Maximum-Likelihood Detection for Energy-Efficient Timing Acquisition in NB-IoT

TL;DR

This paper presents a maximum likelihood cross-correlation detector for NB-IoT timing acquisition that significantly reduces latency and energy consumption compared to auto-correlation methods.

Contribution

It introduces a hardware implementation of the maximum likelihood detector that achieves lower latency and energy use in NB-IoT timing acquisition.

Findings

Detection latency is halved compared to auto-correlation methods.

Energy consumption for timing acquisition is reduced by up to 34%.

Hardware implementation demonstrates practical feasibility.

Abstract

Initial timing acquisition in narrow-band IoT (NB-IoT) devices is done by detecting a periodically transmitted known sequence. The detection has to be done at lowest possible latency, because the RF-transceiver, which dominates downlink power consumption of an NB-IoT modem, has to be turned on throughout this time. Auto-correlation detectors show low computational complexity from a signal processing point of view at the price of a higher detection latency. In contrast a maximum likelihood cross-correlation detector achieves low latency at a higher complexity as shown in this paper. We present a hardware implementation of the maximum likelihood cross-correlation detection. The detector achieves an average detection latency which is a factor of two below that of an auto-correlation method and is able to reduce the required energy per timing acquisition by up to 34%.