Learn to Compress CSI and Allocate Resources in Vehicular Networks

TL;DR

This paper introduces a hybrid deep learning-based resource allocation framework for vehicular networks, combining centralized and distributed schemes to optimize long-term network performance with reduced signaling overhead.

Contribution

It proposes a novel hybrid architecture with deep neural networks for CSI compression and deep Q-networks for resource allocation, improving efficiency and robustness in V2X networks.

Findings

Achieves near-optimal network performance in simulations.

Robust to feedback interval and noise variations.

Reduces signaling overhead with learned CSI compression.

Abstract

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.