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We address the problem of carrier frequency offset (CFO) estimation within the Data Dependent Superimposed Training (DDST) framework for channel estimation. A CFO estimator was recently developed for DDST, which uses two different data dependent training sequences, one for CFO estimation and other for channel estimation. Here, we propose a CFO estimation scheme which combines the estimates using both the data-dependent training sequences to improve the performance. Finally, simulations are presented that verify the theoretical developments.

In this paper, we propose a new iterative approach for superimposed training (ST) that improves synchronisation, DCoffset estimation and channel estimation. While synchronisation algorithms for ST have previously been proposed in [2],[4] and [5], 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.