# Low Complexity Iterative Detection for a Large-scale Distributed MIMO Prototyping System

arXiv: https://arxiv.org/abs/1811.07505 · HTML: https://mlsanity.com/paper/1811.07505/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [Causality-Driven Reinforcement Learning for Joint Communication and Sensing](https://mlsanity.com/paper/2409.15329/) (2024)

## Abstract (arXiv)

In this paper, we study the low-complexity iterative soft-input soft-output (SISO) detection algorithm in a large-scale distributed multiple-input multiple-output (MIMO) system. The uplink interference suppression matrix is designed to decompose the received multi-user signal into independent single-user receptions. An improved minimum-mean-square-error iterative soft decision interference cancellation (MMSE-ISDIC) based on eigenvalue decomposition (EVD-MMSE-ISDIC) is proposed to perform low-complexity detection of the decomposed signals. Furthermore, two iteration schemes are given to improve receiving performance, which are iterative detection and decoding (IDD) scheme and iterative detection (ID) scheme. While IDD utilizes the external information generated by the decoder for iterative detection, the output information of the detector is directly exploited with ID. In particular, a large-scale distributed MIMO prototyping system is introduced and a 32 × 32 (4 user equipments (UEs) and 4 remote antenna units (RAUs), each equipped with 8 antennas) experimental tests at 3.5 GHz was performed. The experimental results show that the proposed iterative receiver greatly outperforms the linear MMSE receiver, since it reduces the average number of error blocks of the system significantly.