The channel estimation is one of important techniques to ensure reliable broadband signal transmission. Broadband channels are often modeled as a sparse channel. Comparing with traditional dense-assumption based linear channel estimation methods, e.g., least mean square/fourth (LMS/F) algorithm, exploiting sparse structure information can get extra performance gain. By introducing -norm penalty...

Channel estimation problem is one of the key technical issues for broadband multiple-input–multiple-output (MIMO) signal transmission. To estimate the MIMO channel, a standard least mean square (LMS) algorithm was often applied to adaptive channel estimation because of its low complexity and stability. The sparsity of the broadband MIMO channel can be exploited to further improve the estimation...

In this study, compressed channel estimation method for sparse multipath two-way relay networks is investigated. Conventional estimation methods, e.g., Least Square (LS) and Minimum Mean Square Error (MMSE), are based on the dense assumption of relay channel and cannot exploit channel sparsity which has been verified by lots of channel measurements. Unlike the previous methods, we propose a com...

Orthogonal time-frequency space (OTFS) modulation, which has recently been proposed in the literature, is one of promising techniques designed 2D Delay-Doppler domain adapted to combat high Doppler fading channels. However, channel estimation scenarios advanced mobile-communication systems still a challenging task. In this paper, problem OTFS focused on. First, simple adaptation generalized ort...

An enhanced power-line communications channel estimation method in discrete multi-tone (DMT) communication system based on sparse Bayesian regression is presented. By exploiting a probabilistic Bayesian learning framework, the sparse model used provides an accurate model for channel estimation in presence of noise and consequently equalization. We consider frequency domain equalization (FEQ) us...

Broadband frequency-selective fading channels usually have the inherent sparse nature. By exploiting the sparsity, adaptive sparse channel estimation (ASCE) methods, e.g., reweighted L1-norm least mean square (RL1-LMS), could bring a performance gain if additive noise satisfying Gaussian assumption. In real communication environments, however, channel estimation performance is often deteriorate...

Due to the channel with characteristic of sparse multi-path in the 60GHz wireless communication system, the channel estimation problem can be attributed to that of sparse signals recovery. And with the consideration of the subspace pursuit (SP) algorithm is superior to the orthogonal matching pursuit (OMP) at reconstruction precision, the channel estimation technique based on the SP algorithm i...

This dissertation is primarily concerned with the estimation of nonlinear communication systems that are modeled by Volterra series. The major methods used for estimating the unknown channel parameters can be classified into two main categories: training-based and blind. First, orthobasis representation and training-based identification through the respective Fourier series are investigated for...