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IEEE OPEN JOURNAL OF signal processing 2022年 3卷 71-93页

作者： Kayaalp, Mert Vlaski, Stefan Sayed, Ali Ecole Polytech Fed Lausanne Adapt Syst Lab CH-1015 Lausanne Switzerland Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

The objective of meta-learning is to exploit knowledge obtained from observed tasks to improve adaptation to unseen tasks. Meta-learners are able to generalize better when they are trained with a larger number of observed tasks and with a larger amount of data per task. Given the amount of resources that are needed, it is generally difficult to expect the tasks, their respective data, and the necessary computational capacity to be available at a single central location. It is more natural to encounter situations where these resources are spread across several agents connected by some graph topology. The formalism of meta-learning is actually well-suited for this decentralized setting, where the learner benefits from information and computational power spread across the agents. Motivated by this observation, we propose a cooperative fully-decentralized multi-agent meta-learning algorithm, referred to as Diffusion-based MAML or Dif-MAML. Decentralized optimization algorithms are superior to centralized implementations in terms of scalability, robustness, avoidance of communication bottlenecks, and privacy guarantees. The work provides a detailed theoretical analysis to show that the proposed strategy allows a collection of agents to attain agreement at a linear rate and to converge to a stationary point of the aggregate MAML objective even in non-convex environments. Simulation results illustrate the theoretical findings and the superior performance relative to the traditional non-cooperative setting.

关键词： Task analysis Adaptation models Data models signal processing algorithms signal processing Optimization Biological system modeling Decentralized optimization diffusion algorithm distributed learning learning to learn meta-learning multi-agent systems networked agents

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Quantum Resources Required to Block-Encode a Matrix of Classical Data

IEEE TRANSACTIONS ON QUANTUM ENGINEERING

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IEEE TRANSACTIONS ON QUANTUM ENGINEERING 2022年第1期3卷 1页

作者： Clader, B. David Dalzell, Alexander M. Stamatopoulos, Nikitas Salton, Grant Berta, Mario Zeng, William J. Goldman Sachs & Co New York NY 10282 USA AWS Ctr Quantum Comp Pasadena CA 91125 USA CALTECH Pasadena CA 91125 USA Amazon Quantum Solut Lab Seattle WA 98170 USA Imperial Coll London Dept Comp London SW7 2BX England Rhein Westfal TH Aachen Inst Quantum Informat D-52062 Aachen Germany

We provide modular circuit-level implementations and resource estimates for several methods of block-encoding a dense NxN matrix of classical data to precision & varepsilon;the minimal-depth method achieves a T-depth of O(log(N/& varepsilon;)), while the minimal-count method achieves a T-count of O(Nlog(1/& varepsilon;)). We examine resource tradeoffs between the different approaches, and we explore implementations of two separate models of quantum random access memory (QRAM). As part of this analysis, we provide a novel state preparation routine with T-depth O(log(N/& varepsilon;)), improving on previous constructions with scaling O(log(2)(N/& varepsilon;)). Our results go beyond simple query complexity and provide a clear picture into the resource costs when large amounts of classical data are assumed to be accessible to quantum algorithms.

关键词： Qubit Logic gates Costs signal processing algorithms Registers Quantum circuit Integrated circuit modeling Block encoding quantum circuit synthesis quantum circuits quantum computing quantum random access memory (QRAM) quantum resources

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Conjugate Gradient Iterative Hard Thresholding for Structured Sparsity

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2022年 3卷 387-402页

作者： Blanchard, Jeffrey D. Grinnell Coll Dept Math & Stat Grinnell IA 50112 USA

Greedy sparse recovery algorithms are studied in the structured sparsity (sparsity in levels) framework. Recovery guarantees are provided for Normalized Iterative Hard Thresholding and Conjugate Gradient Iterative Hard Thresholding in the form of restricted isometry properties for sparsity in levels. Empirical results indicate that CGIHT is comparable to CoSaMP in recovery capability in the structured setting, while maintaining the computational complexity of NIHT. While exploiting structured sparsity improves recovery performance, pessimistic theoretical guarantees mask when practitioners should use these algorithms;the empirical results offer guidance for using the original greedy algorithms over CGIHT in Levels.

关键词： Compressed sensing signal processing algorithms Indexes Sparse representation Iterative algorithms Standards Sensors Compressed sensing conjugate gradient iterative hard thresholding greedy algorithms model-based compressed sensing structured sparsity sparsity in levels sparse approximation

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Impact of Antenna Distribution on Spectral and Energy Efficiency of Cell-Free Massive MIMO With Transmit Power Control algorithms

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2022年 3卷 1615-1629页

作者： Ito, Masaaki Kanno, Issei Yamazaki, Kosuke Kishi, Yoji Chen, Wei-Yu Choi, Thomas Molisch, Andreas F. KDDI Res Inc Wireless Technol Div Saitama 3568502 Japan Univ Southern Calif Ming Hsieh Dept Elect & Comp Engn Los Angeles CA 90089 USA

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems are expected to provide faster and more robust connections to user equipments by cooperation of a massive number of distributed access points (APs), and to be one of the key technologies for beyond 5G (B5G). CF-mMIMO systems with multiple-antenna APs have been investigated from various viewpoints recently. However, no comprehensive analysis of the impact of antenna distribution on CF-mMIMO system performance has been done so far, which is important for practical deployment. Besides spectral efficiency, in B5G, energy efficiency of user equipments is one of the key indicators because various kinds of battery-limited devices connect to the network. Thus, this paper provides a comprehensive performance analysis of the impact of antenna distribution on the performance indicators, while considering several combining/precoding schemes and transmit power control algorithms. For uplink maximal-ratio combining, the concentrated deployment has the best performance thanks to the channel hardening and favorable propagation phenomena. On the other hand, the concentrated deployment prominently suffers from shadowing effects. For uplink/downlink minimum mean-square error combining with transmit power control, the semi-distributed deployments show the best performance, and it implies that we can reduce the number of APs to 1/4 for uplink and to 1/2 for downlink while keeping the same performance as the fully-distributed deployment.

关键词： Antennas Power control Uplink signal processing algorithms Transmitting antennas Shadow mapping Minimax techniques Antenna distribution battery lifetime prolongation cell-free massive MIMO energy efficiency spectral efficiency transmit power control

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A Primer on Zeroth-Order Optimization in signal processing and Machine Learning Principals, recent advances, and applications

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IEEE signal processing MAGAZINE 2020年第5期37卷 43-54页

作者： Liu, Sijia Chen, Pin-Yu Kailkhura, Bhavya Zhang, Gaoyuan Hero, Alfred O., III Varshney, Pramod K. Univ Michigan Ann Arbor MI 48109 USA IBM Thomas J Watson Res Ctr Yorktown Hts NY USA Lawrence Livermore Natl Lab Livermore CA 94550 USA MIT IBM Watson Artificial Intelligence Lab Cambridge MA 02139 USA Univ Michigan Elect Engn & Comp Sci Ann Arbor MI 48109 USA Univ Michigan Engn Ann Arbor MI 48109 USA Syracuse Univ Elect Engn & Comp Sci Syracuse NY 13244 USA

Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many signal processing and machine learning (ML) applications. It is used for solving optimization problems similarly to gradient-based methods. However, it does not require the gradient, using only function evaluations. Specifically, ZO optimization iteratively performs three major steps: gradient estimation, descent direction computation, and the solution update. In this article, we provide a comprehensive review of ZO optimization, with an emphasis on showing the underlying intuition, optimization principles, and recent advances in convergence analysis. Moreover, we demonstrate promising applications of ZO optimization, such as evaluating robustness and generating explanations from black-box deep learning (DL) models and efficient online sensor management.

关键词： Optimization Estimation signal processing algorithms Linear programming signal processing Convergence Approximation error

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FPGA Implementation of Reconfigurable CORDIC Algorithm and a Memristive Chaotic System With Transcendental Nonlinearities

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2022年第7期69卷 2885-2892页

作者： Mohamed, Sara M. Sayed, Wafaa S. Radwan, Ahmed G. Said, Lobna A. Nile Univ Nanoelect Integrated Syst Ctr Giza 12588 Egypt Cairo Univ Fac Engn Engn Math & Phys Dept Giza 12613 Egypt Nile Univ Sch Engn & Appl Sci Giza 12588 Egypt

Coordinate Rotation Digital Computer (CORDIC) is a robust iterative algorithm that computes many transcendental mathematical functions. This paper proposes a reconfigurable CORDIC hardware design and FPGA realization that includes all possible configurations of the CORDIC algorithm. The proposed architecture is introduced in two approaches: multiplier-less and single multiplier approaches, each with its advantages. Compared to recent related works, the proposed implementation overpasses them in the included number of configurations. Additionally, it demonstrates efficient hardware utilization and suitability for potential applications. Furthermore, the proposed design is applied to a memristive chaotic system with different transcendental functions computed using the proposed reconfigurable block. The memristive system design is realized on the Artix-7 FPGA board, yielding throughputs of 0.4483 and 0.3972 Gbit/s for the two approaches of reconfigurable CORDIC.

关键词： Field programmable gate arrays Hardware Chaotic communication signal processing algorithms Computer architecture Adders Trajectory Chaotic systems CORDIC FPGA reconfigurable transcendental

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MSP-MFCC: Energy-Efficient MFCC Feature Extraction Method With Mixed-signal processing Architecture for Wearable Speech Recognition Applications

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IEEE ACCESS 2020年 8卷 48720-48730页

作者： Li, Qin Yang, Yuze Lan, Tianxiang Zhu, Huifeng Wei, Qi Qiao, Fei Liu, Xinjun Yang, Huazhong Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing 100084 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Tsinghua Univ Dept Mech Engn Beijing 100084 Peoples R China

Feature extraction is an essential part of automatic speech recognition (ASR) to compress raw speech data and enhance features, where conventional implementation methods based on the digital domain have encountered energy consumption and processing speed bottlenecks. Thus, we propose a Mixed-signal processing (MSP) architecture to efficiently extract Mel-Frequency Cepstrum Coefficients (MFCC) features. We design MSP-MFCC to pre-process speech signals in the analog domain, which significantly reduces the cost of the analog-to-digital converter (ADC), as well as the computational complexity of the digital backend. Moreover, MSP-MFCC eliminates the time-consuming Fourier transform in the conventional digital realization by improving processing flow. We fabricated the analog part based on 180nm CMOS mixed-signal technology, then measured the chip. The measured results show the energy consumption of MSP-MFCC is 0.72 mu J/frame, and the processing speed is up to 45.79 mu s/frame. MSP-MFCC achieves 95% energy saving and about 6.4 x speedup than state of the art. Further, by using the features extracted by MSP-MFCC, speech recognition simulation reaches the accuracy of 98.2%, which also keeps the leading performance to its current counterparts. The proposed MFCC extractor is competitive for integration in the ultra-low-power always-on wearable speech recognition applications.

关键词： Feature extraction Mel frequency cepstral coefficient Speech recognition Energy efficiency Energy consumption signal processing algorithms Time-domain analysis Mixed signal processing architecture energy-efficient feature extraction mel-frequency cepstrum coefficients (MFCC) wearable speech recognition application

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Uplink Channel Estimation With Reduced Fronthaul Overhead in Cell-Free Massive MIMO Systems

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IEEE WIRELESS COMMUNICATIONS LETTERS 2022年第8期11卷 1718-1722页

作者： Zhao, Tianyu Chen, Shuyi Zhang, Ruoyu Chen, Hsiao-Hwa Guo, Qing Harbin Inst Technol Commun Res Ctr Harbin 150001 Peoples R China Nanjing Univ Sci & Technol Minist Key Lab JGMT Nanjing 210094 Peoples R China Natl Cheng Kung Univ Dept Engn Sci Tainan 70101 Taiwan

This letter focuses on the problem of uplink channel estimation with the reduced fronthaul overhead in cell-free massive multiple-input multiple-output (mMIMO) systems. First, we propose a sub-sampling scheme to reduce the dimension of fronthaul. Then, we exploit the inherent channel sparsity and model the underdetermined channel estimation problem as an off-grid sparse signal recovery problem. Finally, an enhanced sparse Bayesian learning (ESBL) channel estimation algorithm is proposed to refine the sampled grid points and recover the sparse channel iteratively. Simulation results demonstrate that the proposed algorithm achieves a significant reduction on the fronthaul overhead and offers a better channel estimation performance.

关键词： Channel estimation Uplink signal processing algorithms Estimation Sparse matrices Matching pursuit algorithms Central processing Unit Cell-free massive MIMO uplink channel estimation Fronthaul overhead reduction off-grid enhanced sparse Bayesian learning

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Nonconvex Optimization for signal processing and Machine Learning

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IEEE signal processing MAGAZINE 2020年第5期37卷 15-17页

作者： So, Anthony Man-Cho Jain, Prateek Ma, Wing-Kin Scutari, Gesualdo Dept Syst Engn & Engn Management W Lafayette IN USA IIT Kanpur Kanpur Uttar Pradesh India Dept Elect Engn W Lafayette IN USA Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA

The articles in this special section focus on nonconvex optimization for signal processing and machine learning. Optimization is now widely recognized as an indispensable tool in signal processing (SP) and machine learning (ML). Indeed, many of the advances in these fields rely crucially on the formulation of suitable optimization models and deployment of efficient numerical optimization algorithms. In the early 2000s, there was a heavy focus on the use of convex optimization techniques to tackle SP and ML applications. This is largely due to the fact that convex optimization problems often possess favorable theoretical and computational properties and that many problems of practical interest have been shown to admit convex formulations or good convex approximations.

关键词： Special issues and sections Optimization signal processing algorithms Machine learning Convex functions Training data

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Higher Reconstruction Performance with Dual Deep Neural Networks for Wireless Sensor Networks Compressed Temporal signals 12

Higher Reconstruction Performance with Dual Deep Neural Netw...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2023

作者： Rousseau, Sebastien Negrier, Romain Courreges, Fabien Engoti, Frank Itoua Quere, Raymond University of Limoges Xlim Institute Brive-la-Gaillarde19100 France NAIoBEE Company Brive-la-Gaillarde19100 France

ISBN: (纸本)9798350358056

In many applications using wireless sensor networks, the reliability of monitored data is crucial to analyze situations and take decisions. Compressed sensing methods are effective to ensure durability of a wireless sensor network installation and to overcome communication failures by drastically lowering the amount of data to transmit. The method proposed in this paper aims to perform an abstraction of the signals sparsity model required by compressed sensing in order to use a deep learning-based approach. This method is designed to be effective for all kinds of transient or temporally dynamic signals sufficiently regular to be predictable, that is to say signals that can be implicitly modelled by a sparse representation. Through the collaboration of two recurrent neural networks and signal processing algorithms, our temporal method outperforms, in terms of accuracy, state-of-art methods, including distributed compressed sensing, time series imputation and forecasting deep learning methods. Thus, high compression rates up to 95% can be reached while leading to an error of less than 0.5°C in reconstructing temperature signals. © 2023 IEEE.

关键词： compressed sensing deep learning recurrent neural networks signal processing algorithms wireless sensor networks

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