China is the main competitor of the United States in the race for the title of leader in 5G deployment. By 2024, the Middle Kingdom had installed over 1.2 million 5G base stations and already has over 600 million subscribers .
The marketing of non-5G services refers to the promotion of enhanced 4G networks that are presented as precursors or equivalents to 5G. Some mobile network operators marketed upgraded 4G technologies using terms that suggested 5G capability.
Brazil is accelerating the deployment of 5G networks after successful frequency auctions in the 3.5 GHz band. Operators like Claro, Vivo, and TIM are deploying networks in key urban areas, aiming to cover all state capitals by the end of 2024 . Residents of 27 of Brazil's most important cities will then gain access to 5G.
Major suppliers of 5G radio and core systems included Altiostar, Cisco Systems, Datang Telecom/Fiberhome, Ericsson, Huawei, Nokia, Qualcomm, Samsung, and ZTE. Huawei was estimated to hold about 70 percent of global 5G base stations by 2023.
In this paper, we thoroughly study the base station control problem in 5G ultra-dense networks and propose an innovative MAPPO algorithm. The algorithm significantly reduces the overall power consumption of the system by optimizing inter-base station collaboration and interference management while guaranteeing user QoS.
The limited penetration capability of millimeter waves necessitates the deployment of significantly more 5G base stations (the next generation Node B, gNB) than their 4G counterparts to ensure network coverage . Notably, the power consumption of a gNB is very high, up to 3–4 times of the power consumption of a 4G base stations (BSs).
However, the selection of 5G base station locations is also influenced by local terrain and population distribution, and obstacles such as streets, buildings, and trees can significantly impact signal propagation.
Comparison results of 5G base station optimization in general urban areas. As shown in Table 11, the algorithm proposed in this topic reduces the site construction cost by at least 13 %, improves the coverage by at least 5.4 %, and reduces the number of base stations by at least 17.6 % compared to other algorithms.
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