Blind Source Separation For Full-Duplex Systems: Potential and Challenges

Fouda, Mohammed E.; Shen, Chung-An; Eltawil, Ahmed M.

doi:10.1109/ojcoms.2021.3086105

Cited by 10 publications

(3 citation statements)

References 44 publications

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“…One prominent signal processing technique in this context is BSS. Various BSS algorithms and architectures are investigated, considering their potential to improve interference management, channel estimation, beamforming, and resource allocation in these next-generation wireless networks [4], [15], [16], [17], [18], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

A Robust Hybrid Neural Network Architecture for Blind Source Separation of Speech Signals Exploiting Deep Learning

Ansari,

Alnajjar,

Khater

et al. 2023

IEEE Access

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In the contemporary era, blind source separation has emerged as a highly appealing and significant research topic within the field of signal processing. The imperative for the integration of blind source separation techniques within the context of beyond fifth-generation and sixth-generation networks arises from the increasing demand for reliable and efficient communication systems that can effectively handle the challenges posed by high-density networks, dynamic interference environments, and the coexistence of diverse signal sources, thereby enabling enhanced signal extraction and separation for improved system performance. Particularly, audio processing presents a critical domain where the challenge lies in effectively handling files containing a mixture of human speech, silence, and music. Addressing this challenge, speech separation systems can be regarded as a specialized form of human speech recognition or audio signal classification systems that are leveraged to separate, identify, or delineate segments of audio signals encompassing human speech. In various applications such as volume reduction, quality enhancement, detection, and identification, the need arises to separate human speech by eliminating silence, music, or environmental noise from the audio signals. Consequently, the development of robust methods for accurate and efficient speech separation holds paramount importance in optimizing audio signal processing tasks. This study proposes a novel three-way neural network architecture that incorporates transfer learning, a pre-trained dual-path recurrent neural network, and a transformer. In addition to learning the time series associated with audio signals, this network possesses the unique capability of direct context-awareness for modeling the speech sequence within the transformer framework. A comprehensive array of simulations is meticulously conducted to evaluate the performance of the proposed model, which is benchmarked with seven prominent state-of-the-art deep learning-based architectures. The results obtained from these evaluations demonstrate notable advancements in multiple objective metrics. Specifically, our proposed solution showcases an average improvement of 4.60% in terms of short-time objective intelligibility, 14.84% in source-to-distortion ratio, and 9.87% in scale-invariant signal-to-noise ratio. These extraordinary advancements surpass those achieved by the nearest rival, namely the dual-path recurrent neural network time-domain audio separation network, firmly establishing the superiority of our proposed model's performance.

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Section: Introductionmentioning

confidence: 99%

A Robust Hybrid Neural Network Architecture for Blind Source Separation of Speech Signals Exploiting Deep Learning

Ansari,

Alnajjar,

Khater

et al. 2023

IEEE Access

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“…IBFD technology is the key technology to solve this problem. However, since the strong SI signal can obscure the SOI, conventional wisdom holds that two approaches can be used to solve the problem of self-interference [4][5][6] . For the first method, frequency-division duplex bidirectional communication is used, but this method wastes spectrum resources.…”

Section: Introductionmentioning

confidence: 99%

“…The FICA algorithm proposed by M.E. Fouda et al [6] solves the problem of SI suppression. Experiments results show that the SNR of the receiver is successfully reduced by 6 dB as compared to the least square SI cancellation method.…”

Section: Introductionmentioning

confidence: 99%

Multi-domain blind source separation in-band full-duplex technique considering RF impairments

Zhu,

Wang,

Yuan

et al. 2023

J. Phys.: Conf. Ser.

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In-band full-duplex (IBFD) technology can potentially improve spectrum efficiency compared to half-duplex technology. The cancellation of the self-interference (SI) signal is the key to implementing the IBFD system to achieve accurate detection of the signal of interest. In this work, we fully account for the effect of IQ imbalance, phase noise (PN), additional Gaussian white noise (AWGN), and the nonlinear caused by the RF impairments of power amplifier, and adopt the method of canceling SI signal combined with the propagation, analog, and digital domain to cancel the SI signal, but the residual SI signal is still mixed with the signal of interest in the same time and frequency domain. In addition, a novel multi-domain blind source separation (MDBSS) self-interference cancellation method implemented by using the off-the-shelf components is proposed in this paper, which can realize the separation of residual self-interference signals from the signal of interest (SOI). Additionally, the multi-domain blind source separation (MDBSS) self-interference cancellation technique is used to achieve 75 dB of self-interference cancellation performance, which is proven through numerical simulation analysis, and the SOI is successfully separated in the receiver. Moreover, the multi-domain blind source separation (MDBSS) method in this paper can be applied to radar, jammer, and communication systems in the future.

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Blind RF Self-Interference Cancellation for In-Band Distribution Link in ATSC 3.0

Hong

Zhang

et al. 2022

2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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Blind Source Separation For Full-Duplex Systems: Potential and Challenges

Cited by 10 publications

References 44 publications

A Robust Hybrid Neural Network Architecture for Blind Source Separation of Speech Signals Exploiting Deep Learning

A Robust Hybrid Neural Network Architecture for Blind Source Separation of Speech Signals Exploiting Deep Learning

Multi-domain blind source separation in-band full-duplex technique considering RF impairments

Blind RF Self-Interference Cancellation for In-Band Distribution Link in ATSC 3.0

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