A jointsource-channel multiple description (JSC-MD) framework for signal estimation and communication in resource-constrained lossy networks is presented. To keep the encoder complexity at a minimum, a signal is code...
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A jointsource-channel multiple description (JSC-MD) framework for signal estimation and communication in resource-constrained lossy networks is presented. To keep the encoder complexity at a minimum, a signal is coded by a multiple description quantizer (MDQ) with neither entropy nor channelcoding. The code diversity of MDQ and the path diversity of the network are exploited by decoders to combat transmission errors. A key design objective is resource scalability: powerful nodes in the network can perform JSC-MD estimation under the criteria of maximum a posteriori probability (MAP) or minimum mean-square error (MMSE), while primitive nodes resort to simpler MD decoding, all working with the same MDQ code. The application of JSC-MD to distributed estimation of hidden Markov models in a sensor network is demonstrated. The proposed JSC-MD MAP estimator is an algorithm of the longest path in a weighted directed acyclic graph, while the JSC-MD MMSE decoder is an extension of the well-known forward-backward algorithm to multiple descriptions. Both algorithms simultaneously exploit the source memory, the redundancy of the fixed-rate MDQ and the inter-description correlations. They outperform the existing hard-decision MDQ decoders by large margins (up to 8 dB). For Gaussian Markov sources, the complexity of JSC-MD distributed MAP sequence estimation can be made as low as that of typical single description Viterbi-type algorithms.
Fountain codes are a robust solution for data multi-casting to a large number of receivers which experience variable channel conditions and different packet loss rates. However, the standard fountain code design becom...
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Fountain codes are a robust solution for data multi-casting to a large number of receivers which experience variable channel conditions and different packet loss rates. However, the standard fountain code design becomes inefficient if all receivers have access to some side information correlated with the source information. We focus our attention on the cases where the correlation of the source and side information can be modelled by a binary erasure channel (BEC) or by a binary input additive white Gaussian noise channel (BIAWGNC). We analyse the performance of fountain codes in data multicasting with side information for these cases, derive bounds on their performance and provide a fast and robust linear programming optimization framework for code parameters. We demonstrate that systematic Raptor code design can be employed as a possible solution to the problem at the cost of higher encoding/decoding complexity, as it reduces the side information scenario to a channelcoding problem. However, our results also indicate that a simpler solution, non-systematic LT and Raptor codes, can be designed to perform close to the information theoretic bounds.
A channel code is constructed using sparse matrices for stationary memoryless channels that do not necessarily have a symmetric property like a binary symmetric channel. It is also shown that the constructed code has ...
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A channel code is constructed using sparse matrices for stationary memoryless channels that do not necessarily have a symmetric property like a binary symmetric channel. It is also shown that the constructed code has the following remarkable properties. 1. joint source-channel coding: Combining channel code with lossy source code, which is also constructed by sparse matrices, a simpler jointsource-channel code can be constructed than that constructed by the ordinary block code. 2. Universal coding: The constructed channel code has a universal property under a specified condition.
Multimedia communication has become one of the main applications in commercial wireless systems. Multimedia sources, mainly consisting of digital images and videos, have high bandwidth requirements. Since bandwidth is...
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Multimedia communication has become one of the main applications in commercial wireless systems. Multimedia sources, mainly consisting of digital images and videos, have high bandwidth requirements. Since bandwidth is a valuable resource, it is important that its use should be optimized for image and video communication. Therefore, interest in developing new joint source-channel coding (JSCC) methods for image and video communication is increasing. Design of any JSCC scheme requires an estimate of the distortion at different sourcecoding rates and under different channel conditions. The common approach to obtain this estimate is via simulations or operational rate-distortion curves. These approaches, however, are computationally intensive and, hence, not feasible for real-time coding and transmission applications. A more feasible approach to estimate distortion is to develop models that predict distortion at different sourcecoding rates and under different channel conditions. Based on this idea, we present a distortion model for estimating the distortion due to quantization and channel errors in MPEG-4 compressed video streams at different sourcecoding rates and channel bit error rates. This model takes into account important aspects of video compression such as transform coding, motion compensation, and variable length coding. Results show that our model estimates distortion within 1.5 dB of actual simulation values in terms of peak-signal-to-noise ratio.
Optimal design of sequential real-time communication of a Markov source over a noisy channel is investigated. In such a system, the delay between the source output and its reconstruction at the receiver should equal a...
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Optimal design of sequential real-time communication of a Markov source over a noisy channel is investigated. In such a system, the delay between the source output and its reconstruction at the receiver should equal a fixed prespecified amount. An optimal communication strategy must minimize the total expected symbol-by-symbol distortion between the source output and its reconstruction. Design techniques or performance bounds for such real-time communication systems are unknown. In this paper a systematic methodology, based on the concepts of information structures and information states, to search for an optimal real-time communication strategy is presented. This methodology trades off complexity in communication length (linear in contrast to doubly exponential) with complexity in alphabet sizes (doubly exponential in contrast to exponential). As the communication length is usually order of magnitudes bigger than the alphabet sizes, the proposed methodology simplifies the search for an optimal communication strategy. In spite of this simplification, the resultant optimality equations cannot be solved efficiently using existing algorithmic techniques. The main idea is to formulate a zero-delay communication problem as a dynamic team with nonclassical information structure. Then, an appropriate choice of information states converts the dynamic team problem into a centralized stochastic control problem in function space. Thereafter, Markov decision theory is used to derive nested optimality equations for choosing an optimal design. For infinite horizon problems, these optimality equations give rise to a fixed point functional equation. Communication systems with fixed finite delay constraint, a higher-order Markov source, and channels with memory are treated in the same manner after an appropriate expansion of the state space. Thus, this paper presents a comprehensive methodology to study different variations of real-time communication.
In this letter, we prove a published conjecture on the asymptotic uniformity of the outputs of a convolutional encoder under biased inputs. These results are interesting in light of recent research on jointsource-cha...
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In this letter, we prove a published conjecture on the asymptotic uniformity of the outputs of a convolutional encoder under biased inputs. These results are interesting in light of recent research on joint source-channel coding as well as sourcecoding using turbo codes in which the constituent encoders are convolutional codes. In particular, it is well-known that in many situations a good code should result in a uniform distribution on blocks of consecutive encoded symbols. The results presented here provide insights into the choice of encoders in such scenarios.
In a previous paper, Karp, Kieffer, and Duhamel have presented three methods to calculate the parity-check matrix of paraunitary oversampled DFT filter banks. Here is another.
In a previous paper, Karp, Kieffer, and Duhamel have presented three methods to calculate the parity-check matrix of paraunitary oversampled DFT filter banks. Here is another.
An efficient policy allocation algorithm for the transmission of embedded bit streams over noisy channels with feedback is proposed. The transmission is based on the typeII hybrid ARQ/FEC protocol and uses a nested se...
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An efficient policy allocation algorithm for the transmission of embedded bit streams over noisy channels with feedback is proposed. The transmission is based on the typeII hybrid ARQ/FEC protocol and uses a nested sequence C of channel codes to protect the packets. There are also constraints on the total bit budget and on the allowed number of retransmissions per packet. The allocation algorithm assigns different protection policies, each policy being a subset of C, to different packets to maximize the average number of correctly received source bits. We study the performance and the complexity of the proposed scheme through the transmission of images encoded by JPEG2000 over mobile channels with correlated Rayleigh fading. We demonstrate by simulations that the proposed multiple-policy scheme provides significant improvements over a purely FEC scheme with no feedback and also the existing fixed-policy schemes. Our results show that feedback is particularly helpful for poor channel conditions and that the proposed scheme is very robust against changes in the channel signal-to-noise ratio (SNR) and the mobile speed.
作者:
Park, MoonseoKim, Seong-LyunYonsei Univ
Ind Acad Cooperat Fdn Seoul 120749 South Korea Yonsei Univ
Sch Elect & Elect Engn Radio Res Management & Optimizat Lab Seoul 120749 South Korea Ajou Univ
Div Elect & Comp Engn Commun Syst Lab Suwon 443749 South Korea
When the jointsource-channel (JSC) decoder is used for sourcecoding over noisy channels, the JSC decoder may invent errors even though the received data is not corrupted by the channel noise, if the JSC decoder assu...
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When the jointsource-channel (JSC) decoder is used for sourcecoding over noisy channels, the JSC decoder may invent errors even though the received data is not corrupted by the channel noise, if the JSC decoder assumes the channel was noisy. A novel encoder algorithm has been recently proposed to improve the performance of the communications system under this situation. In this letter, we propose another algorithm based on conditional entropy-constrained vector quantizer to further improve the encoder. The algorithm proposed in this letter significantly improves the performance of the communications system when the hypothesized channel bit error rate is high.
We design a channel optimized vector quantizer (COVQ) for symbol-by-symbol maximum a posteriori (MAP) hard-decision demodulated channels. The main objective is to exploit the non-uniformity of the indices representing...
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We design a channel optimized vector quantizer (COVQ) for symbol-by-symbol maximum a posteriori (MAP) hard-decision demodulated channels. The main objective is to exploit the non-uniformity of the indices representing the quantized source via the MAP decoder and iteratively optimize the overall discrete channel (at the symbol level) jointly with the quantizer. We consider memoryless Gaussian and Gauss-Markov sources transmitted over a binary phase-shift keying modulated Rayleigh fading channel. Our scheme has less encoding computational and storage complexity (particularly for noisy channel conditions) than both conventional and soft-decision COVQ systems, which use hard-decision and soft-decision maximum likelihood demodulation, respectively. Furthermore, it provides a notable signal-to-distortion ratio gain over the former system, and in some cases it matches or outperforms the latter one.
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