论文标题
认知无线网络吞吐量最大化，深入增强学习
Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning
论文作者
论文摘要
射频供电的认知无线电网络（RF-CRN）可能是即将到来的现代网络（例如物联网（IoT））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驱动的网络实体需要在本地做出决策，以最大化任何网络环境的不确定性。但是，在复杂和大规模的网络中，状态和行动空间通常很大，现有的表格强化学习技术无法快速找到最佳的国家行动策略。在本文中，提出了深入的加固学习来克服上述缺点，并允许无线网关得出最佳策略以最大程度地提高网络吞吐量。当针对高级DQN技术的基准测试时，我们提出的DQN配置可提供高达1.8倍的性能，总体性能良好。
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.
