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International Conference on Communications

Quantum Annealing-Aided Multi-User Detection: An Application to Uplink Non-Orthogonal Multiple Access

Kouki Yonaga, Kenichi Takizawa

Published May 28, 2023• 7 citations
Publisher

Abstract

A new method to realize computation of the log-likelihood ratio (LLR) in multi-user detection (MUD) by quantum annealing (QA) is proposed herein. One metaheuristic algorithm for solving combinatorial optimization problems is QA. Recently, QA has been applied in various fields to hasten computations. This study specifically examines the application of QA in up-link non-orthogonal multiple access (UL-NOMA) that employs iterative MUD. Our proposed method, QA-aided MUD, uses QA to calculate LLR in iterative MUD. We tested QA-aided MUD with a D-Wave quantum annealer. The error rate analysis demonstrates that QA-aided MUD has comparable performance to that of conventional MUD method. Furthermore, when the annealing time was set to 20 µs, we demonstrated than that the total computation time is 1/10 of the computation time necessary when using a digital computer. We also evaluated the convergence behavior of QA-aided MUD through evolution of mutual information. The results indicate that QA-assisted MUD has almost identical convergence performance to that of the conventional MUD method for LLR calculations.

DOI
10.1109/icc45041.2023.10279631

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A new method to realize computation of the log-likelihood ratio (LLR) in multi-user detection (MUD) by quantum annealing (QA) is proposed herein. One metaheuristic algorithm for solving combinatorial optimization problems is QA. Recently, QA has been applied in various fields to hasten computations. This study specifically examines the application of QA in up-link non-orthogonal multiple access (UL-NOMA) that employs iterative MUD. Our proposed method, QA-aided MUD, uses QA to calculate LLR in iterative MUD. We tested QA-aided MUD with a D-Wave quantum annealer. The error rate analysis demonstrates that QA-aided MUD has comparable performance to that of conventional MUD method. Furthermore, when the annealing time was set to 20 µs, we demonstrated than that the total computation time is 1/10 of the computation time necessary when using a digital computer. We also evaluated the convergence behavior of QA-aided MUD through evolution of mutual information. The results indicate that QA-assisted MUD has almost identical convergence performance to that of the conventional MUD method for LLR calculations.
We present performance evaluation of quantum-annealing-aided multi-user detection (QA-aided MUD) in over-loaded and fully loaded multi-input multi-output systems with orthogonal frequency division multiplexing (MIMO-OFDM). QAaided MUD is a method of iterative multi-user detection (MUD) accelerated by using QA. We previously applied this method to single-input single-output (SISO) single-carrier non-orthogonal multiple access (SC-NOMA) and demonstrated that it achieves error rate and convergence performance comparable to the conventional iterative MUD based on the MAP algorithm. In this study, we have newly developed QA-aided MUD for MIMO-OFDM and conducted performance evaluation under the 3GPP UMa channel model by employing the D-Wave qquantum annealer and other digital annealing techniques. The evaluation results show that the block error rate (BLER) obtained by QA-aided MUD outperforms that of conventional low-complexity MUDs, particularly in overloaded scenarios. It is also observed that the annealing parameters significantly impact BLER in some conditions. Additionally, we present bit error rate (BER) analysis using a few instances, employing both the D-Wave quantum annealer and other digital annealing techniques. The results demonstrate that QA-aided MUD with each annealing technique avoids the error floor and can achieve error-free performance. Finally, we discuss future perspectives by evaluating the computational complexity and computation time of QA-aided MUD.
This study presents an over-the-air (OTA) demonstration of quantum-annealing-aided multi-user detection (QA-aided MUD). QA-aided MUD is a quantum–digital hybrid method that combines iterative multi-user detection (MUD) and quantum annealing (QA). We apply it to multi-input multi-output (MIMO) systems with orthogonal frequency division multiplexing (OFDM) transmission and least squares (LS) channel estimation. The OTA experiments in an outdoor environment demonstrate that QA-aided MUD can successfully detect transmitted signals from eight user equipments.
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.
With the explosive proliferation of mobile devices, the scarcity of communication resources has emerged as a critical issue in the next generation (NG) communication systems. To overcome this, non-orthogonal multiple access (NOMA) has been shown to provide a beneficial spectral efficiency gain, while supporting massive connectivity. Sparse code multiple access (SCMA) is one of the NOMA schemes, but its application is hampered by its high-complexity multi-user detection. Fortunately, quantum search techniques were shown to substantially reduce the complexity of multi-user detectors (MUD). However, the quantum search-aided multiuser detection of SCMA is an open problem at the time of writing. Hence, we conceive a pair of quantum search-aided MUDs for SCMA, namely, a quantum-assisted message passing algorithm (Q-MPA) -based MUD and a quantum-assisted sphere decoder-based MPA (QSD-MPA) MUD.
The potentially excessive complexity of the Maximum Likelihood Multi-User Detector (ML MUD) in large-scale Spatial Division Multiple Access (SDMA) systems dictates the employment of low-complexity sub-optimal MUDs in the context of conventional systems. However, this limitation was circumvented by the recently proposed Dürr-Høyer Algorithm (DHA)-aided Quantum Weighted Sum Algorithm (QWSA)-based Quantum Multi-User Detector (QMUD) employed for performing optimal ML iterative detection in SDMA systems. Focusing our attention on the QWSA, we analyse the QMUD and the evolution of the quantum system with the aid of a simple SDMA uplink scenario. We characterize the performance of the DHA-QWSA QMUD advocated, which is capable of matching the performance of the ML MUD both in terms of its EXIT charts and BER curves.
Detecting active users in a non-orthogonal multiple access (NOMA) network poses a significant challenge for 5G/6G applications. Traditional algorithms tackling this task, relying on classical processors, have to make a compromise between performance and complexity. However, a quantum computing based strategy called quantum annealing (QA) can mitigate this tradeoff. In this paper, we first propose a mapping between the AUD searching problem and the identification of the ground state of an Ising Hamiltonian. Then, we compare the execution times of our QA approach for several code domain multiple access (CDMA) scenarios. We evaluate the impact of the cross-correlation properties of the chosen codes in a NOMA network for detecting the active user's set.