Beamforming Techniques for Non-Orthogonal Multiple Access in 5G Cellular Networks

TL;DR

This paper develops and compares various beamforming techniques for downlink MISO NOMA systems in 5G, demonstrating improved power efficiency and robustness against channel uncertainties.

Contribution

It introduces novel beamforming schemes for NOMA with perfect and imperfect CSI, employing SDR and LMI methods for tractable solutions, and highlights NOMA's power efficiency over OMA.

Findings

Robust beamforming outperforms non-robust in outage probability.

NOMA requires roughly half the power of OMA for same data rates.

Proposed methods effectively handle channel uncertainties.

Abstract

In this paper, we develop various beamforming techniques for downlink transmission for multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) systems. First, a beamforming approach with perfect channel state information (CSI) is investigated to provide the required quality of service (QoS) for all users. Taylor series approximation and semidefinite relaxation (SDR) techniques are employed to reformulate the original non-convex power minimization problem to a tractable one. Further, a fairness-based beamforming approach is proposed through a max-min formulation to maintain fairness between users. Next, we consider a robust scheme by incorporating channel uncertainties, where the transmit power is minimized while satisfying the outage probability requirement at each user. Through exploiting the SDR approach, the original non-convex problem is reformulated in a linear matrix inequality (LMI) form to obtain the optimal solution. Numerical results demonstrate that the robust scheme can achieve better performance compared to the non-robust scheme in terms of the rate satisfaction ratio. Further, simulation results confirm that NOMA consumes a little over half transmit power needed by OMA for the same data rate requirements. Hence, NOMA has the potential to significantly improve the system performance in terms of transmit power consumption in future 5G networks and beyond.