Least mean square (LMS) type adaptive algorithms have attracted much attention due to their low computational complexity. In the scenarios of sparse channel estimation, zero-attracting LMS (ZA-LMS), reweighted ZA-LMS (RZA-LMS) and reweighted -norm LMS (RL1-LMS) have been proposed to exploit channel sparsity. However, these proposed algorithms may hard to make tradeoff between convergence speed ...

Channel estimation problem is one of key technical issues in time-variant multiple-input multiple-output (MIMO) communication systems. To estimate the MIMO channel, least mean square (LMS) algorithm was applied to adaptive channel estimation (ACE). Since the MIMO channel is often described by sparse channel model, such sparsity could be exploited and then estimation performance could be improve...

Cluster-sparse multipath channels, i.e., non-zero taps occurring in clusters, exist frequently in many communication systems, e.g., underwater acoustic (UWA), ultra-wide band (UWB), and multiple-antenna communication systems. Conventional sparse channel estimation methods often ignore the additional structure in the problem formulation. In this paper, we propose an improved compressive channel ...

Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm. CS enables the recovery of high-dimensional sparse signals from much fewer samples than usually required. Further, quite a few recent channel measurement experiments show that many wireless channels also tend to exhibit sparsity. In this case, CS theory can be applicable to sparse channel estimation and its ...

One of the main challenges for a massive multi-input multi-output (MIMO) system is to obtain accurate channel state information despite the increasing number of antennas at the base station. The Bayesian learning channel estimation methods have been developed to reconstruct the sparse channel. However, these existing methods depend heavily on the channel distribution. In this paper, based on sp...

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 algorit...

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 algorit...

Orthogonal Frequency Division Multiplexing is a widely adopted multi carrier modulation in wireless communication systems due to its effective transmission and efficient bandwidth utilization ability. Wireless systems with coherent data detection require the estimation of channel at the receiver. Commonly employed pilot aided channel estimation probes the channel with known sequence called pilo...

Channel Estimation is an essential component in applications such as radar and data communication. In multi path time varying environments, it is necessary to estimate time-shifts, scale-shifts (the wideband equivalent of Doppler-shifts), and the gains/phases of each of the multiple paths. With recent advances in sparse estimation (or " compressive sensing "), new estimation techniques have eme...

Downlink beamforming in FDD Massive MIMO systems is challenging due to the large training and feedback overhead, which is proportional to the number of antennas deployed at the base station, incurred by traditional downlink channel estimation techniques. Leveraging the compressive sensing framework, compressed channel estimation algorithm has been applied to obtain accurate channel estimation w...