Fully Decentralized Massive MIMO Detection Based on Recursive Methods

TL;DR

This paper introduces a fully decentralized Massive MIMO detection algorithm based on recursive methods, eliminating the need for a central node and significantly reducing interconnection data rates, thus enabling scalable and efficient uplink detection.

Contribution

The paper proposes a novel recursive, decentralized detection algorithm for Massive MIMO that converges to zero-forcing solutions without requiring matrix inversion hardware.

Findings

Converges asymptotically to zero-forcing detection.

Reduces interconnection data-rate compared to centralized architectures.

Enables scalable Massive MIMO uplink detection.

Abstract

Algorithms for Massive MIMO uplink detection typically rely on a centralized approach, by which baseband data from all antennas modules are routed to a central node in order to be processed. In case of Massive MIMO, where hundreds or thousands of antennas are expected in the base-station, this architecture leads to a bottleneck, with critical limitations in terms of interconnection bandwidth requirements. This paper presents a fully decentralized architecture and algorithms for Massive MIMO uplink based on recursive methods, which do not require a central node for the detection process. Through a recursive approach and very low complexity operations, the proposed algorithms provide a sequence of estimates that converge asymptotically to the zero-forcing solution, without the need of specific hardware for matrix inversion. The proposed solution achieves significantly lower interconnection data-rate than other architectures, enabling future scalability.