Distributed coding using punctured quasi-arithmetic codes for memory and memoryless sources

This correspondence considers the use of punctured quasi-arithmetic (QA) codes for the Slepian–Wolf problem. These entropy codes are defined by finite state machines for memoryless and first-order memory sources. Puncturing an entropy coded bit-stream leads to an ambiguity at the decoder side. The decoder makes use of a correlated version of the original message in order to remove this ambiguity. A complete distributed source coding (DSC) scheme based on QA encoding with side information at the decoder is presented, together with iterative structures based on QA codes. The proposed schemes are adapted to memoryless and first-order memory sources. Simulation results reveal that the proposed schemes are efficient in terms of decoding performance for short sequences compared to well-known DSC solutions using channel codes.Peer ReviewedPostprint (published version

Distributed Joint Source-Channel Coding With Copula-Function-Based Correlation Modeling for Wireless Sensors Measuring Temperature

Wireless sensor networks (WSNs) deployed for temperature monitoring in indoor environments call for systems that perform efficient compression and reliable transmission of the measurements. This is known to be a challenging problem in such deployments, as highly efficient compression mechanisms impose a high computational cost at the encoder. In this paper, we propose a new distributed joint source-channel coding (DJSCC) solution for this problem. Our design allows for efficient compression and error-resilient transmission, with low computational complexity at the sensor. A new Slepian-Wolf code construction, based on non-systematic Raptor codes, is devised that achieves good performance at short code lengths, which are appropriate for temperature monitoring applications. A key contribution of this paper is a novel Copula-function-based modeling approach that accurately expresses the correlation amongst the temperature readings from colocated sensors. Experimental results using a WSN deployment reveal that, for lossless compression, the proposed Copula-function-based model leads to a notable encoding rate reduction (of up to 17.56%) compared with the state-of-the-art model in the literature. Using the proposed model, our DJSCC system achieves significant rate savings (up to 41.81%) against a baseline system that performs arithmetic entropy encoding of the measurements. Moreover, under channel losses, the transmission rate reduction against the state-of-the-art model reaches 19.64%, which leads to energy savings between 18.68% to 24.36% with respect to the baseline system

Wyner-Ziv coding based on TCQ and LDPC codes and extensions to multiterminal source coding

Driven by a host of emerging applications (e.g., sensor networks and wireless video), distributed source coding (i.e., Slepian-Wolf coding, Wyner-Ziv coding and various other forms of multiterminal source coding), has recently become a very active research area. In this thesis, we first design a practical coding scheme for the quadratic Gaussian Wyner-Ziv problem, because in this special case, no rate loss is suffered due to the unavailability of the side information at the encoder. In order to approach the Wyner-Ziv distortion limit D??W Z(R), the trellis coded quantization (TCQ) technique is employed to quantize the source X, and irregular LDPC code is used to implement Slepian-Wolf coding of the quantized source input Q(X) given the side information Y at the decoder. An optimal non-linear estimator is devised at the joint decoder to compute the conditional mean of the source X given the dequantized version of Q(X) and the side information Y . Assuming ideal Slepian-Wolf coding, our scheme performs only 0.2 dB away from the Wyner-Ziv limit D??W Z(R) at high rate, which mirrors the performance of entropy-coded TCQ in classic source coding. Practical designs perform 0.83 dB away from D??W Z(R) at medium rates. With 2-D trellis-coded vector quantization, the performance gap to D??W Z(R) is only 0.66 dB at 1.0 b/s and 0.47 dB at 3.3 b/s. We then extend the proposed Wyner-Ziv coding scheme to the quadratic Gaussian multiterminal source coding problem with two encoders. Both direct and indirect settings of multiterminal source coding are considered. An asymmetric code design containing one classical source coding component and one Wyner-Ziv coding component is first introduced and shown to be able to approach the corner points on the theoretically achievable limits in both settings. To approach any point on the theoretically achievable limits, a second approach based on source splitting is then described. One classical source coding component, two Wyner-Ziv coding components, and a linear estimator are employed in this design. Proofs are provided to show the achievability of any point on the theoretical limits in both settings by assuming that both the source coding and the Wyner-Ziv coding components are optimal. The performance of practical schemes is only 0.15 b/s away from the theoretical limits for the asymmetric approach, and up to 0.30 b/s away from the limits for the source splitting approach

Universal Source Polarization and an Application to a Multi-User Problem

We propose a scheme that universally achieves the smallest possible compression rate for a class of sources with side information, and develop an application of this result for a joint source channel coding problem over a broadcast channel.Comment: to be presented at Allerton 201

A Comprehensive Review of Distributed Coding Algorithms for Visual Sensor Network (VSN)

Since the invention of low cost camera, it has been widely incorporated into the sensor node in Wireless Sensor Network (WSN) to form the Visual Sensor Network (VSN). However, the use of camera is bringing with it a set of new challenges, because all the sensor nodes are powered by batteries. Hence, energy consumption is one of the most critical issues that have to be taken into consideration. In addition to this, the use of batteries has also limited the resources (memory, processor) that can be incorporated into the sensor node. The life time of a VSN decreases quickly as the image is transferred to the destination. One of the solutions to the aforementioned problem is to reduce the data to be transferred in the network by using image compression. In this paper, a comprehensive survey and analysis of distributed coding algorithms that can be used to encode images in VSN is provided. This also includes an overview of these algorithms, together with their advantages and deficiencies when implemented in VSN. These algorithms are then compared at the end to determine the algorithm that is more suitable for VSN

Hamming distance spectrum of DAC codes for equiprobable binary sources

Distributed Arithmetic Coding (DAC) is an effective technique for implementing Slepian-Wolf coding (SWC). It has been shown that a DAC code partitions source space into unequal-size codebooks, so that the overall performance of DAC codes depends on the cardinality and structure of these codebooks. The problem of DAC codebook cardinality has been solved by the so-called Codebook Cardinality Spectrum (CCS). This paper extends the previous work on CCS by studying the problem of DAC codebook structure.We define Hamming Distance Spectrum (HDS) to describe DAC codebook structure and propose a mathematical method to calculate the HDS of DAC codes. The theoretical analyses are verified by experimental results

How to Achieve the Capacity of Asymmetric Channels

We survey coding techniques that enable reliable transmission at rates that approach the capacity of an arbitrary discrete memoryless channel. In particular, we take the point of view of modern coding theory and discuss how recent advances in coding for symmetric channels help provide more efficient solutions for the asymmetric case. We consider, in more detail, three basic coding paradigms. The first one is Gallager's scheme that consists of concatenating a linear code with a non-linear mapping so that the input distribution can be appropriately shaped. We explicitly show that both polar codes and spatially coupled codes can be employed in this scenario. Furthermore, we derive a scaling law between the gap to capacity, the cardinality of the input and output alphabets, and the required size of the mapper. The second one is an integrated scheme in which the code is used both for source coding, in order to create codewords distributed according to the capacity-achieving input distribution, and for channel coding, in order to provide error protection. Such a technique has been recently introduced by Honda and Yamamoto in the context of polar codes, and we show how to apply it also to the design of sparse graph codes. The third paradigm is based on an idea of B\"ocherer and Mathar, and separates the two tasks of source coding and channel coding by a chaining construction that binds together several codewords. We present conditions for the source code and the channel code, and we describe how to combine any source code with any channel code that fulfill those conditions, in order to provide capacity-achieving schemes for asymmetric channels. In particular, we show that polar codes, spatially coupled codes, and homophonic codes are suitable as basic building blocks of the proposed coding strategy.Comment: 32 pages, 4 figures, presented in part at Allerton'14 and published in IEEE Trans. Inform. Theor