Reliable Communications for Hypersonic Vehicles: A Reinforcement Learning Approach

The ultra-high speed (e.g., typically moving with 1020 Mach) of the hypersonic vehicle (HSV) causes a plasma sheath, which severely degrades the communication performance and results in communication blackouts. In this paper, we propose a deep reinforcement learning (RL)-based HSV reliable communications scheme against jamming, which enables the HSV to select the carrier frequency and transmit power according to the signal quality, the estimated voltage standing wave ratio, flight altitude, flight speed, and angle of attack. Specifically, we design a deep two-level hierarchical structure to compress the high-dimensional state and action space, with the added advantage of leveraging transfer learning to reduce initial exploration and expedite the optimization process. To optimize the learning speed, the dueling architecture is implemented in the deep network to measure the state value and the advantage function of the policies. In contrast to the benchmark, the simulation results indicate that the proposed scheme yields a significant reduction in both bit error rate and transmit power.