Massive MIMO Detection Techniques: A Survey

• Published in: IEEE Communications surveys and tutorials Vol. 21; no. 4; pp. 3109 - 3132
• Main Authors: Albreem, Mahmoud A., Juntti, Markku, Shahabuddin, Shahriar
• Format: Journal Article
• Language: English
• Published: IEEE 01-01-2019
• Subjects: 5G mobile communication; approximate matrix inversion; Belief propagation; Compressed sensing; compressive sensing; detection; lattice reduction; local search; Machine learning; massive MIMO; MIMO communication; sparsity
• ISSN: 2373-745X
• DOI: 10.1109/COMST.2019.2935810

Massive multiple-input multiple-output (MIMO) is a key technology to meet the user demands in performance and quality of services (QoS) for next generation communication systems. Due to a large number of antennas and radio frequency (RF) chains, complexity of the symbol detectors increased rapidly in a massive MIMO uplink receiver. Thus, the research to find the perfect massive MIMO detection algorithm with optimal performance and low complexity has gained a lot of attention during the past decade. A plethora of massive MIMO detection algorithms has been proposed in the literature. The aim of this paper is to provide insights on such algorithms to a generalist of wireless communications. We garner the massive MIMO detection algorithms and classify them so that a reader can find a distinction between different algorithms from a wider range of solutions. We present optimal and near-optimal detection principles specifically designed for the massive MIMO system such as detectors based on a local search, belief propagation and box detection. In addition, we cover detectors based on approximate inversion, which has gained popularity among the VLSI signal processing community due to their deterministic dataflow and low complexity. We also briefly explore several nonlinear small-scale MIMO (2-4 antenna receivers) detectors and their applicability in the massive MIMO context. In addition, we present recent advances of detection algorithms which are mostly based on machine learning or sparsity based algorithms. In each section, we also mention the related implementations of the detectors. A discussion of the pros and cons of each detector is provided.