Doubly selective channel estimation using superimposed training and exponential bases models
EURASIP Journal on Applied Signal Processing
IEEE Transactions on Signal Processing
Enhanced channel estimation using superimposed training based on universal basis expansion
IEEE Transactions on Signal Processing
Super-imposed pilot-aided channel estimation and power allocation for relay systems
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Linearly time-varying channel estimation for MIMO/OFDM systems using superimposed training
IEEE Transactions on Communications
Superimposed training for channel shortening equalization in OFDM
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Optimal superimposed training sequences for channel estimation in MIMO-OFDM systems
EURASIP Journal on Advances in Signal Processing
Full-Hardware Architectures for Data-Dependent Superimposed Training Channel Estimation
Journal of Signal Processing Systems
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Channel estimation for single-input multiple-output (SIMO) time-invariant channels using superimposed training has been recently considered by several authors. A periodic (nonrandom) training sequence is arithmetically added (superimposed) at a low power to the information sequence at the transmitter before modulation and transmission. In particular, in , the channel is estimated using only the first-order statistics of the data under a fixed power allocation to training and under the assumption that the superimposed training sequence at the receiver is time-synchronized with its transmitted counterpart (frame synchronization). In this paper, we remove these restrictions. We first present a performance analysis of the approach of to obtain a closed-form expression for the channel estimation variance. We then address the issue of superimposed training power allocation for complex Gaussian random (Rayleigh) channels. Using the developed channel estimation variance expression, we cast the power allocation problem as one of optimizing a signal-to-noise ratio for equalizer design. Finally, we propose a novel approach for frame synchronization. All the results are illustrated via simulation examples involving frequency-selective Rayleigh fading. Simulation comparisons with an existing approach to frame synchronization is also provided.