Maximum Likelihood Frequency Offset Estimation in Multiple Access Time-Hopping UWB

Abstract

Frequency offset estimation for time-hopping (TH) ultra-wide-band (UWB) is addressed in the literature by relying on an AWGN assumption and by exploiting a periodic preamble appended to each packet. In this paper we generalize these techniques with two aims. First, we identify a solution which does not rely on any periodic structure, but can be implemented with a generic TH format. Second, we identify a solution which is robust to multiple access interference (MAI) by assuming a Gaussian mixture (GM) model for MAI. In fact, GMs have recently been identified as good descriptors of UWB interference, and they provide closed form and limited complexity results. With these ideas in mind, we build a data aided maximum likelihood (ML) estimator. The proposed ML solution shows quasi optimum performance in the Cramer-Rao bound sense, and proves to be robust in meaningful multiple user scenarios.