计算机科学
计算机网络
基站
蜂窝网络
网络数据包
吞吐量
强化学习
无线自组网
无线网络
继电器
无线
电信
人工智能
功率(物理)
物理
量子力学
作者
Son Dinha,Huan Liu,Qi Zhao,Yang Li,Nicholas DeCorte,Genshe Chen
摘要
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.
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