Transmission control and optimization in next-generation hybrid wireless networks: an online reinforcementlearning approach
Transmission control and optimization in next-generation hybrid wireless networks: an online reinforcementlearning approach
复制标题
下一代混合无线网络中的传输控制和优化：在线强化学习方法
DOI：
10.1117/12.2623041
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Chen, G.
中科院分区：
文献类型：
--
作者：
Dinh, S.;Liu, H.;Zhao, Q.;Li, Y.;DeCorte, N.;Chen, G.
下一代（5G及以上）蜂窝网络承诺更高的吞吐量和更低的延迟。然而，信道质量差的移动用户不仅会遭受与基站的低数据速率连接，还会降低蜂窝的总吞吐量并增加总体延迟。在本文中，我们考虑了一个混合蜂窝和移动自组织设备到设备（D2D）网络，它利用广域蜂窝覆盖和高速自组织D2D中继的优势来增强网络性能和可扩展性。也可以部署专用中继设备，如无人驾驶飞行器(uav)/无人机，以进一步改善网络连接，从而提高吞吐量。基站可以将目的为蜂窝信道质量差的移动用户的数据包发送到具有较好蜂窝信道质量的代理移动设备。代理移动设备将数据包转发到目的地，从而显著提高网络吞吐量和延迟。我们制定了数据传输问题，并设计了一种基于在线强化学习的算法，以实现最佳的传输性能。
Next-generation (5G & beyond) cellular networks promise much higher throughput and lower latency. However, mobile users experiencing poor channel quality not only suffer low data-rate connections with the base station but also reduce cell’s aggregate throughput and increase overall delay. In this paper, we consider a hybrid cellular and mobile ad hoc Device-to-Device (D2D) network that leverages the advantages of both wide-area cellular coverage and high-speed ad hoc D2D relaying to enhance network performance and scalability. Dedicated relay devices, such as Unmanned Aerial Vehicles (UAVs)/drones, can also be deployed to further improve network connectivity and thus throughput. The base station may send the packets destined for a mobile user with poor cellular channel quality to a proxy mobile device with better cellular channel quality. The proxy mobile device will relay the packets to the destination, thereby significanltly improving network throughput and delay. We formulate the data transmission problem and design an online reinforcement learning-based algorithm to achieve the best transmission performance.