An energy consumption optimization method and system for a serial relay network under an underwater turbulent channel

Abstract

This invention discloses an energy consumption optimization method and system for a serial relay network in an underwater turbulent channel, belonging to the field of underwater wireless optical communication. Based on an underwater weak turbulence model, this invention optimizes the transmission power of each node in the network under certain interruption probability constraints. First, a system model is established, constructing a LOS wireless optical transmission serial relay network under a weak turbulence distribution. Factors causing underwater optical communication attenuation, such as absorption, scattering, and turbulence, are comprehensively considered to model the turbulence channel. Then, the optimization problem is modeled, optimizing the transmission power of each transmitting node under certain interruption probability constraints. To solve the above problem, a fitting-based power allocation algorithm is proposed. A fitting function is obtained by estimating the Q-function, and the power of each transmitting node is optimized by using the distance between the fitting function and the nodes. This invention can achieve low interruption probability with minimal power cost, providing options for power allocation of nodes in underwater serial relay networks.

Description

Technical Field

[0001] This invention relates to an energy consumption optimization technology for underwater turbulent channel serial relay network nodes, belonging to the field of underwater wireless optical communication technology. Background Technology

[0002] Serial relay networks are a common network topology in underwater communication, used to enhance the reliability and coverage of long-distance underwater communications. In turbulent underwater channels, signal transmission is affected by various factors such as turbulence, absorption, and scattering, all of which impact signal transmission and power distribution. Therefore, serial relay networks need to dynamically adjust signal power distribution to optimize the link, minimize the impact of signal attenuation, and improve signal transmission efficiency to ensure high signal transmission quality.

[0003] In 2020, Emma Zedini et al. published a paper titled "Performance Analysis of Dual-Hop Underwater Wireless Optical Communication Systems Over Mixture Exponential-Generalized Gamma Turbulence Channels" in IEEE Transactions on Communications, studying channel modeling of dual-hop UWOC systems and deriving performance metrics such as outage probability, average bit error rate (BER), and ergodic capacity based on the statistical characteristics of SNR, analyzing the end-to-end performance of the system. In 2021, Sai Li et al. published a paper titled "Performance Analysis of UAV-Based Mixed RF-UWOC Transmission Systems" in IEEE Transactions on Communications, studying channel modeling of hybrid RF UWOC systems. They used performance metrics such as outage probability, average BER, and average channel capacity to demonstrate the correlation between system performance and temperature fluctuations, salinity changes, and bubbles, and investigated the determinants of the diversity order of AF and DF relays. In 2023, Imene Romdhane et al. published an article in IEEE Communications Letters entitled “Outage Probability Analysis for UOWC System Over Oceanic Turbulence With Pointing Errors”, in which they proposed a combined UWOC system, including a direct link with maximum ratio combination and an indirect link assisted by an optical smart reflector. They used outage probability analysis and proved that the proposed combined system can improve the connectivity of high-speed UWOC under different underwater channel conditions.

[0004] Existing research largely focuses on channel modeling and system performance analysis of underwater wireless optical links. A few studies have addressed power allocation and resource optimization techniques for underwater wireless optical networks, but no research has yet considered the power allocation problem of serial networks in turbulent channels. This invention proposes a fitting-based power allocation method for serial relay networks in underwater turbulent channel environments. This method optimizes network energy consumption while satisfying a certain interruption probability, balancing network energy consumption and transmission performance. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide an energy consumption optimization method and system for serial relay networks in underwater turbulent channels, which can effectively allocate energy resources under low interruption probability.

[0006] Technical Solution: To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0007] An energy consumption optimization method for a serial relay network in an underwater turbulent channel includes the following steps:

[0008] Step 1: Construct an information representation model for a LOS wireless optical transmission serial relay network based on underwater weak turbulence distribution. The information sent by the source node S reaches the destination node D after being forwarded by n relay nodes; where n≥2.

[0009] Step 2: Taking into account the path loss caused by absorption and scattering in the underwater channel, as well as the weak turbulence attenuation based on the Log-Normal distribution, construct an underwater turbulence channel model;

[0010] Step 3: For serial relay networks, the system outage probability based on the Q-function is derived using the characteristics of turbulent channels;

[0011] Step 4: Set the total system interrupt probability to meet the threshold P out_th The interruption probability of each segment satisfies the threshold Q. th Under these conditions, optimize the transmission power of each node in the network to build an underwater communication system that balances network energy consumption and transmission performance;

[0012] Step 5: Use the exponential function P x (d x )=a1+a2exp(a3d x By fitting the Q function and minimizing the error between the fitted result and the actual data, the optimal fitting parameters a1, a2, a3 are found to analyze the transmission distance d. x Transmit power P x And the relationship between the probability of interruption;

[0013] Step 6: Select the Q-function threshold set according to the given interruption probability threshold to obtain the fitting function set. Determine the power optimization allocation of each transmitting node with the minimum total energy consumption by using the distance between nodes and the fitting function.

[0014] Preferably, in step 2, the hybrid channel loss of the wireless optical link in underwater turbulence is expressed as:

[0015] H = α 2 L

[0016] Where α represents the attenuation amplitude caused by turbulence, and the attenuation amplitude α of underwater turbulence is modeled as a Log-Normal distribution. L represents the path loss caused by the absorption and scattering of light by the water body, and L represents the transmission loss of the system. and geometric loss Multiplication, transmission loss and geometric loss All of these are functions related to the distance between the transmitting and receiving nodes.

[0017] Preferably, in step 3, the system interruption probability is expressed as:

[0018]

[0019] Where, γ th P represents the signal-to-noise ratio threshold, and ρ represents the responsivity of the photodetector. i,i+1 L represents the transmit power of the i-th relay node. i,i+1 μ represents the path loss between the i-th relay node and the next node. xi,i+1 The attenuation magnitude α caused by turbulence between the i-th relay node and the next node is represented by the following expression: i,i+1 The logarithmic mean, α i,i+1 The logarithmic variance, i = 1, ..., n; P 0,1 L represents the transmit power of the source node. 0,1 This represents the path loss between the source node and the first relay node. The attenuation magnitude α caused by turbulence between the source node and the first relay node is represented. 0,1 The logarithmic mean, The attenuation magnitude α caused by turbulence between the source node and the first relay node is represented. 0,1 The logarithmic variance.

[0020] Preferably, in step 4, the optimization problem modeled is expressed as:

[0021]

[0022]

[0023]

[0024] P i,i+1 >0i=0,1,2,…,n

[0025] Preferably, in step 5, the transmission power P of each transmitting node under set parameters is used. x Using the sample data for fitting, the exponential function P is chosen. x (d x)=a1+a2exp(a3d x The Q-function is fitted using the least squares method; the fitted sample data is obtained by the following formula.

[0026]

[0027] in, P represents channel noise. o ′ ut This represents the preset Q-function threshold. The logarithmic variance of the attenuation magnitude α caused by turbulence is represented. Based on the flicker coefficient Decision, expressed as

[0028] Preferably, in step 6, the interruption probability threshold P is used. out_th and Q th Multiple sets of Q-function thresholds that meet the conditions are set to obtain multiple sets of fitting parameters a = [a1, a2, a3]; based on the distance between nodes and the fitting function P... x (d x )=a1+a2exp(a3d x Multiple sets of power allocation results were obtained, and the optimal node power allocation result for the serial relay network with the minimum total energy consumption in the underwater turbulent channel was determined.

[0029] An energy consumption optimization system for a serial relay network in an underwater turbulent channel includes:

[0030] The system modeling module is used to construct an information representation model of a LOS wireless optical transmission serial relay network based on underwater weak turbulence distribution. The information sent by the source node S reaches the destination node D after being forwarded by n relay nodes; where n≥2.

[0031] The turbulence channel modeling module is used to comprehensively consider the path loss caused by absorption and scattering in the underwater channel, as well as the weak turbulence attenuation based on the Log-Normal distribution, to construct an underwater turbulence channel model.

[0032] The system interruption analysis module is used to derive the system interruption probability based on the Q-function representation for serial relay networks by utilizing the characteristics of turbulent channels.

[0033] The optimization problem modeling module is used to set the total system outage probability to satisfy a threshold P. out_th The interruption probability of each segment satisfies the threshold Q. th Under these conditions, optimize the transmission power of each node in the network to build an underwater communication system that balances network energy consumption and transmission performance;

[0034] The Q-function fitting module is used to fit the exponential function P. x (d x )=a1+a2exp(a3d x By fitting the Q function and minimizing the error between the fitted result and the actual data, the optimal fitting parameters a1, a2, a3 are found to analyze the transmission distance d. x Transmit power P x And the relationship between the probability of interruption;

[0035] Additionally, a power allocation optimization module is used to select a set of Q-function thresholds based on a given interruption probability threshold, obtain a set of fitting functions, and determine the power allocation of each transmitting node with the minimum total energy consumption by using the distance between nodes and the fitting functions.

[0036] A computer system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded onto the processor, it implements the steps of the energy consumption optimization method for a serial relay network in an underwater turbulent channel.

[0037] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the energy consumption optimization method for a serial relay network in an underwater turbulent channel.

[0038] Beneficial Effects: Compared with the prior art, the beneficial effects of this invention are as follows: In the Log-Normal distributed underwater weak turbulence channel serial relay network, the method of this invention comprehensively considers the impact of turbulence, absorption and scattering on underwater wireless optical communication, models the hybrid channel loss of the link, and analyzes the system performance more completely and accurately; it proposes a relay node energy consumption optimization algorithm based on fitting, which approximates the balance relationship between transmission distance, transmission power and interruption probability through fitting function, solves the constructed non-convex optimization model piecewise, reduces the complexity of the solution algorithm, and takes into account both network energy consumption and transmission performance. Attached Figure Description

[0039] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram of a serial relay network model;

[0041] Figure 3 A comparison of fitting performance under different Q-function thresholds;

[0042] Figure 4This is a comparison chart of node deployment distance and total power for the fitting-based power allocation scheme, the equal power allocation scheme, and the power allocation scheme based on the square of the distance in scenario 1.

[0043] Figure 5 This is a comparison chart of node deployment distance and total power for the fitting-based power allocation scheme, the equal power allocation scheme, and the power allocation scheme based on the square of the distance in scenario 2. Detailed Implementation

[0044] The invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0045] The energy consumption optimization method for a serial relay network in an underwater turbulent channel disclosed in this embodiment of the invention includes the following steps: Figure 1 As shown, the specific implementation steps are as follows:

[0046] Step 1: System modeling, such as Figure 2 As shown, a LOS wireless optical transmission serial relay network is constructed based on an underwater weak turbulence distribution. The source node S needs to transmit information to the destination node D through n (n≥2) relay nodes R. All nodes use a DF forwarding strategy to transmit information in the same direction. After receiving the signal, the relay nodes first decode and then re-encode it, finally transmitting the re-encoded signal to the destination node. In the serial relay network system model, the source node S changes the light intensity as the transmitted information and sends it to relay node R1. R1 receives the information by detecting the change in light intensity. Noise exists during this process, so the received information can be represented as...

[0047]

[0048] In the formula, ρ represents the responsivity of the photodetector, P 0,1 H is the optical emission power of the source node. 0,1 This is the hybrid channel loss from the source node to the relay node R1, where x0 represents the information transmitted by the source node S, and n 0,1 It is the noise from source node S to relay node R1;

[0049] Assuming the system only considers LOS links, with unidirectional information transmission between nodes, and relay node R... i The received information can be represented as

[0050]

[0051] In the formula, P i,i+1 H is the transmit power of the i-th node. i,i+1 It is the hybrid channel loss from the i-th node to the (i+1)-th node, x i It is node R i The message sent, ni,i+1 It is the noise from the i-th node to the (i+1)-th node;

[0052] Information is sent from source node S to destination node D via n relay nodes R. Destination node D receives information y. D It can be represented as

[0053] y D =ρP n,n+1 H n,n+1 x n +n n,n+1 (3)

[0054] In the formula, P n,n+1 It is the optical transmission power of the last relay node, H n,n+1 It is the hybrid channel loss from the last relay node to the destination node, n n,n+1 It is the noise from the last relay node to the destination node.

[0055] Step 2: Turbulent Channel Modeling. Considering a weakly turbulent underwater channel, its attenuation amplitude is modeled as a Log-Normal distribution. This is used to calculate the hybrid channel loss of the underwater turbulent wireless optical link. This hybrid channel loss includes attenuation caused by underwater turbulence and path loss caused by light absorption and scattering by the water. The underwater turbulent channel is a signal propagation environment in underwater transmission. This environment contains turbulence with various motion states. The occurrence of these turbulent motions is random, leading to signal attenuation. The hybrid channel loss of the underwater wireless optical link can be expressed as...

[0056] H = α 2 L (4)

[0057] In the formula, L represents the path loss caused by light absorption and scattering by the water body, and α represents the attenuation amplitude caused by turbulence. This implementation mainly considers the underwater weak turbulence channel, and its attenuation amplitude is modeled as a Log-Normal distribution, i.e. The probability density function of α can be expressed as:

[0058]

[0059] In the formula, μ x and These are the expectation and variance of ln(α), respectively. To ensure that the average transmission power is not affected, α is usually standardized, that is, let E(α) = 1 / 2. 2 ) = 1, from which we can deduce

[0060] The path loss of a LOS link is the transmission loss between two nodes. and geometric loss Multiplication can be represented as

[0061]

[0062] Underwater transmission loss It is determined by both absorption loss and scattering loss, as shown below.

[0063]

[0064] In the formula, d sd Let be the Euclidean distance between the two nodes, and c(λ) represent the attenuation coefficient of seawater, which is related to the wavelength λ of light. Its value is a linear combination of the absorption coefficient a(λ) and the scattering coefficient b(λ), and can be expressed as:

[0065] c(λ)=a(λ)+b(λ) (8)

[0066] Geometric loss in marine environments It can be represented as

[0067]

[0068] In the formula, A r η is the aperture area of ​​the optical receiver. t and η r θ0 represents the optical efficiency of the optical transmitter and the optical receiver, respectively; θ0 is the beam divergence angle of the optical transmitter; and θ is the tilt angle between the optical transmitter and the optical receiver.

[0069] Step 3: System Outage Analysis. In a serial relay network, the model performance depends on the worst-performing link among all links. Therefore, the signal-to-noise ratio (SNR) of a serial relay network can be expressed as the minimum SNR among all links.

[0070] γ SD =min(γ) i,i+1 )i=0,1,..,n (10)

[0071] In the formula, i = 0 represents the source node, i = 1, ..., n represents the relay node, and γ i,i+1 The signal-to-noise ratio from the i-th node to the (i+1)-th node is expressed as follows:

[0072]

[0073] In the formula, H is the noise variance from node i to node i+1. i,i+1 Substitute the value of γ into formula (4), i,i+1 It can be simplified to

[0074]

[0075] When the link signal-to-noise ratio γ SDγ is not greater than the signal-to-noise ratio threshold th When a system interrupt occurs, the interrupt probability can be expressed as:

[0076]

[0077] In the formula, γ i,i+1 Substitute the value into formula (12)

[0078]

[0079] Based on the statistical properties of the log-normal distribution, It can be considered to conform to the mean. variance is The log-normal random variable, therefore P out It can be deduced as

[0080]

[0081] Where Q(x) is the Q-function, which is the right-tail function of the standard normal distribution, expressed as:

[0082]

[0083] Step 4: Optimize problem modeling. Considering the actual application scenario, ensure that the total system interruption probability and the interruption probability of each segment meet the threshold P. out_th and Q th Under these conditions, optimize the transmission power of each node in the network to build an underwater communication system that balances network energy consumption and transmission performance;

[0084] This problem can be optimized as follows:

[0085]

[0086] In the formula, P i,i+91 It is the transmission power from node i to node i+1, where node 0 is the source node, node n+1 is the destination node, and the rest are relay nodes.

[0087] Step 5: Fitting the Q-function. By analyzing the system outage probability, the Q-function characterizes the balance between transmission distance, transmission power, and outage probability, and is the breakthrough point for solving the optimization problem. Therefore, a power allocation method based on fitting is proposed. An exponential function P is used. x (d x )=a1+a2exp(a3d x By fitting the Q-function and minimizing the error between the fitted result and the actual data, the optimal fitting parameters are found to further analyze the transmission distance d. x Transmit power P x The relationship between [the probability of interruption] and [the probability of interruption].

[0088] Specifically, firstly, under specific parameter conditions, a preset Q-function threshold is established, and the transmit power P′ of each node is calculated. x The calculation formula is as follows, based on the sampled data:

[0089]

[0090] in, P′ represents channel noise. out This represents the preset Q-function threshold. Based on the flicker coefficient A decision can be expressed as It can be calculated as

[0091]

[0092] Where k0 = 2π / λ represents the wave number, λ is the wavelength, κ is the spatial frequency; ζ represents two types of light waves, ζ = 0 represents spherical waves, ζ = 1 represents plane waves, and Φ(κ) is the spatial power spectrum function of the refractive index fluctuation.

[0093] Then, choose the exponential function P. x (d x )=a1+a2exp(a3d x ), where a = [a1, a2, a3] are parameters to be determined. By selecting different parameters to be determined, the error between the fitting result and the actual data is recorded, and expressed using the least squares method as:

[0094]

[0095] In the formula, Here, N is the residual function, N is the number of samples, and the subscripts i and i-th indicate the i-th sample data. When the set of values ​​with the smallest residual is determined, that is, the optimal fitting result value that is closest to the actual data, the corresponding optimal parameter combination can be obtained.

[0096] Step 6: Optimize node power allocation. Select a suitable set of Q-function thresholds based on the given interruption probability threshold to obtain the fitting parameter set a = [a1, a2, a3], thereby obtaining the fitting function set. Based on the distance between nodes and the fitting function, determine the optimized node power allocation of the serial relay network under the underwater turbulent channel with the minimum total energy consumption.

[0097] The beneficial effects of the method of the present invention can be further illustrated by the following simulation.

[0098] I. Simulation Conditions

[0099] The specific parameter settings for the simulation are shown in Table 1. An equal-power allocation scheme and a power allocation scheme proportional to the square of the distance were selected as comparative algorithms to analyze the optimization effect of the proposed fitting-based power allocation scheme. The results of the fitting-based power allocation scheme are represented by blue dashed lines, the results of the equal-power allocation scheme are represented by red solid lines, and the results of the power allocation scheme proportional to the square of the distance are represented by green solid lines.

[0100] Table 1 Simulation-related parameters

[0101]

[0102] II. Simulation Content and Results

[0103] Simulation 1: Comparison of fitting performance. Four Q-function thresholds are set, and the sample data and fitting curves are represented by blue dots and red curves, respectively.

[0104] Simulation results: such as Figure 3 As shown, the threshold values ​​for four Q functions range from 1 to 10. -1 1-10 -2 1-10 -3 1-10 -4 Four fitting curves were generated from bottom to top. It can be seen that the sample data all lie on the fitting curves, indicating a good fitting effect. Table 2 records the values ​​of the fitting parameters for the four cases.

[0105] Table 2 Fitting parameters under different Q-function thresholds

[0106]

[0107] Simulation 2, performance comparison after optimization, assuming a system outage probability of 10. -1 With 4 relay nodes, two different deployment scenarios are set up:

[0108] Scenario 1: Deploy the transmission distance between adjacent nodes according to [d, d-1, d+1, d, d-1];

[0109] Scenario 2: The transmission distance between adjacent nodes is deployed according to [d+4,d-6,d,d+4,d-6].

[0110] Where d is a variable parameter used to analyze the impact of different transmission distances on the optimization performance of the proposed power allocation scheme. The proposed fitting-based power allocation scheme is compared with the equal power allocation scheme and the power allocation scheme based on the square of the distance.

[0111] Simulation results: The simulation results for scenario 1 and scenario 2 are as follows: Figure 4 and Figure 5 As shown, the total power of all three schemes increases with deployment distance. The proposed fitting-based scheme requires the least total power, verifying that the proposed fitting-based power allocation scheme can effectively reduce energy consumption under low interruption probability.

[0112] Based on the same inventive concept, this invention discloses an energy consumption optimization system for a serial relay network in an underwater turbulent channel, comprising: a system modeling module for constructing an information representation model of a LOS wireless optical transmission serial relay network based on a weak underwater turbulent distribution, wherein information sent by source node S reaches destination node D after being forwarded by n relay nodes; a turbulent channel modeling module for constructing an underwater turbulent channel model by comprehensively considering path loss caused by absorption and scattering in the underwater channel, as well as weak turbulent attenuation based on a Log-Normal distribution; a system outage analysis module for deriving the system outage probability based on a Q-function representation for the serial relay network using the characteristics of the turbulent channel; an optimization problem modeling module for optimizing the transmission power of each node in the network under the condition that the total system outage probability meets a threshold and the outage probability of each segment meets a threshold, so as to construct an underwater communication system that balances network energy consumption and transmission performance; and a Q-function fitting module for using an exponential function P... x (d x )=a1+a2exp(a3d x The system fits the Q-function and finds the optimal fitting parameters by minimizing the error between the fitting result and the actual data to analyze the relationship between transmission distance, transmission power, and interruption probability. Additionally, a power allocation optimization module selects a set of Q-function thresholds based on a given interruption probability threshold to obtain a set of fitting functions. Using the distance between nodes and the fitting functions, the system determines the optimal power allocation for each transmitting node with the minimum total energy consumption.

[0113] Based on the same inventive concept, an embodiment of the present invention discloses a computer system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded onto the processor, it implements the steps of the energy consumption optimization method for a serial relay network in an underwater turbulent channel.

[0114] Based on the same inventive concept, this invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the energy consumption optimization method for a serial relay network in an underwater turbulent channel.

Claims

1. A method for energy consumption optimization of a serial relay network under an underwater turbulent channel, characterized in that, Includes the following steps: Step 1: Construct an information representation model for a LOS wireless optical transmission serial relay network based on underwater weak turbulence distribution. The information sent by the source node S reaches the destination node D after being forwarded by n relay nodes; where n≥2. Step 2: Taking into account the path loss caused by absorption and scattering in the underwater channel, as well as the weak turbulence attenuation based on the Log-Normal distribution, construct an underwater turbulence channel model; Step 3: For serial relay networks, using the characteristics of turbulent channels, derive the system outage probability based on the Q-function, where the Q-function is the right-tail function of a standard normal distribution; Step 4: set the system interruption probability to meet the threshold and the segment interruption probability meets the threshold Under the condition that the network energy consumption and transmission performance are taken into account, the transmission power of each node of the network is optimized to construct an underwater communication system. Step 5: Using the exponential function Fitting the Q function by minimizing the error between the fitted result and the actual data to find the best fitting parameters To analyze the relationship between the transmission distance , the transmission power , and the outage probability; Step 6: Select the Q-function threshold set according to the given interruption probability threshold to obtain the fitting function set. Determine the power optimization allocation of each transmitting node with the minimum total energy consumption by using the distance between nodes and the fitting function.

2. The energy consumption optimization method for underwater turbulent channel lower serial relay network according to claim 1, characterized in that, In step 2, the hybrid channel loss of the wireless optical link in underwater turbulence is expressed as: ； in, This indicates the attenuation magnitude caused by turbulence, specifically the attenuation magnitude of underwater turbulence. The model is a Log-Normal distribution, where L is the path loss caused by light absorption and scattering by the water body, and L represents the transmission loss of the system. and geometric loss Multiplication, transmission loss and geometric loss All of these are functions related to the distance between the transmitting and receiving nodes.

3. The energy consumption optimization method for underwater turbulent channel lower serial relay network according to claim 1, characterized in that, In step 3, the system interruption probability is expressed as: ； in, Indicates the signal-to-noise ratio threshold. Indicates the responsivity of the photodetector; This represents the transmit power of the i-th relay node. This represents the path loss between the i-th relay node and the next node. This represents the attenuation magnitude caused by turbulence between the i-th relay node and the next node. The logarithmic mean, express The logarithmic variance It is the noise variance from the i-th node to the (i+1)-th node. ; This indicates the transmit power of the source node. This represents the path loss between the source node and the first relay node. This represents the attenuation magnitude caused by turbulence between the source node and the first relay node. The logarithmic mean, This represents the attenuation magnitude caused by turbulence between the source node and the first relay node. The logarithmic variance.

4. The energy consumption optimization method for underwater turbulent channel lower serial relay network according to claim 1, characterized in that, In step 4, the optimization problem of modeling is expressed as: ； ； ； ； in, Indicates the signal-to-noise ratio threshold. Indicates the responsivity of the photodetector; This represents the transmit power of the i-th relay node. This represents the path loss between the i-th relay node and the next node. This represents the attenuation magnitude caused by turbulence between the i-th relay node and the next node. The logarithmic mean, express The logarithmic variance It is the noise variance from the i-th node to the (i+1)-th node. ; This indicates the transmit power of the source node. This represents the path loss between the source node and the first relay node. This represents the attenuation magnitude caused by turbulence between the source node and the first relay node. The logarithmic mean, This represents the attenuation magnitude caused by turbulence between the source node and the first relay node. The logarithmic variance.

5. The energy consumption optimization method for a serial relay network in an underwater turbulent channel according to claim 1, characterized in that, In step 5, the transmission power of each transmitting node under set parameters is used. The exponential function was chosen as the sample data for fitting. The Q-function is fitted using the least squares method; the fitted sample data is obtained from the following formula. ； in, Indicates the signal-to-noise ratio threshold. Indicates channel noise. Indicates the responsivity of the photodetector. This represents the preset Q-function threshold. Indicates path loss. Indicates the attenuation magnitude caused by turbulence The logarithmic variance Based on the flicker coefficient Decision, expressed as .

6. The energy consumption optimization method for a serial relay network in an underwater turbulent channel according to claim 1, characterized in that, In step 6, based on the interruption probability threshold and Multiple sets of Q-function thresholds that meet the conditions are set to obtain multiple sets of fitting parameters. Based on the distance between nodes and the fitted function Multiple sets of power allocation results were obtained, and the optimal node power allocation result for the serial relay network with the minimum total energy consumption in the underwater turbulent channel was determined.

7. An energy consumption optimization system for a serial relay network in an underwater turbulent channel, characterized in that, include: The system modeling module is used to construct an information representation model of a LOS wireless optical transmission serial relay network based on underwater weak turbulence distribution. The information sent by the source node S reaches the destination node D after being forwarded by n relay nodes; where n≥2. The turbulence channel modeling module is used to comprehensively consider the path loss caused by absorption and scattering in the underwater channel, as well as the weak turbulence attenuation based on the Log-Normal distribution, to construct an underwater turbulence channel model. The system interruption analysis module is used to derive the system interruption probability based on the Q-function representation for serial relay networks by utilizing the characteristics of turbulent channels. The Q-function is the right-tail function of the standard normal distribution. The optimization problem modeling module is used to set a threshold for the system outage probability. The interruption probability of each segment meets the threshold. Under these conditions, optimize the transmission power of each node in the network to build an underwater communication system that balances network energy consumption and transmission performance; The Q-function fitting module is used to apply exponential functions. By fitting the Q-function and minimizing the error between the fitted result and the actual data, the optimal fitting parameters are found. To analyze transmission distance Transmission power And the relationship between the probability of interruption; Additionally, a power allocation optimization module is used to select a set of Q-function thresholds based on a given interruption probability threshold, obtain a set of fitting functions, and determine the power allocation of each transmitting node with the minimum total energy consumption by using the distance between nodes and the fitting functions.

8. The energy consumption optimization system for a serial relay network in an underwater turbulent channel according to claim 7, characterized in that, In the optimization problem modeling module, the optimization problem is represented as: ； ； ； ； in, Indicates the signal-to-noise ratio threshold. Indicates the responsivity of the photodetector; This represents the transmit power of the i-th relay node. This represents the path loss between the i-th relay node and the next node. This represents the attenuation magnitude caused by turbulence between the i-th relay node and the next node. The logarithmic mean, express The logarithmic variance It is the noise variance from the i-th node to the (i+1)-th node. ; This indicates the transmit power of the source node. This represents the path loss between the source node and the first relay node. This represents the attenuation magnitude caused by turbulence between the source node and the first relay node. The logarithmic mean, This represents the attenuation magnitude caused by turbulence between the source node and the first relay node. The logarithmic variance.

9. A computer system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is loaded into the processor, it implements the steps of the energy consumption optimization method for a serial relay network in an underwater turbulent channel according to any one of claims 1-6.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the energy consumption optimization method for a serial relay network in an underwater turbulent channel according to any one of claims 1-6.

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