Education, Science, Technology, Innovation and Life
Open Access
Sign In

Wireless Communication Base Station Location Selection and Network Optimization Based on Neural Network Algorithm

Download as PDF

DOI: 10.23977/cpcs.2024.080105 | Downloads: 17 | Views: 343

Author(s)

Yi Yao 1

Affiliation(s)

1 Department of Information Science and Engineering, Hunan University of Humanities, Science and Technology, Loudi, Hunan, 417000, China

Corresponding Author

Yi Yao

ABSTRACT

Base station location selection and network optimization are critical to improving the performance of wireless communication networks in terms of latency reduction. To this end, the article proposes leveraging a convolutional neural network (CNN) to improve the accuracy of base station location selection and network latency reduction. The CNN method, based on a three-dimensional representation including signal strength data set, network topology data set, and transmission path data set, is used to select base station location and optimize the multihop relay network for latency reduction. The article presents a following method: location selection and network optimization for the wireless communication network. First, it collects the experimental data set of base station location selection and network optimization, and then uses the training data to train the CNN model to extract features. Once the training is done, the article further optimizes the network parameters and configurations, and ultimately obtains the optimal base station location and network configuration while minimizing network latency. As a result, simulation results indicate that the CNN model has remarkable performance in base station location selection, as well as in network optimization. In summary, the feature extraction and processing ability of CNN are powerful, enabling it to effectively capture factors leading to delay, hence improving the performance of base station location selection and network optimization. The article also demonstrates that the CNN model can be adjusted according to different environments and scenario settings through dynamic tuning.

KEYWORDS

Wireless Communication Base Station Location Selection; Network Optimization; Neural Network Algorithms; Convolutional Neural Network

CITE THIS PAPER

Yi Yao, Wireless Communication Base Station Location Selection and Network Optimization Based on Neural Network Algorithm. Computing, Performance and Communication Systems (2024) Vol. 8: 31-38. DOI: http://dx.doi.org/10.23977/cpcs.2024.080105.

REFERENCES

[1] Bian Qiang, Xu Donghui, Liu Jiangliang, He Jingyuan, Yang Haiyu.Research on the site selection algorithm of wireless broadband base stations based on hilly areas [J].Journal of Weapons and Equipment Engineering, 2023, 44(2):146-152
[2] Yang Fengyong, Zong Jianfeng, Ma Yang. Research on the optimization algorithm of mobile communication network site planning based on the mean drift algorithm optimized by DBSCA clustering algorithm [J].Progress in Applied Mathematics, 2023, 12(3):847-859
[3] Song Juwei, Zhang Fan.Research and application of site selection of wireless private network base stations for wind power plants [J].Power Information and Communication Technology, 2021, 19(2):75-81
[4] Li Xuegang.The relationship between the site selection and basic design of the communication base station tower [J].Tianjin Science and Technology, 2021, 48(5):31-32
[5] Zhang Lingzhi.Wireless communication network base station coverage analysis and base station site selection design [J].Communication world, 2019, 26(8):198-199
[6] Lai C C, Chen C T, Wang L C. On-demand density-aware UAV base station 3D placement for arbitrarily distributed users with guaranteed data rates[J]. IEEE Wireless Communications Letters, 2019, 8(3): 913-916.
[7] Yang J, Ding M, Mao G, et al. Optimal base station antenna downtilt in downlink cellular networks[J]. IEEE Transactions on Wireless Communications, 2019, 18(3): 1779-1791.
[8] Kishk M, Bader A, Alouini M S. Aerial base station deployment in 6G cellular networks using tethered drones: The mobility and endurance tradeoff[J]. IEEE Vehicular Technology Magazine, 2020, 15(4): 103-111.
[9] Xiao Z, Dong H, Bai L, et al. Unmanned aerial vehicle base station (UAV-BS) deployment with millimeter-wave beamforming [J]. IEEE Internet of Things Journal, 2019, 7(2): 1336-1349.
[10] Wang M, Lin Y, Tian Q, et al. Transfer learning promotes 6G wireless communications: Recent advances and future challenges[J]. IEEE Transactions on Reliability, 2021, 70(2): 790-807.
[11] Al-Ahmed S A, Shakir M Z, Zaidi S A R. Optimal 3D UAV base station placement by considering autonomous coverage hole detection, wireless backhaul and user demand[J]. Journal of Communications and Networks, 2020, 22(6): 467-475.
[12] Zeng S, Zhang H, Di B, et al. Reconfigurable intelligent surface (RIS) assisted wireless coverage extension: RIS orientation and location optimization[J]. IEEE Communications Letters, 2020, 25(1): 269-273.
[13] Wei L, Huang C, Alexandropoulos G C, et al. Channel estimation for RIS-empowered multi-user MISO wireless communications [J]. IEEE Transactions on Communications, 2021, 69(6): 4144-4157.
[14] Wang H, Wan L, Dong M, et al. Assistant vehicle localization based on three collaborative base stations via SBL-based robust DOA estimation[J]. IEEE Internet of Things Journal, 2019, 6(3): 5766-5777.
[15] Wu Q, Liu L, Zhang R. Fundamental trade-offs in communication and trajectory design for UAV-enabled wireless network [J]. IEEE Wireless Communications, 2019, 26(1): 36-44. 
[16] Lv Z. The security of Internet of drones. Computer Communications. 2019 Dec 15;148: 208-214.
[17] Jensen Raufelder. Modeling Analysis of Attitude Perception of Engineering Manipulator Supporting Wireless Communication and Internet of Things. Kinetic Mechanical Engineering, 2021, 2(2): 18-26.

Downloads: 2433
Visits: 119906

Sponsors, Associates, and Links


All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.