Joint Design of Access Point Selection and Path Planning for UAV-Assisted Cellular Networks

Unmanned aerial vehicle (UAV)-assisted communication is envisioned as a potential solution to the data traffic explosion in the massive machine-type communications (mMTC) scenario. In this article, we investigate the UAV-assisted cellular networks, where a UAV acts as a flying relay to offload part of the data traffic from the overloaded cell to another. We utilize the practical spatial distribution of data traffic and a convincing air-to-ground channel model. The quality of service (QoS) is defined as a UAV utility function which is designed based on a packet loss ratio (PLR)-related users' cost function to represent the performance improvements brought by the UAV. We formulate a joint optimization problem to maximize the UAV utility function and then decompose it into the subproblems about the access point selection and the UAV path planning, which influence the PLR by influencing the packet collision rate and channel state. Since the access point selection subproblem is NP-hard, a game-theory-based distributed algorithm is proposed, instructing the users to select the base station (BS) or the UAV as the access point autonomously. To achieve the most superior channel state, we solve the UAV path planning subproblem by a deep reinforcement learning (DRL)-based approach, instructing the UAV to take the optimal action in each position. The simulation results show that the proposed access point selection scheme can significantly reduce the average cost of users and the proposed UAV path planning method can achieve a path with smaller average channel pathloss compared with other approaches.