GSM-R wireless field strength coverage prediction algorithm based on PSO-RBF neural network algorithm

• Published in: Journal of physics. Conference series Vol. 2383; no. 1; pp. 12097 - 12102
• Main Authors: Wang, Kaiyan, Zhou, Qinghua
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.12.2022
• Subjects: Algorithms; Cellular communication; Communications systems; Field strength; Mathematical models; Neural networks; Parameters; Particle swarm optimization; Physics; Prediction models; Radial basis function; Railway construction; Service introduction; Wireless communications; Wireless networks
• ISSN: 1742-6588; 1742-6596; 1742-6596
• DOI: 10.1088/1742-6596/2383/1/012097

In the wake of the successive construction of new railway lines, new lines and existing lines are adjacent, approached and surpassed. In the early stage of line construction, if the influence of adjacent lines is not considered in subsequent planning, the original lines need to be adjusted, and the reconstruction of the base station (BS) coverage along the railway increases the hardness of buliding and the invested funds. Therefore, a Global System for Mobile Communications – Railway (GSM-R) wireless field strength coverage prediction model based on particle swarm optimization (PSO) algorithm optimized radial basis function neural network (RBFNN) was proposed. Aiming at the problem of slow network convergence caused by improper selection of network parameters and structure of RBFNN, PSO algorithm is used to optimize the parameters of RBFNN, and combined with the actual measurement data on site, a PSO-RBFNN model is established to simulate and predict the field strength coverage. The results show that the prediction effect of PSO-RBFNN is the best, followed by RBFNN, and the worst of HATA model, which is very beneficial to the future railway GSM-R wireless network coverage and provides a feasible idea.