学习压缩CSI和分配车辆网络中的资源
Learn to Compress CSI and Allocate Resources in Vehicular Networks
摘要：
资源分配对车联网（V2X）网络的性能有着直接而深远的影响。在本文中，我们开发了一种混合体系结构，包括集中式决策和分布式资源共享（C-决策方案），以最大限度地提高所有车辆的长期总和率。为了减少网络信令开销，每辆车都使用深度神经网络来压缩其观察到的信息，然后将其反馈给集中式决策单元。集中式决策单元采用深度Q网络分配资源，然后将决策结果发送给所有车辆。我们还为每个车辆采用了一个量化层，它学习对连续反馈进行量化。此外，我们设计了一种机制来平衡车辆到车辆（V2 V）链路和车辆到基础设施（V2 I）链路的传输。为了进一步促进分布式频谱共享，我们还为每个V2 V链路提出了分布式决策和频谱共享架构（D-Decision方案）。通过大量的仿真结果，我们表明，建议的C-决策和D-决策方案都可以达到接近最优的性能，是强大的反馈间隔的变化，输入噪声和反馈噪声。
英文摘要：
Resource allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. In this paper, we develop a hybrid architecture consisting of centralized decision making and distributed resource sharing (the C-Decision scheme) to maximize the long-term sum rate of all vehicles. To reduce the network signaling overhead, each vehicle uses a deep neural network to compress its observed information that is thereafter fed back to the centralized decision making unit. The centralized decision unit employs a deep Q-network to allocate resources and then sends the decision results to all vehicles. We further adopt a quantization layer for each vehicle that learns to quantize the continuous feedback. In addition, we devise a mechanism to balance the transmission of vehicle-to-vehicle (V2V) links and vehicle-to-infrastructure (V2I) links. To further facilitate distributed spectrum sharing, we also propose a distributed decision making and spectrum sharing architecture (the D-Decision scheme) for each V2V link. Through extensive simulation results, we demonstrate that the proposed C-Decision and D-Decision schemes can both achieve near-optimal performance and are robust to feedback interval variations, input noise, and feedback noise.
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