GPDS: A multi-agent deep reinforcement learning game for anti-jamming secure computing in MEC network.

The openness of Mobile Edge Computing (MEC) networks makes them vulnerable to interference attacks by malicious jammers, which endangers the communication quality of mobile users. To achieve secure computing, the conventional method is that the mobile device reduces the attacker's malicious interference by increasing the transmission power. However, the cost of power defense is unacceptable in MEC with resource shortages. Therefore, this paper considers a novel defense strategy based on time-varying channel and describes the malicious interference countermeasure process as a multi-user intelligent game model. Because the interference model and interference strategy are unknown, this paper proposes a deep reinforcement learning multi-user random Game with Post-Decision State (named GPDS) to intelligently resist intelligent attackers. In the GPDS algorithm, mobile users need to obtain the communication quality, spectrum availability, and jammer's strategy from the state of the blocked channel. The reward of the optimal decision strategy is defined as the expected value of the maximum channel throughput, and the potential optimal channel selection strategy is obtained through Nash equilibrium. After GPDS training, mobile users can learn the optimal channel switching strategy after multi-step training. The experimental results show that the proposed GPDS achieves better anti-jamming performance, compared with SOTA algorithms. • We first model the MEC anti-jammer as a multi-user continuous game. • A post decision state is proposed to deal with dynamic unknown information. • A minimax gradient strategy is proposed to realize learning generalization. • State-of-the-art methods are outperformed in both parameters and performance. [ABSTRACT FROM AUTHOR]