DRL-based Cross-layer Design for PHY Scheduling and Congestion Control in Anti-jamming Communications

Data-driven cross-layer secure design is expected to provide effective support for high-reliability and high-speed 6G network services. In this paper, we study the joint anti-jamming decision problem for phy-layer scheduling and congestion control in the transport layer. To address the challenges of the extremely huge action space and the simultaneous existence of multi-dimensional and multi-scale action variables, we propose a hierarchical DRL anti-jamming algorithm. Firstly, we unify the time scales of the variables by defining the state space, action space, and reward function, and construct them as a Markov decision process. Second, we make decisions in different dimensions sequentially through a hierarchical learning algorithm, which compresses the size of the action space while maintaining the correlation between variables. Simulation results show that the proposed cross-layer design method can realize a good adaptation between the lower layer and the transport layer in the context of anti-jamming requirements, which significantly improves the user QoS and system throughput compared with the baseline algorithms.