Trajectory Design and Power Allocation for Drone-Assisted NR-V2X Network with Dynamic NOMA/OMA

TL;DR

This paper proposes a dynamic NOMA/OMA scheme for drone-assisted vehicular networks, optimizing trajectory and power to improve sum-rate and fairness, with the scheme adapting based on link conditions and complexity considerations.

Contribution

It introduces a dynamic NOMA/OMA scheme with joint trajectory and power optimization for UAV-assisted vehicular networks, considering link conditions and decoding complexity.

Findings

NOMA outperforms OMA at high SNR unless links are weak or similar.

OMA achieves better min-rate than NOMA at high SNR.

The proposed iterative algorithm effectively optimizes trajectory and power allocation.

Abstract

In this paper, we find trajectory planning and power allocation for a vehicular network in which an unmanned-aerial-vehicle (UAV) is considered as a relay to extend coverage for two disconnected far vehicles. We show that in a two-user network with an amplify-and-forward (AF) relay, non-orthogonal-multiple-access (NOMA) always has better or equal sum-rate in comparison to orthogonal-multiple-access (OMA) at high signal-to-noise-ratio (SNR) regime. However, for the cases where i) base station (BS)-to-relay link is weak, or ii) two users have similar links, or iii) BS-to-relay link is similar to relay-to-weak user link, applying NOMA has negligible sum-rate gain. Hence, due to the complexity of successive-interference-cancellation (SIC) decoding in NOMA, we propose a dynamic NOMA/OMA scheme in which OMA mode is selected for transmission when applying NOMA has only negligible gain. Also, we show that OMA always has better min-rate than NOMA at high SNR regime. Further, we formulate two optimization problems which maximize the sum-rate and min-rate of the two vehicles. These problems are non-convex, and hence we propose an iterative algorithm based on alternating-optimization (AO) method which solves trajectory and power allocation sub-problems by successive-convex-approximation (SCA) and difference-of-convex (DC) methods, respectively. Finally, the above-mentioned performance is confirmed by simulations.