Iterative Synchronisation and DC-Offset Estimation using Superimposed Training
S. M. A. Moosvi, Des McLernon, Enrique Alameda-Hernandez · 2007
In this paper, we propose a new iterative approach for superimposed training (ST) that improves synchronisation, DC-offset estimation and channel estimation. While synchronisation algorithms for ST have previously been proposed in A.G. Orozco-Lugo et al. (2004), J.K. Tugnait and W. Luo (2004) and E. Alameda-Hernandez et al., due to interference from the data they performed sub-optimally, resulting in channel estimates with unknown delays. These delay ambiguities (also present in the equaliser) were estimated in previous papers in a non-practical manner. In this paper we avoid the need for estimation of this delay ambiguity by iteratively removing the effect of the data "noise". The result is a BER performance superior to all other ST algorithms that have not assumed a-priori synchronisation.