论文标题
一种增强学习方法，用于有效的机会主义车辆到云数据传输
A Reinforcement Learning Approach for Efficient Opportunistic Vehicle-to-Cloud Data Transfer
论文作者
论文摘要
预计车辆众包将成为智能运输系统（ITS）域中数据驱动优化的关键催化剂。然而，由车辆到云的变速器引起的大规模机器型通信（MMTC）的预期增长将面临与容量相关的挑战的蜂窝网络基础设施。在不引入其他物理基础架构的情况下实现救济的一种认知方法是将机会性数据传输应用于延迟耐受性应用程序。在此，客户以渠道感知的方式安排其数据传输，以避免重新转载和干扰其他单元用户。在本文中，我们介绍了一种针对此类资源软件数据传输的新颖方法，该方法通过基于强化学习的决策来汇集了对网络质量预测的监督学习。在不同方案的多个移动网络运营商（MNOS）的公共蜂窝网络中，使用数据驱动的网络模拟和现实世界实验进行了性能评估。在大多数情况下，提出的传输方案显着超过了最先进的概率方法，并且与常规的周期性数据传输相比，在上行链路中，上行链路上的数据速率提高高达181％，在下行链路传输方向上最多可提高270％。
Vehicular crowdsensing is anticipated to become a key catalyst for data-driven optimization in the Intelligent Transportation System (ITS) domain. Yet, the expected growth in massive Machine-type Communication (mMTC) caused by vehicle-to-cloud transmissions will confront the cellular network infrastructure with great capacity-related challenges. A cognitive way for achieving relief without introducing additional physical infrastructure is the application of opportunistic data transfer for delay-tolerant applications. Hereby, the clients schedule their data transmissions in a channel-aware manner in order to avoid retransmissions and interference with other cell users. In this paper, we introduce a novel approach for this type of resourceaware data transfer which brings together supervised learning for network quality prediction with reinforcement learningbased decision making. The performance evaluation is carried out using data-driven network simulation and real world experiments in the public cellular networks of multiple Mobile Network Operators (MNOs) in different scenarios. The proposed transmission scheme significantly outperforms state-of-the-art probabilistic approaches in most scenarios and achieves data rate improvements of up to 181% in uplink and up to 270% in downlink transmission direction in comparison to conventional periodic data transfer.
