A Resource Allocation Method for WPCN Underwater Acoustic Communication System

Abstract

The present invention discloses a resource allocation method for a WPCN underwater acoustic communication system. Through OFDM modulation technology and WPCN wireless power supply technology, this method can perform wireless energy replenishment for the terminal sensors of an underwater acoustic communication network while ensuring communication quality. This method is equipped with multiple transmitting transducers (multiple antennas) for transmitting energy at the access point S. By performing energy beamforming on the antenna array of the access point S, the energy signal is sent to the terminal sensor R, maximizing the received energy of the terminal sensor R. The terminal sensor R uses the collected energy for information transmission. The present invention aims to maximize the energy collected by the system, splits the optimization problem into two sub-optimization problems, namely jointly optimizing the beamforming matrix and the time slot allocation factor, obtains the optimal beamforming matrix and the optimal time slot allocation factor, realizes the maximization of energy collection, and can significantly improve the efficiency of wireless energy transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater acoustic communication, and particularly to a resource allocation method for an underwater acoustic communication system applicable to a Wireless Powered Communication Network (WPCN). Background Art

[0002] Underwater acoustic communication is widely used in civil and commercial fields such as marine environment monitoring and disaster warning, coastal monitoring and port security. However, due to the particularity of the ocean and underwater environment, the application and development of underwater acoustic communication technology have been facing huge challenges, including long communication delay, severe Doppler effect and multipath effect, narrow available bandwidth, and limited energy in underwater acoustic communication networks, etc.

[0003] The transmission process of WPCN is divided into two stages: the first stage is downlink wireless energy transfer, where the base station (access point) transfers energy to the terminal node, and the terminal node collects energy; the second stage is uplink wireless information transfer, where the terminal node uses the energy collected in the first stage to send information to the base station. A prominent advantage of WPCN is that the ambient noise and interference received by the receiver can also be used as an energy source for energy collection, which can significantly improve the energy collection efficiency and effectively solve the problem that the life cycle of traditional underwater acoustic communication networks is limited by the battery energy of nodes.

[0004] Currently, only a few studies have proposed methods to supplement energy for underwater acoustic communication nodes through wireless energy collection technology. A patent for invention named "Resource Allocation Method and Device for Underwater Acoustic Communication System with Energy Collection Ability" with publication number CN110138460B and publication date August 18, 2020, proposes to separate the acoustic wave signal received by the underwater terminal node into two signals, and perform energy collection and signal detection simultaneously. However, this method assumes that the ambient noise is Gaussian white noise, which does not conform to the actual situation, and the ambient noise is not collected as part of the energy source during the energy collection process; in addition, this method only considers using a part of the power of the received signal for energy collection, and the energy that can be collected is relatively low. Summary of the Invention

[0005] The purpose of the present invention is to solve the above-mentioned defects in the prior art, and provide a WPCN underwater acoustic communication resource allocation method. This method is aimed at the application scenario of a point-to-point WPCN underwater acoustic communication system, considering that the access point S (hereinafter all represented by S) is equipped with multiple transmitting transducers (multiple antennas) for energy transmission. By performing energy beamforming on the antenna array of the access point S, the energy signal is sent to the terminal sensor R (hereinafter all represented by R), so as to maximize the received energy of the terminal sensor R, and the terminal sensor R uses the collected energy for information transmission.

[0006] The object of the present invention can be achieved by adopting the following technical solutions:

[0007] A resource allocation method for a WPCN underwater acoustic communication system. The WPCN underwater acoustic communication system includes an access point S equipped with N transmitting transducers and 1 receiving transducer, and a terminal sensor R equipped with 1 transmitting transducer and 1 receiving transducer. The transmitting transducer is also called an antenna. The transmission time is T. In the τ0T time of the first stage, the access point S transmits energy to the terminal sensor R, and the terminal sensor R performs energy harvesting, where τ0 is a time slot allocation factor used to allocate the time ratio of energy transmission and information transmission; in the (1 - τ0)T time of the second stage, the terminal sensor R uses the energy harvested in the first stage to transmit information to the access point S, and the signals transmitted in both stages will be modulated by the Orthogonal Frequency Division Multiplexing (OFDM) technology, and the obtained OFDM symbol block is transmitted via K subcarriers. The resource allocation method includes the following steps:

[0008] S1. The access point S broadcasts an instruction to the terminal sensor R, requiring the terminal sensor R to send a training symbol sequence to the access point S. The access point S estimates the channel state information between the access point S and the terminal sensor R according to the received training symbol sequence. The channel vector between the access point S and the terminal sensor R is G k =[G1[k], G2[k], …, G n [k]…, G N [k]] T , where [·] T represents the transpose operation on a matrix or vector, n = 1, 2, …, N, k = 1, 2, …, K, and G n [k] is the channel state information between the nth antenna of the access point S and the terminal sensor R on the kth subcarrier;

[0009] S2. The terminal sensor R broadcasts an instruction to the access point S, requiring the access point S to send a training symbol sequence to the terminal sensor R. The terminal sensor R performs channel estimation according to the received training symbol sequence to obtain the channel state information H = [H[1], H[2], …, H[k], …, H[K]] T , where H[k] is the channel state information between the terminal sensor R and the access point S on the kth subcarrier;

[0010] S3. The access point S generates a K-dimensional energy signal vector X = [x1, x2, …, x k ,…x K T , where, x​k is the information bit sent from the access point S to the terminal sensor R on the n-th antenna and the k-th subcarrier, and the information sent on the k-th subcarrier is the same; define the energy beamforming matrix W of the access point S = [w1, w2, …, w k …, w K , where w k = [w1[k], w2[k], …, w n [k] …, w N [k]] T is the beamforming vector of the access point S corresponding to the k-th subcarrier, and w n [k] is the beamforming factor of the access point S on the n-th antenna and the k-th subcarrier;

[0011] S4. Define the optimization problem of maximizing the harvested energy in the WPCN underwater acoustic communication system, and solve for the optimal value of the beamforming matrix W of the access point S through graph optimization and the optimal value of the time slot allocation factor τ0 wherein, is the optimal beamforming vector on the k-th subcarrier, and w n [k] * is the optimal beamforming factor of the access point S on the n-th antenna and the k-th subcarrier;

[0012] S5. The access point S uses the optimal value W * of the beamforming matrix W of the underwater acoustic transducer array to perform beamforming on the transmitted energy signal vector X of the access point S to obtain the transmitted signal and transmit the signal to the terminal sensor R within the first stage τ0 * T time; the terminal sensor R performs energy harvesting and uses the harvested energy for information transmission within the second stage (1 - τ0 * )T time.

[0013] Furthermore, the process of step S4 is as follows:

[0014] S4.1. Define the optimization problem of maximizing the system harvested energy as the following objective function P1:

[0015] P1:

[0016] subject to C1: r0 ≥ r th

[0017] C2:

[0018] C3: 0 ≤ τ0 ≤ 1

[0019] wherein, E is the total energy harvested by the terminal sensor R, where |·| represents the modulo operation, and N(f k ) is the power spectral density of non-white Gaussian noise on the k-th subcarrier, where f k is the frequency of the k-th subcarrier, r0 is the achievable information rate, η is the energy conversion efficiency of the energy receiver, r th is the transmission rate threshold, and P S is the maximum transmit power of the access point S;

[0020] S4.2. Solve the optimization problem of maximizing the energy collected by the system through the convex optimization toolbox CVX as follows:

[0021] S4.2.1. Initialize the beamforming matrix W = [w1, w2,..., w k ,…, w K of the underwater acoustic transducer array of the access point S and the time slot allocation factor τ0 to obtain the initial values and the feasible points (W (0) , ) and (W (1) , ), where represents the energy beam transmitted by the access point S in the 0th iteration, represents the energy beam transmitted by the access point S in the 1st iteration, is the energy transmission time allocated to the access point S in the 0th iteration, is the energy transmission time allocated to the access point S in the 1st iteration. Let Set the convergence tolerance ε and the initial value of the iteration variable n = 1;

[0022] S4.2.2. Use the feasible points W (n) and to solve the optimization problem in step S4.1 using the convex optimization toolbox CVX to obtain the optimal solution

[0023] S4.2.3. Use and to solve the optimization problem in step S4.1 using the convex optimization toolbox CVX to obtain the optimal solution

[0024] S4.3. According to the objective function P1 in step S4.1, calculate the total energy collected by the WPCN underwater acoustic communication system in the nth and (n - 1)th iterations, which are respectively respectively represent the optimal energy beams transmitted by the access point S in the (n - 1)th and nth iterations, is the optimal energy transmission time assigned to access point S in the (n - 1)-th and n-th iterations;

[0025] S4.4. Judgment Whether it holds. If holds, stop the iteration and output the optimal solution of the optimization problem Otherwise, let n = n + 1, and return to step S4.2.2.

[0026] Furthermore, in the step S4.1, the value of N(f k ) is related to the frequency f k of the k-th subcarrier, which is different from ordinary Gaussian white noise. The sources of environmental noise mainly come from turbulence, shipping, wind and waves, and thermal noise, and are greatly affected by frequency, which is closer to the real underwater environment.

[0027] Furthermore, in the step S4.1, the WPCN underwater acoustic communication system also uses environmental noise and interference as energy sources for energy harvesting, which can significantly improve the energy harvesting efficiency.

[0028] Furthermore, in the step S4.1, E is the maximum harvested energy of the WPCN underwater acoustic communication system. The larger the value of E, the higher the energy harvested by the WPCN underwater acoustic communication system, and the better the scheme.

[0029] The present invention has the following advantages and effects compared with the prior art:

[0030] 1. The present invention uses OFDM technology and WPCN wireless power supply technology to wirelessly supplement energy for underwater acoustic nodes, which can avoid the operation of replacing batteries or the nodes themselves due to the exhaustion of battery energy of underwater acoustic sensor nodes, greatly extend the lifespan of underwater acoustic sensor nodes and underwater acoustic networks, and thus significantly reduce the operating cost of underwater acoustic networks.

[0031] 2. The present invention takes the beamforming matrix of the signal transmitted by the access point and the time slot allocation factor as the optimization objects, obtains the maximum harvested energy of the sensor terminal nodes while ensuring the communication quality, thus maximizing the energy supplement for the terminal sensor nodes of the underwater acoustic communication network, and uses the AO (Alternating Optimization) technology to split the multi-variable problem into sub-problems of single-variable optimization, and the algorithm implementation is simple.

[0032] 3. The WPCN technology applied in the present invention has the outstanding advantage that the environmental noise and interference received by the receiving end can also be used as energy sources for energy harvesting, which can significantly improve the energy harvesting efficiency. Description of the Drawings

[0033] The accompanying drawings described herein are used to provide a further understanding of the present invention, and constitute a part of this application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention, and do not constitute an improper limitation to the present invention. In the drawings:

[0034] Figure 1 It is a schematic diagram of the application model of the WPCN underwater acoustic system resource system allocation method disclosed in the present invention;

[0035] Figure 2 It is a flowchart of the WPCN underwater acoustic system resource system allocation method disclosed in the present invention;

[0036] Figure 3 It is a flowchart of the steps for obtaining the optimal solutions of the access point beamforming matrix and the time slot allocation factor in the present invention;

[0037] Figure 4 It is a simulation diagram of the system collected energy changing with the transmission power before and after optimization in Embodiment 1 of the present invention;

[0038] Figure 5 It is a simulation diagram of the system collected energy changing with the transmission power before and after optimization in Embodiment 2 of the present invention. Detailed implementation manners

[0039] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] Embodiment 1

[0041] This embodiment discloses a resource allocation method for a WPCN underwater acoustic communication system. A resource allocation method for a WPCN underwater acoustic communication system, the WPCN underwater acoustic communication system includes an access point S equipped with 3 transmitting transducers and 1 receiving transducer and a terminal sensor R equipped with 1 transmitting transducer and 1 receiving transducer. The transmitting transducer is also called an antenna, the transmission time is T. During the τ0T time in the first stage, the access point S transmits energy to the terminal sensor R, and the terminal sensor R performs energy harvesting, where τ0 is the time slot allocation factor for allocating the time ratio of energy transmission and information transmission; during the (1 - τ0)T time in the second stage, the terminal sensor R uses the energy harvested in the first stage to transmit information to the access point S, and the signals transmitted in both stages will be modulated by the Orthogonal Frequency Division Multiplexing (OFDM) technology, and the obtained OFDM symbol block is transmitted via 4 subcarriers. The resource allocation method includes the following steps:

[0042] S1. The access point S broadcasts an instruction to the terminal sensor R, requiring the terminal sensor R to send a training signal to the access point S. The access point S estimates the channel state information between the access point S and the terminal sensor R according to the received training symbol sequence. The channel vector between the access point S and the terminal sensor R is G k =[G1[k], G2[k], G3[k]] T , where [·] T represents the transpose operation on a matrix or vector, and G n [k] is the channel state information between the nth antenna of S and the terminal sensor R on the kth subcarrier, where n = 1, 2, 3; k = 1, 2, 3, 4;

[0043] S2. The terminal sensor R broadcasts an instruction to the access point S, requiring the access point S to send a training symbol sequence to the terminal sensor R. The terminal sensor R performs channel estimation according to the received training symbol sequence to obtain the channel state information H = [H[1], H[2], H[3], H[4]] between the terminal sensor R and the access point S T , where H[k] is the channel state information between the terminal sensor R and the access point S on the kth subcarrier;

[0044] S3. The access point S generates a K-dimensional energy signal vector X = [x1, x2, x3, x4] T , where, x k is the information bit sent by the access point S to the terminal sensor R on the nth antenna and the kth subcarrier, and the information transmitted on the kth subcarrier is the same; define the energy beamforming matrix W = [w1, w2, w3, w4] of the access point S and the time slot allocation factor τ0, where wk = [w1[k], w2[k], w3[k]] T is the beamforming vector of S corresponding to the k-th subcarrier, w n [k] is the beamforming factor of the k-th subcarrier on the n-th antenna of access point S;

[0045] S4. The access point S obtains the optimal value of the beamforming matrix W of the underwater acoustic transducer array and the optimal value of the time slot allocation factor τ0 where is the optimal beamforming vector on the k-th subcarrier, w n [k] * is the optimal beamforming factor of the k-th subcarrier on the n-th antenna of access point S, and the specific steps are as follows:

[0046] S4.1: Define the optimization problem of maximizing the system's harvested energy as the following objective function P1:

[0047] P1:

[0048] subject to C1: r0 ≥ r th

[0049] C2:

[0050] C3: 0 ≤ τ0 ≤ 1

[0051] where E is the total energy collected by the terminal sensor R, where |·| represents the modulo operation, N(f k ) is the non-white Gaussian noise power spectral density on the k-th subcarrier, where f k is the frequency of the k-th subcarrier, r0 is the achievable information rate, where η is the energy conversion efficiency of the energy receiver, r th is the transmission rate threshold, P S is the maximum transmit power of S;

[0052] S4.2. Solve the optimization problem of maximizing the system's harvested energy through the convex optimization toolbox CVX, and the process is as follows:

[0053] S4.2.1. Initialize the beamforming matrix W = [w1, w2, w3, w4] of the underwater acoustic transducer array of access point S and the time slot allocation factor τ0 to obtain the initial values and the feasible points (W (0) , ) and (W (1) , ), where denotes the energy beam transmitted by the access point S in the 0th iteration, denotes the energy beam transmitted by the access point S in the 1st iteration, is the energy transmission time allocated to the access point S in the 0th iteration, is the energy transmission time allocated to the access point S in the 1st iteration. Let Set the convergence tolerance ε and the initial value of the iteration variable n = 1;

[0054] S4.2.2. Use the feasible point W (n) and Use the convex optimization toolbox CVX to solve the optimization problem described in step 4.1 to obtain the optimal solution at the nth iteration

[0055] S4.2.3. Use and Use the convex optimization toolbox CVX to solve the optimization problem described in step 4.1 to obtain the optimal solution at the nth iteration

[0056] S4.3. According to the objective function P1 in step 4.1, calculate the total energy collected by the WPCN underwater acoustic communication system at the nth and (n - 1)th iterations, which are respectively respectively denote the energy beams transmitted by the access point S at the (n - 1)th and nth iterations, is the energy transmission time allocated to the access point S in the 0th iteration, is the energy transmission time allocated to the access point S in the 1st iteration;

[0057] S4.4. Judge whether holds. If holds, stop the iteration and output the optimal solution of the optimization problem Otherwise, let n = n + 1, and return to step 4.2.2;

[0058] S5. The access point S uses the optimal value W of the underwater acoustic transducer array beamforming matrix W * , to perform beamforming on the transmitted energy signal vector X of the access point S to obtain the transmitted signal In the first stage τ0 * T time, send x0 to the terminal sensor R. The terminal sensor R performs energy harvesting and uses the harvested energy for information transmission in the second stage (1 - τ0 * )T time;

[0059] Figure 4The simulation diagram showing the change of the energy collected by the system of the embodiment with the transmission power. The legend from top to bottom represents the comparison results of the WPCN optimization method and the WIPT optimization method proposed by the present invention. It can be seen from the figure that as the transmission power gradually increases, the energy collected by the system gradually increases. The optimization method proposed by the present invention achieves better results than the WIPT optimization method in most cases, indicating that the method proposed by the present invention is feasible.

[0060] Embodiment 2

[0061] This embodiment discloses a resource allocation method for a WPCN underwater acoustic communication system. A WPCN underwater acoustic communication system resource allocation method, the WPCN underwater acoustic communication system includes an access point S equipped with 4 transmitting transducers and 1 receiving transducer and a terminal sensor R equipped with 1 transmitting transducer and 1 receiving transducer. The transmitting transducer is also called an antenna, and the transmission time is T. In the first stage of τ0T time, the access point S transmits energy to the terminal sensor R, and the terminal sensor R performs energy harvesting, where τ0 is the time slot allocation factor for allocating the time ratio of energy transmission and information transmission; in the second stage of (1 - τ0)T time, the terminal sensor R uses the energy harvested in the first stage to transmit information to the access point S, and the signals transmitted in both stages will be modulated by the Orthogonal Frequency Division Multiplexing (OFDM) technology, and the obtained OFDM symbol block is transmitted via 6 subcarriers. The resource allocation method includes the following steps:

[0062] S1. The access point S broadcasts an instruction to the terminal sensor R, requiring the terminal sensor R to send a training signal to the access point S. The access point S estimates the channel state information between the access point S and the terminal sensor R according to the received training symbol sequence. The channel vector between the access point S and the terminal sensor R is G k =[G1[k], G2[k], G3[k], G4[k]] T , where [·] T represents the transpose operation on a matrix or vector, and G n [k] is the channel state information between the nth antenna of S and the terminal sensor R on the kth subcarrier, where n = 1, 2, 3, 4, and k = 1, 2, 3, 4, 5, 6;

[0063] S2. The terminal sensor R broadcasts an instruction to the access point S, requiring the access point S to send a training symbol sequence to the terminal sensor R. The terminal sensor R performs channel estimation according to the received training symbol sequence to obtain the channel state information H = [H[1], H[2], H[3], H[4], H[5], H[6]] between the terminal sensor R and the access point ST , where \(H[k]\) is the channel state information between the terminal sensor \(R\) and the access point \(S\) on the \(k\)-th subcarrier;

[0064] S3. The access point \(S\) generates a \(K\)-dimensional energy signal vector \(X = [x_1,x_2,x_3,x_4,x_5,x_6]\) T , where \(x\) k is the information bit sent by the access point \(S\) to the terminal sensor \(R\) on the \(n\)-th antenna and the \(k\)-th subcarrier, and the information sent on the \(k\)-th subcarrier is the same; define the energy beamforming matrix \(W = [w_1,w_2,w_3,w_4,w_5,w_6]\) of the access point \(S\) and the time slot allocation factor \(\tau_0\), where \(w\) k \(= [w_1[k],w_2[k],w_3[k],w_4[k]]\) T is the beamforming vector corresponding to the \(k\)-th subcarrier of the access point \(S\), and \(w\) n [k]\) is the beamforming factor of the \(k\)-th subcarrier on the \(n\)-th antenna of the access point \(S\);

[0065] S4. The access point \(S\) obtains the optimal value of the beamforming matrix \(W\) of the underwater acoustic transducer array and the optimal value of the time slot allocation factor \(\tau_0\) where is the optimal beamforming vector on the \(k\)-th subcarrier, and \(w\) n [k]\) * is the optimal beamforming factor of the \(k\)-th subcarrier on the \(n\)-th antenna of \(S\), and the specific steps are as follows:

[0066] S4.1. Define the optimization problem of maximizing the system collected energy as the following objective function \(P1\):

[0067] P1:

[0068] subject to \(C1: r_0\geq r\) th

[0069] C2:

[0070] C3: \(0\leq\tau_0\leq1\)

[0071] where \(E\) is the total energy collected by the terminal sensor \(R\), where \(|\cdot|\) represents the modulus operation, and \(N(f\) k ) is the non-white Gaussian noise power spectral density on the \(k\)-th subcarrier, where \(f\) k is the frequency of the \(k\)-th subcarrier, and \(r_0\) is the achievable information rate, where

[0072]

[0073] where η is the energy conversion efficiency of the energy receiver, and r th is the transmission rate threshold, and P S is the maximum transmission power of the access point S;

[0074] S4.2. Solve the optimization problem of maximizing the energy collected by the system through the convex optimization toolbox CVX. The process is as follows:

[0075] S4.2.1. Initialize the beamforming matrix W = [w1, w2, w3, w4, w5, w6] of the underwater acoustic transducer array of the access point S and the time slot allocation factor τ0 to obtain the initial values and the feasible points (W (0) , ) and (W (1) , ), where represents the energy beam transmitted by the access point S in the 0th iteration, represents the energy beam transmitted by the access point S in the 1st iteration, is the energy transmission time allocated to the access point S in the 0th iteration, is the energy transmission time allocated to the access point S in the 1st iteration. Let Set the convergence tolerance ε and the initial value of the iteration variable n = 1;

[0076] S4.2.2. Use the feasible points W (n) and to solve the optimization problem described in step 4.1 using the convex optimization toolbox CVX to obtain the optimal solution at the nth iteration

[0077] S4.2.3. Use and to solve the optimization problem described in step 4.1 using the convex optimization toolbox CVX to obtain the optimal solution at the nth iteration

[0078] S4.3. According to the objective function P1 in step S4.1, calculate the total energy collected by the WPCN underwater acoustic communication system in the nth and (n - 1)th iterations, which are respectively respectively represent the energy beams transmitted by the access point S in the (n - 1)th and nth iterations, is the energy transmission time allocated to the access point S in the 0th iteration, is the energy transmission time allocated to the access point S in the 1st iteration;

[0079] S4.4. Judge whether holds. If holds, stop the iteration and output the optimal solution of the optimization problem Otherwise, let n = n + 1, and return to step S4.2.2;

[0080] S5. The access point S uses the optimal value W of the beamforming matrix W of the underwater acoustic transducer array * , to perform beamforming on the transmitted energy signal vector X of the access point S to obtain the transmitted signal In the first stage τ0 * T, x0 is sent to the terminal sensor R. The terminal sensor R performs energy harvesting and uses the harvested energy for information transmission in the second stage (1 - τ0 * )T;

[0081] Figure 5 It is a simulation diagram showing that the energy collected by the system of the embodiment changes with the transmission power. The legends from top to bottom respectively represent the comparison results of the WPCN optimization method and the WIPT optimization method proposed by the present invention. It can be seen from the figure that as the transmission power gradually increases, the energy collected by the system gradually increases. The optimization method proposed by the present invention has better effects than the WIPT optimization method, indicating that the method proposed by the present invention is feasible.

[0082] The above embodiments are the preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. A resource allocation method for a WPCN underwater acoustic communication system. The WPCN underwater acoustic communication system includes an access point S equipped with N transmitting transducers and 1 receiving transducer, and a terminal sensor R equipped with 1 transmitting transducer and 1 receiving transducer. The transmitting transducer is also called an antenna. The transmission time is T. In the first stage of τ0T time, the access point S transmits energy to the terminal sensor R, and the terminal sensor R performs energy harvesting, where τ0 is the time slot allocation factor used to allocate the time ratio of energy transmission and information transmission. In the second stage of (1 - τ0)T time, the terminal sensor R uses the energy harvested in the first stage to transmit information to the access point S. Moreover, the signals transmitted in both stages will be modulated by the orthogonal frequency division multiplexing OFDM technology, and the obtained orthogonal frequency division multiplexing OFDM symbol block is transmitted via K subcarriers. It is characterized in that, The resource allocation method includes the following steps: S1. The access point S broadcasts an instruction to the terminal sensor R, requesting the terminal sensor R to send a training symbol sequence to the access point S. The access point S estimates the channel state information between the access point S and the terminal sensor R based on the received training symbol sequence. The channel vector between the access point S and the terminal sensor R is G k =[G1[k], G2[k], …, G n [k]…, G N [k]] T , where [·] T represents the transpose operation on a matrix or vector. n = 1, 2, …, N, k = 1, 2, …, K, and G n [k] is the channel state information between the nth antenna of the access point S and the terminal sensor R on the kth subcarrier; S2. The terminal sensor R broadcasts an instruction to the access point S, requesting the access point S to send a training symbol sequence to the terminal sensor R. The terminal sensor R performs channel estimation based on the received training symbol sequence to obtain the channel state information H = [H[1], H[2], …, H[K]] between the terminal sensor R and the access point S T , where H[k] is the channel state information between the terminal sensor R and the access point S on the k-th subcarrier; S3. The access point S generates a K-dimensional energy signal vector X = [x1, x2, …, x k , …, x K T , where xk is the information bit sent from the access point S to the terminal sensor R on the k-th subcarrier on the n-th antenna. The information sent on the k-th subcarrier is the same. Define the energy beamforming matrix W of the access point S as W = [w1, w2, …, w k …, w K , where w k = [w1[k], w2[k], …, w n [k] …, w N [k]] T is the beamforming vector of the access point S corresponding to the k-th subcarrier, and w n [k] is the beamforming factor of the access point S on the n-th antenna for the k-th subcarrier;​ S4. Define the optimization problem of maximizing the harvested energy in the WPCN underwater acoustic communication system, and solve for the optimal values of the access point S beamforming matrix W and the time slot allocation factor τ0 through convex optimization and the optimal value of the time slot allocation factor τ0 wherein is the optimal beamforming vector on the k-th subcarrier, and w n [k] * is the optimal beamforming factor of the k-th subcarrier on the n-th antenna of the access point S; The process of step S4 is as follows: S4.

1. Define the optimization problem of maximizing the system's harvested energy as the following objective function P1: where \(E\) is the total energy collected by the terminal sensor \(R\), and \(E\) is also the maximum energy collected by the WPCN underwater acoustic communication system. where \(|\cdot|\) represents the modulo operation, and \(N(f\) k ) is the power spectral density of non-white Gaussian noise on the \(k\)-th subcarrier, where \(f\) k is the frequency of the \(k\)-th subcarrier. \(r_0\) is the achievable information rate, \(\eta\) is the energy conversion efficiency of the energy receiver, \(r\) th is the transmission rate threshold, \(P\) S is the maximum transmit power of the access point \(S\), and the value of \(N(f\) k ) is related to the frequency \(f\) of the \(k\)-th subcarrier. k ​ S4.

2. Solve the optimization problem of maximizing the system's harvested energy through the convex optimization toolbox CVX. The process is as follows: S4.2.

1. Initialize the beamforming matrix W = [w1, w2, …, w k , …, w K and the time slot allocation factor τ0 of the underwater acoustic transducer array at access point S to obtain the initial values as well as the feasible points and where represents the energy beam transmitted by access point S in the 0th iteration, represents the energy beam transmitted by access point S in the 1st iteration, is the energy transmission time allocated to access point S in the 0th iteration, is the energy transmission time allocated to access point S in the 1st iteration. Let Set the convergence tolerance ε and the initial value of the iteration variable n = 1; S4.2.

2. Utilize the feasible point W (n) and Use the convex optimization toolbox CVX to solve the optimization problem in step S4.1 to obtain the optimal solution at the nth iteration S4.2.

3. Use and the convex optimization toolbox CVX to solve the optimization problem in step S4.1, and obtain the optimal solution at the nth iteration S4.

3. Calculate the total energy collected by the WPCN underwater acoustic communication system in the nth and (n - 1)th iterations according to the objective function P1 in step S4.1, which are respectively respectively represent the optimal energy beams transmitted by the access point S in the (n - 1)th and nth iterations, are respectively the optimal energy transmission times allocated to the access point S in the (n - 1)th and nth iterations; S4.

4. Judgment Whether it holds. If holds, stop the iteration and output the optimal solution of the optimization problem Otherwise, let n = n + 1, and return to step S4.2.2; S5. The access point S uses the optimal value W of the beamforming matrix W of the underwater acoustic transducer array to perform beamforming on the transmitted energy signal vector X of the access point S to obtain a transmitted signal * , and sends the signal to the terminal sensor R within the first stage τ0 T; the terminal sensor R performs energy harvesting and uses the harvested energy for information transmission within the second stage (1 - τ0 * )T. * ​ 2. The resource allocation method of a WPCN underwater acoustic communication system according to claim 1, characterized in that, In the step S4.1, N(f k ) is different from ordinary Gaussian white noise.

3. A resource allocation method for a WPCN underwater acoustic communication system according to claim 1, characterized in that In step S4.1, the WPCN underwater acoustic communication system uses environmental noise and interference as energy sources for energy harvesting.

4. A resource allocation method for a WPCN underwater acoustic communication system according to claim 1, characterized in that, In step S4.1, the larger the value of E, the higher the energy harvested by the WPCN underwater acoustic communication system and the better the solution.

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