The ever-increasing and unbalanced traffic load in cellular vehicle-to-everything (C-V2X) networks have increased the network congestion and led to user dissatisfaction. To relieve the network congestion and improve the traffic load balance, in this paper, we propose an intelligent software defined C-V2X network framework to enable flexible and low-complexity traffic offloading by decoupling the network data plane from the control plane. In the data plane, the cellular traffic offloading and the vehicle assisted traffic offloading are jointly performed. In the control plane, deep learning is deployed to reduce the software defined network (SDN) control complexity and improve the traffic offloading efficiency. Under the proposed framework, we investigate the traffic offloading problem, which can be formulated as a multi-objective optimization problem. Specifically, the first objective maximizes the cellular access point (AP) throughput with consideration of the load balance by associating the users with the APs. The second objective maximizes the vehicle throughput with consideration of the vehicle trajectory by associating the delay-insensitive users with the vehicles. The two objectives are coupled by the association between the cellular APs and the vehicles. A deep learning based online-offline approach is proposed to solve the multi-objective optimization problem. The online stage decouples the optimization problem into two sub-problems and utilizes the ‘Pareto optimal’ to find the solutions. The offline stage utilizes deep learning to learn from the historical optimization information of the online stage and helps predict the optimal solutions with reduced complexity. Numerical results are provided to validate the advantages of our proposed traffic offloading approach via deep learning in C-V2X networks.

中文翻译：

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深度学习在智能软件定义的蜂窝 V2X 网络中实现流量卸载

蜂窝车对一切（C-V2X）网络中不断增加和不平衡的流量负载增加了网络拥塞并导致用户不满。为了缓解网络拥塞并改善流量负载平衡，在本文中，我们提出了一种智能软件定义的 C-V2X 网络框架，通过将网络数据平面与控制平面解耦来实现灵活且低复杂度的流量卸载。在数据平面，蜂窝流量分流和车辆辅助流量分流是联合进行的。在控制平面上部署深度学习，降低软件定义网络（SDN）控制复杂度，提高流量分流效率。在提出的框架下，我们研究了流量卸载问题，可以表述为一个多目标优化问题。具体而言，第一个目标通过将用户与 AP 关联，在考虑负载平衡的情况下最大化蜂窝接入点 (AP) 吞吐量。第二个目标是通过将延迟不敏感的用户与车辆相关联，在考虑车辆轨迹的情况下最大化车辆吞吐量。这两个目标通过蜂窝 AP 和车辆之间的关联进行耦合。提出了一种基于深度学习的在线-离线方法来解决多目标优化问题。在线阶段将优化问题分解为两个子问题，并利用“帕累托最优”找到解决方案。离线阶段利用深度学习从在线阶段的历史优化信息中学习，帮助预测具有降低复杂度的最优解决方案。提供了数值结果以验证我们通过 C-V2X 网络中的深度学习提出的流量卸载方法的优势。