Prediction Models of an Indoor Smart Antenna System Using Artificial Neural Networks
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
Blind adaptive equalization of MIMO systems: new recursive algorithms and convergence analysis
IEEE Transactions on Circuits and Systems Part I: Regular Papers
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Blind channel estimation and blind equalization of single-input multiple-output communications channels is considered using only the second-order statistics of the data. Estimation of (partial) channel impulse response and design of finite-length minimum mean-square error blind equalizers is investigated. The basis of the approach is the design of multiple zero-forcing equalizers that whiten the noise-free data at multiple delays. In the past such an approach has been considered using just one zero-forcing equalizer at zero-delay. Infinite-impulse response channels are allowed. The proposed approach also works when the "subchannel" transfer functions have common zeros so long as the common zeros are minimum-phase zeros. The channel length or model orders need not be known. Three illustrative simulation examples using 4-QAM and 16-QAM signals are provided where the proposed approach is compared with several existing approaches