Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
EURASIP Journal on Wireless Communications and Networking - Special issue on OFDMA architectures, protocols, and applications
Optimal training sequence for MIMO wireless systems in colored environments
IEEE Transactions on Signal Processing
Non-redundant precoding and PAPR reduction in MIMO OFDM systems with ICA based blind equalization
IEEE Transactions on Wireless Communications
Optimal superimposed training sequences for channel estimation in MIMO-OFDM systems
EURASIP Journal on Advances in Signal Processing
| Hi-index | 35.69 |
Channel estimation for multiple-input multiple-output (MIMO) time-invariant channels using superimposed training is considered. A user-specific periodic (nonrandom) training sequence is arithmetically added (superimposed) at a low power to each user's information sequence at the transmitter before modulation and transmission. Two versions of a two-step approach are adopted where in the first step we estimate the channel using only the first-order statistics of the data. Using the estimated channel from the first step, a linear minimum mean-square error (MMSE) equalizer and hard decisions, or a Viterbi detector, are used to estimate the information sequence. In the second step of the two-step approach a deterministic maximum-likelihood (DML) approach based on a Viterbi detector or a linear MMSE equalizer-based approach is used to iteratively estimate the MIMO channel and the information sequences sequentially. We also present a performance analysis of the first-order statistics-based approach 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 for MIMO systems arising from spatial multiplexing of a single-user signal. Illustrative simulation examples are provided