Cell-Free Massive MIMO with Nonorthogonal Pilots for Internet of Things

TL;DR

This paper proposes an advanced cell-free massive MIMO system with nonorthogonal pilots and optimal channel estimation to significantly improve throughput and energy efficiency for IoT devices operating at low power.

Contribution

It introduces a novel approach combining nonorthogonal pilots with LMMSE estimation and large-scale fading based SINR analysis for IoT in cell-free massive MIMO.

Findings

40% throughput improvement in uplink and downlink

95% energy efficiency gain

Over three times uplink performance compared to small-cell IoT systems

Abstract

We consider Internet of Things (IoT) organized on the principles of cell-free massive MIMO. Since the number of things is very large, orthogonal pilots cannot be assigned to all of them even if the things are stationary. This results in an unavoidable pilot contamination problem, worsened by the fact that, for IoT, since the things are operating at very low transmit power. To mitigate this problem and achieve a high throughput, we use cell-free systems with optimal linear minimum mean squared error (LMMSE) channel estimation, while traditionally simple suboptimal estimators have been used in such systems. We further derive the analytical uplink and downlink signal-to-interference-plus-noise ratio (SINR) expressions for this scenario, which depends only on large scale fading coefficients. This allows us to design new power control algorithms that require only infrequent transmit power adaptation. Simulation results show a 40% improvement in uplink and downlink throughputs and 95% in energy efficiency over existing cell-free wireless systems and at least a three-fold uplink improvement over known IoT systems based on small-cell systems.