Compressed Sensing with Applications in Wireless Networks

Leinonen, Markus; Codreanu, Marian; Giannakis, Georgios B.

doi:10.1561/2000000107

Cited by 14 publications

(12 citation statements)

References 126 publications

(226 reference statements)

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“…This results in high speed reconstruction and substantial less encoding complexity. The use of parallel processing also increases the process of encryption [12].…”

Section: Figurementioning

confidence: 99%

“…To improve the quality of recovered signal, kronecker based approach is used. This idea is further explored in [12], where for the reconstruction of signal, enhanced weighted greedy analysis pursuit algorithm is used. The problem addressed here is to solve the weighted optimization problem using enhanced greedy algorithm in the presence of impulsive noise.…”

Section: Figurementioning

confidence: 99%

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Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

Awan

Amir

Maqsood

et al. 2021

ITC

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Fetal ECG extraction from abdominal ECG is critical task for telemonitoring of fetus which require lot of understanding to the subject. Conventional source separation methods are not efficient enough to separate FECG from huge multichannel ECG. Thus use of compression technique is needed to compress and reconstruct ECG signal without any significant losses in quality of signal. Compressed sensing shows promising results for such tasks. However, current compressed sensing theory is not so far that successful due to the non-sparsity and strong noise contamination present in ECG signal. The proposed work explores the concept of block compressed sensing to reconstruct non-sparse FECG signal using GFOCUSS algorithm. The main objective of this paper is not only to successfully reconstruct the ECG signal but to efficiently separate FECG from abdominal ECG. The proposed algorithm is explained in very extensive manner for all experiments. The key feature of proposed method is, that it doesn’t affect the interdependence relation between multichannel ECG. The useof walsh sensing matrix made it possible to achieve high compression ratio. Experimental results shows that even at very high compression ratio, successful FECG reconstruction from raw ECG is possible. These results are validated using PSNR, SINR, and MSE. This shows the framework, compared to other algorithms such as current blocking CS algorithms, rackness CS algorithm and wavelet algorithms, can greatly reduce code execution time during data compression stage and achieve better reconstruction in terms of MSE, PSNR and SINR.

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“…This results in high speed reconstruction and substantial less encoding complexity. The use of parallel processing also increases the process of encryption [12].…”

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

Awan

Amir

Maqsood

et al. 2021

ITC

View full text Add to dashboard Cite

show abstract

“…The realizations of x are assumed to be independent and identically distributed across time. We assume that vector 2 x is S-sparse, i.e., it has at most S non-zero entries, x 0 = S ≤ N . The a priori probabilities of the sparsity patterns are unknown.…”

Section: A Source Signal and Compressive Measurementsmentioning

confidence: 99%

“…I N a myriad of wireless applications and signal acquisition tasks, information signals are sparse, i.e., they contain many zero-valued elements, either naturally or after a transformation [2]. Sparse signals are encountered in, e.g., environmental monitoring [3], source localization [4], spectrum sensing [5], and signal/anomaly detection [6].…”

Section: Introductionmentioning

confidence: 99%

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Low-Complexity Vector Quantized Compressed Sensing via Deep Neural Networks

Leinonen¹,

Codreanu²

2020

IEEE Open J. Commun. Soc.

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Sparse signals, encountered in many wireless and signal acquisition applications, can be acquired via compressed sensing (CS) to reduce computations and transmissions, crucial for resource-limited devices, e.g., wireless sensors. Since the information signals are often continuous-valued, digital communication of compressive measurements requires quantization. In such a quantized compressed sensing (QCS) context, we address remote acquisition of a sparse source through vector quantized noisy compressive measurements. We propose a deep encoder-decoder architecture, consisting of an encoder deep neural network (DNN), a quantizer, and a decoder DNN, that realizes low-complexity vector quantization aiming at minimizing the mean-square error of the signal reconstruction for a given quantization rate. We devise a supervised learning method using stochastic gradient descent and backpropagation to train the system blocks. Strategies to overcome the vanishing gradient problem are proposed. Simulation results show that the proposed non-iterative DNN-based QCS method achieves higher rate-distortion performance with lower algorithm complexity as compared to standard QCS methods, conducive to delay-sensitive applications with large-scale signals.

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Stochastic reaction and diffusion systems in molecular communications: Recent results and open problems

Egan¹,

Akdeniz²,

Tang³

2022

Digital Signal Processing

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Chemical reactions and diffusion are two basic mechanisms governing the dynamics of molecules in a fluid.As such, they play a critical role in molecular communication for channel modeling, design of detection rules, implementation of molecular circuits for computation, and modeling interactions with external biochemical systems. For finite numbers of information-carrying molecules, stochastic models naturally arise with the simplest example given by the Wiener process, often known as Brownian motion. Nevertheless, the Wiener process fails to be accurate when external forces, friction, and chemical reactions are present. Recently, there have been several contributions that tailor molecular communication systems to these more challenging channel conditions. In this paper, we first overview a general family of stochastic models of reaction and diffusion systems, including both Langevin diffusion and the reaction-diffusion master equation. These models form a basis for the use of these models as molecular communication channels, from which modulation and detection schemes can be developed. We survey recent results on the design of these schemes, with a focus on a recently developed approach which is robust to a wide range of channel models, known as equilibrium signaling. We then turn to the implementation of these detection schemes and related parameter estimation problems via stochastic molecular circuits, based on stochastic chemical reaction networks. Finally, interactions between molecular communication systems and stochastic biological systems as well as open problems are discussed.Our overarching goal is to highlight how the consideration of general stochastic models of reaction and diffusion can be utilized in order to widen the application of molecular communications both within engineered systems, and also as motivation for advances in the mathematical characterization of these models.

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Compressed Sensing with Applications in Wireless Networks

Cited by 14 publications

References 126 publications

Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

Low-Complexity Vector Quantized Compressed Sensing via Deep Neural Networks

Stochastic reaction and diffusion systems in molecular communications: Recent results and open problems

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