A first-order statistical method for channel estimation

Channel estimation and symbol detection for block transmission using data-dependent superimposed training

“…A time-domain estimator based on synchronized averaging of the received signal was developed in [3], [5], and [6]. The same estimator can be obtained using the frequency domain [4], which will be used here since equalization will be carried out in this domain.…”

“…Recent contributions are based on superimposing a known periodic sequence on the data [3]- [7]. In [7], the deterministic (or sample) mean of each data block was removed prior to transmission, and this approach was shown to reduce the effect of the unknown data on the performance of both channel estimation and equalization.…”

“…The data symbols are assumed to be zero mean, independent and identically distributed random variables drawn from a finite alphabet, e.g., phase shift keying or quadrature amplitude modulation; let denote the data symbol power. The channel coefficients can be 1070-9908/$20.00 © 2005 IEEE consistently estimated using the first-order statistics of the received signal [3]- [5]. In order to simplify channel estimation, is often chosen to be periodic; let denote its period, and assume that is an integer.…”

Superimposed Training-Aided Channel Estimation for Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing Systems over High-Mobility Environment

“…Although the channel estimates are in general reliable, extra bandwidth or time slot is required for transmitting known pilots. In recent years, an alternative approach, referred to as superimposed training (ST), has been extensively studied in [8]- [19] [26]- [28]. In the idea of ST, additional periodic training sequences are arithmetically added to information sequence in time-or frequency-domain.…”

“…Meanwhile, the information sequences are viewed as interference to channel estimation since pilot symbols are superimposed at a low power to the information sequences at the transmitter. The existing ST-based channel estimations are mainly restricted to the case where the channel is linearly time-invariant (LTI), where the channel transfer function can be estimated by using first-order statistics [8]- [13] [17]- [18]. In the latest contributions, J. K. Tugnait [16] extended the conventional ST to time-varying environment where the LTV channels are modeled by complex exponential bases.…”