AN ENERGY-EFFICIENT DEEP REINFORCEMENT LEARNING-BASED ANTI-JAMMING STRATEGY FOR NEXT-GENERATION WIRELESS NETWORKS
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DOI:
https://doi.org/10.34238/tnu-jst.15082Abstract
Reliable wireless communication under malicious jamming remains a critical challenge for next-generation wireless networks. This paper proposes an energy-efficient anti-jamming strategy based on Deep Reinforcement Learning to enhance communication reliability while reducing energy consumption. The anti-jamming decision process is modeled as a Markov Decision Process, where a Deep Reinforcement Learning agent adaptively selects the transmission channel and power level based on the observed network state. Simulation results demonstrate that the proposed approach achieves faster learning convergence and significantly improves communication performance compared to conventional and random strategies. In particular, the proposed scheme increases the Signal to Interference plus Noise Ratio by approximately 6–8 dB and improves throughput by about 25–30%. Moreover, the average energy consumption is reduced by nearly 15–20% through adaptive power control. These results indicate that the proposed Deep Reinforcement Learning based framework provides a robust and energy-efficient solution for anti-jamming communications in next-generation wireless networks.Downloads
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