ALI-MAPPO: Attention on Local Information Aided MAPPO Algorithm for Power Allocation of Wireless Cognitive Jamming Systems

Intelligent decision-making is an essential sub-task for wireless electromagnetic countermeasures, with the objective of imposing effective electronic jamming based on the observation and estimation of rival equipment. With the proliferation of radar networks, the effectiveness of a single jammer is being questioned, raising research interest in jamming resource allocation (JRA) of cognitive jamming system. This paper presents a study on the multi-agent collaborative jamming power allocation (MACJPA) task to achieve the overall barrage jamming of a radar network using limited jamming resources. This task is first formulated as a decentralized partially observable Markov decision process (Dec-POMDP) to be oriented towards the fully cooperative setting. And we introduce an improved centralized-training-withdecentralized-execution (CTDE) paradigm, namely, designing an “Attention on Local Information” aided multi-agent proximal policy optimization (ALI-MAPPO) network. The core motivation of ALI is that the customized global information with weights is more conducive to learning effective individual value functions. Through adequate network training, the cognitive jamming system utilizes observation information and well-trained policies make optimal decisions individually. In addition, we incorporate radar anti-jamming signal processing features, such as frequency agility and uncertainty in parameter estimation, into the scenario design. Extensive experimental results demonstrate that the ALI-MAPPO enables the cognitive jamming system to generate robust policies and obtain the best jamming performance compared to other existing MARL methods.