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.
@article{arxiv.1903.04918,
title = {A Deep Learning Based Resource Allocation Scheme in Vehicular Communication Systems},
author = {Mimi Chen and Jiajun Chen and Xiaojing Chen and Shunqing Zhang and Shugong Xu},
journal= {arXiv preprint arXiv:1903.04918},
year = {2019}
}
Comments
arXiv admin note: text overlap with arXiv:1903.00165