Improved Carrier Frequency Offset Estimation using Data-Dependent Superimposed Training

Abstract

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.

Channel and carrier frequency offset estimation based on projection onto a bidimensional basis

“…For single carrier transmissions, [6] proposes the use of time multiplexed pilots in a two-step algorithm; first, a coarse estimation of the CFO is made and subsequently a fine estimation is carried out. In the case of implicit training (training sequence superimposed onto data), CFO estimation and compensation was studied in [7][8][9].…”

“…For single carrier transmissions, [6] proposes the use of time multiplexed pilots in a two-step algorithm; first, a coarse estimation of the CFO is made and subsequently a fine estimation is carried out. In the case of implicit training (training sequence superimposed onto data), CFO estimation and compensation was studied in [7][8][9]. However, for time-varying channels, we can only cite [10], where a method is proposed that combines the use of an exponential basis (for each time-variant channel coefficient) and pilot sequences for OFDM systems.…”