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arXiv · 1903.04918

A Deep Learning Based Resource Allocation Scheme in Vehicular Communication Systems

Mimi Chen·Jiajun Chen·Xiaojing Chen·Shunqing Zhang·Shugong Xu
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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.

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Mimi Chen, Jiajun Chen, Xiaojing Chen, Shunqing Zhang, Shugong Xu. 2019-03-10. A Deep Learning Based Resource Allocation Scheme in Vehicular Communication Systems. https://arxiv.org/abs/1903.04918

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