A Novel Resource Allocation scheme for NOMA-V2X-Femtocell with Channel Aggregation

Abstract

Vehicle to everything (V2X) in heterogeneous networks concurrently retains multiple communication links within a channel: such as vehicle to vehicle (V2V), Vehicle to macro base station (V2C), and cellular user equipment to femtocell base station (U2F). To provide high spectral efficiency, there were many efforts such as non-orthogonal multiple access (NOMA) and channel aggregation. However, combining these schemes on the top of NOMA-V2X-femtocell is extremely challenging as it increases the number of dimensions to be considered. To address this issue, this paper proposes a new genetic deep learning algorithm. It employs a genetic algorithm (GA) to find a pair of communication links per channel in a way to maximize the throughput and a neural network to reduce the dimension gradually. The neural network is trained to predicts which pair can be part of the final result. The suitable pairs are marked by deep learning, then they are not shuffled in the subsequent generations. The simulation results show that the proposed scheme achieved higher throughput greater than 20%, compared to the existing GA.