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International Conference on Communications

Quantum Annealing for Next-Generation MU-MIMO Detection: Evaluation and Challenges

J. C. De Luna Ducoing, Konstantinos Nikitopoulos

Published May 16, 2022• 19 citations
Publisher

Abstract

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.

DOI
10.1109/icc45855.2022.9839195

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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.
Digital annealing has been successfully applied to solving combinatorial optimization (CO) problems. It is more flexible, robust, and easier to deploy on edge platforms compared to its counterparts including quantum annealing and analog and in-memory Ising machines. In this work, we apply digital annealing to compute-intensive communication digital signal processing problems, including multi-user detection in multiple-input and multiple-output (MU-MIMO) wireless communication systems and decoding low-density parity-check (LDPC) codes. We show that digital annealing can achieve near maximum likelihood (ML) accuracy for MIMO detection with even lower complexity than the conventional minimum mean square error (MMSE) detection. In LDPC decoding, we enhance digital annealing by introducing a new cost function that improves decoding accuracy and reduces computational complexity compared to the standard formulations.
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.
Quantum Annealing (QA)-accelerated MIMO detection is an emerging research approach in the context of NextG wireless networks. The opportunity is to enable large MIMO systems and thus improve wireless performance. The approach aims to leverage QA to expedite the computation required for theoretically optimal but computationally-demanding Maximum Likelihood detection to overcome the limitations of the currently deployed linear detectors. This paper presents X-ResQ, a QA-based MIMO detector system featuring fine-grained quantum task parallelism that is uniquely enabled by the Reverse Annealing (RA) protocol. Unlike prior designs, X-ResQ has many desirable system properties for a parallel QA detector and has effectively improved detection performance as more qubits are assigned. In our evaluations on a state-of-the-art quantum annealer, fully parallel X-ResQ achieves near-optimal throughput (over 10 bits/s/Hz) for 4×64\times64×6 MIMO with 16-QAM using six levels of parallelism with 240 qubits and 220 μ220~μ220 μs QA compute time, achieving 2.5--5×\times× gains compared against other tested detectors. For more comprehensive evaluations, we implement and evaluate X-ResQ in the non-quantum digital setting. This non-quantum X-ResQ demonstration showcases the potential to realize ultra-large 1024×10241024\times10241024×1024 MIMO, significantly outperforming other MIMO detectors, including the state-of-the-art RA detector classically implemented in the same way.
Quantum Annealing (QA)-accelerated MIMO detection is an emerging research approach in the context of NextG wireless networks. The opportunity is to enable large MIMO systems and thus improve wireless performance. The approach aims to leverage QA to expedite the computation required for theoretically optimal but computationally-demanding Maximum Likelihood detection to overcome the limitations of the currently deployed linear detectors. This paper presents X-ResQ, a QA-based MIMO detector system featuring flexible parallelism that is uniquely enabled by quantum Reverse Annealing (RA). Unlike prior designs, X-ResQ has many desirable parallel QA system properties and has effectively improved detection performance as more qubits are assigned. In our evaluations on a state-of-the-art quantum annealer, fully parallel X-ResQ achieves near-optimal throughput for 4 × 6 MIMO with 16-QAM using approx. 240 qubits achieving 2.5–5× gains compared against other classical and quantum detectors. We also implement and evaluate X-ResQ in the non-quantum digital setting for more comprehensive evaluations. This classical X-ResQ showcases the potential to realize ultra-large 1024 × 1024 MIMO, significantly outperforming other MIMO detectors, including the state-of-the-art RA detector classically implemented in the same way.
In a multi-user system with multiple antennas at the base station, precoding techniques in the downlink broadcast channel allow users to detect their respective data in a non-cooperative manner. Vector Perturbation Precoding (VPP) is a non-linear variant of transmit-side channel inversion that perturbs user data to achieve full diversity order. While promising, finding an optimal perturbation in VPP is known to be an NP-hard problem, demanding heavy computational support at the base station and limiting the feasibility of the approach to small MIMO systems. This work proposes a radically different processing architecture for the downlink VPP problem, one based on Quantum Annealing (QA), to enable the applicability of VPP to large MIMO systems. Our design reduces VPP to a quadratic polynomial form amenable to QA, then refines the problem coefficients to mitigate the adverse effects of QA hardware noise. We evaluate our proposed QA based VPP (QAVP) technique on a real Quantum Annealing device over a variety of design and machine parameter settings. With existing hardware, QAVP can achieve a BER of 10−4with 100µs compute time, for a 6 × 6 MIMO system using 64 QAM modulation at 32 dB SNR.
Optimal MIMO detection is one of the most computationally challenging tasks in wireless systems. We show that the quantum-inspired computing approach based on Coherent Ising Machines (CIMs) is a promising candidate for performing near-optimal MIMO detection. We propose a novel regularized Ising formulation for MIMO detection that mitigates a common error floor issue in the direct approach adopted in the existing literature on MIMO detection using Quantum Annealing. We evaluate our methods using a simplified, quantum-inspired model and show that our methods can achieve a near-optimal performance for several Large MIMO systems, like16×16,20×2016\times 16,20\times 2016×16,20×20, and24×2424\times 2424×24MIMO with BPSK modulation.