Low SNR Uplink CFO Estimation for Energy Efficient IoT Using LTE

Abstract

Machine Type Communications (MTC) is one of the prominent solutions to enable the Internet of Things (IoT). With a large number of IoT applications envisioned over the cellular network, the Third Generation Partnership Project (3GPP) has initiated the support for MTC in the Long Term Evolution (LTE)/ LTE-Advanced (LTE-A) standards. A significant portion of the MTC devices is expected to be low-complexity and low-power User Equipment (UE), requiring an energy efficient mode of operation. In addition, many such UEs can be located in the regions of low network coverage. In this paper, we show that an accurate estimation and compensation of the residual carrier frequency offset (CFO) at the base-station (eNB) results in a reduction in energy consumption for MTC devices in low coverage. For robust and accurate CFO estimation in low coverage, we propose a Maximum Likelihood (ML) based CFO estimation technique that works for data and/or pilot repetitions in LTE/LTE-A uplink. Through simulations, we illustrate that our technique shows a significant performance improvement over the conventional CFO estimation technique using the phase angle of the correlation between the repeated data. We determine that residual CFO estimation and compensation at the eNB results in 22.5%-55.2% reduction in energy consumption of the MTC devices, when compared to the case without CFO compensation.

Keywords

• Machine Type Communications (MTC)
• Long Term Evolution (LTE)
• carrier frequency offset (CFO) estimation
• Maximum Likelihood (ML)
• coverage enhancement
• Narrow-band Internet of Things (NB-IoT)