A method for joint power and relay allocation based on underwater wireless optical communication

Abstract

The application discloses a relay and power joint distribution method based on underwater wireless optical communication and belongs to the technical field of wireless optical communication. The system average error rate performance expression derived is used as a target function, and the Hungarian algorithm is used to distribute relay nodes for source nodes; based on the obtained source node and relay node pairing combination, the particle swarm algorithm is used to optimize the power distribution of the source node and the relay node under the constraint condition of total power, with the minimum system average error rate as the target; based on the updated power distribution result of the source node and the relay node, the system average error rate is calculated; if the absolute value of the difference between the new system average error rate and the original system average error rate is greater than a set threshold value, the Hungarian algorithm is used again to distribute relay nodes for source nodes, and the particle swarm algorithm is used to optimize the power distribution, until the absolute value of the difference between the current system average error rate and the last system average error rate is less than the set threshold value. The relay and power joint distribution method based on underwater wireless optical communication provided by the application avoids the defects of large operation amount and high complexity of the exhaustive search method, better realizes the relay distribution and power distribution problems in the weak ocean turbulence environment with lower complexity, and improves the performance of the multi-user multi-relay underwater wireless optical communication system.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of underwater wireless optical communication (UWOC) and relates to a relay distribution and power distribution problem of a wireless optical communication system in a weak marine turbulent environment. BACKGROUND

[0002] The ocean contains a large amount of data and information about life, climate and energy resources. In the field of ocean exploration, underwater Internet of Things (a global network composed of intelligent interconnected underwater objects) has become a promising technology.

[0003] As a medium, the underwater wireless optical communication channel has the advantages of low delay, high-speed transmission and high security. However, due to the adverse effects of absorption and scattering, the UWOC system has the problems of serious signal loss and limited transmission range. In order to overcome these shortcomings, the system introduces relay-assisted communication. At present, there have been some researches on the performance of relay-assisted UWOC system, and the feasibility of relay assistance has been verified.

[0004] The multi-user multi-relay scheme may be more in line with the actual application of information transmission of multiple source nodes in underwater Internet of Things. In underwater Internet of Things, multiple source nodes need to select appropriate relay nodes and allocate transmission power, so as to effectively complete the information transmission of multiple users, therefore, it is necessary to conduct in-depth research on the allocation of relay nodes and power resources. For underwater wireless communication systems, the existing researches are mostly focused on the resource allocation problem of underwater acoustic waves. In the acoustic wave system, there are power allocation methods and adaptive relay selection and power allocation methods aimed at reducing the near-far effect, while there are few researches on the resource allocation problem of underwater optical communication. Because the channel models of underwater acoustic communication and underwater optical communication are completely different, the relay selection and power allocation methods designed for underwater acoustic networks cannot be directly applied to underwater wireless optical networks.

[0005] At present, there is no report on the joint allocation method of relay and power based on the system average bit error rate performance of underwater wireless optical channel. In the underwater wireless optical communication network considering absorption, scattering and marine turbulence, the research on the joint relay distribution and power distribution based on the system average bit error rate is very important to improve the performance of UWOC system, and has certain guiding significance for the actual deployment of relay UWOC system. SUMMARY

[0006] The application aims to improve the performance of multi-user multi-relay UWOC, and proposes a method for joint allocation of relay and power based on underwater wireless optical communication system, which has good performance in reducing the system average bit error rate.

[0007] Technical solution: A relay and power joint allocation method based on underwater wireless optical communication, comprising the following steps:

[0008] Step one, establish a multi-user multi-relay network system average bit error rate analysis model in a lognormal distribution weak turbulence environment;

[0009] Step two, according to the average bit error rate matrix formed by all source nodes and all relay nodes, the optimal allocation of relay nodes for source nodes is obtained by using the Hungarian algorithm, and the allocation matrix is obtained.

[0010] Step three, according to the allocation matrix, the power allocation of all source nodes and relay nodes is determined by using particle swarm algorithm to minimize the system average bit error rate, and the updated average bit error rate matrix is calculated according to the optimized power allocation of source nodes and relay nodes.

[0011] Step four, repeat steps two to three until the absolute value of the difference between the current system average bit error rate and the last system average bit error rate is less than a sufficiently small threshold ε.

[0012] The step one, establishing a multi-user multi-relay network system average bit error rate analysis model in a lognormal distribution weak turbulence environment is realized by the following steps.

[0013] 1.1 Considering the influence of underwater optical turbulence, scattering and absorption of water, a channel model considering the comprehensive influence of turbulence and path loss is given:

[0014] H=α 2 l

[0015] In the formula, α represents the amplitude attenuation caused by ocean turbulence, and l represents the path loss caused by absorption and scattering. The optimization method is carried out in weak ocean turbulence environment, so α can be modeled as a lognormal distribution, and its probability density function is:

[0016]

[0017] In the formula, α = exp(X), X is a random variable conforming to Gaussian distribution with mean μ X and variance .

[0018] The path loss caused by absorption and scattering can be expressed as:

[0019]

[0020] In the formula, A r , η t , η rrespectively, are the receiving aperture area and the optical efficiency of the transmitter and receiver. θ0and θ are the beam divergence angle of the transmitter and the tilt angle between the transmitter and the receiver, respectively. c(λ) is the attenuation coefficient, which is determined by the optical wavelength used and the turbidity of the seawater;

[0021] 1.2 Signal transmission model of the multi-user multi-relay underwater wireless optical communication system is established:

[0022] The system is a multi-user multi-relay network environment with M source nodes, M relay nodes and one destination node.

[0023] The first stage of transmission (first hop), each signal source selects a relay to assist in transmitting information to the receiving node. For wireless optical systems, the instantaneous optical power is proportional to the current, so in s i The selected relay r j receives the signal, which can be represented as:

[0024] y ij，R = ρP i，1 H ij，1 x i + n ij，1

[0025] In the above formula, x i is the signal transmitted from the source s i , P i，1 is the transmission power at the source s i , ρ represents the responsivity of the photodetector, H ij，1 and n ij，1 are the channel loss and noise between the signal source s i and its selected relay r j .

[0026] The second stage of transmission (second hop), each relay receives information from the corresponding source and then decodes the received information. After the selected relay decodes the received information, it re-encodes and forwards it to the destination node. The final signal at the destination node can be represented as:

[0027] y ij，D = ρP i，2 H ij，2 x i ′ + n ij，2

[0028] In the above formula, x i ′ is the re-encoded information, P i，2 is the transmission power at the relay r j , ρ represents the responsivity of the photodetector, H ij，2 and n ij，2 are the selected relay r jChannel loss and noise between the destination node.

[0029] 1.3 The expression of the system average BER is achieved by the following steps:

[0030] According to OOK modulation, the BER of the first hop and the second hop can be expressed as:

[0031]

[0032]

[0033] In the above formula, σ ij，1 and σ ij，2 are the standard deviations of the noise n ij，1 and n ij，2 , respectively. Q(·) represents the Q function,

[0034] Considering that the destination node will only receive correct information when both hops correctly detect information or both hops incorrectly detect information, the end-to-end BER of any source node to the destination node in the relay-assisted UWOC system can be expressed as:

[0035] BER ij = BER ij，1 + BER ij，2 - 2 BER ij，1 BER ij，2

[0036] Since BER ij，1 BER ij，2 is very small compared to BER ij，1 + BER ij，2 , the above formula can be simplified to obtain:

[0037] BER ij ≈ BER ij，1 + BER ij，2

[0038] When considering the weak turbulence effect of the lognormal distribution, the average BER calculation formula of the underwater wireless optical communication system is as follows:

[0039]

[0040] The above integral formula is complex and difficult to solve, and numerical calculation is performed on it by Gauss-Hermite quadrature formula:

[0041]

[0042] Similarly,

[0043]

[0044] Further, there are:

[0045]

[0046] In the above formula, m is the order of approximation, w q is the m-order approximation weight (Q = 1, 2, 3…, m), x q is the zero point of the m-order Hermite polynomial.

[0047] Therefore, the system average bit error rate can be represented as:

[0048]

[0049] In the above formula, P T is the total transmission power of all sources and relays. ξ ij is a binary variable, taking the value of 0 or 1, representing the selection relationship between the source and the relay.

[0050] In step two, the Hungarian algorithm is used to perform the optimal allocation of the relay nodes for the source nodes, and the specific steps of obtaining the allocation matrix are as follows:

[0051] 2.1 Initialize the parameter value: set the total transmission power as P T , and set the alternating iteration algorithm precision as a threshold value ε that is small enough; set the initial value of the transmission power of all sources and relays as P s (0) = (P T / 2M) M×1 , P R (0) = (P T / 2M) M×1 ; set the initial value of the iteration number as n = 0, and set the initial value of the system average bit error rate as Set the coordinates of the source nodes, relay nodes and destination nodes in the form of the rectangular coordinate system (x, y, z), where x and y are the coordinate axes in the horizontal plane, and z axis represents the depth; calculate the distances of all candidate transmission paths according to the position coordinates, then calculate the noise variance and path loss of all transmission paths according to the distances, and finally obtain the initial average bit error rate matrix

[0052] 2.2 Based on the system average bit error rate, use the Hungarian algorithm to perform the pairing selection of the source nodes and the relay nodes, so as to obtain the allocation matrix that minimizes the system average bit error rate, thereby obtaining the optimal allocation pair ξ (n) = {ξ ij} of the source nodes and the relay nodes.

[0053] In the third step, the optimal solution of power allocation is solved by using particle swarm intelligence optimization algorithm, and the specific steps are as follows:

[0054] 3.1 initializing parameters of the particle swarm algorithm, i.e. maximum iteration number, population size, particle dimension, inertia weight, learning factor, speed boundary and position boundary;

[0055] 3.2 initializing the speed and position of the particle according to the population size, particle dimension, speed boundary and position boundary, and judging whether the constraint condition of power is satisfied; if the constraint condition is not satisfied, the speed and position of the particle which does not satisfy the constraint condition are reinitialized;

[0056] 3.3 calculating fitness: the fitness of each particle is calculated according to the system average bit error rate formula, the best position found so far by each particle (individual historical best position) is initialized, and the particle with the minimum fitness is found based on the best position, and the position of the particle is the best position of all particles so far (group historical best position);

[0057] 3.4 updating the speed and position in iteration: in the iteration process, the speed of each particle in the particle swarm is updated and boundary processing is performed;

[0058] 3.5 judging the power constraint condition, if the power constraint condition is satisfied, the fitness of each particle position of the particle swarm is calculated according to the system average bit error rate formula, and the fitness is compared with the fitness of the last time, and the individual historical best fitness is updated: if the power constraint condition is not satisfied, the individual historical best fitness is the individual historical best fitness of the last time; the fitness of all particles is compared, and the group historical best fitness and the group historical best position are updated;

[0059] 3.6 repeating steps 3.4 and 3.5 until the maximum iteration number is reached, and the group historical best position is the selected power allocation result of the source and relay matching pair, and the group historical best fitness is the corresponding system average bit error rate.

[0060] In the fourth step, the alternating optimization step is as follows:

[0061] Steps two to three are repeated until the absolute value of the difference between the current system average bit error rate and the last system average bit error rate is less than a sufficiently small threshold value ε.

[0062] Advantages: compared with the prior art, the technical scheme of the present application has the following advantages:

[0063] The application provides a relay and power joint distribution method based on underwater wireless optical communication, avoids the defects of large operation amount and high complexity of an exhaustive search method, and better realizes relay distribution and power distribution in a weak ocean turbulence environment with low complexity, and improves the performance of a multi-user multi-relay UWOC system. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 is a relay and power joint distribution method based on underwater wireless communication according to the application, and a realization block diagram of the method is shown in the figure;

[0065] Figure 2 is a system average bit error rate diagram of the application under three different conditions of the alternating optimization distribution method (AO), the distance-based distribution method (DRA) and the equal power distribution method (EPA).

[0066] is a system average bit error rate diagram of the AO, DRA and EPA methods with total power change.

[0067] Figure 3 DETAILED DESCRIPTION

[0068] The application will be further described below in combination with specific simulation, and it should be understood that the simulation is only used for describing the application and does not limit the scope of the application, and after reading the application, various equivalent modifications of the application made by those skilled in the art fall within the scope defined by the claims attached to the application.

[0069] The application is a relay and power joint distribution method based on underwater wireless optical communication, and the specific implementation method and application example are described as follows:

[0070] Step 1, an analysis model of a multi-user multi-relay network system average bit error rate in a lognormal distribution weak turbulence environment is established by the following steps:

[0071] 1.1 Considering the influence of underwater optical turbulence and the influence of water scattering and absorption, a channel model considering the comprehensive influence of turbulence and path loss is given:

[0072] H = a 2 l (1)

[0073] In the formula, a represents the amplitude attenuation caused by ocean turbulence, and l represents the path loss caused by absorption and scattering. The optimization method is performed in a weak ocean turbulence environment, and therefore a can be modeled as a lognormal distribution, and the probability density function is:

[0074]

[0075] In the formula, a = exp (X), and X is a mean value of mu​X , variance of a Gaussian distribution. α is normalized so that can be expressed as The scintillation index of a plane wave is given by

[0076]

[0077] where λ is the wavelength and k0= 2π / λ is the wavenumber. Φ n (κ) is a model for the spatial power spectrum of refractive-index turbulence in seawater. It depends on the temperature and salinity profiles.

[0078] The path loss due to absorption and scattering can be expressed as

[0079]

[0080] In the above equation, A r , η t , η r are the aperture areas of the receiver and the transmitter and receiver optical efficiencies, respectively. θ0and θ are the beam divergence angle of the transmitter and the tilt angle between the transmitter and receiver, respectively. c(λ) is the attenuation coefficient, which depends on the optical wavelength used and the general turbidity of the seawater

[0081] 1.2 Establish the signal transmission model of the multi-user multi-relay underwater wireless optical communication system:

[0082] The system described is a multi-user multi-relay network environment with M source nodes, M relay nodes and one destination node.

[0083] In the first stage of transmission (first hop), each signal source selects a relay to assist in transmitting information to the receiving node. For a wireless optical system, the instantaneous optical power is proportional to the current, and in s i The signal received at the selected relay r j can be expressed as:

[0084] y ij，R = ρP l，1 H ij，1 x i + n ij，1 (5)

[0085] In the above equation, x i is the signal transmitted from the source s i , P i，1 is the transmission power at the source s i , ρ represents the responsivity of the photodetector, H ij，1 and n ij，1 are the signal source si The channel loss and noise between the selected relay r j and the destination node.

[0086] The second stage (second hop) of transmission, each relay receives information from the corresponding source and then decodes the received information, after the selected relay decodes the received information, re-encodes and forwards to the destination node, the final destination node signal can be represented as:

[0087] y ij，D = pP i，2 H ij，2 x i ' + n ij，2 (6)

[0088] In the above formula, x i ' is the re-encoded information, P i，2 is the transmit power at the relay r j , p represents the responsivity of the photodetector, H ij，2 and n ij，2 are the channel loss and noise between the selected relay r j and the destination node.

[0089] The noise of the underwater wireless optical communication system mainly consists of background noise, thermal noise and dark current noise, which are independent of each other and can be modeled as additive white Gaussian noise (AWGN). Therefore, the total noise can also be modeled as AWGN with mean 0 and variance . The background noise variance is where q is the electronic charge, p is the responsivity of the photodetector, P BG is the background noise power, and B is the electronic bandwidth. η and h are the quantum efficiency and Planck constant of the photodetector, respectively. f = c / λ is the frequency of the light source expressed in light speed c, and λ is the wavelength of the light source. The thermal noise variance W where K is the Boltzmann constant, T e is the equivalent temperature, F is the system noise factor, and R L is the load resistance. The dark current noise variance is where I DC is the dark current. The total noise variance is

[0090] 1.3 Expression of the average bit error rate of the system is realized by the following steps:

[0091] According to OOK modulation, the bit error rates of the first hop and the second hop can be represented as:

[0092]

[0093] In the above formula, σij，1 and σ ij，2 are the standard deviations of the noise n ij，1 and n ij，2 respectively. Q(·) denotes the Gaussian Q-function,

[0094] Considering that the destination node will receive correct information only when both hops correctly detect the information or both hops incorrectly detect the information, the end-to-end bit error rate (BER) of the relay-assisted UWOC system from any source node to the destination node can be expressed as:

[0095] BER ij = BER ij，1 + BER ij，2 - 2 BER ij，1 BER ij，2 (8)

[0096] Since BER ij，1 BER ij，2 is very small compared to BER ij，1 + BER ij，2 , the above equation can be simplified to obtain:

[0097] BER ij ≈ BER ij，1 + BER ij，2 (9)

[0098] When the weak turbulence effect on the lognormal distribution is considered, the average BER of the underwater wireless optical communication system can be calculated as follows:

[0099]

[0100] The above integral equation is complex and difficult to solve, and it is numerically calculated by using the Gauss-Hermite quadrature formula:

[0101]

[0102] Similarly,

[0103]

[0104] Using equations (11) and (12), equation (10) can be transformed into:

[0105]

[0106] In the above equation, m is the order of approximation, w q is the m-order approximation weight (Q = 1, 2, 3…, m), and x q is the zero point of the m-order Hermite polynomial.

[0107] The optimization problem can be expressed as:

[0108]

[0109]

[0110] In the above equation, P T is the total transmission power of all sources and relays. ξ ij is a binary variable, which takes value 0 or 1, indicating the selection relationship between source and relay.

[0111] This is a non-deterministic polynomial problem, which involves a mixed integer programming problem. This method uses an alternating optimization to solve the problem. The problem is divided into two sub-problems, one is to determine the selection of source and relay pairing, and the other is to optimize the power allocation of all transmission nodes including sources and relays. For the first sub-problem, the Hungarian allocation algorithm is used to solve the matching problem. The second sub-problem is to optimize the power allocation after determining the matching relationship between source and relay. Particle swarm optimization is used to find the optimal solution of power allocation through the cooperation and information sharing between individuals in the group.

[0112] Step 2-4, the flow chart of solving the mixed integer programming problem by using the alternating optimization method is shown in Figure 1 , and the specific generation steps are as follows:

[0113] Step 2, according to the average error rate matrix formed by all source nodes and all relay nodes, the Hungarian algorithm is used to allocate the best relay nodes for the source nodes to minimize the system average error rate, and the allocation matrix is obtained.

[0114] 2.1 Initialize the parameter value: set the total transmission power as P T , and set the alternating iteration algorithm precision as a small enough threshold value ε. The initial value of the transmission power of all sources and relays is P s (0) = (P T / 2M) M×1 , P R (0) = (P T / 2M) M×1 . The initial value of the iteration number is n = 0, and the initial value of the system average error rate is Set the coordinates of the source nodes, relay nodes and destination nodes in the form of the rectangular coordinate system (x, y, z), where x and y are the coordinate axes in the horizontal plane, and z represents the depth. Calculate the distance of all candidate transmission paths according to the position coordinates, then calculate the noise variance, flicker index and path loss of all transmission paths according to the distance, and finally obtain the initial average error rate matrix according to the end-to-end error rate formula

[0115] 2.2 Based on the system average BER, the source nodes and relay nodes are paired using the Hungarian algorithm to obtain the allocation matrix that minimizes the system average BER, thereby obtaining the optimal allocation pair of source nodes and relay nodes ξ (n) = {ξ ij}.

[0116] Next, the power allocation optimization problem when the source and relay matching is completed is solved. In the case of relay allocation determination, the power optimization problem can be rewritten as:

[0117]

[0118]

[0119] In the above formula, represents the average BER of the transmission link of the signal sent by the source node s i to the destination node with the assistance of the selected relay node r j .

[0120] Step 3, the optimal solution of power allocation is solved by using the particle swarm intelligent optimization algorithm. The specific steps are as follows:

[0121] 3.1 Initialize the parameters of the particle swarm algorithm, that is, the maximum number of iterations ger, the population size N, the particle dimension dim (the value is 2M), the inertia weight w, the learning factors c1 and c2, the speed boundary and the position boundary;

[0122] 3.2 Initialize the speed and position of the particle swarm particles according to the population size, the number of variables, the speed boundary and the position boundary, and judge whether the power constraint condition formula (17) is satisfied; if the constraint condition is not satisfied, re-initialize the speed and position of the particles that do not satisfy the constraint condition;

[0123] 3.3 Calculate the fitness: traverse the entire particle swarm, calculate the fitness of each particle according to the system average BER formula (16), and initialize the best position found so far for each particle (individual historical best position) p best On this basis, the particle with the minimum fitness is found, and its position is the best position found so far for all particles (group historical best position) g best ;

[0124] 3.4 Iterative update of speed and position: in the l+1th iteration, the speed v kl of the kth particle is updated to v k(l+1) , and the boundary processing is performed on v k(l+1) , and then the formula loc k(l+1) = lockl +v kl Update each particle position of the population and do boundary processing for the position; velocity update is realized through velocity update formula, which is: v k(l+1) = w x v kl + c1 x rand x (loc kl -p kl ) + c2 x rand x (loc kl -p gl ), wherein rand represents random number with value of 0-1, p kl is the known best position of the individual, and p gl is the known best position of the population;

[0125] 3.5Judge the power constraint condition formula (17), if it is satisfied, calculate the fitness of each particle of the particle swarm according to the system average error rate formula (16), and compare it with the fitness of the last time, update the individual historical best fitness; if it is not satisfied, the individual historical best fitness is the individual historical best fitness of the last time; compare the fitness of all particles, update the group historical best fitness and the group historical best position;

[0126] 3.6Repeat steps 3.4 and 3.5 until the maximum number of iterations is reached, and the final group historical best position is the selected source and relay matching pair power allocation result, and the group historical best fitness is the corresponding system average error rate;

[0127] Step 4, repeat steps 2 to 3 until the difference between the current system average error rate and the last system average error rate is less than a sufficiently small threshold value ε.

[0128] Simulation of relay and power joint allocation method based on underwater wireless optical communication:

[0129] Next, we will provide some simulation results to verify the performance of the above-mentioned joint algorithm (AO) of relay selection and power allocation. The important parameters used in the simulation are shown in Table 1. It is shown that the above-mentioned relay and power joint allocation method based on the derived underwater wireless optical communication system average error rate is feasible.

[0130] Table 1 Main simulation parameter settings

[0131] Parameter Value Parameter Value c(λ) 0.1514 [theta0] 10° η 0.8 θ 0° q 1.602*10 -19 C]]> P BD ]]> 6.34 x 10 -11 W]] h 6.626 x 10 -34 J·S]]> B 5 MHz λ 532 nm K 1.380622 x 10 -23 J / K c 2.26 x 10 8 m / s [CAT e ]]> 290K t ]]> ​ 0.9 [R L ]] 100 Ω r ]]> ​ 0.9 F 4 A r ]]> 0.01m 2 ]] I DC ]] 1.226*10 -9 A]]>

[0132] For a multi-user multi-relay network, the simulation scene is designed as follows, in which the source and relay are deployed in two cubes with a side length of 8m. The centroid of the source node is at (4, 4, 20), the centroid of the relay node is at (x, 4, 20), and the destination node is at (40, 4, 20). In the figure, G M，MThis represents a UWOC network environment with M sources and M relays.

[0133] We simulated and compared the performance of the AO algorithm with distance-based relay selection and power allocation (DRA) and equal power allocation (EPA) methods. DRA selects relays with the goal of minimizing the average transmission distance from the source node to the destination node, and then uses a particle swarm optimization algorithm for power allocation. EPA performs equal power allocation between the source and relays, and allocates relays with the goal of minimizing the system's average bit error rate.

[0134] Set the centroid coordinate x of the relay node to 26. For example... Figure 2 As shown, the three UWOC network environments are: total power 32dB m G 2，2 G with a total power of 32dBm 3，3 And a total power of 34dBm G 4，4 Under these conditions, the proposed AO method outperforms the simulation results of DRA and EPA in terms of system average bit error rate. This demonstrates that the UWOC system using the AO method can achieve a lower system average bit error rate.

[0135] To further compare the performance of the proposed AO with DRA and EPA, the following was demonstrated in G 4，4 and G 5，5 The system average bit error rate of these three methods varies with total power in two cases. Observation. Figure 3 It can be seen that the proposed AO algorithm exhibits better system bit error rate performance compared to DRA and EPA. Since relay allocation and power allocation may influence each other, the proposed AO algorithm iteratively solves these tasks alternately, resulting in better simulation results. The proposed joint relay and power allocation algorithm can significantly improve the bit error rate performance of UWOC systems, which is of great significance for the practical application of UWOC systems.

[0136] The above description is merely an embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for joint power and relay allocation based on underwater wireless optical communication, characterized in that, The method comprises the following steps: 1) establishing an analysis model of the average bit error rate of a multi-user multi-relay network system in a lognormal weak turbulence environment; 2) obtaining an average bit error rate matrix according to all the links formed by all source nodes and all relay nodes, and using the Hungarian algorithm to perform optimal allocation of the relay nodes for the source nodes to minimize the system average bit error rate, to obtain an allocation matrix, specifically comprising: 2a) initialization of parameter values: total transmission power is set as P T , the precision of the alternate iteration algorithm is set as a small enough threshold value ε; the initial value of the transmission power of all sources and relays P s (0) = (P T / (2M)) Mx1 , M represents the number of source nodes and also the number of relay nodes, P R (0) = (P T / (2M)) M×1 ; the initial value of the iteration number n = 0, and the initial value of the system average bit error rate The coordinates of the source nodes, relay nodes and destination nodes are set in the form of a rectangular coordinate system (x, y, z), in which x and y are the coordinate axes in the horizontal plane, and z represents the depth; the distances of all candidate transmission paths are calculated according to the position coordinates, the noise variance and path loss of all transmission paths are calculated according to the distances, and finally the initial average bit error rate matrix is obtained according to the end-to-end bit error rate formula 2b) Pairing selection of source node and relay node using Hungarian algorithm to get the allocation matrix that makes the system average bit error rate minimum, thus getting the best allocation pair of source node and relay node ξ (n) = {ξ ij}; 3) using the particle swarm algorithm to determine the power allocation of all the source nodes and the selected relay nodes according to the allocation matrix, to minimize the system average bit error rate, and calculating the updated average bit error rate matrix according to the optimized power allocation of the source nodes and the relay nodes; 4) repeating steps 2) to 3) until the difference between the current system average bit error rate and the last system average bit error rate is less than a sufficiently small threshold ε.

2. The method of claim 1, wherein, In step 1), the analysis model of the average bit error rate of a multi-user multi-relay network system in a lognormal weak turbulence environment is established by the following steps: 1a) considering the influence of underwater optical turbulence and the scattering and absorption of water, a channel model considering the comprehensive influence of turbulence and path loss is given: H＝α 2 l In the formula, α represents the amplitude attenuation caused by ocean turbulence, and l represents the path loss caused by absorption and scattering; the optimization method is performed in a weak ocean turbulence environment, and α can be modeled as a lognormal distribution, and the probability density function is: In the above equation, a = exp(X), X is a random variable conforming to a Gaussian distribution with mean μ X and variance . The path loss caused by absorption and scattering can be expressed as: In the above formula, A r , η t , η r are the receiving end aperture area and the optical efficiencies of the transmitting end and the receiving end, respectively. θ0 and θ respectively represent the beam divergence angle of the transmitter and the inclination angle between the transmitter and the receiver; c(λ) is the attenuation coefficient, which is determined by the optical wavelength used and the turbidity of seawater; 1b) a signal transmission model of a multi-user multi-relay underwater wireless optical communication system is established: The system is a multi-user multi-relay network environment with M source nodes, M relay nodes and one destination node. In the first phase, i.e. the first hop, each signal source selects a relay to assist in delivering information to the receiving node. For a wireless optical system, the instantaneous optical power is proportional to the current, and at the source s i The selected relay r j The signal received at the source s may be represented as: y ij,R = pP i,,1 H ij,1 x i +n ij,1 In the above equation, x i is the signal transmitted from source s i , P i，1 is the transmit power at source s i , p represents the responsivity of the photodetector, H ij，1 and n ij，1 are the channel loss and noise between the signal source s i and its selected relay r j , respectively. In the second stage of transmission, i.e., the second hop, the relay receives information from the corresponding source and then decodes the received information, and after the selected relay decodes the received information, it re-encodes and forwards it to the destination node, and the signal of the destination node can be expressed as: y ij，D = pP i，2 H ij，2 x i +n ij，2 In the above formula, x′ i It is re-encoded information, P i，2 It is a relay r j The emission power at the location, ρ represents the responsivity of the photodetector, H ij，2 and n ij，2 These are the selected relays r j Channel loss and noise between the destination node and the target node; 1c) the expression of the system average bit error rate is achieved by the following steps: According to OOK modulation, the bit error rates of the first hop and the second hop can be expressed as: In the above equation, σ ij，1 and σ ij，2 are the standard deviations of the noise n ij,1 and n ij，2 respectively: Q(·) denotes the Q-function, Considering that the destination node will only receive correct information when both hops correctly detect information or both hops incorrectly detect information, the end-to-end bit error rate of any source node to the destination node in the relay-assisted UWOC system can be expressed as: BER ij = BER ij，1 + BER ij，2 - 2 BER ij，1 BER ij，2 Due to BER ij，1 BER ij，2 Compared to BER ij，1 + BER ij，2 Very small, simplifying the above equation, we get: BER ij ≈BER ij，1 +BER ij，2 When considering the influence of lognormal weak turbulence, the average bit error rate calculation formula of the underwater wireless optical communication system is as follows: The numerical calculation is performed by the Gauss-Hermite quadrature formula: Similarly, Further, there is: In the above equation, m is the order of approximation, w q is the mth order approximation weight, q = 1, 2, 3,..., m, x q is the zero point of the mth order Hermite polynomial. The system average bit error rate can be expressed as: In the above equation, ξ ij is a binary variable that takes the value 0 or 1, indicating the selection relationship between the source and the relay.

3. The method of claim 1, wherein, In step 3), the specific steps of solving the optimal solution of power allocation by using the particle swarm intelligent optimization algorithm are as follows: 3a) initializing the parameters of the particle swarm algorithm, i.e., the maximum number of iterations, the population size, the particle dimension, the inertia weight, the learning factor, the speed boundary, and the position boundary; 3b) initializing the velocity and position of the particle according to the population size, particle dimension, velocity boundary and position boundary, judging whether the constraint condition of power is satisfied or not; if the constraint condition is not satisfied, re-initializing the velocity and position of the particle which does not satisfy the constraint condition; 3c) calculating the fitness: traversing the whole particle swarm, calculating the fitness of each particle according to the system average error rate formula, initializing the best position found by each particle so far, i.e. individual historical best position, on this basis, finding the particle with the minimum fitness, whose position is the best position found by all particles so far, i.e. swarm historical best position; 3d) iteratively updating the velocity and position: in the iteration process, updating the velocity and position of each particle in the particle swarm and performing boundary processing on the velocity and position; 3e) judging the power constraint condition, if the constraint condition is satisfied, calculating the fitness of each particle in the particle swarm according to the system average error rate formula, comparing the fitness with the fitness in the last time, updating the individual historical best fitness; if the constraint condition is not satisfied, the individual historical best fitness is the individual historical best fitness in the last time; comparing the fitness of all particles, updating the swarm historical best fitness and swarm historical best position; 3f) repeating step 3d) to step 3e) until the set maximum iteration number is reached, the final swarm historical best position is the power allocation result of the selected source and relay matching pair, and the swarm historical best fitness is the corresponding system average error rate.

4. The method of claim 1, wherein, In the step 4), the alternating optimization steps are as follows: repeating step 2) to step 3) until the absolute value of the difference between the current system average error rate and the system average error rate in the last time is less than a sufficiently small threshold value ε.

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