Energy-Efficient Backscatter Aided Uplink NOMA Roadside Sensor Communications under Channel Estimation Errors

TL;DR

This paper proposes an energy-efficient NOMA-based framework for backscatter wireless sensors in intelligent transportation systems, optimizing power and reflection coefficients under channel uncertainties to enhance network performance.

Contribution

It introduces a novel two-stage alternating optimization algorithm (AOBWS) for joint power and reflection coefficient optimization considering channel estimation errors.

Findings

AOBWS achieves near-optimal energy efficiency with low complexity.

The algorithm outperforms benchmark exhaustive search in practical scenarios.

Simulation confirms robustness under channel uncertainties.

Abstract

This work presents non-orthogonal multiple access (NOMA) enabled energy-efficient alternating optimization framework for backscatter aided wireless powered uplink sensors communications for beyond 5G intelligent transportation system (ITS). Specifically, the transmit power of carrier emitter (CE) and reflection coefficients of backscatter aided roadside sensors are optimized with channel uncertainties for the maximization of the energy efficiency (EE) of the network. The formulated problem is tackled by the proposed two-stage alternating optimization algorithm named AOBWS (alternating optimization for backscatter aided wireless powered sensors). In the first stage, AOBWS employs an iterative algorithm to obtain optimal CE transmit power through simplified closed-form computed through Cardano's formulae. In the second stage, AOBWS uses a non-iterative algorithm that provides a closed-form expression for the computation of optimal reflection coefficient for roadside sensors under their quality of service (QoS) and a circuit power constraint. The global optimal exhaustive search (ES) algorithm is used as a benchmark. Simulation results demonstrate that the AOBWS algorithm can achieve near-optimal performance with very low complexity, which makes it suitable for practical implementations.