Two-Layer Non-Alternating Joint Optimization of Trajectory Planning, User Association, and Radio Resource Management for UAV-Assisted Uplink IoT Data Collection: A Weighted Sum Power Minimization Approach

Abstract

Unmanned aerial vehicle (UAV)-assisted data collection from battery-constrained Internet of Things (IoT) devices faces a fundamental trade-off between communication reliability and device energy depletion. This paper jointly optimizes UAV trajectory, user association, and radio resource management (RRM) to minimize the uplink weighted sum transmit powers, where each device’s weight is dynamically and inversely related to its residual energy, while treating UAV propulsion energy as a feasibility constraint. To circumvent the initialization trap of conventional alternating optimization (AO), we propose a two-layer non-alternating framework. The inner layer solves the per-slot RRM problem analytically via KKT conditions for a fixed UAV position, yielding analytical power allocation and a unique bandwidth solution, while user association is determined by an incremental greedy algorithm. The outer layer formulates trajectory planning (TP) as a Markov decision process (MDP), enabling single-pass trajectory synthesis without cross-layer iteration, thereby inherently avoiding initialization sensitivity. The framework supports the genetic algorithm (GA) and limited depth-first search (DFS) as trajectory solvers, with the deep Q-network (DQN) as a promising future extension, each offering distinct optimality–complexity trade-offs. Simulation results show that the proposed scheme consistently outperforms conventional iterative baselines across various network configurations.