Blind Massive MIMO for Dense IoT Networks

Abstract

In this paper, we investigate the challenges of downlink communication in heavy payload Internet of Things (IoT) networks supported by frequency division duplexing (FDD) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The substantial overhead required for obtaining channel state information at the transmitter (CSIT) is crucial for achieving high spectral efficiency through conventional massive MIMO techniques; however, it hinders the deployment of ultra-reliable low-latency communications (URLLC) and incurs significant energy expenditure, particularly in dense IoT networks. To address this challenge, we propose an innovative CSIT-Free MIMO precoding method, termed circulant information classification via linear estimation (CIRCLE). Our primary contribution lies in the design of a CSIT-independent (or deterministic) precoding scheme, which is constructed by leveraging the circulant permutation of the discrete Fourier transform (DFT) matrix. This design facilitates interference-free signal combining at the IoT devices. Through theoretical analysis and simulations, we validate the effectiveness of the proposed CIRCLE method.