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International Conference on Advances in Information Technology and Mobile Communication (AIM 2011)

作者： Majumder, Saikat Verma, Shrish Natl Inst Technol Dept Elect & Telecommun Raipur Madhya Pradesh India

ISBN: (纸本)9783642205729

In this paper we propose a scheme for rate adaptive lossless distributed source coding scheme for wireless sensor network. We investigate the distributed source-channel coding of correlated sources when correlation parameter is not fixed or may change during sensor network operation. For achieving rate adaptability we propose the puncturing and extension of IRA code depending on the value of correlation between two sources and the quality of channel. In our scheme we need to transmit only incremental redundancy for decreased correlation or fall in channel quality to meet energy constraints and reducing computation cost.

关键词： distributed source-channel coding IRA code sensor networks

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Low-Delay Analog distributed Joint source-channel coding using SIRENs 29

Low-Delay Analog Distributed Joint Source-Channel Coding usi...

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29th European Signal Processing Conference (EUSIPCO)

作者： Xuan, Ziwei Narayanan, Krishna Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

ISBN: (纸本)9789082797060

We consider the design of joint source-channel coding (JSCC) schemes for two multiterminal source-channel coding problems - namely, the transmission of a Gaussian source with side information at the receiver and the transmission of bivariate Gaussian sources over two independent Gaussian channels. Our focus is on low-delay transmission. We formulate the design problem as optimization of an autoencoder (AE) model, and show that sinusoidal representation networks (SIRENs) are a good choice due to their inherent periodicity and the ability of stretched sinusiods to cover the source space. We show that SIRENs outperform parametric ReLU based networks. The complexity of the proposed method scales better with source dimension than the best traditional schemes known in the literature while their performance is comparable to or better than that of the best traditional schemes. We demonstrate that the spontaneously learned encoder mappings share resemblance to the classical Wyner-Ziv mappings for JSCC with side information, and exhibits structured patterns in the case of distributed coding that are interpretable.

关键词： distributed source-channel coding joint source-channel coding SIRENs deep learning

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distributed Joint source coding and Trellis Coded Modulation for Symbol-Based Markov sources

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2018年第5期67卷 4031-4041页

作者： Izhar, Mohd Azri Mohd Aljohani, Abdulah Jeza Ng, Soon Xin Hanzo, Lajos Univ Teknol Malaysia UTM Razak Sch Engn & Adv Technol Wireless Commun Ctr Kuala Lumpur 54100 Malaysia Univ Teknol Malaysia UTM Razak Sch Engn & Adv Technol Ubiquitous Broadband Access Network Grp Kuala Lumpur 54100 Malaysia Umm Alqura Univ Engn Fac Elect Engn Dept Mecca 21955 Saudi Arabia Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

A distributed joint source-channel coding scheme based on a unity-rate code (URC) assisted trellis coded modulation (TCM) is proposed, which exploits the spatiotemporal correlation of symbol-based sources. More specifically, asymmetric distributed source coding of two spatially correlated Markov sources is considered, where one of the sources is assumed to be perfectly decoded and to be available at the receiver of the other source as side information. In order to exploit the temporal correlation statistics, an iterative decoding process exchanging extrinsic information between the amalgamated URC-assisted TCM and a soft-symbol source decoder employing a modified symbol-based maximum a posteriori algorithm is invoked. Furthermore, the Slepian-Wolf (SW) bound of symbol-based sources having spatiotemporal correlation is derived and the benefits of exploiting the spatiotemporal correlation using the proposed coding scheme are demonstrated by our extrinsic information transfer chart analysis. It is shown from our simulation results that upon exploiting the spatiotemporal correlation of the sources, the proposed coding scheme is capable of operating within 0.02 b of the SW bound.

关键词： Joint source-channel coding distributed source coding distributed source-channel coding coded modulation iterative decoding Slepian-Wolf coding

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Error-Resilient and Complexity-Constrained distributed coding for Large Scale Sensor Networks

Error-Resilient and Complexity-Constrained Distributed Codin...

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11th ACM/IEEE Conference on Information Processing in Sensing Networks

作者： Viswanatha, Kumar Ramaswamy, Sharadh Saxena, Ankur Rose, Kenneth Univ Calif Santa Barbara Santa Barbara CA 93106 USA

ISBN: (纸本)9781450312271

There has been considerable interest in distributed source coding within the compression and sensor network research communities in recent years, primarily due to its potential contributions to low-power sensor networks. However, two major obstacles pose an existential threat on practical deployment of such techniques in real world sensor networks, namely, the exponential growth of decoding complexity with network size and coding rates, and the critical requirement for error-resilience given the severe channel conditions in many wireless sensor networks. Motivated by these challenges, this paper proposes a novel, unified approach for large scale, error-resilient distributed source coding, based on an optimally designed classifier-based decoding framework, where the design explicitly controls the decoding complexity. We also present a deterministic annealing (DA) based global optimization algorithm for the design clue to the highly non-convex nature of the cost function, which further enhances the performance over basic greedy iterative descent technique. Simulation results on data, both synthetic and from real sensor networks, provide strong evidence that the approach opens the door to practical deployment of distributed coding in large sensor networks. It not only yields substantial gains in terms of overall distortion, compared to other state-of-the-art techniques, but also demonstrates how its decoder naturally scales to large networks while constraining the complexity, thereby enabling performance gains that increase with network size.

关键词： distributed source-channel coding Large scale sensor networks Error resilient coding

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On the scaling laws of dense wireless sensor networks: The data gathering channel

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IEEE TRANSACTIONS ON INFORMATION THEORY 2005年第3期51卷 1229-1234页

作者： El Gamal, H Ohio State Univ Dept Comp Engn Columbus OH 43210 USA

We consider dense wireless sensor networks deployed to observe arbitrary random fields. The requirement is to reconstruct an estimate of the random field at a certain collector node. This creates a many-to-one data gathering wireless channel. In this note, we first characterize the transport capacity of many-to-one dense wireless networks subject to a constraint on the total average power. In particular, we show that the transport capacity scales as circle minus (log (N)) when the number of sensors N grows to infinity and the total average power remains fixed. We then use this result along with some information-theoretic tools to derive sufficient and necessary conditions that characterize the set of observable random fields by dense sensor networks. In particular, for random fields that can be modeled as discrete random sequences, we derive a certain form of source/channel coding separation theorem. We further show that one can achieve any desired nonzero mean-square estimation error for continuous, Gaussian, and spatially bandlimited fields through a scheme composed of single-dimensional quantization, distributed Slepian-Wolf source coding, and the proposed antenna sharing strategy. Based on our results, we revisit earlier conclusions about the feasibility of data gathering applications using dense sensor networks.

关键词： distributed source-channel coding relay channel sensor networks the many-to-one channel the separation principle

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Low-Delay Analog Joint source-channel coding With Deep Learning

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IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第1期71卷 40-51页

作者： Xuan, Ziwei Narayanan, Krishna Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77840 USA

We consider the design of low-delay joint source-channel coding (JSCC) schemes for the transmission of discrete-time analog sources over noisy channels based on deep neural networks. The design problem is addressed as optimization of an autoencoder model, and several scenarios are discussed. For point-to-point communication of independent and identically distributed (i.i.d) Gaussian sources and Gauss-Markov sources over additive-white Gaussian noise (AWGN) channels, the encoder and decoder are constructed using recurrent neural networks (RNNs). With minimum prior knowledge used for design, the performance of these RNNs-based models is optimized using fine tuning techniques during training. Sinusoidal representation networks (SIRENs)-based models are proposed and optimized for three JSCC problems namely, transmitting multivariate Gaussian sources over AWGN channels, transmitting i.i.d Gaussian sources with side information at the decoder, and for communicating correlated sources over orthogonal Gaussian channels. We show that these deep learning-based JSCC schemes perform comparably or better than state-of-the-art (SOTA) traditional schemes. The proposed scheme can extend flexibly to different pairs of source and channel dimensions. Moreover, the spontaneously learned encoder mappings exhibit structured patterns that are interpretable.

关键词： Decoding AWGN channels Receivers Noise measurement Training Distortion Recurrent neural networks Joint source-channel coding deep learning RNNs SIRENs distributed source-channel coding

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Error/Erasure-Resilient and Complexity-Constrained Zero-Delay distributed coding for Large-Scale Sensor Networks

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ACM TRANSACTIONS ON SENSOR NETWORKS 2015年第2期11卷 35-35页

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

There has been considerable interest in distributed source coding (DSC) in recent years, primarily due to its potential contributions to low-power sensor networks. However, two major obstacles pose an existential threat to practical deployment of such techniques: the exponential growth of decoding complexity with network size and coding rates and the critical requirement of resilience to bit errors and erasures, given the severe channel conditions in many wireless sensor network applications. This article proposes a novel, unified approach for large-scale, error/erasure-resilient DSC that incorporates an optimally designed, nearest neighbor classifier-based decoding framework, where the design explicitly controls performance versus decoding complexity. Motivated by the highly nonconvex nature of the cost function, we present a deterministic annealing-based optimization algorithm for the joint design of the system parameters, which further enhances the performance over the greedy iterative descent technique. Simulation results on both synthetic and real sensor network data provide strong evidence for performance gains compared to other state-of-the-art techniques and may open the door to practical deployment of DSC in large sensor networks. Moreover, the framework provides a principled way to naturally scale to large networks while constraining decoder complexity, thereby enabling performance gains that increase with network size.

关键词： Algorithms Theory Experimentation distributed source-channel coding large-scale sensor networks error-resilient coding complexity-constrained decoding

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