Deep Signal Recovery with One-bit Quantization

Khobahi, Shahin; Naimipour, Naveed; Soltanalian, Mojtaba; Eldar, Yonina C.

doi:10.1109/icassp.2019.8683876

Cited by 58 publications

(46 citation statements)

References 19 publications

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“…sidered estimation of an unknown single parameter in a wireless sensor network with multiple single-antenna nodes. On the other hand, [13] used the deep unfolding technique to solve multi-dimensional signal recovery problem with one-bit quantization.…”

Section: Related Workmentioning

confidence: 99%

ADMM-Based One-Bit Quantized Signal Detection for Massive MIMO Systems With Hardware Impairments

Demir

Björnson

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper considers signal detection in massive multiple-input multiple-output (MIMO) systems with general additive hardware impairments and one-bit quantization. First, we present the quantization-unaware and Bussgang decomposition-based linear receivers by generalizing them for the considered hardware impairment model. We propose an optimization problem to estimate the uplink data signals by choosing a suitable cost function that treats the unquantized received signal at the base station as the variable. We exploit the additional structure of the one-bit quantization and signal modulation by including proper constraints. To solve the non-convex quadratically-constrained quadratic programming (QCQP) problem, we propose an ADMM-based algorithm with closed-form update equations. Then, we replace the harsh projectors in the updates with their soft versions to improve the detection performance. We show that the proposed ADMM-based algorithm outperforms the state-ofthe-art linear receivers significantly in terms of bit error rate (BER) and the performance gain increases with the number of antennas and users.Index Terms-MIMO signal detection, massive MIMO, onebit quantization, ADMM, hardware impairments.

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Section: Related Workmentioning

confidence: 99%

ADMM-Based One-Bit Quantized Signal Detection for Massive MIMO Systems With Hardware Impairments

Demir

Björnson

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…The work [ 32 ] used DNNs to compress and quantize high-dimensional channel state information in a massive MIMO feedback setup. The design of DNNs for processing one-bit quantized measurements in the digital domain, i.e., in the presence of task-ignorant quantizers, was considered for signal recovery in [ 33 ]; while DNN-based MIMO receivers with one-bit quantizers were studied in [ 34 , 35 ]. To the best of our knowledge, despite the importance of quantization with scalar ADCs in digital signal processing, the application of deep learning in such systems has not yet been studied.…”

Section: Introductionmentioning

confidence: 99%

Deep Task-Based Quantization

Shlezinger

Eldar

2021

Entropy

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Quantizers play a critical role in digital signal processing systems. Recent works have shown that the performance of acquiring multiple analog signals using scalar analog-to-digital converters (ADCs) can be significantly improved by processing the signals prior to quantization. However, the design of such hybrid quantizers is quite complex, and their implementation requires complete knowledge of the statistical model of the analog signal. In this work we design data-driven task-oriented quantization systems with scalar ADCs, which determine their analog-to-digital mapping using deep learning tools. These mappings are designed to facilitate the task of recovering underlying information from the quantized signals. By using deep learning, we circumvent the need to explicitly recover the system model and to find the proper quantization rule for it. Our main target application is multiple-input multiple-output (MIMO) communication receivers, which simultaneously acquire a set of analog signals, and are commonly subject to constraints on the number of bits. Our results indicate that, in a MIMO channel estimation setup, the proposed deep task-bask quantizer is capable of approaching the optimal performance limits dictated by indirect rate-distortion theory, achievable using vector quantizers and requiring complete knowledge of the underlying statistical model. Furthermore, for a symbol detection scenario, it is demonstrated that the proposed approach can realize reliable bit-efficient hybrid MIMO receivers capable of setting their quantization rule in light of the task.

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“…Although the data-driven approaches can handle large and complex datasets, they are ignorant to the underlying problem-level reasoning that may be available. Therefore, it is vital to develop a hybrid data-driven and domain-knowledge-aware framework to enhance the accuracy and efficiency of deep learning-based COVID-19 diagnosis using CT/X-ray images, while reducing the computational cost as much as possible (we refer an interested reader to consult [4, 5, 6, 7] and the references therein for a detailed explanation of the existing model-based deep learning models).…”

Section: Introductionmentioning

confidence: 99%

CoroNet: A Deep Network Architecture for Semi-Supervised Task-Based Identification of COVID-19 from Chest X-ray Images

Khobahi

Agarwal

Soltanalian

2020

Preprint

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In late 2019, a new Coronavirus disease, referred to as Corona virus disease 2019 , emerged in Wuhan city, Hubei, China, and resulted in a global pandemic-claiming a large number of lives and affecting billions all around the world. The current global standard used in diagnosis of COVID-19 in suspected cases is the real-time polymerase chain reaction (RT-PCR) test. Although the RT-PCR remains the standard reference for diagnosis purposes, it is a timeconsuming and expensive test, and moreover, it usually suffers from high rates of false-negatives. Several early works have reported that the sensitivity of the chest Computed Tomography (CT) and the chest X-ray imaging are noticeably greater than that of the RT-PCR test at the initial representations of the disease, making them great candidates for developing new and sophisticated methodologies for analysis and classification of COVID-19 cases. In this paper, we establish the use of a rapid, non-invasive and cost-effective X-ray-based method as a key diagnosis and screening tool for COVID-19 at early and intermediate stages of the disease. To this end, we develop a novel and sophisticated deep learning-based signal and image processing technique as well as classification methodology for analyzing X-ray images specific to COVID-19 disease. Specifically, we consider a semi-supervised learning methodology based on AutoEncoders to first extract the infected legions in chest X-ray manifestation of COVID-19 and other Pneumonia-like diseases (as well as healthy cases). Then, we utilize this highly-tailored deep architecture to extract the relevant features specific to each class (i.e., healthy, non-COVID pneumonia, and COVID-19) and train a powerful yet efficient classifier to perform the task of automatic diagnosis. Furthermore, the semi-supervised nature of the proposed framework enables us to efficiently exploit the limited available dataset on COVID-19 while exploiting the vast amount of available X-ray dataset for healthy and non-COVID classes. Moreover, such a semi-supervised approach does not require an expert-annotated lesion area for each class. Our numerical investigations demonstrate that the proposed framework outperforms the state-of-theart methods for COVID-19 identification while employing approximately ten times fewer training parameters as compared to other existing methodologies for classification of the COVID-19 from X-ray images (facilitating efficient training in a limited data regime). We further develop explainable artificial intelligence tools that can explain the diagnosis by using attribution maps while providing an indispensable tool for the radiologist in triage state. We have made the codes of our proposed framework publicly available to the research and healthcare community 1 .

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Deep Signal Recovery with One-bit Quantization

Cited by 58 publications

References 19 publications

ADMM-Based One-Bit Quantized Signal Detection for Massive MIMO Systems With Hardware Impairments

ADMM-Based One-Bit Quantized Signal Detection for Massive MIMO Systems With Hardware Impairments

Deep Task-Based Quantization

CoroNet: A Deep Network Architecture for Semi-Supervised Task-Based Identification of COVID-19 from Chest X-ray Images

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