Robust Deep Reinforcement Learning for Interference Avoidance in Wideband Spectrum

Mohamed A. Aref, S. Jayaweera
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引用次数: 5

Abstract

This paper presents a design of a cognitive engine for interference and jamming resilience based on deep reinforcement learning (DRL). The proposed scheme is aimed at finding the spectrum opportunities in a heterogeneous wideband spectrum. In this paper we discuss a specific DRL mechanism based on double deep Q-learning (DDQN) with a convolutional neural network (CNN) to successfully learn such interference avoidance operation over a wideband partially observable environment. It is shown, through simulations, that the proposed technique has a low computational complexity and significantly outperforms other techniques in the literature, including other DRL-based approaches.
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基于鲁棒深度强化学习的宽带干扰避免
提出了一种基于深度强化学习(DRL)的干扰和抗干扰认知引擎的设计。该方案旨在寻找异构宽带频谱中的频谱机会。在本文中,我们讨论了一种基于双深度q学习(DDQN)和卷积神经网络(CNN)的特定DRL机制,以成功地在宽带部分可观察环境中学习这种干扰避免操作。通过模拟表明,该技术具有较低的计算复杂度,并且显著优于文献中的其他技术,包括其他基于drl的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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