RIS Assisted Anti Jamming Backscatter Communication Using Deep Reinforcement Learning
DOI:
https://doi.org/10.54654/isj.v1i27.6384Keywords:
Anti-jamming communication, ambient backscatter, Deep Q-network (DQN), reconfigurable intelligent surface, wireless IoT networksTóm tắt
Backscatter communication is a promising low-power solution for large scale Internet of Things (IoT) networks; however, it is highly susceptible to intentional jamming and dynamic interference. This paper proposes a Deep Reinforcement Learning (DRL) - based anti-jamming framework for reconfigurable intelligent surface (RIS) - assisted backscatter systems. The problem is formulated as a Markov decision process, where an agent adaptively optimizes RIS reflection coefficients without prior environmental knowledge. A deep Q-network with experience replay and a target network ensures stable learning in high-dimensional state spaces. Simulation results show convergence within 600 – 800 episodes. Compared with random RIS configurations and RIS-free systems, the proposed scheme improves SINR by 3 – 6 dB and over 8 dB, respectively, under moderate-to-strong jamming. Moreover, BER is reduced by nearly one order of magnitude, and throughput increases by 25% -40%. These results demonstrate robust and adaptive interference mitigation for future IoT and 6G networks
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