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Autoencoder-Based Error Correction Coding for one-bit quantization

IEEE TRANSACTIONS ON COMMUNICATIONS 2020年第6期68卷 3440-3451页

作者： Balevi, Eren Andrews, Jeffrey G. Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

This paper proposes a novel deep learning-based error correction coding scheme for AWGN channels under the constraint of one-bit quantization in receivers. Specifically, it is first shown that the optimum error correction code that minimizes the probability of bit error can be obtained by perfectly training a special autoencoder, in which "perfectly" refers to converging the global minima. However, perfect training is not possible in most cases. To approach the performance of a perfectly trained autoencoder with a suboptimum training, we propose utilizing turbo codes as an implicit regularization, i.e., using a concatenation of a turbo code and an autoencoder. It is empirically shown that this design gives nearly the same performance as to the hypothetically perfectly trained autoencoder, and we also provide a theoretical proof of why that is so. The proposed coding method is as bandwidth efficient as the integrated (outer) turbo code, since the autoencoder exploits the excess bandwidth from pulse shaping and packs signals more intelligently thanks to sparsity in neural networks. Our results show that the proposed coding scheme at finite block lengths outperforms conventional turbo codes even for QPSK modulation. Furthermore, the proposed coding method can make one-bit quantization operational even for 16-QAM.

关键词： Decoding Training quantization (signal) Turbo codes Communication systems Error correction codes symbol Deep learning error correction coding one-bit quantization

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Distributed Estimation over Fading Channels Using one-bit quantization

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2009年第12期8卷 5779-5784页

作者： Wu, Tao Cheng, Qi Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

The problem of distributed estimation of an unknown parameter in noise is considered. Sensor observations are compressed using a one-bit quantizer and then transmitted to a fusion center over fading channels. We propose a mean estimator which requires only the knowledge of the mean of the channel gain and a sign estimator where the signs of the received signals are used for parameter estimation. Our analysis shows that these two estimators are not only computationally efficient, but also achieve comparable performance of maximum likelihood estimation. A robust iterative algorithm is proposed to address the design issue of local quantization thresholds.

关键词： Distributed estimation flat fading channel one-bit quantization

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Joint Angle-Frequency Estimation Using Nested Sampling with one-bit quantization

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2020年第8期39卷 4187-4197页

作者： Han, Xiaodong Shu, Ting He, Jin Yu, Wenxian Shanghai Jiao Tong Univ Shanghai Key Lab Intelligent Sensing & Recognit Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

The problem of joint angle and frequency estimation is considered in this paper. An algorithm using spatial-temporal nested sampling and one-bit quantization is developed. It is shown that under moderate condition when the signal-to-noise ratio is low enough, the defined one-bit correlation matrix has linear relationship with the unquantized one, and therefore, subspace-based algorithms can be applied to this one-bit correlation matrix directly. The enhanced identification performance, offered by the nested sampling, can also be provided when one-bit quantization is used. Simulation results are provided to verify the performance of the presented algorithm.

关键词： Angle and frequency estimation Nested sampling one-bit quantization

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UNO: Unlimited Sampling Meets one-bit quantization

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 997-1014页

作者： Eamaz, Arian Mishra, Kumar Vijay Yeganegi, Farhang Soltanalian, Mojtaba Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA US DEVCOM Army Res Lab Adelphi MD 20783 USA

Recent results in one-bit sampling provide a framework for a relatively low-cost, low-power sampling, at a high rate by employing time-varying sampling threshold sequences. Another recent development in sampling theory is unlimited sampling, which is a high-resolution technique that relies on modulo ADCs to yield an unlimited dynamic range. In this paper, we leverage the appealing attributes of the two afore mentioned techniques to propose a novel unlimited one-bit(UNO) sampling approach. In this framework, the information on the distance between the input signal value and the threshold is stored and utilized to accurately reconstruct the one-bit sampled signal. We then utilize this information to accurately reconstruct the signal from its one-bit samples via the randomized Kaczmarz algorithm (RKA). In the presence of noise, we employ the recent plug-and-play (PnP) priors technique with alternating direction method of multipliers (ADMM) to exploit integration of state-of-the-art regularizers in the reconstruction process. Numerical experiments with RKA and PnP-ADMM-based reconstruction illustrate the effectiveness of our proposed UNO, including its superior performance compared to the one-bit Sigma Delta sampling

关键词： quantization (signal) Signal reconstruction Signal processing algorithms Receivers Signal resolution Sigma-delta modulation Sensors Kaczmarz algorithm one-bit quantization PnP-ADMM modulo ADCs unlimited sampling

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Error Correction Coding for one-bit quantization With CNN-Based AutoEncoder

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IEEE COMMUNICATIONS LETTERS 2022年第8期26卷 1814-1818页

作者： Zeng, Rui Lu, Zhilin Wang, Jintao Song, Jian Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing 100084 Peoples R China

Recently, a deep learning based error correction coding scheme is proposed to compensate for the severe distortion due to one-bit quantization. However, the fully-connected (FC) layers aided autoencoder is too heavy, resulting in high storage cost. In this letter, a novel convolutional autoencoder named ECCNet is introduced to lighten the scheme. Additionally, the soft quantization function is introduced to overcome the gradient mismatch. The squeeze and excitation (SE) block is applied for further performance boosting. Simulations show that the BER performance of the proposed ECCNet outperforms the previous state-of-the-art method under 16-QAM modulation with fewer parameters required. Furthermore, the proposed autoencoder design has impressive robustness in near Gaussian multi-path fading channels.

关键词： quantization (signal) Symbols Turbo codes Encoding Decoding Modulation Error correction codes Error correction coding one-bit quantization convolutional neural network squeeze and excitation network

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Underwater Wireless Optical Communication With one-bit quantization: A Hybrid Autoencoder and Generative Adversarial Network Approach

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第10期22卷 6432-6444页

作者： Zou, Cong Yang, Fang Song, Jian Han, Zhu Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Key Lab Digital TV Syst Guangdong Prov & Shenzhen Res Inst Shenzhen 518057 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

Compared with underwater acoustic communication, underwater wireless optical communication (UWOC) has the advantages of wide communication bandwidth, high transmission speed and strong directional transmission, which make it have broad application prospects. However, the absorption and scattering effects in underwater environments cause a UWOC system to suffer from severe channel fading. To reduce the system complexity and power loss, the on- -off-keying (OOK) modulation scheme and one-bit analog-to-digital converter are considered in this work. Besides, to deal with the complex underwater optical channels and the nonlinearity of the one-bit quantization, a novel deep learning based architecture integrating the autoencoder (AE) and generative adversarial network (GAN), named hybrid AE-GAN, is developed. In the proposed hybrid AE-GAN, the generator in GAN is responsible for a generalized channel equalization, which aims to equalize both the impairments from underwater optical channels and the distortion from one-bit quantization. While the decoder in AE and the discriminator in GAN share the same network to distinguish true and fake as well as reconstruct the sent messages simultaneously. Meanwhile, the one-bit quantized signal is regarded as conditional information for GAN to assist in targeted channel equalization for various signals. Moreover, a specialized loss function and training strategy for the hybrid AE-GAN are also presented, which can optimize AE and GAN at the same time, and deal with the problem that the binarization of OOK stymies the gradient backpropagation. The simulation results indicate that the proposed hybrid AE-GAN can achieve superior performance.

关键词： Underwater wireless optical communication autoencoder generative adversarial network channel equalization one-bit quantization

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DEEP SIGNAL RECOVERY WITH one-bit quantization 44

DEEP SIGNAL RECOVERY WITH ONE-BIT QUANTIZATION

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Khobahi, Shahin Naimipour, Naveed Soltanalian, Mojtaba Eldar, Yonina C. Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA Technion Israel Inst Technol Dept Elect Engn Haifa Israel

ISBN: (纸本)9781479981311

Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference. In this paper, we consider the application of the deep unfolding technique in the problem of signal reconstruction from its one-bit noisy measurements. Namely, we propose a model-based machine learning method and unfold the iterations of an inference optimization algorithm into the layers of a deep neural network for one-bit signal recovery. The resulting network, which we refer to as DeepRec, can efficiently handle the recovery of high-dimensional signals from acquired one-bit noisy measurements. The proposed method results in an improvement in accuracy and computational efficiency with respect to the original framework as shown through numerical analysis.

关键词： Deep learning deep unfolding MIMO communications big data machine learning neural network maximum likelihood one-bit quantization

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Unlimited Sampling of FRI Signals with Dithered one-bit quantization 57

Unlimited Sampling of FRI Signals with Dithered One-Bit Quan...

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57th Asilomar Conference on Signals, Systems and Computers

作者： Eamaz, Arian Yeganegi, Farhang Mishra, Kumar Vijay Soltanalian, Mojtaba Univ Illinois ECE Dept Chicago IL 60607 USA US Army US DEVCOM Res Lab Adelphi MD 20783 USA

ISBN: (纸本)9798350325744

Recent research in sampling theory suggests utilizing unlimited sampling with one-bit quantization time-varying threshold (UNO) for bandlimited signals. The UNO framework addresses the problems associated with the finite dynamic range and limited quantization levels in conventional ADCs. However, the input signal in many engineering applications is not bandlimited. Of particular interest are the finite rate of innovation (FRI) signals that are characterized by finitely many samples per unit of time and need not be bandlimited. In this work, leveraging upon the generalized FRI sampling theory, we introduce a novel sampling approach for FRI signals that utilizes low-cost one-bit ADCs. We extend this one-bit generalized FRI (OBi-GeFRI) sampling to include unlimited sampling for FRI signals in the form of a new unlimited one-bit FRI (UNOFRI) framework. We rely on the highly efficient random Kaczmarz algorithm, a linear feasibility problem solver, for signal reconstruction in both OBi-GeFRI and UNOFRI. Our numerical examples illustrate the viability of these sampling techniques to tackle the shortcomings of the state-of-the-art.

关键词： FRI signals Kaczmarz algorithm one-bit quantization time-varying sampling threshold unlimited sampling

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A Distributed Compressed Sensing Scheme Based on one-bit quantization 79

A Distributed Compressed Sensing Scheme Based on One-Bit Qua...

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IEEE 79th Vehicular Technology Conference (VTC-Spring)

作者： Tian, Yun Xu, Wenbo Wang, Yue Yang, Hongwen Beijing Univ Posts & Telecommun Minist Educ Key Lab Univ Wireless Commun Beijing 100876 Peoples R China Huawei Technol Co Ltd Res Dept Hisilicon Beijing 100095 Peoples R China

ISBN: (纸本)9781479944828

For multi-node networks, a distributed compressed sensing scheme based on one-bit quantization is proposed, where signals at each node consist of common component and innovation component. To reduce the transmission cost, each node derives the measurements as the sign information of the compressed samples by using one-bit quantization. Based on the received sign information from different nodes, two joint recovery algorithms are designed to recover the signal of each node. Simulation results show that the proposed scheme outperforms both the independent reconstruction scheme and the scheme based on multi-bit quantization.

关键词： Compressed sensing one-bit quantization Sparsity Sub-sampling

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Cascaded binary classifiers for blind Beam Alignment in mmWave MIMO using one-bit quantization

Cascaded binary classifiers for blind Beam Alignment in mmWa...

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IEEE International Conference on Communications (IEEE ICC)

作者： Ktari, Aymen Ghauch, Hadi Rekaya, Ghaya Telecom Paris Dept COMELEC Paris France

ISBN: (纸本)9798350333077

This paper proposes a new approach for partial and blind Machine Learning (ML)-based Beam Alignment (BA) for massive mmWave MIMO. It models an uplink scenario using one-bit quantization through a low-complexity fully-analog system architecture. The proposed BA is based on sub-sampled codebooks holding possible beam patterns at UE and BS. We propose to sound a small subset of beams based on instantaneous Received Signal Energies (RSE). These sounded RSE values are then quantized into binary integers. The proposed cascaded structure of Binary Logistic Regression (BLR) aims to iteratively filter the large dataset input-matrix (by deleting low-RSE beams) into a smaller one where our benchmark, the Exhaustive BA is feasible and the signaling overhead remains low. In addition to the theoretical monotonicconvergence guarantees, BLR has good classification quality and low computational complexity. Our extensive numerical simulations illustrate encountering the large signaling overhead problem with high prediction accuracy using one-bit quantization scheme and 14% of the total beam samples.

关键词： Millimeter Wave MIMO massive antennas (ML)-based Beam Alignment blind BA one-bit quantization binary classification logistic regression

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