In communication systems, efficient use of the spectrum is an indispensable concern. Recently the use of compressed sensing for the purpose of estimating Orthogonal Frequency Division Multiplexing (OFDM) sparse multipath channels has been proposed to decrease the transmitted overhead in form of the pilot subcarriers which are essential for channel estimation. In this paper, we investigate the p...

This paper derives the channel estimation of a discrete cosine transform-(DCT-) based orthogonal frequency-division multiplexing (OFDM) system over a frequency-selective multipath fading channel. Channel estimation has been proved to improve system throughput and performance by allowing for coherent demodulation. Pilot-aided methods are traditionally used to learn the channel response. Least sq...

Abstract: This paper proposes a novel scheme for multi-static passive radar processing, based on soft-input soft-output processing and Bayesian sparse estimation. In this scheme, each receiver estimates the probability of target presence based on its received signal and the prior information received from a central processor. The resulting posterior target probabilities are transmitted to the c...

Orthogonal time frequency space (OTFS) modulation is a 2-dimensional (2D) modulation scheme designed in the delay-Doppler domain, unlike traditional modulation schemes which are designed in the time-frequency domain. Through a series of 2D transformations, OTFS converts a doubly-dispersive channel into an almost non-fading channel in the delay-Doppler domain. In this domain, each symbol in a fr...

We present a computationally efficient blind sequential detection method for data transmitted over a sparse intersymbol interference channel. Unlike blind sequential detection methods designed for general channels, the proposed method exploits the channel sparsity by using estimated channel sparsity to assist in the detection of the transmitted sequence. A Gaussian mixture model is used to desc...

Channel estimation at millimeter wave (mmWave) is challenging when large antenna arrays are used. Prior work has leveraged the sparse nature of mmWave channels via compressed sensing based algorithms for channel estimation. Most of these algorithms, though, assume perfect synchronization and are vulnerable to phase errors that arise due to carrier frequency offset (CFO) and phase noise. Recentl...

Two-way relay network (TWRN) was introduced to realize high-data rate transmission and to improve spatial diversity over the frequency-selective fading channel. However, channel state information (CSI) is needed due to the requirement from coherent data detection and the self-data removal at terminals. Traditional training-based linear probing techniques are able to achieve the accurate CSI by ...

A group-constrained maximum correntropy criterion (GC-MCC) algorithm is proposed on the basis of the compressive sensing (CS) concept and zero attracting (ZA) techniques and its estimating behavior is verified over sparse multi-path channels. The proposed algorithm is implemented by exerting different norm penalties on the two grouped channel coefficients to improve the channel estimation perfo...

The least mean squares (LMS) algorithm is one of the most popular recursive parameter estimation methods. In its standard form it does not take into account any special characteristics that the parameterized model may have. Assuming that such model is sparse in some domain (for example, it has sparse impulse or frequency response), we aim at developing such LMS algorithms that can adapt to the ...