Multi-target spectrum allocation method based on undisposal order preference quantum goose group algorithm

Abstract

The invention aims at providing a multi-target spectrum allocation method based on an undisposal order preference quantum goose group algorithm, which comprises the following steps of: building a graph theory coloring model of cognitive radio spectrum allocation, initializing the position of the quantum geese and the quantum speed, carrying out the undisposal order preference and congestion degree calculation on individuals in the population according to the adaptability, sequencing the individuals with the same undisposal order preference levels in sequence from higher congestion degrees to lower congestion degrees, carrying out evolution on the population by a quantum goose group evolution rule, generating new quantum speed and position, carrying out undisposal order preference on obtained solutions in an elite solution set nonDomQGSAList and selecting the solutions with the undisposal solution level being 1 as the final Pareto front end solution set. The method solves the discrete multi-target optimization problem, designs the novel undisposal order preference quantum goose group algorithm as the evolution strategy and has the advantages that the convergence speed is high, and the precision is high. In addition, the method provided by the invention has a wider application range.

Description

technical field [0001] The invention relates to a spectrum allocation method of cognitive radio. Background technique [0002] With the development of wireless communication, the shortage of wireless spectrum resources has become a bottleneck restricting the sustainable development of wireless communication. The research report of the Federal Council of the United States shows that the current fixed frequency spectrum allocation policy makes the spectrum utilization rate extremely low, many frequency bands are not fully utilized, while other frequency bands are extremely crowded. Cognitive radio technology provides a possibility to solve the shortage of wireless spectrum resources. This technology enables cognitive users to use vacant spectrum without interfering with licensed users and other cognitive users. Cognitive users can sense the surrounding spectrum environment, search for available spectrum resources, and perform dynamic spectrum access, thereby improving the ca...

AI Technical Summary

Problems solved by technology

The accuracy of the algorithm is not high enough, and it cannot consider the maximum network benefit and fairness among users at the same time

"Cognitive radio spectrum allocation using evolutionary algorithms" published by Zhijin Zhao et al. The application of artificial intelligence algorithms (including genetic algorithm, quantum genetic algorithm and particle swarm algorithm) is proposed to solve the spectrum allocation problem. Although the accuracy is improved compared with the sensitive graph theory coloring algorithm, it still cannot solve the multi-objective problem of cognitive radio spectrum allocation.

[0005] The multi-objective allocation problem of cognitive radio spectrum can be regarded as a combinatorial optimization problem, which is an NP-hard problem, and it is difficult to find the optimal solution in a limited time

Images