To improve user experience and allocate reasonably spectrum resources, the quality of experience (QoE) for user is introduced as an indicator to express the user's satisfaction with the service provided. The QoE‐driven resource allocation optimization algorithm for orthogonal frequency‐division multiple is designed to maximize the user's QoE and optimize resource allocation in vehicular cloud (VC) long‐term evolution (LTE) networks. Therein, a novel QoE model which reflects delay, transmission rate, and service price by mean opinion score (MOS) is proposed to identify different services. Furthermore, the characteristics of delay, transmission rate, and service price are also analyzed specifically at different time slots, respectively. Simulation results show that the proposed resource allocation algorithm could achieve a better QoE in the field of user satisfaction and throughput compared with other traditional algorithms, especially when the number of vehicles is greater than 30 or 22, respectively. Meanwhile, the impact of fairness on QoE and throughput are also improved apparently when the number of vehicles is small. In end, the QoE‐mode brings performance improvements on delay, transmission rate, and service price compared with other single X mode.

中文翻译：

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车辆云长期演进网络中经验驱动的资源分配的质量

为了改善用户体验并合理分配频谱资源，引入了用户体验质量（QoE）作为指标，以表达用户对所提供服务的满意度。正交频分复用的QoE驱动资源分配优化算法旨在最大化用户的QoE并优化车辆云（VC）长期演进（LTE）网络中的资源分配。其中，提出了一种新颖的QoE模型，该模型通过平均意见评分（MOS）反映延迟，传输速率和服务价格，以识别不同的服务。此外，还分别在不同的时隙分别分析了延迟，传输速率和服务价格的特性。仿真结果表明，与其他传统算法相比，本文提出的资源分配算法在用户满意度和吞吐量方面可以实现更好的QoE，特别是当车辆数量分别大于30或22时。同时，当车辆数量较少时，公平性对QoE和吞吐量的影响也得到明显改善。最后，与其他单一X模式相比，QoE模式在延迟，传输速率和服务价格方面带来了性能上的改进。