Low Complexity ML Synchronization for 3GPP NB-IoT

Narrow band internet of things (NB-IoT) is a 3GPP standard introduced in Rel-13 for supporting IoT applications characterized by low cost, small data rate and long battery life. Applications typically need small data exchanges between long sleep cycles, hence needs to acquire frequency and time synchronization from network before setting up communication. This mandates the need for synchronization algorithms optimized in terms of both power and performance. But due to extreme channel conditions NB-IoT applications are expected to support, it is challenging for algorithms to optimize both, hence typically design approaches trade-off between low complexity and performance. In this paper, we propose a maximum likelihood synchronization algorithm that achieves optimal performance at low complexity. We describe approaches for reducing complexity by lowering sampling rate and using optimal frequency hypotheses placement. Using simulations, we show that our algorithm can improve synchronization performance by 50% while using 18.75% lesser computations compared to state of the art.