m,1 ,f m,2 ,f m,3 ,f m,4 ,f m,5 ,f m,6 ] T Indicates terminal U m Subcarrier allocation method, where The value of is 0 or 1, where f m,k =1 indicates that subcarrier k is allocated to terminal U m , f m,k =0 means that subcarrier k is not allocated to terminal U m ; represents the signal-to-noise ratio, where where ξ m =0.9 indicates terminal U m Energy conversion efficiency, h m In the process of downlink energy transmission, the hybrid access point H-AP and the terminal U m The channel state information between L and 2 means that each terminal occupies 2 subcarriers, σ 2 represents the noise variance of the hybrid access point HA P as a receiver, g m =[g m,1 ,g m,2 ,g m,3 ,g m,4 ,g m,5 ,g m,6 ] T Indicates the terminal U during uplink information transmission m The channel state information between the hybrid access point H-AP, where g m,k Indicates the uplink information transmission terminal U m Channel state information on subcarrier k; f m′ Indicates terminal U m′ The subcarrier allocation method; Constraint C1 is the constraint of energy transmission time and information transmission time; Constraint C2 represents the number of subcarriers occupied by each terminal; Constraint C3 represents f m,k Possible values; Constraint C4 indicates that the subcarrier allocations of any two terminals are different;

[0070] S2. Subcarrier allocation scheme f m =[f m,1 ,f m,2 ,f m,3 ,f m,4 ,f m,5 ,f m,6 ] T The energy collection time coefficient τ0 is jointly optimized, including:

[0071] S2.1, fixed energy collection time coefficient τ0, subcarrier allocation scheme f m =[f m,1 ,f m,2 ,f m,3 ,f m,4 ,f m,5 ,f m,6 ] T Optimize and obtain the optimal subcarrier allocation scheme that maximizes energy efficiency gain Specifically include:

[0072] S2.1.1, let τ0 = 0.5, m = 1, and randomly generate a factor graph matrix S = (c1, c2, ..., c j ,...,c9), where the j-th column c of the factor graph matrix S j =(c j,1 ,c j,2 ,c j,3 ,c j,4 ,c j,5 ,c j,6 ) T represents a subcarrier allocation method, where The value of is randomly generated 0 or 1;

[0073] S2.1.2. For terminal node U m ,make in R j (τ0,c j ) indicates terminal U m Using c j The throughput of the subcarriers and c J Indicates terminal U m The optimal subcarrier of terminal U m Use the optimal subcarrier allocation scheme

[0074] S2.1.3, determine whether m≥9 is true, if so, let The loop stops; otherwise, let S = S\c J , where S\c J Indicates removing c from the factor graph matrix S J , let m=m+1, and return to step S2.1.2;

[0075] S2.2. Optimal subcarrier allocation scheme obtained using step S2.1 Find the optimal solution for the energy harvesting time coefficient The specific steps are as follows:

[0076] S2.2.1. Convert the optimization problem of maximizing system energy efficiency into a convex problem Where λ is an auxiliary variable, and its value at the nth iteration is in is the value of the optimization variable τ0 at the n-1th iteration, express and optimal subcarrier allocation scheme The corresponding total system throughput;

[0077] S2.2.2. Set the convergence tolerance ε = 10-3 , the initial value of the iteration variable n = 1 and the 0th iteration value

[0078] S2.2.3. Utilization and optimal subcarrier allocation scheme Update λ (n) , use the convex optimization toolkit CVX to solve the optimization problem described in step S2.2.1 and obtain the optimal solution at the nth iteration

[0079] S2.2.4. Judgment Is it true? If true, the iteration ends. Output optimal energy collection time coefficient and optimal subcarrier allocation scheme Otherwise, set n=n+1 and return to step S2.2.3.

[0080] Figure 5 This is a simulation diagram of the system energy efficiency of Example 2 that changes with the noise power. The legend represents the optimization method proposed in the present invention and the non-optimized results from top to bottom. It can be seen from the figure that as the noise power gradually increases, the system energy efficiency gradually decreases. The system energy efficiency of the optimization method proposed in the present invention is much greater than the non-optimized system energy efficiency, which shows that the method proposed in the present invention is feasible.

[0081] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A wireless power supply communication network energy efficiency optimization method based on SCMA, wherein the communication system model used in the wireless power supply communication network energy efficiency optimization method includes M terminals , a hybrid access point H-AP, The following communication system model is referred to as the system. Each terminal and access point has a single antenna. The number of subcarriers is K. M terminals are multiplexed on K subcarriers. The system energy efficiency is optimized within a transmission time slot. The time of a transmission time slot is defined as T. T is divided into two stages. In the first stage, During this time, the hybrid access point sends RF signals to all terminals for wireless charging. In the second phase Within this time, all terminal nodes use all the collected energy to send information to the access point through SCMA technology. The energy collection time coefficient The value range is , characterized in that, The wireless power supply communication network energy efficiency optimization method comprises the following steps: S1. Define the optimization problem of maximizing system energy efficiency as follows: in, is the system energy efficiency, Indicates the transmit power of the hybrid access point H-AP. is the total system throughput, where Indicates terminal The throughput of Indicates terminal Subcarrier allocation method, When represents subcarrier Assign to terminal , Indicates subcarrier Not assigned to terminal ; represents the signal-to-noise ratio, where , Indicates terminal The energy conversion efficiency, Indicates the hybrid access point H-AP and the terminal during downlink energy transmission. The channel state information between Indicates the number of subcarriers occupied by each terminal, represents the noise variance of the hybrid access point H-AP as a receiver, Indicates the terminal during uplink information transmission The channel status information between the hybrid access point H-AP, where Indicates the uplink information transmission terminal In subcarrier Channel state information on Indicates terminal The subcarrier allocation method; Constraint C1 is the constraint of energy transmission time and information transmission time; Constraint C2 represents the number of subcarriers occupied by each terminal; Constraint C3 represents Possible values; Constraint C4 indicates that the subcarrier allocations of any two terminals are different; S2. Subcarrier allocation scheme and the energy harvesting time factor The joint optimization process is as follows: S2.1, Fixed energy collection time coefficient , for subcarrier allocation scheme Optimize and obtain the subcarrier allocation scheme that maximizes energy efficiency gain ; S2.

2. Optimal subcarrier allocation scheme obtained using step S2.1 , find the optimal solution for energy harvesting time .

2. According to a method for optimizing energy efficiency of a wireless power supply communication network based on SCMA according to claim 1, it is characterized in that: The process of step S2.1 is as follows: S2.1.

1. Settings Initial value, , randomly generate a factor graph matrix that satisfies the constraints C2, C3 and C4 , where the factor graph matrix No. List represents a subcarrier allocation method, where The value of is randomly generated 0 or 1; S2.1.

2. For terminal nodes ,make ,in , Indicates terminal use The throughput of the subcarriers and , Indicates terminal The optimal subcarrier of the terminal Use the optimal subcarrier allocation scheme ; S2.1.

3. Judgment Is it established? If established, let , the cycle stops; otherwise, ,in Represents the matrix from the factor graph Remove ,make , return to step S2.1.

2.

3. According to a method for optimizing energy efficiency of a wireless power supply communication network based on SCMA according to claim 2, it is characterized in that: The process of step S2.2 is as follows: S2.2.

1. Convert the optimization problem of maximizing system energy efficiency into a convex problem ,in is an auxiliary variable, The value at the nth iteration is ,in is the optimization variable In the The value at the iteration, express and optimal subcarrier allocation scheme The corresponding total system throughput; S2.2.

2. Setting Convergence Tolerance , iterative variable initial value and the 0th iteration value ; S2.2.

3. Utilization and optimal subcarrier allocation scheme renew , use the convex optimization toolkit CVX to solve the optimization problem in step S2.2.1 and obtain the optimal solution at the nth iteration ; S2.2.

4. Judgment Is it true? If true, the iteration ends. , output the optimal energy collection time coefficient and optimal subcarrier allocation scheme ; Otherwise, , return to step S2.2.

3.

4. According to a method for optimizing energy efficiency of a wireless power supply communication network based on SCMA according to claim 1, it is characterized in that: In step S1, the transmission power of the hybrid access point H-AP is The value is 10W.

5. According to a method for optimizing energy efficiency of a wireless power supply communication network based on SCMA according to claim 1, it is characterized in that: In the step S1, is the system energy efficiency, The larger the value, the higher the energy efficiency of the system and the better the solution.

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