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To estimate multiple-input multiple-output (MIMO) channels, invariable step-size normalized least mean square (ISSNLMS) algorithm was applied to adaptive channel estimation (ACE). Since the MIMO channel is often described by sparse channel model due to broadband signal transmission, such sparsity can be exploited by adaptive sparse channel estimation (ASCE) methods using sparse ISS-NLMS algorithms...
Multipath channels often exhibit sparse structures in the multiple-input single-output orthogonal frequency division multiplexing (MISO-OFDM) systems. Conventional linear channel estimation methods, such as least squares (LS), are based on the implicit assumption of rich multipath which results in low spectral efficiency. In this paper, exploiting the channel sparsity, we propose a novel sparse channel...
Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) is the promising technology for next generation communication systems due to high throughput. Due to the coherent receiving and demodulation at the receiver, accurate channel state information (CSI) is indispensable. Conventional rich assumption-based channel estimators have been proposed at the cost of enough training...
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