Channel allocation method, device and storage medium based on cognitive wireless network

Abstract

The present application discloses a channel allocation method, device and storage medium based on a cognitive wireless network. The method includes: 1) determining the encoding method, and generating an initial group; 2) determining a fitness function, and calculating each individual in the group 3) Calculate the average fitness and maximum fitness of the population, sort the population according to the fitness value and divide them into two groups, 4) population crossover, 5) population variation, 6) population update, 7) iteration Termination condition judgment. This application builds a channel allocation model for maximizing network throughput based on cognitive wireless networks, proposes to use adaptive genetic algorithm to solve the problem, and obtains channel allocation results for maximizing network throughput, thereby solving the problem of single method and communication blockage in channel allocation for marine fishing boats It provides a better guarantee for the work coordination and emergency rescue among fishing boats at sea.

Description

technical field

[0001] The present application relates to the field of communication technologies, and in particular to a channel allocation method, device and storage medium based on cognitive wireless networks. Background technique

[0002] With the continuous development of modern electronic communication technology, data interaction at sea is becoming more and more frequent, such as data interaction between ship electronic equipment, data interaction between ships and information centers, etc. These data interactions are all carried out through marine radio, and its performance directly affects the quality of data communication .

[0003] When the ultrashort wave communication network of marine fishing boats divides the frequency band of fishery communication, the frequency band of fishery ultrashort wave communication is divided into multiple independent channels. The division and allocation method is single, resulting in signal congestion in some channels and a decrea...

Examples

Embodiment 1

[0071] This embodiment takes the flow of the adaptive genetic algorithm as the basic framework. Due to the limitations of the existing genetic algorithm itself, the optimization process is prone to premature and local convergence problems. In order to solve the existing genetic algorithm channel allocation in ocean fishing boats, Improve the genetic algorithm to realize variable parameters, so that the crossover rate and mutation rate can be adaptively changed with the fitness of the group, thereby providing the best crossover rate and mutation rate relative to a certain solution, enhancing the global search ability of the algorithm, and avoiding the algorithm from falling into local Optimum, improve search efficiency. The specific steps of the channel allocation method based on the cognitive wireless network described in this embodiment include:

[0072] 1) Determine the coding method and generate the initial group

[0073] Establish user connection graph based on graph theo...

Embodiment 2

[0104]The effects of the present application will be further described below in combination with simulation experiments.

[0105] In order to verify the effect and function of the adaptive genetic algorithm in channel allocation of marine fishing boats, this application designs a simulation experiment to explore the performance improvement of the improved algorithm in channel allocation such as throughput and channel conflict. Assume that all users at sea are randomly distributed in an area of ​​1000m*1000m in the form of a distributed network, assuming that 40 users are randomly generated in the area, including 2 authorized users and 38 cognitive users, such as Figure 4 As shown, the authorized user's coverage is r PU =400m, the communication distance of cognitive users is r com = 250m, the interference range of cognitive users is r inter =2.2×r com .

[0106] Based on the settings of the above simulation environment, the average value of the experimental results repeate...

Embodiment 3

[0113] Figure 8 A schematic structural diagram of the electronic device provided in Embodiment 3 of the present application, such as Figure 8 As shown, the device includes: a memory 801 and a processor 802;

[0114] Memory 801, for storing computer programs;

[0115] Wherein, the processor 802 executes the computer program in the memory 801, so as to implement the methods provided by the above method embodiments.

[0116] In this embodiment, the processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0117] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory (cache), etc., fo...

technical field

[0001] The present application relates to the field of communication technologies, and in particular to a channel allocation method, device and storage medium based on cognitive wireless networks. Background technique

[0002] With the continuous development of modern electronic communication technology, data interaction at sea is becoming more and more frequent, such as data interaction between ship electronic equipment, data interaction between ships and information centers, etc. These data interactions are all carried out through marine radio, and its performance directly affects the quality of data communication .

[0003] When the ultrashort wave communication network of marine fishing boats divides the frequency band of fishery communication, the frequency band of fishery ultrashort wave communication is divided into multiple independent channels. The division and allocation method is single, resulting in signal congestion in some channels and a decrea...

Examples

Embodiment 1

[0071] This embodiment takes the flow of the adaptive genetic algorithm as the basic framework. Due to the limitations of the existing genetic algorithm itself, the optimization process is prone to premature and local convergence problems. In order to solve the existing genetic algorithm channel allocation in ocean fishing boats, Improve the genetic algorithm to realize variable parameters, so that the crossover rate and mutation rate can be adaptively changed with the fitness of the group, thereby providing the best crossover rate and mutation rate relative to a certain solution, enhancing the global search ability of the algorithm, and avoiding the algorithm from falling into local Optimum, improve search efficiency. The specific steps of the channel allocation method based on the cognitive wireless network described in this embodiment include:

[0072] 1) Determine the coding method and generate the initial group

[0073] Establish user connection graph based on graph theo...

Embodiment 2

[0104]The effects of the present application will be further described below in combination with simulation experiments.

[0105] In order to verify the effect and function of the adaptive genetic algorithm in channel allocation of marine fishing boats, this application designs a simulation experiment to explore the performance improvement of the improved algorithm in channel allocation such as throughput and channel conflict. Assume that all users at sea are randomly distributed in an area of ​​1000m*1000m in the form of a distributed network, assuming that 40 users are randomly generated in the area, including 2 authorized users and 38 cognitive users, such as Figure 4 As shown, the authorized user's coverage is r PU =400m, the communication distance of cognitive users is r com = 250m, the interference range of cognitive users is r inter =2.2×r com .

[0106] Based on the settings of the above simulation environment, the average value of the experimental results repeate...

Embodiment 3

[0113] Figure 8 A schematic structural diagram of the electronic device provided in Embodiment 3 of the present application, such as Figure 8 As shown, the device includes: a memory 801 and a processor 802;

[0114] Memory 801, for storing computer programs;

[0115] Wherein, the processor 802 executes the computer program in the memory 801, so as to implement the methods provided by the above method embodiments.

[0116] In this embodiment, the processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0117] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory (cache), etc., fo...

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