Blind Massive MIMO for Dense IoT Networks

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.