Measurement of Large-Scale Cluster Power Characteristics for Geometric Channel Models

A General Zero Attraction Proportionate Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification

“…For example, for the signal transmissions in wireless communication in hilly-terrain environments, the channel response always exhibits a multi-path fading selecting phenomenon due to the delays of the different propagation paths [22]. Thus, the measurement result shows a sparse characteristic for such channels [23]. Furthermore, this phenomenon happens over wireless voice transmission systems and networks, in which the echo paths are demonstrated as active ones and some unknown bulk delays occur because of the propagation of the network, buffer delays and encoding processing [24].…”

A sparsity-aware proportionate normalized maximum correntropy criterion algorithm for sparse system identification in non-Gaussian environment

“…Furthermore, the early studies showed that multipath channel and echo channel are typical models in practical applications [1], [2], [3], which are usually sparse. As a sparse channel, it has a typical feature that the major of the channel responses are close to zeros or equal to zeros, while only few channel responses are non-zero ones [4].…”

Norm Penalized Joint-Optimization NLMS Algorithms for Broadband Sparse Adaptive Channel Estimation

“…Additionally, the broadband multi-path channel or underwater communication channel might be a sparse channel, which has been studied in recent decades [13][14][15][16][17]. From the measurement of the wireless channel, the channel impulse response can be regarded as sparse channel.…”