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Quantum Error Mitigation Relying on Permutation Filtering

IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第3期70卷 1927-1942页

作者： Xiong, Yifeng Ng, Soon Xin Hanzo, Lajos Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

Quantum error mitigation (QEM) is a class of promising techniques capable of reducing the computational error of variational quantum algorithms tailored for current noisy intermediate-scale quantum computers. The recently proposed permutation-based methods are practically attractive, since they do not rely on any a priori information concerning the quantum channels. In this treatise, we propose a general framework termed as permutation filters, which includes the existing permutation-based methods as special cases. In particular, we show that the proposed filter design algorithm always converge to the global optimum, and that the optimal filters can provide substantial improvements over the existing permutation-based methods in the presence of narrowband quantum noise, corresponding to large-depth, high-error-rate quantum circuits.

关键词： Quantum computing Quantum algorithm Qubit Computers Optimization Task analysis signal processing algorithms Quantum error mitigation permutation filtering permutation symmetry variational quantum algorithms

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Performance Analysis of Single-Photon Detector with Real-Time signal processing 26

Performance Analysis of Single-Photon Detector with Real-Tim...

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26th Silicon Nanoelectronics Workshop

作者： Manivannan, Revathi Satoh, Hiroaki Inokawa, Hiroshi Shizuoka Univ Grad Sch Sci & Technol Shizuoka Japan Shizuoka Univ Res Inst Elect Shizuoka Japan

ISBN: (纸本)9784863487819

In this report, we experimentally verify the attainable performance of SOI MOSFET photon detector based on single-charge counting in terms of maximum count rate (MCR). We evaluated the generation rate of holes for different light intensity by a dedicated signal processing algorithm implemented in field programmable gate array (FPGA) for real-time operation.

关键词： MOSFET signal processing algorithms Detectors Logic gates Real-time systems Silicon Performance analysis

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Deep Learning for Massive MIMO Uplink Detectors

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2022年第1期24卷 741-766页

作者： Albreem, Mahmoud A. Alhabbash, Alaa H. Shahabuddin, Shahriar Juntti, Markku Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates Islamic Univ Gaza Palestinian ICT Res Agcy Gaza Palestine Nokia Mobile Networks Oulu 90620 Finland Univ Oulu Ctr Wireless Commun Oulu 90014 Finland

Detection techniques for massive multiple-input multiple-output (MIMO) have gained a lot of attention in both academia and industry. Detection techniques have a significant impact on the massive MIMO receivers' performance and complexity. Although a plethora of research is conducted using the classical detection theory and techniques, the performance is deteriorated when the ratio between the numbers of antennas and users is relatively small. In addition, most of classical detection techniques are suffering from severe performance loss and/or high computational complexity in real channel scenarios. Therefore, there is a significant room for fundamental research contributions in data detection based on the deep learning (DL) approach. DL architectures can be exploited to provide optimal performance with similar complexity of conventional detection techniques. This paper aims to provide insights on DL based detectors to a generalist of wireless communications. We garner the DL based massive MIMO detectors and classify them so that a reader can find the differences between various architectures with a wider range of potential solutions and variations. In this paper, we discuss the performance-complexity profile, pros and cons, and implementation stiffness of each DL based detector's architecture. Detection in cell-free massive MIMO is also presented. Challenges and our perspectives for future research directions are also discussed. This article is not meant to be a survey of a mature-subject, but rather serve as a catalyst to encourage more DL research in massive MIMO.

关键词： Massive MIMO Detectors Wireless communication Machine learning signal processing algorithms Frequency modulation Deep learning Massive MIMO detection deep learning detection networks message passing sphere decoding cell-free massive MIMO deep convolutional neural networks

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Radio Map Estimation

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IEEE signal processing MAGAZINE 2022年第6期39卷 53-72页

作者： Romero, Daniel Kim, Seung-Jun Univ Minnesota Digital Technol Ctr Minneapolis MN 55455 USA Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Univ Agder Dept Informat & Commun Technol N-4879 Grimstad Norway NEC Labs Amer Princeton NJ USA Univ Minnesota Minneapolis MN 55455 USA Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA

Radio maps characterize quantities of interest in radio communication environments, such as the received signal strength and channel attenuation, at every point of a geographical region. Radio map estimation (RME) typically entails interpolative inference based on spatially distributed measurements. In this tutorial article, after presenting some representative applications of radio maps, the most prominent RME methods are discussed. Starting from simple regression, the exposition gradually delves into more sophisticated algorithms, eventually touching upon state-of-the-art techniques. To gain insight into this versatile toolkit, illustrative toy examples will also be presented.

关键词： Wireless communication Radio frequency Estimation signal processing algorithms Data models Probabilistic logic

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Zeroth and First Order Stochastic Frank-Wolfe algorithms for Constrained Optimization

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 2119-2135页

作者： Akhtar, Zeeshan Rajawat, Ketan Indian Inst Technol Kanpur Dept Elect Engn Kanpur 208016 Uttar Pradesh India

This paper considers stochastic convex optimization problems with two sets of constraints: (a) deterministic constraints on the domain of the optimization variable, which are difficult to project onto;and (b) deterministic or stochastic constraints that admit efficient projection. Problems of this form arise frequently in the context of semidefinite programming as well as when various NP-hard problems are solved approximately via semidefinite relaxation. Since projection onto the first set of constraints is difficult, it becomes necessary to explore projection-free algorithms, such as the stochastic Frank-Wolfe (FW) algorithm. On the other hand, the second set of constraints cannot be handled in the same way, and must be incorporated as an indicator function within the objective function, thereby complicating the application of FW methods. Similar problems have been studied before;however, they suffer from slow convergence rates. This work, equipped with momentum based gradient tracking technique, guarantees fast convergence rates on par with the best-known rates for problems without the second set of constraints. Zeroth-order variants of the proposed algorithms are also developed and again improve upon the state-of-the-art rate results. We further propose the novel trimmed FW variants that enjoy the same convergence rates as their classical counterparts, but are empirically shown to require significantly fewer calls to the linear minimization oracle speeding up the overall algorithm. The efficacy of the proposed algorithms is tested on relevant applications of sparse matrix estimation, clustering via semidefinite relaxation, and uniform sparsest cut problem.

关键词： signal processing algorithms Optimization Convergence Minimization Smoothing methods Clustering algorithms Estimation Optimization algorithms Gradient Methods Machine Learning

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Linear Version of Parseval's Theorem

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IEEE ACCESS 2022年 10卷 27230-27241页

作者： Hassanzadeh, Mohammad Shahrrava, Behnam Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

Parseval's theorem states that the energy of a signal is preserved by the discrete Fourier transform (DFT). Parseval's formula shows that there is a nonlinear invariant function for the DFT, so the total energy of a signal can be computed from the signal or its DFT using the same nonlinear function. In this paper, we try to answer the question of whether there are linear invariant functions for the DFT, and how they can be found, along with their potential applications in digital signal processing. In order to answer this question, we first prove that the only linear equations that are preserved by the DFT are its orthogonal projections. Then, using Hilbert spaces and adjoint operators, we propose an algorithm that computes all linear invariant functions for the DFT. These linear invariant functions are also shown to be useful and important in a variety of signal-processing applications, particularly for finding some boundaries for transformed signals without explicitly evaluating the DFT, and vice versa. Additionally, using the proposed identities, we demonstrate that the average of a circular auto-correlation function for a large class of signals is preserved by the DFT. Finally, the results reported in this paper are verified for several short-length and long-length DFTs, including a 256-point DFT.

关键词： Discrete Fourier transforms Hilbert space Eigenvalues and eigenfunctions signal processing algorithms Time-frequency analysis Symmetric matrices Spectral analysis Adjoint operators discrete Fourier transform (DFT) Hilbert spaces invariant functions Parseval's theorem

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Intelligent Fault Diagnosis Method for Gear Transmission Systems Based on Improved Multi-Scale Reverse Dispersion Entropy and Swarm Decomposition

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1-13页

作者： Wang, Hongwei Sun, Wenlei He, Li Zhou, Jianxing Xinjiang Univ Sch Mech Engn Urumqi 830046 Peoples R China

Based on the non-stationary and non-linear acceleration signals, a rapid data-driven method for fault diagnosis in gear transmission systems, which is based on swarm decomposition (SWD) algorithm, improved multi-scale reverse dispersion entropy (improved MRDE) algorithm, and bidirectional long short-term memory (Bi-LSTM) network, is proposed. First, every segment in the original signals is decomposed into several oscillatory components (OCs) with simple fault information by the SWD algorithm. Second, the proposed improved MRDE algorithm is adopted to further extract the features of the original signal and the decomposed signals under different scale factors, and the features are combined into a next bigger feature vector. Finally, the datasets composed of feature vectors are divided into train and test datasets to train and validate the Bi-LSTM network, so as to recognize and classify different fault signals intelligently. The proposed method of fault diagnosis in this article is verified by the signals under different types of faults are collected from the wind turbine drivetrain diagnostics simulator (WTDDS). And the results of the experiment show that it can recognize and classify the types of gear transmission system's fault diagnosis quickly and accurately, and has its advantages in stability, determination, and efficiency.

关键词： signal processing algorithms Fault diagnosis Entropy Gears Feature extraction Classification algorithms Stability analysis Bidirectional long short-term memory (Bi-LSTM) deep learning feature extraction improved multi-scale reverse dispersion entropy (improved MRDE) intelligent fault diagnosis swarm decomposition (SWD)

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Image Compression Using Stochastic-AFD Based Multisignal Sparse Representation

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IEEE TRANSACTIONS ON IMAGE processing 2022年 31卷 5317-5331页

作者： Dai, Lei Zhang, Liming Li, Hong Univ Macau Fac Sci & Technol Taipa Macau Peoples R China Huazhong Univ Sci & Technol Sch Math & Stat Wuhan 430074 Hubei Peoples R China

Adaptive Fourier decomposition (AFD) is a newly developed signal processing tool that can adaptively decompose any single signal using a Szego kernel dictionary. To process multiple signals, a novel stochastic-AFD (SAFD) theory was recently proposed. The innovation of this study is twofold. First, a SAFD-based general multi-signal sparse representation learning algorithm is designed and implemented for the first time in the literature, which can be used in many signal and image processing areas. Second, a novel SAFD based image compression framework is proposed. The algorithm design and implementation of the SAFD theory and image compression methods are presented in detail. The proposed compression methods are compared with 13 other state-of-the-art compression methods, including JPEG, JPEG2000, BPG, and other popular deep learning-based methods. The experimental results show that our methods achieve the best balanced performance. The proposed methods are based on single image adaptive sparse representation learning, and they require no pre-training. In addition, the decompression quality or compression efficiency can be easily adjusted by a single parameter, that is, the decomposition level. Our method is supported by a solid mathematical foundation, which has the potential to become a new core technology in image compression.

关键词： Image coding Dictionaries signal processing algorithms Transform coding Kernel Training Discrete cosine transforms Stochastic adaptive Fourier decomposition sparse representation image compression image reconstruction

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Proper Definitions of Dirichlet Conditions and Convergence of Fourier Representations [Lecture Notes]

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IEEE signal processing MAGAZINE 2022年第5期39卷 77-84页

作者： Singh, Pushpendra Singhal, Amit Fatimah, Binish Gupta, Anubha Joshi, Shiv Dutt Natl Inst Technol Hamirpur Dept Elect & Commun Engn Hamirpur 177005 India Netaji Subhas Univ Technol Dept Elect & Commun Delhi 110078 India CMR Inst Technol Dept Elect & Commun Engn Bangalore 560037 Karnataka India Indraprastha Inst Informat Technol Dept Elect & Commun Engn Delhi 110020 India Indian Inst Technol Dept Elect Engn Delhi 110016 India

Fourier theory is the backbone of signal processing (SP) and communication engineering. It has been widely used in almost all branches of science and engineering in numerous applications since its inception. However, Fourier representations such as Fourier series (FS) and Fourier transform (FT) may not exist for some signals that fail to fulfill a predefined set of Dirichlet conditions (DCs). We note a subtle gap in explaining these conditions as available in popular SP literature. For example, original DCs require a signal to have bounded variations (BVs) over one time period for the convergence of FS, where there can be, at most, a countably infinite number of maxima and minima and, at most, a countably infinite number of discontinuities of finite magnitude. However, a large body of the literature replaces this statement with the requirements of a finite number of maxima and minima over one time period, and a finite number of finite discontinuities over one time period. Due to the latter, some signals fulfilling the original DCs are incorrectly perceived as not having convergent FS (CFS) representation. A similar problem holds in the description of DCs for the FT. Likewise, although it is easy to relate the first DC with the finite value of FS or FT coefficients, the relevance of second and third DCs, as specified in SP literature, is hard to assimilate.

关键词： Fourier transforms signal processing algorithms Fourier series Convergence

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Fixed-Horizon Active Hypothesis Testing and Anomaly Detection

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 4170-4185页

作者： Kartik, Dhruva Mitra, Urbashi Univ Southern Calif Ming Hsieh Dept Elect & Comp Engn Los Angeles CA 90007 USA

Determining the presence of an anomaly or whether a system is safe or not is a problem with wide applicability. The model adopted for this problem is that of verifying whether a multi-component system has anomalies or not. Components can be probed over time individually or as groups in a data-driven manner. The collected observations are noisy and contain information on whether the selected group contains an anomaly or not. The aim is to minimize the probability of incorrectly declaring the system to be free of anomalies while ensuring that the probability of correctly declaring it to be safe is sufficiently large. This problem is modeled as an active hypothesis testing problem in the Neyman-Pearson setting. Asymptotically optimal rates and strategies are characterized. The general strategies are data driven and outperform previously proposed asymptotically optimal methods in the finite sample regime. Furthermore, novel component-selection are designed and analyzed in the non-asymptotic regime. For a specific class of problems admitting a key form of symmetry, strong non-asymptotic converse and achievability bounds are provided which are tighter than previously proposed bounds.

关键词： Testing Anomaly detection Numerical models Safety Error probability signal processing algorithms Sensors Anomaly detection chernoff-stein lemma controlled sensing hypothesis testing

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