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IEEE Consumer Communications and Networking Conference

Over-the-air Demonstration of Quantum-Annealing-Aided Multi-user Detection in MIMO-OFDM Systems

Kouki Yonaga, Kenichi Takizawa

Published Jan 9, 2026• 0 citations
Publisher

Abstract

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.

DOI
10.1109/ccnc65079.2026.11366386

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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.
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.
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.
The high complexity of numerous optimal classical communication schemes, such as the Maximum Likelihood (ML) and Maximum A posteriori Probability (MAP) Multi-User Detector (MUD) designed for coherent detection or the ML and MAP Multiple-Symbol Differential Detectors (MSDD) conceived for non-coherent receivers often prevents their practical implementation. In this thesis we commence with a review and tutorial on Quantum Search Algorithms (QSA) and propose a number of hard-output and iterative Quantum-assisted MUDs (QMUD) and MSDDs (QMSDD). We employ a QSA, termed as the Durr-Hyer Algorithm (DHA) that finds the minimum of a function in order to perform near-optimal detection with quadratic reduction in the computational complexity, when compared to that of the ML MUD / MSDD. Two further techniques conceived for reducing the complexity of the DHA-based Quantum-assisted MUD (QMUD) are also proposed. These novel QMUDs / QMSDDs are employed in the uplink of various multiple access systems, such as Direct Sequence Code Division Multiple Access systems, Space Division Multiple Access systems as well as in Direct-Sequence Spreading and Slow Subcarrier Hopping SDMA systems amalgamated with Orthogonal Frequency Division Multiplexing and Interleave Division Multiple Access systems. Furthermore, we follow a quantum approach to achieve the same performance as the optimal Soft Input Soft-Output (SISO) classical detectors by replacing them with a quantum algorithm, which estimates the weighted sum of all the evaluations of a function. We propose a SISO QMUD / QMSDD scheme, which is the quantum-domain equivalent of the MAP MUD / MSDD. Both our EXtrinsic Information Transfer (EXIT) charts and Bit Error Ratio (BER) curves show that the computational complexity of the proposed QMUD / QMSDD is significantly lower than that of the MAP MUD / MSDD, whilst their performance remains equivalent. Moreover, we propose two additional families of iterative DHA-based QMUD / QMSDDs for performing near-optimal MAP detection exhibiting an even lower tunable complexity than the QWSA QMUD. Several variations of the proposed QMUD / QMSDDs have been developed and they are shown to perform better than the state-of-the-art low-complexity MUDs / MSDDs at a given complexity. Their iterative decoding performance is investigated with the aid of non-Gaussian EXIT charts.
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.
Multi-user (MU), multiple-input, multiple-output (MIMO) detection has been extensively investigated, and many techniques have been proposed. However, further performance improvements may be constrained by limitations in classical computation. The motivation for this work is to test whether a machine that exploits quantum principles can offer improved performance over conventional detection approaches. This paper presents an evaluation of MIMO detection based on quantum annealing (QA) when run on an actual QA quantum processing unit (QPU) and describes the challenges and potential improvements. The evaluations show promising results in some cases, such as near-optimality in a QPSK-modulated 8×8 MIMO case, but poor results in other cases, such as for larger systems or when using 16-QAM. We show that some challenges of QA detection include dealing with integrated control errors (ICE), the limited dynamic range of QA QPUs, an exponential increase in the number of qubits to the problem size, and a high computation overhead. Solving these challenges could make QA-based detection superior to conventional approaches and bring a new generation of MU-MIMO detection methods.
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.