Self-Interference Suppression in Doubly-Selective Channel Estimation Using Superimposed Training

Channel estimation for frequency-selective time-varying channels is considered using superimposed training. The authors employ a Discrete Prolate Spheroidal Basis Expansion Model (DPS-BEM) to describe the time-varying channel. A periodic (non-random) training sequence is arithmetically added (superimposed) at low power to the information sequence at the transmitter before modulation and transmission. In existing first-order statistics-based channel estimators, the information sequence acts as interference resulting in a poor Signal-to-Noise Ratio (SNR). In this paper a data-dependent superimposed training sequence is used to either totally or partially cancel out the effects of the unknown information sequence at the receiver on channel estimation.