Towards Quantum Annealing for Multi-user NOMA-based Networks

TL;DR

This paper explores the application of Quantum Annealing to improve signal decoding in multi-user NOMA networks, showing comparable error performance to brute force methods but with longer execution times, and suggests parallelization for speedup.

Contribution

It introduces a QA-aided ML decoder for NOMA networks that matches brute force performance and discusses potential for parallelization to enhance speed.

Findings

QA decoder matches brute force BER performance

QA outperforms SIC in error rate

Parallelization can reduce execution time

Abstract

Quantum Annealing (QA) uses quantum fluctuations to search for a global minimum of an optimization-type problem faster than classical computers. To meet the demand for future internet traffic and mitigate the spectrum scarcity, this work presents the QA-aided maximum likelihood (ML) decoder for multi-user non-orthogonal multiple access (NOMA) networks as an alternative to the successive interference cancellation (SIC) method. The practical system parameters such as channel randomness and possible transmit power levels are taken into account for all individual signals of all involved users. The brute force (BF) and SIC signal detection methods are taken as benchmarks in the analysis. The QA-assisted ML decoder results in the same BER performance as the BF method outperforming the SIC technique, but the execution of QA takes more time than BF and SIC. The parallelization technique can be a potential aid to fasten the execution process. This will pave the way to fully realize the potential of QA decoders in NOMA systems.