A Deep Learning Based Resource Allocation Scheme in Vehicular Communication Systems

TL;DR

This paper proposes a CNN-based resource allocation scheme for vehicular communication systems that effectively balances capacity and latency requirements, achieving near-optimal performance with significantly reduced computational time.

Contribution

It introduces a novel deep learning approach that decomposes the resource allocation problem into classification and regression tasks for real-time decision making.

Findings

CNN achieves similar performance to exhaustive search.

Significantly reduces CPU runtime to 3.62%.

Effective in balancing capacity and latency in vehicular networks.

Abstract

In vehicular communications, intracell interference and the stringent latency requirement are challenging issues. In this paper, a joint spectrum reuse and power allocation problem is formulated for hybrid vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Recognizing the high capacity and low-latency requirements for V2I and V2V links, respectively, we aim to maximize the weighted sum of the capacities and latency requirement. By decomposing the original problem into a classification subproblem and a regression sub-problem, a convolutional neural network (CNN) based approach is developed to obtain real-time decisions on spectrum reuse and power allocation. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 3.62% of its CPU runtime.