Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Two-Dimensional Pilot-Symbol-Aided Channel Estimation by Wiener Filtering
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Low-Complexity LMMSE-Based MIMO-OFDM Channel Estimation Via Angle-Domain Processing
IEEE Transactions on Signal Processing
Time-Variant Channel Estimation Using Discrete Prolate Spheroidal Sequences
IEEE Transactions on Signal Processing
Joint Twofold-Iterative Channel Estimation and Multiuser Detection for MIMO-OFDM Systems
IEEE Transactions on Wireless Communications - Part 2
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This paper proposes a low-complexity two-dimensional channel estimator for MIMO-OFDM systems derived from a time-frequency variant channel estimator previously proposed. The estimator exploits both time and frequency correlations of the wireless channel via use of Slepian-basis expansions. The computational saving comes from replacing a two-dimensional Slepian-basis expansion with two serially-concatenated one-dimensional Slepian-basis expansions. Performance in terms of Normalized Mean Square Error (NMSE) vs. Signal-to-Noise Ratio (SNR) have been analyzed via numerical simulations and compared with the original estimator. The analysis of the performance takes into account the impact of both system and channel parameters.