Optimization for Paralyzing G2A Communication Network: A DRL-Based Joint Path Planning and Jamming Power Allocation Approach

Abstract

This letter investigates the jammer path planning and jamming power allocation problem during airborne deterrence operation (ADO) in highly dynamic environments. In response to airborne threats posed by enemy aircraft formations, jammers must rely on perceptual information to plan trajectories and emit jamming signals to paralyze the ground-to-air (G2A) communication networks. Unlike traditional static scenarios, the high mobility of both sides presents significant challenges. Most works only study jamming solutions for static ground or single airborne targets, failing to address multiple airborne targets. We propose a joint path planning and jamming power allocation approach based on deep reinforcement learning (JPPJPA-DRL). This approach considers the impact of flight paths on receiving antenna gain, models the ADO as a Markov Decision Process (MDP), and uses the proximal policy optimization (PPO) algorithm to generate optimized path points and jamming power allocation schemes. In addition, a scientific reward function is designed to guide the learning process, and a visual communication countermeasure simulation platform is developed. The results show that the proposed approach can efficiently paralyze G2A communication networks, outperforming the baseline.