Grant-Free Resource Allocation for NOMA V2X Uplink Systems Using a Genetic Algorithm Approach

Abstract

While NOMA-V2V (non-orthogonal multiple accesscan-vehicle-to-vehicle) effectively achieve massive connectivity requirements in 5G network systems, minimizing communication latency is a very crucial challenge. To address the latency problem, we propose a channel allocation method called hyper-fraction, which divides the road into many zones and allocates a channel to each zone. Then, a vehicle located within the corresponding zone uses the channel allocated to the zone. Hyper-fraction will allow the system to minimize communication latency between a user equipment (UE) and a base station (BS) caused by scheduling processes and consequentially reduce the overall latency of the system. In the simulation, a novel concept of genetic algorithm (GA) is utilized, called GA with continuous pool. It is an approach to enable conventional GA to solve optimization problems for continuous situations within much less computation, especially in situations where the elements in the system keep moving such as vehicular networks. As a result, GA with continuous pool is proven to be an effective heuristic method to improve throughput rate, as well as hyper-fraction improving the latency of NOMA V2V and vehicle-to-infrastructure (V2I) systems.