Experimental Exploration of Unlicensed Sub-GHz Massive MIMO for Massive Internet-of-Things

TL;DR

This paper experimentally investigates how massive MIMO technology in the unlicensed sub-GHz band can support a large number of IoT devices with improved reliability and lower power consumption, demonstrating its potential for future IoT networks.

Contribution

It provides the first experimental analysis of massive MIMO in the unlicensed sub-GHz band for IoT, validating its advantages for supporting many devices with reduced power and increased reliability.

Findings

Massive MIMO enhances IoT device support in unlicensed sub-GHz band.

Array gain and channel hardening enable lower transmit power for IoT nodes.

Spatial separation with large arrays increases network capacity.

Abstract

Due to the increase of Internet-of-Things (IoT) devices, IoT networks are getting overcrowded. Networks can be extended with more gateways, increasing the number of supported devices. However, as investigated in this work, massive MIMO has the potential to increase the number of simultaneous connections, while also lowering the energy expenditure of these devices. We present a study of the channel characteristics of massive MIMO in the unlicensed sub-GHz band. The goal is to support IoT applications with strict requirements in terms of number of devices, power consumption, and reliability. The assessment is based on experimental measurements using both a uniform linear and a rectangular array. Our study demonstrates and validates the advantages of deploying massive MIMO gateways to serve IoT nodes. While the results are general, here we specifically focus on static nodes. The array gain and channel hardening effect yield opportunities to lower the transmit-power of IoT nodes while also increasing reliability. The exploration confirms that exploiting large arrays brings great opportunities to connect a massive number of IoT devices by separating the nodes in the spatial domain. In addition, we give an outlook on how static IoT nodes could be scheduled based on partial channel state information.