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首页> 外文期刊>Wireless Communications Letters, IEEE >Dynamic Spectrum Anti-Jamming in Broadband Communications: A Hierarchical Deep Reinforcement Learning Approach
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Dynamic Spectrum Anti-Jamming in Broadband Communications: A Hierarchical Deep Reinforcement Learning Approach

机译：宽带通信中动态频谱抗干扰：分层深度加强学习方法

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摘要

In this letter, the frequency selection problem in jamming environment with large number of optional frequencies is investigated. With numerous optional actions in the wider frequency band scenario, most of existing anti-jamming methods will become ineffective, since the convergence time and computational complexity will grow exponentially with the number of actions. To cope with the above challenge, a novel hierarchical deep reinforcement learning algorithm which does not need to know the jamming patterns and channel model is proposed. The proposed algorithm divides the frequency selection problem in the broadband into two steps via two subnetworks: Firstly, the frequency band is selected by the band selection network, and then the specific frequency is selected in this frequency band by the frequency selection network. Simulation results show that the proposed algorithm avoids multiple different jammings effectively and achieves satisfactory throughput with less calculation.
机译：在这封信中，研究了具有大量可选频率的干扰环境中的频率选择问题。在更广泛的频段场景中具有许多可选动作，大多数现有的抗干扰方法将变得无效，因为收敛时间和计算复杂性将以行动的数量呈指数级增长。为了应对上述挑战，提出了一种不需要知道干扰模式和信道模型的新型分层深度加强学习算法。所提出的算法通过两个子网将宽带中的频率选择问题划分为两个步骤：首先，频带由频带选择网络选择，然后通过频率选择网络在该频带中选择特定频率。仿真结果表明，该算法有效避免了多种不同的干扰，并通过较少计算实现了令人满意的吞吐量。

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