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distributed Joint source-Channel coding-Based Adaptive Dynamic Network coding

IEEE ACCESS 2020年 8卷 86715-86731页

作者： Aljohani, Abdulah Jeza Ng, Soon Xin King Abdulaziz Univ Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst Jeddah 21589 Saudi Arabia Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

distributed source coding (DSC) schemes rely on separate encoding but joint decoding of statistically dependent sources, which exhibit correlation. More specifically, distributed Joint source-Channel coding (DJSC) is associated with the scenario, where the correlated source signals are transmitted through a noisy channel. On one hand, employing DSC or DJSC schemes exploits the existing correlation between sensors resulting in minimising the transmission energy required by the sources, while maintaining reliable communication. On the other hand, Network coding (NC) is an efficient data transport technique leveraging network efficiency, by allowing Relay Nodes (RNs) in a communication network to combine multiple data packets received via the incoming links before transmitting them to the Destination Node (DN). In this paper, the bandwidth -efficient distributed Joint Turbo Trellis -Coded Modulation (DJTTCM) aided by both Dynamic Network coding (DNC) and Adaptive DNC (ADNC)-based cooperative transmission schemes are proposed. Both systems are proposed for supporting correlated source transmissions over hostile channels experiencing both small-scale and large-scale fading in which the RNs dynamically transmit its non -binary linear combinations to the DN. A substantial gain of 19.5 dB was attained at a correlation coefficient of rho = 0.8 over its counterpart dispensing with NC.

关键词： distributed source coding distributed joint source-channel coding dynamic network coding Slepian-wolf coding network coding joint channel-coding and network-coding joint channel-coding and network-coding

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Streamlined Genome Sequence Compression using distributed source coding

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CANCER INFORMATICS 2014年第Sup1期13卷 123-131页

作者： Wang, Shuang Jiang, Xiaoqian Chen, Feng Cui, Lijuan Cheng, Samuel Univ Calif San Diego Div Biomed Informat La Jolla CA 92093 USA Univ Oklahoma Sch Elect & Comp Engn Tulsa OK 73019 USA

We aim at developing a streamlined genome sequence compression algorithm to support alternative miniaturized sequencing devices, which have limited communication, storage, and computation power. Existing techniques that require heavy client (encoder side) cannot be applied. To tackle this challenge, we carefully examined distributed source coding theory and developed a customized reference-based genome compression protocol to meet the low-complexity need at the client side. Based on the variation between source and reference, our protocol will pick adaptively either syndrome coding or hash coding to compress subsequences of changing code length. Our experimental results showed promising performance of the proposed method when compared with the state-of-the-art algorithm (GRS).

关键词： genome compression distributed source coding graphical model

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Reducing Channel Estimation and Feedback Overhead in IRS-Aided Downlink System: A Quantize-Then-Estimate Approach

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第2期24卷 1325-1338页

作者： Wang, Rui Wang, Zhaorui Liu, Liang Zhang, Shuowen Jin, Shi Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China Chinese Univ Hong Kong Shenzhen CUHK Shenzhen FNii Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Shenzhen CUHK Shenzhen SSE Shenzhen 518172 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

Channel state information (CSI) acquisition is essential for the base station (BS) to fully reap the beamforming gain in intelligent reflecting surface (IRS)-aided downlink communication systems. Recently, Wang et al. (2020) revealed a strong correlation in different users' cascaded channels stemming from their common BS-IRS channel component, and leveraged such a correlation to significantly reduce the pilot transmission overhead in IRS-aided uplink communication. In this paper, we aim to exploit the above channel property to reduce the overhead for both pilot and feedback transmission in IRS-aided downlink communication. Note that in the downlink, the distributed users merely receive the pilot signals containing their own CSI and cannot leverage the correlation in different users' channels, which is in sharp contrast to the uplink counterpart considered in Wang et al. (2020). To tackle this challenge, this paper proposes a novel "quantize-then-estimate" protocol in frequency division duplex (FDD) IRS-aided downlink communication. Specifically, the users quantize and feed back their received pilot signals, instead of the estimated channels, to the BS. After de-quantizing the pilot signals received by all the users, the BS estimates all the cascaded channels by leveraging their correlation, similar to the uplink scenario. Under this protocol, we manage to propose efficient user-side quantization and BS-side channel estimation methods. Moreover, we analytically quantify the pilot and feedback transmission overhead to reveal the significant performance gain of our proposed scheme over the conventional "estimate-then-quantize" scheme.

关键词： Channel estimation Downlink Protocols Quantization (signal) Correlation Uplink Feeds Vectors Wireless communication Transmitting antennas Intelligent reflecting surface (IRS) channel estimation channel feedback distributed source coding

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distributed source-Channel coding Based on Real-Field BCH Codes

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2014年第5期62卷 1171-1184页

作者： Vaezi, Mojtaba Labeau, Fabrice McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0E9 Canada

We use real-number codes to compress statistically dependent sources and establish a new framework for distributed lossy source coding in which we compress sources before, rather than after, quantization. This change in the order of binning and quantization blocks makes it possible to model the correlation between continuous-valued sources more realistically and compensate for the quantization error partially. We then focus on the asymmetric case, i.e., lossy source coding with side information at the decoder. The encoding and decoding procedures are described in detail for a class of real-number codes called discrete Fourier transform (DFT) codes, both for the syndrome- and parity-based approaches. We leverage subspace-based decoding to improve the decoding and by extending it we are able to perform distributed source coding in a rate-adaptive fashion to further improve the decoding performance when the statistical dependency between sources is unknown. We also extend the parity-based approach to the case where the transmission channel is noisy and thus we perform distributed joint source-channel coding in this context. The proposed system is well suited for low-delay communications, as the mean-squared reconstruction error (MSE) is shown to be reasonably low for very short block length.

关键词： BCH-DFT codes distributed source coding joint source-channel coding parity real-number codes syndrome

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A distributed source autoencoder of local visual descriptors for 3D reconstruction

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PATTERN RECOGNITION LETTERS 2021年 146卷 193-199页

作者： Milani, Simone Univ Padua Dept Informat Engn Via Gradenigo 6-B I-35131 Padua Italy

This paper presents a local descriptor coding scheme for multicamera surveillance and 3D reconstruction embedding an autoencoder into a traditional distributed source coding strategy. The proposed solution permits shifting most of the computational complexity at the decoder/receiver and exploiting the correlation among descriptors of different cameras (thus reducing the coded bit rate) without increasing the inter-device communication load. Experimental results show that the proposed scheme permits obtaining a satisfying accuracy with respect to the most recent solutions while generating a limited bit rate. (c) 2021 Elsevier B.V. All rights reserved.

关键词： distributed vision networks distributed source coding Autoencoder Local descriptor coding Structure-from-Motion

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Greedy Iterative and Meta-Heuristic Clustering With Coded Caching and Slepian-Wolf Compression for Correlated Content

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IEEE ACCESS 2024年 12卷 12323-12334页

作者： Rosen, Benjamin Cheng, Ling Univ Witwatersrand Sch Elect & Informat Engn ZA-2000 Johannesburg South Africa

Content caching has emerged as an effective approach to combat the increasing strains on our current network infrastructure. This method is further improved when combining caching with source coding. However, additional complexity is incurred by creating this hybrid method, as the source coding component comes with associated feasibility constraints and decoding costs. This paper presents an approach to balance this complexity with the coding gains by selecting the best-performing subset of files to compress, while the others are left uncoded. This problem is shown to be NP-hard in general and difficult to solve in an iteration-free manner. To this end, two novel approaches are outlined: an iterative-based solution, which uses the features of the entropy function to derive the most suitable files to compress jointly, and a meta-heuristic version, which is based on the Genetic Algorithm. When compared to an exhaustive search, the proposed solutions are found to be sub-optimal but falling above the 90th percentile of all possible solutions on average. Significantly, the iterative method produces results within one percentile of the meta-heuristic approach yet it finds a solution 2.31 times faster. The iterative approach has an additional benefit, in that it is able to predict the relative gains when adding more files to a compression group. It is thus able to terminate prematurely if the estimated gains are less than a chosen threshold.

关键词： Encoding Metaheuristics Entropy Complexity theory Iterative methods Servers Content management Cache storage Slepian-wolf coding distributed source coding coded caching iterative optimisation

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Neural distributed Compressor Discovers Binning

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2024年 5卷 246-260页

作者： Ozyilkan, Ezgi Balle, Johannes Erkip, Elza NYU Dept Elect & Comp Engn Brooklyn NY 11201 USA Google Res New York NY 10011 USA

We consider lossy compression of an information source when the decoder has lossless access to a correlated one. This setup, also known as the Wyner-Ziv problem, is a special case of distributed source coding. To this day, practical approaches for the Wyner-Ziv problem have neither been fully developed nor heavily investigated. We propose a data-driven method based on machine learning that leverages the universal function approximation capability of artificial neural networks. We find that our neural network-based compression scheme, based on variational vector quantization, recovers some principles of the optimum theoretical solution of the Wyner-Ziv setup, such as binning in the source space as well as optimal combination of the quantization index and side information, for exemplary sources. These behaviors emerge although no structure exploiting knowledge of the source distributions was imposed. Binning is a widely used tool in information theoretic proofs and methods, and to our knowledge, this is the first time it has been explicitly observed to emerge from data-driven learning.

关键词： distributed source coding binning Wyner-Ziv coding learning lossy compression neural networks rate-distortion theory

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Secure source coding Resilient Against Compromised Users via an Access Structure

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2024年 5卷 478-492页

作者： ZivariFard, Hassan Chou, Remi A. Columbia Univ Dept Elect Engn New York NY 10027 USA Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA

Consider a source and multiple users who observe the independent and identically distributed (i.i.d.) copies of correlated Gaussian random variables. The source wishes to compress its observations and store the result in a public database such that (i) authorized sets of users are able to reconstruct the source with a certain distortion level, and (ii) information leakage to non-authorized sets of colluding users is minimized. In other words, the recovery of the source is restricted to a predefined access structure. The main result of this paper is a closed-form characterization of the fundamental trade-off between the source coding rate and the information leakage rate. As an example, threshold access structures are studied, i.e., the case where any set of at least t users is able to reconstruct the source with some predefined distortion level and the information leakage at any set of users with a size smaller than t is minimized.

关键词： distributed source coding secure source coding secure data storage equivocation-rate capacity side information rate-distortion region access structure compromised users

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Q-Ary distributed Arithmetic coding for Uniform Q-Ary sources

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IEEE TRANSACTIONS ON INFORMATION THEORY 2023年第1期69卷 47-74页

作者： Fang, Yong Changan Univ Sch Informat Engn Xian 710064 Shaanxi Peoples R China

Laplacian distribution is widely used to model the differences between correlated continuous or nonbinary signals, e.g., the predictive residues of videos. Usually, distributed coding of correlated nonbinary sources, e.g., distributed video coding, is implemented by binary or nonbinary Low-Density Parity-Check (LDPC) codes. In this paper, as an alternative, we attempt using nonbinary distributed Arithmetic coding (DAC) to implement distributed coding of uniform nonbinary sources with Laplace-distributed correlation. To analyze Q -ary DAC for uniform Q -ary sources, following the methodology developed in our prior work for binary DAC, we define and deduce Coset Cardinality Spectrum (CCS) from both fixed-length and variable-length perspectives, whose physical meanings are explained in detail;while for binary DAC, our prior work totally ignored the subtle difference between these two perspectives. Compared with binary DAC, an important advantage of nonbinary DAC is that, the mapping from source symbols to coding intervals is so flexible that there are many parameters that can be tuned to achieve better performance;while for binary DAC, there are very few tunable parameters, making it very hard to achieve better performance. This paper proposes a simple method to map source symbols onto coding intervals, which results in a very lightweight codec, while possessing a good Manhattan distance distribution. Then this paper deduces the formula of path metrics for decoder design by making use of CCS. All theoretical analyses are perfectly verified by simulation results. Most important, experimental results show that $Q$ -ary DAC achieves significantly better performance than LDPC codes for distributed coding of uniform $Q$ -ary sources with Laplace-distributed correlation.

关键词： distributed source coding Slepian-Wolf coding distributed arithmetic coding coset cardinality spectrum nonbinary sources

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A Context-Aware CEO Problem

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IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第12期71卷 6979-6992页

作者： Seo, Daewon Lim, Sung Hoon Kim, Yongjune Daegu Gyeongbuk Inst Sci & Technol DGIST Dept Elect Engn & Comp Sci Daegu 42988 South Korea Hallym Univ Sch Informat Sci Chunchon 24252 South Korea Pohang Univ Sci & Technol POSTECH Dept Elect Engn Pohang 37673 South Korea Yonsei Univ Inst Convergence Res & Educ Adv Technol Seoul 03722 South Korea

In many sensor network applications, a fusion center often has additional valuable information, such as context data, which cannot be obtained directly from the sensors. Motivated by this, we study a generalized CEO problem where a CEO has access to context information. The main contribution of this work is twofold. Firstly, we characterize the asymptotically optimal error exponent per rate as the number of sensors and sum rate grow without bound. The proof extends the Berger-Tung coding scheme and the converse argument by Berger et al. (1996) taking into account context information. The resulting expression includes the minimum Chernoff divergence over context information. Secondly, assuming that the sizes of the source and context alphabets are respectively |X | and |S|, we prove that it is asymptotically optimal to partition all sensors into at most (|X| 2) |S| groups and have the sensors in each group adopt the same encoding scheme. Our problem subsumes the original CEO problem by Berger et al. (1996) as a special case if there is only one letter for context information;in this case, our result tightens its required number of groups from (|X| 2) + 2 to (|X| 2). We also numerically demonstrate the effect of context information for a simple Gaussian scenario.

关键词： CEO problem side information distributed source coding error exponent

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