Carrier Frequency Offset Estimation using Data-Dependent Superimposed Training

In this paper we propose, for the first time, a solution to the problem of carrier frequency offset (CFO) estimation within the Data Dependent Superimposed Training (DDST) framework for channel estimation. While time division multiplexed (TDM) trained systems can use the TDM sequence to determine the CFO, the original attraction of DDST for channel estimation was that it avoided any TDM training. So in this paper we show how CFO estimation can still be very effectively performed with the DDST algorithm, while continuing to preclude the need for any additional bandwidth-consuming TDM training. Finally, simulations are presented that verify the theoretical developments.