Spectral Efficiency Optimization for Next Generation NOMA-enabled IoT Networks

Wali Ullah Khan, Ju Liu, Furqan Jameel, Vishal Sharma, Riku Jäntti, Zhu Han

Research output: Contribution to journalArticle

Abstract

Non-orthogonal multiple access (NOMA) is a rapidly emerging paradigm with the capability to improve the spectral efficiency of data-driven, intelligence inspired, and highly digitized sixth-generation (6G) wireless networks. In the backdrop of ever-evolving NOMA techniques, this article presents a novel resource optimization framework for maximizing the spectral efficiency (SE) of the Internet-of-things (IoT) networks using power domain NOMA. The proposed framework considers a limited number of frequency blocks in the IoT network and provides an optimal power and frequency block allocation method. Different practical constraints like successive interference cancellation (SIC) complexity, ensuring the minimum gap of received power among different IoT equipment over the same frequency block for successful SIC operation, quality of services requirements, and IoT equipment's transmit powers have also been taken into account. Accordingly, a non-convex optimization problem has been formulated for resource management where the objective of spectral efficiency is coupled by both frequency block and power allocation. To effectively solve this problem, the resource optimization problem is decoupled into two subproblems for frequency block assignment and power allocation. A suboptimal algorithm has been designed for frequency block assignment and a new optimal sequential quadratic programming (SQP) approach is employed to solve the non-convex power control subproblem. For the sake of fair comparison, a low complexity suboptimal NOMA power allocation scheme, based on Karush?Kuhn?Tucker (KKT) conditions, is also provided. The results unveil that the proposed optimal resource management scheme significantly outperforms the suboptimal scheme in terms of the total spectral efficiency of the overall IoT network.
Original languageEnglish
Number of pages13
JournalIEEE Transactions on Vehicular Technology
DOIs
Publication statusAccepted - 10 Nov 2020

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