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

Abstract-- Channel estimation for frequency-selective timevarying channels is considered using superimposed training. We 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-tonoise 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. In total cancellation, at certain frequencies, the information-bearing components are nulled. To compensate for this information loss, we propose a partially-datadependent (PDD) superimposed training scheme where a tradeoff is made between i...

• Type: Conference
• Year: 2007
• Where: ICC
• Authors: Shuangchi He, Jitendra K. Tugnait
• Keywords: Channel Estimation, Communications, ICC 2007, Information Sequence, Superimposed Training