Blind Massive MIMO for Dense IoT Networks

TL;DR

This paper introduces a CSIT-free precoding technique for massive MIMO in dense IoT networks, reducing overhead and energy use while maintaining high spectral efficiency in mmWave FDD systems.

Contribution

The paper proposes the CIRCLE precoding method that eliminates the need for channel state information at the transmitter in massive MIMO IoT networks.

Findings

CIRCLE achieves interference-free signal combining.

The method reduces CSIT overhead and energy consumption.

Theoretical analysis and simulations confirm effectiveness.

Abstract

In this paper, we investigate the downlink communication challenges in heavy-load Internet-of-Things (IoT) networks supported by frequency-division-duplexing (FDD) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The excessive overhead required for obtaining channel state information at the transmitter (CSIT) is essential to achieve high spectral efficiency through conventional massive MIMO techniques; however, it hinders the deployment of ultra-reliable low-latency communications (URLLC) and leads to significant energy expenditure, particularly in dense IoT networks. To address this challenge, we propose an innovative CSIT-Free MIMO precoding method, referred to as CIRculant information Classification via Linear Estimation (CIRCLE). Our major contribution is the design of a CSIT-independent (or deterministic) precoding, which is constructed by leveraging the circulant permutation of the discrete Fourier transform (DFT) matrix. This design enables interference-free signal combining at the IoT devices. Through theoretical analysis and simulations, we verify the effectiveness of the proposed CIRCLE method.