# Noncoherent Multiuser Massive SIMO for Low-Latency Industrial IoT Communications

## TL;DR

This paper introduces a noncoherent detection framework for multiuser massive SIMO systems in industrial IoT, using a new constellation design and power optimization to enhance low-latency, high-reliability communication without channel estimation.

## Contribution

It proposes a novel UDCG-based constellation design and a max-min KL distance optimization for noncoherent massive SIMO systems, improving error performance in low-latency IIoT applications.

## Key findings

- Outperforms existing max-min Euclidean distance methods in error performance.
- Better error performance than conventional coherent ZF receivers for cell edge users.
- Effective in large antenna array scenarios with reduced latency and overhead.

## Abstract

In this paper, we consider a multiuser massive single-input multiple-output (SIMO) enabled Industrial Internet of Things (IIoT) communication system. To reduce the latency and overhead caused by channel estimation, we assume that only the large-scale fading coefficients are available. We employ a noncoherent maximum-likelihood (ML) detector at the receiver side which does not need the instantaneous channel state information (CSI). For such a massive SIMO system, we present a new design framework to assure that each transmitted signal matrix can be uniquely determined in the noise-free case and be reliably estimated in noisy cases. The key idea is to utilize a new concept called the uniquely decomposable constellation group (UDCG) based on the practically used quadrature amplitude modulation~(QAM) constellation. To improve the average error performance when the antenna array size is scaled up, we propose a max-min Kullback-Leibler (KL) distance design by carrying out optimization over the transmitted power and the sub-constellation assignment. Finally, simulation results show that the proposed design outperforms significantly the existing max-min Euclidean distance based method in terms of error performance. Moreover, our proposed approach also has a better error performance than the conventional coherent zero-forcing (ZF) receiver with orthogonal training for cell edge users.