System for 5G local area network and satellite communication in fleet

Abstract

The invention relates to the technical field of radio communication, and discloses a system for 5G local area network and satellite communication in a fleet, and the system comprises a data collection module which is used for collecting communication feature data, geographic positions and control parameters of M ships in the fleet, and representing the communication feature data, geographic positions and control parameters through first feature data; the preprocessing and feature extraction module is used for preprocessing the first feature data to obtain second feature data, extracting coupling features according to the second feature data, and forming third feature data by the coupling features after normalization processing and the second feature data; the communication quality score generation module is used for generating communication quality scores of the M ships by using a first scoring formula according to the third feature data of the M ships; and the control parameter optimization module is used for optimizing the control parameters of the M ships according to the improved starfish optimization algorithm to obtain an optimal control parameter set so as to maximize the communication performance of the fleet. According to the invention, the maximization of the overall communication performance of the fleet is realized.

Description

Technical Field

[0001] The present invention belongs to the field of radio communication technology, and in particular relates to a system for 5G local area network and satellite communication within a fleet. Background Art

[0002] With the continuous development of the shipping industry, efficient radio communication between fleets has become a key technology to improve the efficiency and safety of maritime operations. At present, the bandwidth allocation ratio, transmission power and channel coding rate that affect the quality of fleet radio communication usually rely on manual experience for adjustment. Although it can improve the communication quality to a certain extent, it cannot be adjusted in time according to the actual situation. It lacks flexibility and accuracy and cannot respond to changes in communication needs in time, making the communication quality within the fleet not optimal. For example, the coverage of 5G networks is limited, and the signal attenuation is serious when far away from land. Although satellite communications can maintain signal coverage in distant sea areas, they are limited by bandwidth and delay. Therefore, systems that rely on static and empirical adjustments will lead to unstable communication quality in practical applications, and the communication quality cannot be optimized. Summary of the invention

[0003] The present invention provides a system for 5G local area network and satellite communication within a fleet, which solves the technical problems in related technologies of relying on manual experience to adjust communication parameters and lacking flexibility and real-time performance.

[0004] The present invention provides a system for 5G local area network and satellite communication within a fleet, comprising:

[0005] A data collection module is used to collect communication characteristic data, geographic location and control parameters of M ships in the fleet, and represent them through first characteristic data, wherein the mth data unit of the first characteristic data represents the communication characteristic data, geographic location and control parameters of the mth ship, the communication characteristic data includes: signal strength, delay rate, throughput, bit error rate, network load and packet loss rate, the control parameters include: bandwidth allocation ratio, transmission power and channel coding rate, and M and m are custom parameters;

[0006] A preprocessing and feature extraction module, used for preprocessing the first feature data to obtain the second feature data, extracting the coupling feature according to the second feature data, and combining the normalized coupling feature and the second feature data into the third feature data;

[0007] A communication quality score generating module, configured to generate communication quality scores of the M ships using a first scoring formula according to the third feature data of the M ships;

[0008] The control parameter optimization module is used to optimize the control parameters of M ships according to the improved starfish optimization algorithm to obtain the optimal control parameter set to maximize the communication performance of the fleet.

[0009] Further, the first characteristic data is preprocessed to obtain the second characteristic data, and the specific steps include:

[0010] S201, for missing values ​​of signal strength in the first characteristic data of a ship, fill in the missing values ​​using the signal strength of the ship with the shortest Euclidean distance to the ship;

[0011] S202, for missing values ​​of features other than signal strength in the first feature data of the vessel, use the average value of the feature values ​​other than the missing value to perform filling processing;

[0012] S203, using a maximum-minimum normalization method to normalize the processed first feature data to obtain second feature data.

[0013] Further, the coupling feature is extracted according to the second feature data, and the specific steps include:

[0014] S301, extracting a dynamic communication efficiency feature according to the second feature data, wherein the calculation formula of the dynamic communication efficiency feature is: ,in, represents the dynamic communication efficiency characteristics, T represents the throughput, Indicates the packet loss rate, represents the delay rate, represents the bit error rate, Indicates the network load, Indicates the heading angle of the ship. Indicates the bandwidth allocation ratio. represents the transmission power, represents the channel coding rate;

[0015] S302: extracting a channel stability feature according to the second feature data. The calculation formula of the channel stability feature is: ,in, represents the channel stability characteristic, S represents the signal strength, represents the weight coefficient of packet loss rate on channel stability, represents the cosine value of the ship's heading, represents the weight coefficient of delay rate on channel stability, Indicates the weight coefficient of network load on channel stability;

[0016] S303, extracting a network pressure index feature according to the second feature data, the calculation formula of the network pressure index feature is: ,in, Represents the network stress index characteristics, It represents the correction factor of bit error rate, Indicates the weight coefficient of transmission power on network pressure in high throughput scenarios;

[0017] S304: extracting a channel utilization feature according to the second feature data. The calculation formula of the channel utilization feature is: ,in, represents the channel utilization characteristics, Represents a logarithmic function The weight coefficient of represents the first weight coefficient, Represents the second weight coefficient.

[0018] Further, the communication quality scores of the M ships are generated using a first scoring formula according to the third characteristic data of the M ships. The first scoring formula is: ;

[0019] in, represents the communication quality score of the ship, N represents the number of features in the third feature data, a represents the feature index in the third feature data, represents the weight of the a-th feature, Represents the feature value of the ath feature in the third feature data.

[0020] Further, the weight of each feature in the third feature data in the first scoring formula is calculated according to the XGBoost algorithm. Specifically, the weight is calculated using the split gain method. The specific steps include:

[0021] S401, configure model parameters, which include: tree depth, learning rate, and training rounds;

[0022] S402, using the XGBoost algorithm to construct a decision tree. Specifically, starting from the root node, the third feature data of the M ships is used as the initial data, a split feature is selected according to the split gain formula, the initial data is divided into left and right child nodes, and a subtree is recursively constructed until the depth of the tree is reached;

[0023] S403, for each tree in the XGBoost algorithm, find a node set that uses feature i as a split node, and calculate the split gain of each node to obtain a total split gain of feature i;

[0024] S404, normalize the split gain of each feature to obtain the weight of each feature.

[0025] Furthermore, the control parameters of the M ships are optimized according to the improved starfish optimization algorithm to obtain the optimal control parameters. The specific steps include:

[0026] S501, generating P initial solutions that meet the constraint conditions based on uniform distribution;

[0027] S502, constructing an objective function according to the communication quality scores of the M ships;

[0028] S503, calculating the fitness value of each solution according to the objective function, and taking the solution with the highest fitness value as the current optimal solution;

[0029] S504, simulating the exploration behavior of the starfish, and updating each solution using the first update formula according to the current optimal solution. The first update formula is: ,in, represents the jth solution in the k+1th iteration, represents the jth solution in the kth iteration, k represents the current iteration number, and j represents the index of the solution. and is a random number between 0 and 1. represents the current optimal solution, represents a random angle;

[0030] S505, simulating the regeneration stage of the starfish, using the second update formula to update each solution, the second update formula is: , where K represents the maximum number of iterations;

[0031] S506, when the current number of iterations reaches the maximum number of iterations, the solution with the maximum current fitness value is used as the optimal control parameter set, otherwise return to S503.

[0032] Furthermore, the objective function of the improved starfish optimization algorithm is: ;

[0033] Among them, F represents the fitness value of the solution, b represents the ship index, represents the communication quality score of the b-th ship.

[0034] The beneficial effects of the present invention are as follows: the present invention realizes dynamic improvement of the fleet's communication quality by collecting the communication characteristic data, geographical location and control parameters of each ship in the fleet and combining with the improved starfish optimization algorithm; the method ensures that the fleet achieves stable communication performance and maximizes the overall communication efficiency of the fleet;

[0035] The present invention adopts an improved starfish optimization algorithm to optimize the control parameters of each ship in the fleet by simulating the exploration behavior and regeneration stage of the starfish; the optimization process not only ensures the efficiency of the global search, but also makes fine adjustments in the local area, effectively improving the communication quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a module schematic diagram of a system for 5G local area network and satellite communication within a fleet of the present invention. DETAILED DESCRIPTION

[0037] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0038] like Figure 1 As shown, a system for 5G local area network and satellite communication within a fleet includes:

[0039] The data collection module 101 is used to collect the communication characteristic data, geographical location and control parameters of the M ships in the fleet, and represent them through the first characteristic data, wherein the mth data unit of the first characteristic data represents the communication characteristic data, geographical location and control parameters of the mth ship, the communication characteristic data includes: signal strength, delay rate, throughput, bit error rate, network load and packet loss rate, the control parameters include: bandwidth allocation ratio, transmission power and channel coding rate, and M and m are custom parameters;

[0040] The preprocessing and feature extraction module 102 is used to preprocess the first feature data to obtain the second feature data, extract the coupling feature according to the second feature data, and form the third feature data with the normalized coupling feature and the second feature data;

[0041] A communication quality score generating module 103, configured to generate communication quality scores of the M ships using a first scoring formula according to the third feature data of the M ships;

[0042] The control parameter optimization module 104 is used to optimize the control parameters of the M ships according to the improved starfish optimization algorithm to obtain an optimal control parameter set to maximize the communication performance of the fleet.

[0043] In one embodiment of the present invention, communication characteristic data, geographic location and control parameters are collected through the communication equipment and network monitoring tools of the unmanned ship. The geographic location is represented by two-dimensional coordinates of longitude and latitude. The bandwidth allocation ratio represents the bandwidth ratio allocated to each ship and is represented by real number coding. The sum of the bandwidth allocation ratios of M ships is 1. The transmission power is represented by real number coding. The channel coding rate is represented by real number coding, wherein the unit of signal strength is dBm, the unit of delay rate is ms, the unit of throughput is Mbps, and the unit of transmission power is W.

[0044] In one embodiment of the present invention, the first feature data is preprocessed to obtain the second feature data, and the specific steps include:

[0045] S201, for missing values ​​of signal strength in the first characteristic data of a ship, fill in the missing values ​​using the signal strength of the ship with the shortest Euclidean distance to the ship. Specifically, the Euclidean distance between the ships is calculated according to the geographical locations of the ships to obtain the ship with the shortest distance to the ship.

[0046] S202, for missing values ​​of features other than signal strength in the first feature data of the vessel, use the average value of the feature values ​​other than the missing value to perform filling processing;

[0047] S203, using a maximum-minimum normalization method to normalize the processed first feature data to obtain second feature data.

[0048] In one embodiment of the present invention, the coupling feature is extracted according to the second feature data, and the specific steps include:

[0049] S301, extracting a dynamic communication efficiency feature according to the second feature data, wherein the calculation formula of the dynamic communication efficiency feature is: ,in, represents the dynamic communication efficiency characteristics, T represents the throughput, Indicates the packet loss rate, represents the delay rate, represents the bit error rate, Indicates the network load, Indicates the heading angle of the ship. Indicates the bandwidth allocation ratio. represents the transmission power, represents the channel coding rate, Indicates the amount of effective data transmitted. Indicates communication loss;

[0050] S302: extracting a channel stability feature according to the second feature data. The calculation formula of the channel stability feature is: ,in, represents the channel stability characteristic, S represents the signal strength, represents the weight coefficient of packet loss rate on channel stability, represents the cosine value of the ship's heading, represents the weight coefficient of delay rate on channel stability, Represents the weight coefficient of network load on channel stability, It represents the interaction between transmission power and bandwidth, and is used to express the additional impact of resource utilization;

[0051] S303, extracting a network pressure index feature according to the second feature data, the calculation formula of the network pressure index feature is: ,in, Represents the network stress index characteristics, It represents the correction factor of bit error rate, Indicates the weight coefficient of transmission power on network pressure in high throughput scenarios, represents the nonlinear correction of the bit error rate, Used to simulate the impact of transmission power on network pressure in high-throughput scenarios;

[0052] S304: extracting a channel utilization feature according to the second feature data. The calculation formula of the channel utilization feature is: ,in, represents the channel utilization characteristics, Represents a logarithmic function The weight coefficient of represents the first weight coefficient, Used to indicate the efficiency of channel resources, represents the second weight coefficient, Indicates the compensation effect of channel coding rate on packet loss rate.

[0053] In one embodiment of the present invention, the coupling feature and the second feature data are combined to form the third feature data. Specifically, the third feature data includes: signal strength, delay rate, throughput, bit error rate, network load, packet loss rate, bandwidth allocation ratio, transmission power, channel coding rate, dynamic communication efficiency feature, channel stability feature, network pressure index feature and channel utilization feature.

[0054] In one embodiment of the present invention, the communication quality scores of the M ships are generated using a first scoring formula according to the third characteristic data of the M ships. The first scoring formula is: ;

[0055] in, represents the communication quality score of the ship, N represents the number of features in the third feature data, a represents the feature index in the third feature data, represents the weight of the a-th feature, Represents the feature value of the ath feature in the third feature data.

[0056] In one embodiment of the present invention, the weight of each feature in the third feature data in the first scoring formula is calculated according to the XGBoost algorithm. Specifically, the weight is calculated using the split gain method. The specific steps include:

[0057] S401, configuring model parameters, the model parameters include: tree depth, learning rate, training rounds, preferably, the tree depth is set to 6, the learning rate is set to 0.1, and the training rounds are set to 100 times;

[0058] S402, using the XGBoost algorithm to build a decision tree. Specifically, starting from the root node, the third feature data of the M ships is used as the initial data, the split feature is selected according to the split gain formula, the initial data is divided into left and right child nodes, and the subtree is recursively constructed until the depth of the tree is reached, wherein the split gain formula is: , represents the split gain, and Represent the gradient and of the initial data in the left child node and the right child node respectively, and Represent the sum of the second-order derivatives in the left child node and the right child node respectively, G represents the gradient sum of the current node, and H represents the sum of the second-order derivatives of the current node. Represents the complexity coefficient, which is used to control the complexity of the split gain;

[0059] S403, for each tree in the XGBoost algorithm, find a node set that uses feature i as a split node, and calculate the split gain of each node to obtain a total split gain of feature i;

[0060] S404, normalize the split gain of each feature to obtain the weight of each feature.

[0061] In one embodiment of the present invention, the control parameters of M ships are optimized according to the improved starfish optimization algorithm to obtain the optimal control parameters. The specific steps include:

[0062] S501, generating P initial solutions that meet the constraints based on uniform distribution, specifically, each solution represents a combination of control parameters of M ships, and the constraints include: the sum of bandwidth allocation ratios of the M ships is 1; the transmission power and channel coding rate have their maximum and minimum values ​​according to physical conditions;

[0063] For transmission power and channel coding, P solutions are generated by uniform distribution and are ensured to be within the value range. For bandwidth allocation ratio, P solutions are randomly generated while meeting the constraints.

[0064] S502, constructing an objective function according to the communication quality scores of the M ships;

[0065] S503, calculating the fitness value of each solution according to the objective function, and taking the solution with the highest fitness value as the current optimal solution;

[0066] S504, simulating the exploration behavior of the starfish, and updating each solution using the first update formula according to the current optimal solution. The first update formula is: ,in, represents the jth solution in the k+1th iteration, represents the jth solution in the kth iteration, k represents the current iteration number, and j represents the index of the solution. and is a random number between 0 and 1. represents the current optimal solution, Indicates random angles, simulating the movement pattern of starfish;

[0067] S505, simulating the regeneration stage of the starfish, using the second update formula to update each solution, the second update formula is: , where K represents the maximum number of iterations, preferably, K is 200;

[0068] S506, when the current number of iterations reaches the maximum number of iterations, the solution with the maximum current fitness value is used as the optimal control parameter set, otherwise return to S503.

[0069] In one embodiment of the present invention, the objective function of the improved starfish optimization algorithm is: ;

[0070] Among them, F represents the fitness value of the solution, b represents the ship index, represents the communication quality score of the b-th ship.

[0071] In one embodiment of the present invention, an improved starfish optimization algorithm is used to optimize the control parameters of M ships. During the optimization process, the update amplitude of the solution is controlled by exponential decay. The exploration behavior of the starfish is simulated in the early stage, and the regeneration behavior of the starfish is simulated in the later stage, ensuring efficient global search and fine local optimization, and finding the optimal set of control parameters for the entire fleet.

[0072] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. A system for 5G local area network and satellite communication within a fleet, characterized in that: include: A data collection module is used to collect communication characteristic data, geographic location and control parameters of M ships in the fleet when communicating through the 5G network and satellite, and represent them through first characteristic data, wherein the mth data unit of the first characteristic data represents the communication characteristic data, geographic location and control parameters of the mth ship, the communication characteristic data includes: signal strength, delay rate, throughput, bit error rate, network load and packet loss rate, the control parameters include: bandwidth allocation ratio, transmission power and channel coding rate, and M and m are custom parameters; A preprocessing and feature extraction module, used for preprocessing the first feature data to obtain the second feature data, extracting the coupling feature according to the second feature data, and combining the normalized coupling feature and the second feature data into the third feature data; A communication quality score generating module, configured to generate communication quality scores of the M ships using a first scoring formula according to the third feature data of the M ships; The control parameter optimization module is used to optimize the control parameters of M ships according to the improved starfish optimization algorithm to obtain the optimal control parameter set.

2. According to claim 1, a system for 5G local area network and satellite communication within a fleet, characterized in that: Preprocessing the first characteristic data to obtain the second characteristic data includes: S201, for missing values ​​of signal strength in the first characteristic data of a ship, fill in the missing values ​​using the signal strength of the ship with the shortest Euclidean distance to the ship; S202, for missing values ​​of features other than signal strength in the first feature data of the vessel, use the average value of the feature values ​​other than the missing value to perform filling processing; S203, using a maximum-minimum normalization method to normalize the processed first feature data to obtain second feature data.

3. The system of 5G local area network and satellite communication within a fleet according to claim 1, characterized in that: Extracting coupling features according to the second feature data, specifically comprising: S301, extracting a dynamic communication efficiency feature according to the second feature data, wherein the calculation formula of the dynamic communication efficiency feature is: ,in, represents the dynamic communication efficiency characteristics, T represents the throughput, Indicates the packet loss rate, represents the delay rate, represents the bit error rate, Indicates the network load, Indicates the heading angle of the ship. Indicates the bandwidth allocation ratio. represents the transmission power, represents the channel coding rate; S302: extracting a channel stability feature according to the second feature data. The calculation formula of the channel stability feature is: ,in, represents the channel stability characteristic, S represents the signal strength, represents the weight coefficient of packet loss rate on channel stability, represents the cosine value of the ship's heading, represents the weight coefficient of delay rate on channel stability, Indicates the weight coefficient of network load on channel stability; S303, extracting a network pressure index feature according to the second feature data, the calculation formula of the network pressure index feature is: ,in, Represents the network stress index characteristics, Represents the correction factor of the bit error rate, Indicates the weight coefficient of transmission power on network pressure in high throughput scenarios; S304: extracting a channel utilization feature according to the second feature data. The calculation formula of the channel utilization feature is: ,in, represents the channel utilization characteristics, Represents a logarithmic function The weight coefficient of represents the first weight coefficient, Represents the second weight coefficient.

4. The system for 5G local area network and satellite communication within a fleet according to claim 1, characterized in that: The communication quality scores of the M ships are generated using the first scoring formula according to the third characteristic data of the M ships. The first scoring formula is: ； in, represents the communication quality score of the ship, N represents the number of features in the third feature data, a represents the feature index in the third feature data, represents the weight of the a-th feature, Represents the feature value of the ath feature in the third feature data.

5. A fleet internal 5G local area network and satellite communication system according to claim 4, characterized in that: The weight of each feature in the third feature data in the first scoring formula is calculated according to the XGBoost algorithm. Specifically, the weight is calculated using the split gain method. The specific steps include: S401, configure model parameters, which include: tree depth, learning rate, and training rounds; S402, using the XGBoost algorithm to construct a decision tree. Specifically, starting from the root node, the third feature data of the M ships is used as the initial data, a split feature is selected according to the split gain formula, the initial data is divided into left and right child nodes, and a subtree is recursively constructed until the depth of the tree is reached; S403, for each tree in the XGBoost algorithm, find a node set that uses feature i as a split node, and calculate the split gain of each node to obtain a total split gain of feature i; S404, normalize the split gain of each feature to obtain the weight of each feature.

6. The system for 5G local area network and satellite communication within a fleet according to claim 1, characterized in that: The control parameters of M ships are optimized according to the improved starfish optimization algorithm to obtain the optimal control parameters. The specific steps include: S501, generating P initial solutions that meet the constraint conditions based on uniform distribution; S502, constructing an objective function according to the communication quality scores of the M ships; S503, calculating the fitness value of each solution according to the objective function, and taking the solution with the highest fitness value as the current optimal solution; S504, simulating the exploration behavior of the starfish, and updating each solution using the first update formula according to the current optimal solution. The first update formula is: ,in, represents the jth solution in the k+1th iteration, represents the jth solution in the kth iteration, k represents the current iteration number, and j represents the index of the solution. and is a random number between 0 and 1. represents the current optimal solution, represents a random angle; S505, simulating the regeneration stage of the starfish, using the second update formula to update each solution, the second update formula is: , where K represents the maximum number of iterations; S506, when the current number of iterations reaches the maximum number of iterations, the solution with the maximum current fitness value is used as the optimal control parameter set, otherwise return to S503.

7. A fleet internal 5G local area network and satellite communication system according to claim 6, characterized in that: The objective function of the improved starfish optimization algorithm is: ； Among them, F represents the fitness value of the solution, b represents the ship index, represents the communication quality score of the b-th ship.

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