Drone-Cell Trajectory Planning and Resource Allocation for Highly Mobile Networks: A Hierarchical DRL Approach

Abstract

Drone cell (DC) is envisioned to enable the dynamic service provisioning for radio access networks (RANs), in response to the spatial and temporal unevenness of user traffic. In this article, we propose a hierarchical deep reinforcement learning (DRL)-based multi-DC trajectory planning and resource allocation (HDRLTPRA) scheme for high-mobility users. The objective is to maximize the accumulative network throughput while satisfying user fairness, DC power consumption, and DC-to-ground link quality constraints. To address the high uncertainties of the environment, we decouple the multi-DC TPRA problem into two hierarchical subproblems, i.e., the higher level global trajectory planning (GTP) subproblem and the lower level local TPRA (LTPRA) subproblem. First, the GTP subproblem is to address trajectory planning for multiple DCs in the RAN over a long time period. To solve the subproblem, we propose a multiagent DRL-based GTP (MARL-GTP) algorithm in which the nonstationary state space caused by the multi-DC environment is addressed by the multiagent fingerprint technique. Second, based on the GTP results, each DC solves the LTPRA subproblem independently to control the movement and transmit power allocation based on the real-time user traffic variations. A deep deterministic policy gradient (DEP)-based LTPRA (DEP-LTPRA) algorithm is then proposed to solve the LTPRA subproblem. With the two algorithms addressing both subproblems at different decision granularities, the multi-DC TPRA problem can be resolved by the HDRLTPRA scheme. Simulation results show that 40% network throughput improvement can be achieved by the proposed HDRLTPRA scheme over the nonlearning-based TPRA scheme.