Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
OFDM for Wireless Multimedia Communications
OFDM for Wireless Multimedia Communications
Joint MIMO Channel Tracking and Symbol Decoding Using Kalman Filtering
IEEE Transactions on Signal Processing
Optimal training design for MIMO OFDM systems in mobile wireless channels
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Multi-input multi-output fading channel tracking and equalizationusing Kalman estimation
IEEE Transactions on Signal Processing
A performance bound for prediction of MIMO channels
IEEE Transactions on Signal Processing
CHANNEL ESTIMATION FOR WIRELESS OFDM SYSTEMS
IEEE Communications Surveys & Tutorials
Experimental characterization of the MIMO wireless channel: data acquisition and analysis
IEEE Transactions on Wireless Communications
Improved bayesian MIMO channel tracking for wireless communications: incorporating a dynamical model
IEEE Transactions on Wireless Communications
A road to future broadband wireless access: MIMO-OFDM-Based air interface
IEEE Communications Magazine
Capacity of MIMO systems based on measured wireless channels
IEEE Journal on Selected Areas in Communications
BER analysis for MIMO-OFDM beamforming with MRC under channel prediction and interpolation errors
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Hi-index | 35.68 |
The performance of a mobile multiple-input multiple-output orthogonal-frequency-division multiplexing (MIMO-OFDM) system depends on the ability of the system to accurately account for the effects of the frequency-selective time-varying channel at every symbol time and at every frequency subcarrier. Typically, pilot symbols are strategically placed at various times over various subcarriers in order to calculate sample channel estimates and then these estimates are interpolated or extrapolated forward to provide channel estimates where no pilot data was transmitted. Performance is highly dependent on the distribution of the pilots with respect to the coherence time and coherence bandwidth of the channel, and on the chosen channel parameterization. In this paper, a vector formulation of the Cramér-Rao bound (CRB) for biased estimators and for functions of parameters is used to derive a lower bound on the channel estimation and prediction error of such a system. Numerical calculations using the bound demonstrate the benefits of multiple antennas for channel estimation and prediction and illustrate the impact of modeling errors on estimation performance when using channel models based on calibrated arrays.