Training-based Bayesian MIMO channel and channel norm estimation

Authors:
Emil Bjornson;Bjorn Ottersten
Affiliations:
ACCESS Linnaeus Center, Signal Processing Lab, Royal Institute of Technology (KTH), SE-100 44 Stockholm, Sweden;ACCESS Linnaeus Center, Signal Processing Lab, Royal Institute of Technology (KTH), SE-100 44 Stockholm, Sweden
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Abstract

Training-based estimation of channel state information in multi-antenna systems is analyzed herein. Closed-form expressions for the general Bayesian minimum mean square error (MMSE) estimators of the channel matrix and the squared channel norm are derived in a Rayleigh fading environment with known statistics at the receiver side. When the second-order channel statistics are available also at the transmitter, this information can be exploited in the training sequence design to improve the performance. Herein, mean square error (MSE) minimizing training sequences are considered. The structure of the general solution is developed, with explicit expressions at high and low SNRs and in the special case of uncorrelated receive antennas. The optimal length of the training sequence is equal or smaller than the number of transmit antennas.