How to improve delay-sensitive traffic throughput is an open issue in vehicular communication networks, where a great number of vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) links coexist. To address this issue, this paper proposes to employ a hybrid deep transfer learning scheme to allocate radio resources. Specifically, the traffic throughput maximization problem is first formulated by considering interchannel interference and statistical delay guarantee. The effective capacity theory is then applied to develop a power allocation scheme on each channel reused by a V2I and a V2V link. Thereafter, a deep transfer learning scheme is proposed to obtain the optimal channel assignment for each V2I and V2V link. Simulation results validate that the proposed scheme provides a close performance guarantee compared to a globally optimal scheme. Besides, the proposed scheme can guarantee lower delay violation probability than the schemes aiming to maximize the channel capacity.

中文翻译：

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超越5G车载网络的有效容量最大化：一种混合式深度传输学习方法

在车辆通信网络中，如何提高对延迟敏感的流量吞吐量是一个悬而未决的问题，在该网络中，大量的车辆到基础设施（V2I）和车辆到车辆（V2V）链接共存。为了解决这个问题，本文提出采用混合深度转移学习方案来分配无线电资源。具体地，首先通过考虑信道间干扰和统计时延保证来提出业务吞吐量最大化问题。然后，将有效容量理论应用于在V2I和V2V链路重用的每个通道上开发功率分配方案。此后，提出了一种深度转移学习方案，以获得每个V2I和V2V链路的最佳信道分配。仿真结果证明，与全局最优方案相比，该方案提供了紧密的性能保证。此外，与旨在最大化信道容量的方案相比，所提出的方案可以保证更低的延迟违规概率。