Clustering and Power Optimization for NOMA Multi-Objective Problems

TL;DR

This paper develops a joint clustering and power optimization framework for adaptive NOMA/OMA uplink transmissions, balancing energy efficiency for IoT and spectral efficiency for eMBB, demonstrating superior performance over existing methods.

Contribution

It introduces a novel clustering and power allocation algorithm that adaptively selects between NOMA and OMA for multi-objective uplink optimization.

Findings

Outperforms existing clustering and MA selection methods.

Effectively balances energy and spectral efficiency.

Enhances uplink transmission performance in IoT and eMBB scenarios.

Abstract

This paper considers uplink multiple access (MA) transmissions, where the MA technique is adaptively selected between Non Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA). Two types of users, namely Internet of Things (IoT) and enhanced mobile broadband (eMBB) coexist with different metrics to be optimized, energy efficiency (EE) for IoT and spectral efficiency (SE) for eMBB. The corresponding multi-objective power allocation problems aiming at maximizing a weighted sum of EE and SE are solved for both NOMA and OMA. Based on the identification of the best MA strategy, a clustering algorithm is then proposed to maximize the multi-objective metric per cluster as well as NOMA use. The proposed clustering, power allocation and MA selection algorithm is shown to outperform other clustering solutions and non-adaptive MA techniques.