Jamming-Resilient Wideband Cognitive Radios with Multi-Agent Reinforcement Learning

Mohamed A. Aref, S. Jayaweera
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引用次数: 1

Abstract

This article presents a design of a wideband autonomous cognitive radio (WACR) for anti-jamming and interference-avoidance. The proposed system model allows multiple WACRs to simultaneously operate over the same spectrum range producing a multi-agent environment. The objective of each radio is to predict and evade a dynamic jammer signal as well as avoiding transmissions of other WACRs. The proposed cognitive framework is made of two operations: sensing and transmission. Each operation is helped by its own learning algorithm based on Q-learning, but both will be experiencing the same RF environment. The simulation results indicate that the proposed cognitive anti-jamming technique has low computational complexity and significantly outperforms non-cognitive sub-band selection policy while being sufficiently robust against the impact of sensing errors.
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基于多智能体强化学习的抗干扰宽带认知无线电
本文提出了一种宽带自主认知无线电(WACR)的抗干扰和抗干扰设计。所提出的系统模型允许多个wacr在相同的频谱范围内同时运行,从而产生一个多智能体环境。每个无线电的目标是预测和躲避动态干扰信号以及避免其他wacr的传输。所提出的认知框架由两个操作组成:感知和传递。每个操作都有自己的基于q学习的学习算法,但它们都将经历相同的RF环境。仿真结果表明,所提出的认知抗干扰技术具有较低的计算复杂度,显著优于非认知子带选择策略,同时对感知误差的影响具有足够的鲁棒性。
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