Quantum Annealing for Large MIMO Downlink Vector Perturbation Precoding

TL;DR

This paper introduces a quantum annealing-based approach to vector perturbation precoding in large MIMO systems, significantly reducing computational complexity and enabling practical implementation for systems with many antennas.

Contribution

It proposes a novel quantum annealing architecture for VPP, transforming the problem into a quadratic form suitable for QA, and demonstrates its effectiveness on real hardware for large MIMO systems.

Findings

Achieves BER of 10^{-4} in 100μs for 6x6 MIMO with 64 QAM at 32 dB SNR

Enables VPP application to large MIMO systems with existing quantum hardware

Reduces computational complexity of VPP using QA-based processing

Abstract

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 $10^{-4}$ with 100$\mu$s compute time, for a 6$\times$6 MIMO system using 64 QAM modulation at 32 dB SNR.