1 research outputs found
A Deep Learning Based Resource Allocation Scheme in Vehicular Communication Systems
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.Comment: arXiv admin note: text overlap with arXiv:1903.0016