An implementation procedure is proposed in the paper for the cooperative operation and deployment scheme of optimizing the location of 5G heterogeneous base stations, which aims to optimally reduce the setup cost and strengthen the signal coverage while deploying 5G base . . An implementation procedure is proposed in the paper for the cooperative operation and deployment scheme of optimizing the location of 5G heterogeneous base stations, which aims to optimally reduce the setup cost and strengthen the signal coverage while deploying 5G base . . ation are critical to improving the performance of wireless communication networks in terms of latency reduction. To this end, the article proposes leveraging a convolutio al neural network (CNN) to improve the accuracy of base station location selection and network latency reduction. The CNN. . Based on factors such as base station construction cost, signal coverage, and Euclidean distance between base stations, this paper constructs a multi-objective planning and loca-tion model combined with genetic algorithm, and conducts algorithm simulation. Finally, the simulation experiment results. . Academic Journal of Computing & Information Science ISSN 2616-5775 Vol. 5, Issue 7: 15-19, DOI: 10. 25236/AJCIS. 050703 Published by Francis Academic Press, UK -15- Best base station location with a given area as an example Hao Zhang1, Guanghua Li1, Yaping Wang1,*, Shitong Wang2, Xinyan Wang2. . Enterprises can harness the advantages of 5G private networks for businesses with support from the Third Generation Partnership Project (3GPP) standards, and more. In order to provide comprehensive coverage of 5G new radio (NR) private network, 5G NR measurement applications running on a signal. . With the large-scale deployment of 5G technology, the rationality of communication base station siting is crucial for network performance, construction costs, and operational efficiency. Traditional site selection methods rely heavily on manual experience, exhibiting strong subjectivity and. . This work was supported in part by Fujian Provincial Department of Science and Technology, China, under Grant No. 2016J01330, the Education Department of Fujian Province, China, under Grant No.
