IRL-Based Co-Channel Interference Optimization for UAV Cellular Network Communication Links

In urban cellular networks, uncrewed aerial vehicles (UAVs) serve as critical aerial nodes and are extensively deployed. However, during mission execution, they are susceptible to co-channel interference (CCI) with terrestrial networks, which can adversely impact quality of service (QoS). This paper develops an optimization model for uplink communication links in spectrum-sharing cellular networks involving UAVs and proposes a joint optimization framework for UAV trajectory planning and transmission power control. Specifically, we introduce a Double and Dueling Deep Q-Network via Inverse Reinforcement Learning (D3QN-IRL) algorithm featuring a prioritized experience replay mechanism with replay time, designed to investigate the impact of varying ground user density on optimization problems. The proposed algorithm integrates expert demonstrations with Deep Reinforcement Learning (DRL), guiding UAVs to avoid interference hotspots within dynamic jamming environments and autonomously reduce transmission power when approaching densely populated user areas. Through this process, the UAV is capable of progressively learning the optimal policy to achieve joint optimization of trajectory and power allocation. Simulation results demonstrate that the proposed approach significantly outperforms existing methods in terms of throughput, signal-to-interference-plus-noise ratio (SINR), and interference management, validating its effectiveness and potential for deployment in complex wireless communication environments.