Sum Rate Fairness Trade-off-based Resource Allocation Technique for MISO NOMA Systems

Haitham Al-Obiedollah,Alister G Burr,Zhiguo Ding,Kanapathippillai Cumanan,Octavia A Dobre,Jeyarajan Thiyagalingam

doi:10.1109/wcnc.2019.8886335

Abstract

In this paper, we propose a beamforming design that jointly considers two conflicting performance metrics, namely the sum rate and fairness, for a multiple-input single-output non-orthogonal multiple access system. Unlike the conventional rate-aware beamforming designs, the proposed approach has the flexibility to assign different weights to the objectives (i.e., sum rate and fairness) according to the network requirements and the channel conditions. In particular, the proposed design is first formulated as a multi-objective optimization problem, and subsequently mapped to a single objective optimization (SOO) problem by exploiting the weighted sum approach combined with a prior articulation method. As the resulting SOO problem is non-convex, we use the sequential convex approximation technique, which introduces multiple slack variables, to solve the overall problem. Simulation results are provided to demonstrate the performance and the effectiveness of the proposed approach along with detailed comparisons with conventional rate-aware-based beamforming designs.

Highlights

Non-orthogonal multiple access (NOMA) has been proposed as a novel multiple access scheme to overcome the relatively poor spectral-efficiency of the conventional orthogonal multiple access (OMA) schemes [1], [2]
We propose a novel beamforming design that jointly considers the conflicting performance metrics, i.e., the sum rate and fairness in a multiple-input single-output (MISO) NOMA system
We have proposed a sum rate-fairness tradeoff-based beamforming design for a MISO NOMA system

Summary

Introduction

Non-orthogonal multiple access (NOMA) has been proposed as a novel multiple access scheme to overcome the relatively poor spectral-efficiency of the conventional orthogonal multiple access (OMA) schemes [1], [2]. The sum rate maximization (SRM)-based design maximizes the sum rate of all users in the cell, without taking the individual users rates into account [6] This approach significantly degrades the rates of the users with weaker channel conditions. In WSRM, higher weights are assigned to weaker users’ rates to maintain the fairness between users in terms of their achievable rates None of these conventional rate-aware-based designs consider either the instantaneous rate-requirements of the users, or the variations of the users’ channel strengths due to the mobility of the users. The SRM-based design is not capable of achieving a reasonable throughput for all users in a system where the channel strengths of the users vary significantly Such cases, the weakest user will suffer from low quality of service. The FI of the system with K users is defined as follows [12], [13]:

Objectives

Methods

Results

Conclusion