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

作者： Cohen, Alejandro Shlezinger, Nir Salamatian, Salman Eldar, Yonina C. Medard, Muriel MIT Res Lab Elect 77 Massachusetts Ave Cambridge MA 02139 USA Weizmann Inst Sci Math & CS Rehovot Israel

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066315

Analog signals processed in digital hardware are quantized into a discrete bit-constrained representation. quantization is typically carried out using analog-to-digital converters (ADCs), operating in a serial scalar manner. In some applications, a set of analog signals are acquired individually and processed jointly. Such setups are referred to as distributed quantization. In this work we propose a distributed quantization scheme for representing a set of sparse time sequences acquired using conventional scalar ADCs. Our approach utilizes tools from secure group testing theory to exploit the sparse nature of the acquired analog signals, obtaining a compact and accurate representation while operating in a distributed fashion. We then show how our technique can be implemented when the quantized signals are transmitted over a multi-hop communication network providing a low-complexity network policy for routing and signal recovery. Our numerical evaluations demonstrate that the proposed scheme notably outperforms conventional methods based on the combination of quantization and compressed sensing tools.

关键词： distributed quantization sparsity group testing

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Sensor Selection and distributed quantization for Energy Efficiency in Massive MTC

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第12期69卷 8518-8533页

作者： Liesegang, Sergi Munoz, Olga Pascual-Iserte, Antonio Univ Politecn Cataluna Dept Signal Theory & Commun Barcelona 08034 Spain

This paper presents an estimation approach within the framework of uplink massive machine-type-communications (mMTC) that considers the energy limitations of the devices. We focus on a scenario where a group of sensors observe a set of parameters and send the measured information to a collector node (CN). The CN is responsible for estimating the original observations, which are spatially correlated and corrupted by measurement and quantization noise. Given the use of Gaussian sources, the minimum mean squared error (MSE) estimation is employed and, when considering temporal evolution, the use of Kalman filters is studied. Based on that, we propose a device selection strategy to reduce the number of active sensors and a quantization scheme with adjustable number of bits to minimize the overall payload. The set of selected sensors and quantization levels are, thus, designed to minimize the MSE. For a more realistic analysis, communication errors are also included by averaging the MSE over the error decoding probabilities. We evaluate the performance of our strategy in a practical mMTC system with synthetic and real databases. Simulation results show that the optimization of the payload and the set of active devices can reduce the power consumption without compromising the estimation accuracy.

关键词： quantization (signal) Estimation Power demand Noise measurement Kalman filters Correlation Standards Machine-type-communications parameter estimation sensor selection distributed quantization mean squared error Kalman filter

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On distributed quantization for Classification

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2020年第1期1卷 237-249页

作者： Hanna, Osama A. Ezzeldin, Yahya H. Sadjadpour, Tara Fragouli, Christina Diggavi, Suhas Univ Calif Los Angeles Elect & Comp Engn Dept Los Angeles CA 90095 USA

We consider the problem of distributed feature quantization, where the goal is to enable a pretrained classifier at a central node to carry out its classification on features that are gathered from distributed nodes through communication constrained channels. We propose the design of distributed quantization schemes specifically tailored to the classification task: unlike quantization schemes that help the central node reconstruct the original signal as accurately as possible, our focus is not reconstruction accuracy, but instead correct classification. Our work does not make any a priori distributional assumptions on the data, but instead uses training data for the quantizer design. Our main contributions include: we prove NP-hardness of finding optimal quantizers in the general case;we design an optimal scheme for a special case;we propose quantization algorithms, that leverage discrete neural representations and training data, and can be designed in polynomial-time for any number of features, any number of classes, and arbitrary division of features across the distributed nodes. We find that tailoring the quantizers to the classification task can offer significant savings: as compared to alternatives, we can achieve more than a factor of two reduction in terms of the number of bits communicated, for the same classification accuracy.

关键词： distributed quantization inference communication-bounded inference quantization with deep learning

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quantization of distributed Data for Learning

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2021年第3期2卷 987-1001页

作者： Hanna, Osama A. Ezzeldin, Yahya H. Fragouli, Christina Diggavi, Suhas Univ Calif Los Angeles Elect & Comp Engn Dept Los Angeles CA 90095 USA Univ Southern Calif Elect & Comp Engn Dept Los Angeles CA 90089 USA

We consider machine learning applications that train a model by leveraging data distributed over a trusted network, where communication constraints can create a performance bottleneck. A number of recent approaches propose to overcome this bottleneck through compression of gradient updates. However, as models become larger, so does the size of the gradient updates. In this paper, we propose an alternate approach to learn from distributed data that quantizes data instead of gradients, and can support learning over applications where the size of gradient updates is prohibitive. Our approach leverages the dependency of the computed gradient on data samples, which lie in a much smaller space in order to perform the quantization in the smaller dimension data space. At the cost of an extra gradient computation, the gradient estimate can be refined by conveying the difference between the gradient at the quantized data point and the original gradient using a small number of bits. Lastly, in order to save communication, our approach adds a layer that decides whether to transmit a quantized data sample or not based on its importance for learning. We analyze the convergence of the proposed approach for smooth convex and non-convex objective functions and show that we can achieve order optimal convergence rates with communication that mostly depends on the data rather than the model (gradient) dimension. We use our proposed algorithm to train ResNet models on the CIFAR-10 and ImageNet datasets, and show that we can achieve an order of magnitude savings over gradient compression methods. These communication savings come at the cost of increasing computation at the learning agent, and thus our approach is beneficial in scenarios where communication load is the main problem.

关键词： distributed quantization machine learning communication-constrained learning quantization with deep learning

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quantization IN MULTISENSOR RANDOM SIGNAL-DETECTION

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IEEE TRANSACTIONS ON INFORMATION THEORY 1995年第1期41卷 204-215页

作者： BLUM, RS Department of Electrical Engineering and Computer Science Lehigh University Bethlehem PA USA

Optimum detection schemes based on quantized data are of great interest in radar and sonar applications. The design and properties of multisensor schemes are considered here for detection of weak random signals in additive, possibly non-Gaussian, noise. Signal-to-noise ratios are assumed unknown and the signals at the different sensors may be statistically dependent. Analytical expressions describing the best way to fuse the quantized observations for cases with any given observation sample size are provided. The best schemes for originally quantizing the observations are also studied for the case of asymptotically large observation sample sizes. These schemes are shown to minimize the mean-squared error between the best weak-signal test statistic based on unquantized observations and the best weak-signal test statistic based on quantized observations (under signal absent). Numerical results indicate it is sometimes best for each quantizer to use different size alphabets when a quantizer is located at each sensor.

关键词： quantization FOR DETECTION ASYMPTOTICALLY OPTIMUM quantization distributed quantization distributed DETECTION DECENTRALIZED DETECTION

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Joint source-decoding in large scale sensor networks using Markov random field models

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SIGNAL PROCESSING 2010年第12期90卷 3134-3146页

作者： Yahampath, Pradeepa Univ Manitoba Dept Elect & Comp Engn Winnipeg MB R3T 5V6 Canada

An approach to scalable joint source decoding in large-scale sensor networks, based on Markov-random filed (MRF) modeling of the spatio-temporal correlation in the observations is presented. This approach exploits the correlation among a multitude of sensors for joint decoding at a central decoder, while using simple distributed quantizers in individual sensors. The decoder derivations are provided for Slepian-Wolf coded quantization based on both sample-by-sample (scalar) binning and vector binning schemes constructed via channel code partitioning. Simulation results are presented to demonstrate the performance achievable with the proposed decoding approach. (C) 2010 Elsevier B.V. All rights reserved.

关键词： distributed quantization Slepian-Wolf coding Multi-terminal source coding Markov-random field models

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On Scalable distributed Coding of Correlated Sources

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2010年第5期58卷 2875-2883页

作者： Saxena, Ankur Rose, Kenneth Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

This paper considers the problem of scalable distributed coding of correlated sources that are communicated to a central unit, a setting typically encountered in sensor networks. As communication channel conditions may vary with time, it is often desirable to guarantee a base layer of coarse information during channel fades, as well as robustness to channel link (or sensor) failures. The main contribution is twofold. First, we consider the special case of multistage distributed coding, and show that naive combination of distributed coding with multistage coding yields poor rate-distortion performance, due to underlying conflicts between the objectives of these two coding methods. An appropriate system paradigm is developed, which allows such tradeoffs to be explicitly controlled. Next, we consider the unconstrained scalable distributed coding problem. Although a standard "Lloyd-style" distributed coder design algorithm is easily generalized to encompass scalable coding, the algorithm performance is heavily dependent on initialization and will virtually always converge to a poor local minimum. We propose an effective initialization scheme for such a system, which employs a properly designed multistage distributed coder. We present iterative design techniques and derive the necessary conditions for optimality for both multistage and unconstrained scalable distributed coding systems. Simulation results show substantial gains for the proposed multistage distributed coding system over naive extensions which incorporate scalability in a multistage distributed coding system. Moreover, the proposed overall scalable distributed coder design consistently and substantially outperforms the randomly initialized "Lloyd-style" scalable distributed coder design.

关键词： distributed quantization multistage coding scalable coding sensor networks

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Feedback quantization strategies for multiuser diversity systems

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IEEE TRANSACTIONS ON INFORMATION THEORY 2007年第4期53卷 1386-1400页

作者： Lau, Alan Pak Tao Kschischang, Frank R. Univ Toronto Edward S Rogers Sr Dept Eelct & Comp Engn Toronto ON M5S 3G4 Canada

In a system utilizing multiuser diversity, regular feedback of channel-quality predictions to the base station is required for each user. Typically, the measure of channel quality must be quantized at each mobile station before it can be sent back. In this paper, we present two distributed scalar quantization schemes that optimize two different performance criteria: a) the minimization of the probability P-e of incorrectly identifying the user with the best channel quality and b) maximization of the resulting throughput R. For a typical Rayleigh-fading system with 30 users per sector, numerical optimization results show that the P-e and R realized by the uniform quantization strategy with 16 quantization levels for each user can be achieved by only three quantization levels using the two proposed strategies. A practical approximation of the proposed schemes is studied and is shown to provide near-optimal performance for both performance criteria as the number of quantization levels becomes large.

关键词： channel fading distributed quantization multiuser diversity order statistics

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Serial quantization for Sparse Time Sequences

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 3299-3314页

作者： Cohen, Alejandro Shlezinger, Nir Salamatian, Salman Eldar, Yonina C. Medard, Muriel MIT Elect Res Lab Cambridge MA 02139 USA Ben Gurion Univ Negev Sch Elect Engn IL-84105 Beer Sheva Israel Weizmann Inst Sci IL-7610001 Rehovot Israel

Sparse signals are encountered in a broad range of applications. In order to process these signals using digital hardware, they must first be sampled and quantized using an analog-to-digital convertor (ADC), which typically operates in a serial scalar manner. In this work, we propose an serial quantization of sparse time sequences (SQuaTS) method inspired by group testing theory. This method is designed to reliably and accurately quantize sparse signals acquired in a sequential manner using the serial scalar ADCs. Unlike previously proposed approaches that combine quantization and compressed sensing (CS), our SQuaTS scheme updates its representation on each incoming analog sample and does not require the complete signal to be observed or stored in analog prior to quantization. We characterize the asymptotic tradeoff between the accuracy and quantization rate of SQuaTS as well as its computational burden. We also propose a variation of SQuaTS that trades the quantization rate for computational efficiency. Next, we show how SQuaTS can be naturally extended to distributed quantization scenarios, where a set of jointly sparse time sequences are acquired individually and processed jointly. Our numerical results demonstrate that SQuaTS is capable of achieving substantially improved representation accuracy over previous CS-based schemes without requiring the complete set of analog signal samples to be observed prior to signal quantization, making this method an attractive approach for acquiring sparse time sequences.

关键词： quantization (signal) Computational modeling Reliability theory Performance gain Reliability engineering Hardware Complexity theory quantization distributed quantization sparsity group testing

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Improving performance of distributed detection networks: an entropy-based optimization

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SIGNAL PROCESSING 2001年第12期81卷 2479-2491页

作者： Pomorski, D Desrousseaux, C Cite Sci Univ Sci & Technol Lille UPRESA 8021 Lab Automat & Informat Ind Lille F-59655 Villeneuve Dascq France

The reduction of communication costs is the key focus of distributed detection networks. On the contrary, their performance is lower compared to centralized networks, because the fusion center does not receive all the available information to make the final decision. The optimization of a parallel distributed network with N sensors leads to a set of 2(N) + N nonlinear equations, that can only be solved in particular cases, assuming statistical independence of the local observations, and for small-sized networks. In order to reduce the number of equations to solve, Shannon's entropy is used to select relevant sensors for the decision process. Once the relevant sensors are selected, an entropy-based criterion is used to optimize the network: local thresholds are determined, and a binary decision tree represents the fusion rule. This approach provides a fusion center using efficient and simple procedures. These optimization techniques are applied to the distributed quantization problem: we consider a compromise between the broadcast bandwidth of the fusion center and quality of the decision system, in order to approximate centralized detection performance. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： centralized/distributed detection Shannon's entropy decision tree distributed quantization

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