Related papers: On Sparse Channel Estimation
In this paper, we have evaluated various methods of time-frequency-selective fading channels estimation in OFDM system and some of them improved under time varying conditions. So, these different techniques will be studied through different…
Recent efforts to obtain high data rates in wireless systems have focused on what can be achieved in systems that have nonlinear or coarsely quantized transceiver architectures. Estimating the channel in such a system is challenging because…
In this paper, we present a Bayesian channel estimation algorithm for multicarrier receivers based on pilot symbol observations. The inherent sparse nature of wireless multipath channels is exploited by modeling the prior distribution of…
Broadband wireless channel is a time dispersive and becomes strongly frequency selective. In most cases, the channel is composed of a few dominant coefficients and a large part of coefficients is approximately zero or zero. To exploit the…
We study the estimation of distance d between two wireless nodes by means of their wideband channels to a third node, called observer. The motivating principle is that the channel impulse responses are similar for small d and drift apart…
Due to the over-parameterization of neural networks, many model compression methods based on pruning and quantization have emerged. They are remarkable in reducing the size, parameter number, and computational complexity of the model.…
In this paper we demonstrate improved techniques to extend coherent processing intervals for passive radar processing, with the Murchison Widefield Array. Specifically, we apply a two stage linear range and Doppler migration compensation by…
We propose a channel estimation scheme based on joint sparsity pattern learning (JSPL) for massive multi-input multi-output (MIMO) orthogonal time-frequency-space (OTFS) modulation aided systems. By exploiting the potential joint sparsity…
Terahertz (THz) communications is considered as one of key solutions to support extremely high data demand in 6G. One main difficulty of the THz communication is the severe signal attenuation caused by the foliage loss, oxygen/atmospheric…
The calibration of modern radio interferometers is a significant challenge, specifically at low frequencies. In this perspective, we propose a novel iterative calibration algorithm, which employs the popular sparse representation framework,…
Accurate channel impulse response (CIR) is required for coherent detection and it can also help improve communication quality of service in next-generation wireless communication systems. One of the advanced systems is multi-input…
Generative graph models struggle to scale due to the need to predict the existence or type of edges between all node pairs. To address the resulting quadratic complexity, existing scalable models often impose restrictive assumptions such as…
New methods that exploit sparse structures arising in smart grid networks are proposed for the state estimation problem when data injection attacks are present. First, construction strategies for unobservable sparse data injection attacks…
In a realistic wireless environment, the multi-antenna channel usually exhibits spatially correlation fading. This is more emphasized when a large number of antennas is densely deployed, known as holographic massive MIMO (multiple-input…
Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the large number of base…
The estimation of a sparse vector in the linear model is a fundamental problem in signal processing, statistics, and compressive sensing. This paper establishes a lower bound on the mean-squared error, which holds regardless of the…
Spatial wideband effects are known to affect channel estimation and localization performance in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Based on perturbation analysis, we show that the spatial…
In this paper, we tackle channel estimation in millimeter-wave hybrid multiple-input multiple-output systems by considering off-grid effects. In particular, we assume that spatial parameters can take any value in the angular domain, and…
Accurate channel estimation is essential for broadband wireless communications. As wireless channels often exhibit sparse structure, the adaptive sparse channel estimation algorithms based on normalized least mean square (NLMS) have been…
In this paper, we study the problem of sparse channel estimation via a collaborative and fully distributed approach. The estimation problem is formulated in the angular domain by exploiting the spatially common sparsity structure of the…