Electromagnetic Spectrum Allocation Method for Multi-Service Irregular Frequency-Using Devices in the Space-Air-Ground Integrated Network

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 23; p. 9227
• Main Authors: Meng, Yongchao, Qi, Peihan, Lei, Qian, Zhang, Zhengyu, Ren, Jinyang, Zhou, Xiaoyu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.11.2022; MDPI
• Subjects: Algorithms; Analysis; Communication; Computer Simulation; Electric waves; Electromagnetic interference; Electromagnetic radiation; Electromagnetic waves; Electromagnetism; Genetic algorithms; Heuristic; Information industry; Internet of Things; Methods; micro genetic algorithm; niche method; Optimization; orthogonal experiment; priority; Resonant frequencies; Satellites; space–air–ground integrated network; Spectrum allocation; Unmanned aerial vehicles; micro genetic algorithm; priority; niche method; space–air–ground integrated network; orthogonal experiment; spectrum allocation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22239227

The management and allocation of electromagnetic spectrum resources is the inner driving force of the construction of the space-air-ground integrated network. Existing spectrum allocation methods are difficult to adapt to the scenario where the working bandwidth of multi-service frequency-using devices is irregular and the working priorities are different. In this paper, an orthogonal genetic algorithm based on the idea of mixed niches is proposed to transform the problem of frequency allocation into the optimization problem of minimizing the electromagnetic interference between frequency-using devices in the integrated network. At the same time, a system model is constructed that takes the minimum interference effect of low-priority-to-high-priority devices as the objective function and takes the protection frequency and natural frequency as the constraint conditions. In this paper, we not only introduce the thought of niches to improve the diversity of the population but also use an orthogonal uniform crossover operator to improve the search efficiency. At the same time, we use a standard genetic algorithm and a micro genetic algorithm to optimize the model. The global searchability and local search precision of the proposed algorithm are all improved. Simulation results show that compared with the existing methods, the proposed algorithm has the advantages of fast convergence, strong stability and good optimization effect.