Alternative Normalized-Preconditioning for Scalable Iterative Large-MIMO Detection

TL;DR

This paper introduces ANPID, a scalable iterative detector for large-MIMO systems that achieves near-optimal detection, low complexity, fast convergence, and robustness to spatial non-stationarity, outperforming existing methods.

Contribution

The paper proposes a novel ANPID detector combining damping demodulation with stationary iterative methods for scalable large-MIMO detection.

Findings

ANPID achieves near-ML detection performance.

It has low computational complexity, proportional to the number of transmit antennas.

ANPID demonstrates superior performance in highly-loaded large MIMO scenarios.

Abstract

Signal detection in large multiple-input multiple-output (large-MIMO) systems presents greater challenges compared to conventional massive-MIMO for two primary reasons. First, large-MIMO systems lack favorable propagation conditions as they do not require a substantially greater number of service antennas relative to user antennas. Second, the wireless channel may exhibit spatial non-stationarity when an extremely large aperture array (ELAA) is deployed in a large-MIMO system. In this paper, we propose a scalable iterative large-MIMO detector named ANPID, which simultaneously delivers 1) close to maximum-likelihood detection performance, 2) low computational-complexity (i.e., square-order of transmit antennas), 3) fast convergence, and 4) robustness to the spatial non-stationarity in ELAA channels. ANPID incorporates a damping demodulation step into stationary iterative (SI) methods and alternates between two distinct demodulated SI methods. Simulation results demonstrate that ANPID fulfills all the four features concurrently and outperforms existing low-complexity MIMO detectors, especially in highly-loaded large MIMO systems.