基于深度强化学习的认知无线电网络吞吐量最大化
Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning
摘要：
射频驱动的认知无线电网络（RF-CRN）很可能成为物联网（IoT）等即将到来的现代网络的眼睛和耳朵，需要增加分散和自主操作。为了被认为是自主的，RF供电的网络实体需要在本地做出决策，以在任何网络环境的不确定性下最大化网络吞吐量。然而，在复杂的大规模网络中，状态和动作空间通常很大，现有的表格强化学习技术无法快速找到最优的状态-动作策略。在本文中，提出了深度强化学习来克服上述缺点，并允许无线网关导出最佳策略以最大化网络吞吐量。当以先进的DQN技术为基准时，我们提出的DQN配置提供了高达1.8倍的性能加速，具有良好的整体性能。
英文摘要：
Radio Frequency powered Cognitive Radio Networks (RF-CRN) are likely to be the eyes and ears of upcoming modern networks such as Internet of Things (IoT), requiring increased decentralization and autonomous operation. To be considered autonomous, the RF-powered network entities need to make decisions locally to maximize the network throughput under the uncertainty of any network environment. However, in complex and large-scale networks, the state and action spaces are usually large, and existing Tabular Reinforcement Learning technique is unable to find the optimal state-action policy quickly. In this paper, deep reinforcement learning is proposed to overcome the mentioned shortcomings and allow a wireless gateway to derive an optimal policy to maximize network throughput. When benchmarked against advanced DQN techniques, our proposed DQN configuration offers performance speedup of up to 1.8x with good overall performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
