A Comprehensive Survey on Blind Source Separation for Wireless Adaptive Processing: Principles, Perspectives, Challenges and New Research Directions

Luo, Zhi-Quan; Li, Chengjie; Zhu, Long

doi:10.1109/access.2018.2879380

Cited by 68 publications

(44 citation statements)

References 106 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[2,10,13,25,26] With the onset of the artificial intelligence (AI) era, blind source separation (BSS) methods are gaining significant interest nowadays. BSS has proven a great potential in multiple practical areas, e.g., wireless communication systems, text analysis, seismic monitoring, signal separation, stock prediction, and image and biomedical signal processing [27,28].…”

Section: Introductionmentioning

confidence: 99%

“…A number of novel algorithms related to BSS have been developed recently, and these have played an important role in many disciplines [28]. These algorithms can be systemized in several ways taking into account the source separation condition or the restrictions to the source features, including independent component analysis (ICA) [29], sparse component analysis (SCA) [30], nonnegative matrix factorization (NMF) [31], and bounded component analysis (BCA) [32].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Blind Source Separation for the Aggregation of Machine Learning Algorithms: An Arrhythmia Classification Case

et al. 2020

View full text Add to dashboard Cite

In this work, we present an application of the blind source separation (BSS) algorithm to reduce false arrhythmia alarms and to improve the classification accuracy of artificial neural networks (ANNs). The research was focused on a new approach for model aggregation to deal with arrhythmia types that are difficult to predict. The data for analysis consisted of five-minute-long physiological signals (ECG, BP, and PLETH) registered for patients with cardiac arrhythmias. For each patient, the arrhythmia alarm occurred at the end of the signal. The data present a classification problem of whether the alarm is a true one—requiring attention or is false—should not have been generated. It was confirmed that BSS ANNs are able to detect four arrhythmias—asystole, ventricular tachycardia, ventricular fibrillation, and tachycardia—with higher classification accuracy than the benchmarking models, including the ANN, random forest, and recursive partitioning and regression trees. The overall challenge scores were between 63.2 and 90.7.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blind Source Separation for the Aggregation of Machine Learning Algorithms: An Arrhythmia Classification Case

et al. 2020

View full text Add to dashboard Cite

show abstract

“…With the rapid proliferation of wireless sensor and mobile communication services, the scarce frequency resource, as well as underutilization frequency allocation scheme will be difficult to meet the increasing communication demands [1], [2]. This problem will give rise to several harmful impacts, including the frequency crowd, spectrum interference, and low efficient spectrum access.…”

Section: Introductionmentioning

confidence: 99%

“…Half-duplex cognitive radio (HD-CR) indicates that the SU must lay off when the sensing work is executed. Although this operation can avoid affecting the sensing decision by the selfinterference (SI), this handling mechanism results in a static sensing problem that will degrade the data transmission rate or throughput of the SU [1], [9]. In order to conquer this dilemma, the full-duplex cognitive radio (FD-CR) has been strongly recommended for its promising application in future wireless networks.…”

Section: Introductionmentioning

confidence: 99%

“…Only when the PU is vacant in frequency band, the SU is granted to work. This scenario will give rise to an intrinsic limitation so that the cognitive radio (CR) is compelled to be synchronized with PU data transmission frames and to sense the activity of PU at the beginning part of each PU data frame [1], [9], [13]. In addition, the conventional spectrum sensing methods are usually trapped in prior information utilization.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Full-Duplex Cognitive Radio Using Guided Independent Component Analysis and Cumulant Criterion

Luo

Zhu

2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

Due to the ever-increasing wireless service requirements, the issues of a limited frequency resource and underutilized frequency allocation scheme as well as frequency crowd have come to become increasingly severe. It gives rise to being difficult to effectively eliminate the spectrum interference and fulfill an efficient spectrum access mechanism using the congested frequency band. For this purpose, this paper conceives a simultaneous transmission and sensing scheme based on the guided independent component analysis combined with the cumulant-based non-Gaussian criterion for fulfilling full-duplex cognitive radio (FD-CR). The philosophy of this proposed scheme is that the ICA based blind source separation principle is firstly utilized for blind separation of the observation mixtures, and then the separated signals are recognized through correlation processing and non-Gaussian criterion based on cumulant. In particular, the known secondary user signal is used as a signature signal for assisting ICA framework construction for blind extraction of signals. As a key component of machine learning, the ICA theory can contribute significantly to conquering the static sensing problem and residual self-interference influence in existing conventional cognitive radio systems. The simulation experiments and analysis corroborate the effectiveness of the proposed method and system design. In addition, the low complexity and performance enhancement of the proposed method are also validated by comparing with that of the two practical FD-CR schemes. INDEX TERMS Full-duplex, cognitive radio, blind source separation, independent component analysis, cumulant.

show abstract

Blind Source Separation of Electromagnetic Signals Based on Swish-Tasnet

Chen,

Liu,

Mao

et al. 2024

Circuits Syst Signal Process

View full text Add to dashboard Cite

A Comprehensive Survey on Blind Source Separation for Wireless Adaptive Processing: Principles, Perspectives, Challenges and New Research Directions

Cited by 68 publications

References 106 publications

Blind Source Separation for the Aggregation of Machine Learning Algorithms: An Arrhythmia Classification Case

Blind Source Separation for the Aggregation of Machine Learning Algorithms: An Arrhythmia Classification Case

Full-Duplex Cognitive Radio Using Guided Independent Component Analysis and Cumulant Criterion

Blind Source Separation of Electromagnetic Signals Based on Swish-Tasnet

Contact Info

Product

Resources

About