Energy-Efficient Operation of Massive MIMO in Industrial Internet-of-Things Networks

Abstract

Massive multiple-input-multiple-output (MIMO) can be effectively applied to the data gathering system of Industrial Internet-of-Things (IIoT) networks. For long-term maintenance of industrial electronic systems with battery-limited IIoT devices, it is essential to increase the energy efficiency (EE) of the system. The high EE should be achieved with ultrareliability and low latency because there are a lot of critical information for the IIoT networks. With this in mind, in this article, we propose high EE operation schemes for the massive MIMO-based IIoT networks. An orthogonal multiple access (OMA) scheme is used and a signal clipping technique is applied to increase the EE of the industrial data gathering system. Clipping distortion for uplink massive MIMO with massive IIoT connectivity is analyzed, and we show that clipping distortion of maximum ratio (MR) processing is directly proportional to the number of service antennas and the number of IIoT devices, while that of zero-forcing (ZF) processing can be reduced as the number of IIoT devices increases. We define the EE metric and derive the closed-form inverses of the EE metric to determine the relevant parameters. Based on the derived closed-form equations, we introduce the EE operation schemes using low-latency parameter determination methods. Simulation results validate the theoretical analysis.