TY - GEN
T1 - Towards Quantum Annealing for Multi-user NOMA-based Networks
AU - Gabdulsattarov, Eldar
AU - Rabie, Khaled
AU - Li, Xingwang
AU - Nauryzbayev, Galymzhan
N1 - Funding Information:
This research was funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP13068122). It was also supported in part by the Nazarbayev University CRP Grant no. 11022021CRP1513.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Quantum Annealing (QA) uses quantum fluctuations to search for a global minimum of an optimization-type problem faster than classical computer. 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.
AB - Quantum Annealing (QA) uses quantum fluctuations to search for a global minimum of an optimization-type problem faster than classical computer. 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.
KW - maximum likelihood (ML)
KW - non-orthogonal multiple access (NOMA)
KW - Quantum annealing
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U2 - 10.1109/VTC2022-Fall57202.2022.10012769
DO - 10.1109/VTC2022-Fall57202.2022.10012769
M3 - Conference contribution
AN - SCOPUS:85147004056
T3 - IEEE Vehicular Technology Conference
BT - 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022
Y2 - 26 September 2022 through 29 September 2022
ER -