Game Theory and Multi–Agent DRL Based Anti-Jamming Transmission for Integrated Air-Ground Network

This paper proposes an anti-jamming transmission algorithm based on game theory and multi-agent deep reinforcement learning (MADRL) for the integrated air-ground network. For uplink process, we propose UAV deployment schemes based on congestion game model and dynamic networking schemes based on coalition game, aiming to counteract malicious jamming from ground and air jammers, effectively enhancing the anti-jamming transmission capability of air-ground networks. For downlink process, to address the joint trajectory and power optimization problem, a partially observable Markov decision process (POMDP) framework is utilized, follows a centralized training and distributed execution framework. During the centralized training process, experiences of each agent interacting with the environment are stored in an experience replay pool and then used to train the soft actor-critic network. This process is conducted on the high altitude platforms (HAP) to alleviate the burden of unmanned aerial vehicle (UAV). During the distributed execution process, each UAV uses the trained actor network to output actions based on observations and adjust its flight position and transmission power for joint service provision. To update the parameters of the soft actor-critic network, an improved proximal policy optimization (PPO) algorithm is proposed. Simulation results demonstrate that the proposed method outperforms traditional algorithms in terms of achieving higher system achievable sum rate, lower power consumption, and faster convergence speed.