Correction of Radio Wave Propagation Prediction Model Based on Improved Seagull Algorithm in Tunnel Environment

In the urban rail transit (URT) environment, the radio wave propagation prediction model and communication system planning are very important. However, due to the complexity of the tunnel propagation environment, the current prediction model can not fully cover the radio wave propagation process in...

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Autores principales:	Yunshui Zheng, Rui Yan, Yang Liu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Urban rail transit radio wave propagation prediction model path loss seagull algorithm continuous-wave test Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/cc82691a323747f7856e5a7543eecd3f
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id	oai:doaj.org-article:cc82691a323747f7856e5a7543eecd3f
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spelling	oai:doaj.org-article:cc82691a323747f7856e5a7543eecd3f2021-11-18T00:02:54ZCorrection of Radio Wave Propagation Prediction Model Based on Improved Seagull Algorithm in Tunnel Environment2169-353610.1109/ACCESS.2021.3122300https://doaj.org/article/cc82691a323747f7856e5a7543eecd3f2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9584899/https://doaj.org/toc/2169-3536In the urban rail transit (URT) environment, the radio wave propagation prediction model and communication system planning are very important. However, due to the complexity of the tunnel propagation environment, the current prediction model can not fully cover the radio wave propagation process in the tunnel. In this paper, the propagation mechanism area is divided based on the segmentation approach. Different propagation models are used for different propagation mechanism areas to predict path loss more quickly and accurately. To improve the accuracy of the prediction model, this paper proposes an improved seagull optimization algorithm (ISOA). First, to address the shortcomings of the seagull optimization algorithm (SOA) such as easy premature convergence and slow convergence speeds, two improved methods of random search and periodic disturbance are proposed. Then, in order to verify the effectiveness and feasibility of the improved algorithm, the benchmark function is used to test the optimization performance of the ISOA and gray wolf optimization, the SOA, and particle swarm optimization. The results show that the optimization performance of ISOA is the most significant. Finally, the ISOA is used to fit and correct the continuous wave test data for a rectangular tunnel and an arch tunnel. The results show that the corrected propagation model has a higher degree of fit with the measured data than the single standard propagation model (SPM) model. The modified propagation model thus has guiding significance for the deployment of time-division long-term (TD-LTE) evolution networks in the tunnel environment.Yunshui ZhengRui YanYang LiuIEEEarticleUrban rail transitradio wave propagation prediction modelpath lossseagull algorithmcontinuous-wave testElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 149569-149581 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Urban rail transit radio wave propagation prediction model path loss seagull algorithm continuous-wave test Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Urban rail transit radio wave propagation prediction model path loss seagull algorithm continuous-wave test Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yunshui Zheng Rui Yan Yang Liu Correction of Radio Wave Propagation Prediction Model Based on Improved Seagull Algorithm in Tunnel Environment
description	In the urban rail transit (URT) environment, the radio wave propagation prediction model and communication system planning are very important. However, due to the complexity of the tunnel propagation environment, the current prediction model can not fully cover the radio wave propagation process in the tunnel. In this paper, the propagation mechanism area is divided based on the segmentation approach. Different propagation models are used for different propagation mechanism areas to predict path loss more quickly and accurately. To improve the accuracy of the prediction model, this paper proposes an improved seagull optimization algorithm (ISOA). First, to address the shortcomings of the seagull optimization algorithm (SOA) such as easy premature convergence and slow convergence speeds, two improved methods of random search and periodic disturbance are proposed. Then, in order to verify the effectiveness and feasibility of the improved algorithm, the benchmark function is used to test the optimization performance of the ISOA and gray wolf optimization, the SOA, and particle swarm optimization. The results show that the optimization performance of ISOA is the most significant. Finally, the ISOA is used to fit and correct the continuous wave test data for a rectangular tunnel and an arch tunnel. The results show that the corrected propagation model has a higher degree of fit with the measured data than the single standard propagation model (SPM) model. The modified propagation model thus has guiding significance for the deployment of time-division long-term (TD-LTE) evolution networks in the tunnel environment.
format	article
author	Yunshui Zheng Rui Yan Yang Liu
author_facet	Yunshui Zheng Rui Yan Yang Liu
author_sort	Yunshui Zheng
title	Correction of Radio Wave Propagation Prediction Model Based on Improved Seagull Algorithm in Tunnel Environment
title_short	Correction of Radio Wave Propagation Prediction Model Based on Improved Seagull Algorithm in Tunnel Environment
title_full	Correction of Radio Wave Propagation Prediction Model Based on Improved Seagull Algorithm in Tunnel Environment
title_fullStr	Correction of Radio Wave Propagation Prediction Model Based on Improved Seagull Algorithm in Tunnel Environment
title_full_unstemmed	Correction of Radio Wave Propagation Prediction Model Based on Improved Seagull Algorithm in Tunnel Environment
title_sort	correction of radio wave propagation prediction model based on improved seagull algorithm in tunnel environment
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/cc82691a323747f7856e5a7543eecd3f
work_keys_str_mv	AT yunshuizheng correctionofradiowavepropagationpredictionmodelbasedonimprovedseagullalgorithmintunnelenvironment AT ruiyan correctionofradiowavepropagationpredictionmodelbasedonimprovedseagullalgorithmintunnelenvironment AT yangliu correctionofradiowavepropagationpredictionmodelbasedonimprovedseagullalgorithmintunnelenvironment
_version_	1718425251281371136

Correction of Radio Wave Propagation Prediction Model Based on Improved Seagull Algorithm in Tunnel Environment

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