An overview of robust compressive sensing of sparse signals in impulsive noise

Ramirez, Ana B.; Carrillo, Rafael E.; Arce, Gonzalo R.; Barner, Kenneth E.; Sadler, Brian M.

doi:10.1109/eusipco.2015.7362907

Cited by 8 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, since the impulse in the time domain corresponds to the constant in the frequency domain, very strong time domain impulses will give negative impact to most of frequency domain symbols. Since the span of impulse noise is short in time and thus can be considered as a sparse vector, we can use the CS technique to mitigate this noise [51], [52]. First, the discrete time complex baseband equivalent channel model for the OFDM signal is expressed as…”

Section: B Sparse Estimationmentioning

confidence: 99%

Compressed Sensing for Wireless Communications: Useful Tips and Tricks

Choi

Shim

Ding

et al. 2017

IEEE Commun. Surv. Tutorials

306

186

View full text Add to dashboard Cite

As a paradigm to recover the sparse signal from a small set of linear measurements, compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. However, it is not easy to come up with simple and easy answers to the issues raised while carrying out research on CS. The main purpose of this paper is to provide essential knowledge and useful tips that wireless communication researchers need to know when designing CS-based wireless systems.First, we present an overview of the CS technique, including basic setup, sparse recovery algorithm, and performance guarantee. Then, we describe three distinct subproblems of CS, viz., sparse estimation, support identification, and sparse detection, with various wireless communication applications. We also address main issues encountered in the design of CS-based wireless communication systems. These include potentials and limitations of CS techniques, useful tips that one should be aware of, subtle points that one should pay attention to, and some prior knowledge to achieve better performance. Our hope is that this article will be a useful guide for wireless communication researchers and even nonexperts to grasp the gist of CS techniques. DRAFT size being proportional to the sparsity level of the input signal are enough to reconstruct the original signal. In fact, in many real-world applications, signals of interest are sparse or can be approximated as a sparse vector in a properly chosen basis. Sparsity of underlying signals simplifies the acquisition process, reduces memory requirement and computational complexity, and further enables to solve the problem which has been believed to be unsolvable.In the last decade, CS techniques have spread rapidly in many disciplines such as medical imaging, machine learning, computer science, statistics, and many others. Also, various wireless communication applications exploiting the sparsity of a target signal have been proposed in recent years. Notable examples, among many others, include channel estimation, interference December 21, 2016 DRAFT also discuss the sparse signal recovery algorithm and performance guarantee of the CS technique under which accurate or stable recovery of the sparse signal is ensured. In Section III, we describe the basic wireless communication system model and then discuss each subproblems of CS related to wireless communications. Depending on the sparse structure of the desired signal vector, CS problems can be divided into three subproblems: sparse estimation, support identification, and sparse detection. We explain the subproblem with the specific wireless communication applications. Developing successful CS technique for the specific wireless application requires good understanding on key issues. These include properties of system matrix and input vector, algorithm selection/modification/design, system setup and performance requirements. I...

show abstract

Section: B Sparse Estimationmentioning

confidence: 99%

Compressed Sensing for Wireless Communications: Useful Tips and Tricks

Choi

Shim

Ding

et al. 2017

IEEE Commun. Surv. Tutorials

306

186

View full text Add to dashboard Cite

show abstract

“…Even if impulse noise only lasts a short period of time, it affects a wide frequency range. By regarding impulse noise as a sparse vector, CS technique has been exploited to mitigate such type of impulse noise [67].…”

Section: B Compressive Sensing Aided Impulse Noise Cancellationmentioning

confidence: 99%

Sparse Representation for Wireless Communications: A Compressive Sensing Approach

Qin

Fan

Liu

et al. 2018

IEEE Signal Process. Mag.

213

View full text Add to dashboard Cite

Sparse representation can efficiently model signals in different applications to facilitate processing. In this article, we will discuss various applications of sparse representation in wireless communications, with focus on the most recent compressive sensing (CS) enabled approaches. With the help of the sparsity property, CS is able to enhance the spectrum efficiency and energy efficiency for the fifth generation (5G) networks and Internet of Things (IoT) networks. This article starts from a comprehensive overview of CS principles and different sparse domains potentially used in 5G and IoT networks. Then recent research progress on applying CS to address the major opportunities and challenges in 5G and IoT networks is introduced, including wideband spectrum sensing in cognitive radio networks, data collection in IoT networks, and channel estimation and feedback in massive MIMO systems. Moreover, other potential applications and research challenges on sparse representation for 5G and IoT networks are identified. This article will provide readers a clear picture of how to exploit the sparsity properties to process wireless signals in different applications.

show abstract

“…Based on this idea, a further enhancement in IN support estimation is proposed in [11], where CS-based support estimation is used first as a coarse estimation, which is later refined in a second round by exploiting a priori information on the IN samples distribution. To enhance the precision in IN support detection that can be achieved by CS-based schemes, a basis pursuit (BP) algorithm is proposed in [12], [13]. Some other algorithms, which require a priori information on the sparsity level of the signal and are based on subspace pursuit and compressive sampling matching pursuit (CoSaMP), are proposed in [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Maximum-likelihood Detection of Impulsive Noise Support for Channel Parameter Estimation

Mestre

Payaró

Shrestha

2019

2019 27th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

In this paper, we consider the problem of estimating channel parameters in the presence of impulsive noise (IN). To that end, two novel maximum-likelihood based IN support detection techniques are proposed for the cases where the IN is modeled to be a deterministic quantity or a random one. For the deterministic case, an exact closed-form expression for the distribution of the joint likelihood statistic is provided whereas, in the random case, an exact expression of its asymptotic distribution is derived. In both cases, the computed distribution of the likelihood statistic enables the joint estimation of the channel parameters and the detection of the IN support with guarantees on the false alarm probability for the samples that are estimated to be in the IN support set. The goodness of the proposed expressions is validated via numerical simulations.

show abstract

An overview of robust compressive sensing of sparse signals in impulsive noise

Cited by 8 publications

References 21 publications

Compressed Sensing for Wireless Communications: Useful Tips and Tricks

Compressed Sensing for Wireless Communications: Useful Tips and Tricks

Sparse Representation for Wireless Communications: A Compressive Sensing Approach

Maximum-likelihood Detection of Impulsive Noise Support for Channel Parameter Estimation

Contact Info

Product

Resources

About