Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Microwave Mobile Communications
Microwave Mobile Communications
On Limits of Wireless Communications in a Fading Environment when UsingMultiple Antennas
Wireless Personal Communications: An International Journal
Multi-input multi-output fading channel tracking and equalizationusing Kalman estimation
IEEE Transactions on Signal Processing
Layered space-time multiuser detection over wireless uplink systems
IEEE Transactions on Wireless Communications
Optimal placement of training for frequency-selective block-fading channels
IEEE Transactions on Information Theory
Adaptive algorithms for channel equalization with soft decision feedback
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Achievable rate of MIMO channels with data-aided channel estimation and perfect interleaving
IEEE Journal on Selected Areas in Communications
Capacity limits of MIMO channels
IEEE Journal on Selected Areas in Communications
Improved decision-directed recursive least squares MIMO channel tracking
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
EURASIP Journal on Wireless Communications and Networking
Locally Defined Principal Curves and Surfaces
The Journal of Machine Learning Research
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A new approach for joint data estimation and channel tracking for multiple-input multiple-output (MIMO) channels is proposed based on the decision-directed recursive least squares (DD-RLS) algorithm. RLS algorithm is commonly used for equalization and its application in channel estimation is a novel idea. In this paper, after defining the weighted least squares cost function it is minimized and eventually the RLS MIMO channel estimation algorithm is derived. The proposed algorithm combined with the decision-directed algorithm (DDA) is then extended for the blind mode operation. From the computational complexity point of view being O(3) versus the number of transmitter and receiver antennas, the proposed algorithm is very efficient. Through various simulations, the mean square error (MSE) of the tracking of the proposed algorithm for different joint detection algorithms is compared with Kalman filtering approach which is one of the most well-known channel tracking algorithms. It is shown that the performance of the proposed algorithm is very close to Kalman estimator and that in the blind mode operation it presents a better performance with much lower complexity irrespective of the need to know the channel model.