Energy-Efficient Coverage and Capacity Enhancement With Intelligent UAV-BSs Deployment in 6G Edge Networks

With the development of 5G/6G networks, the number of wireless users is growing exponentially, and the application scenarios are increasingly diversified. Using unmanned aerial vehicles as base stations (UAV-BSs) to serve ground users has become a trend for wide area coverage and capacity enhancement for rapid access of service in 6G networks. However, as UAV-BSs have limited energy or battery storage, solutions to optimize energy efficiency while providing high-quality services are necessary. Therefore, this paper mainly concentrates on the energy-efficient deployment of coverage-aimed UAV-BSs (Co-UAV-BSs) and capacity-aimed UAV-BSs (Ca-UAV-BSs) for the coverage and capacity enhancement of ground communication under disaster areas or burst data traffic. First, Co-UAV-BSs are deployed with DQN algorithm to to get the UAV-BSs' optimal flight paths, which mainly adopted to detect out of service users in such areas. Then the users are completely clustered based on the detection results. After that, Co-UAV-BSs and Ca-UAV-BSs are deployed hierarchically based on the user distribution and sought to optimize the energy efficiency with acceptable user services. Still, DQN algorithm and the A3C algorithm are used for obtaining all the UAV-BSs' location deployment and users' best connections. The simulation results show that the dynamic flying path requires less energy than the fixed path for user detecting. For the coverage and capacity enhancement, it reveals the solution we proposed could provide high-quality service for users with high energy efficiency comparing to traditional algorithms.

Automated summary

With the development of 5G/6G networks, the use of unmanned aerial vehicles as base stations for ground users has become a trend to enhance coverage and capacity in fast access scenarios. This paper focuses on the energy-efficient deployment of UAV-BSs for coverage and capacity enhancement in disaster areas or burst data traffic. By using DQN and A3C algorithms, optimal flight paths and user connections are obtained, showing that dynamic flying paths consume less energy for user detection and the proposed solution provides high-quality service with high energy efficiency.