Better Late Than Never: GAN-Enhanced Dynamic Anti-Jamming Spectrum Access With Incomplete Sensing Information

Abstract

This letter studies the problem of anti-jamming spectrum access in dynamic and unknown environments with incomplete spectrum sensing information. Most existing studies were based on the assumption that users have perfect spectrum information, thus they worked poorly with incomplete sensing information. To overcome the shortcoming, we present a robust intelligent anti-jamming spectrum access scheme which includes spectrum sensing, completion, learning and access. To complete the missing spectrum data, we design a generative adversarial network (GAN) based spectrum completion network. Based on deep reinforcement learning (DRL), a channel selection network is introduced to choose effective anti-jamming channel access policy. Simulation results show that the proposed approach is able to avoid complex jamming attacks effectively and outperforms conventional DRL-based approach with incomplete spectrum information.