The Experts below are selected from a list of 67089 Experts worldwide ranked by ideXlab platform
Gonzalo Vazquez-vilar - One of the best experts on this subject based on the ideXlab platform.
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Multi-Class Source-Channel Coding
IEEE Transactions on Information Theory, 2016Co-Authors: Irina E. Bocharova, Adrià Tauste Campo, Albert Guillen I Fabregas, Alfonso Martinez, Boris D. Kudryashov, Gonzalo Vazquez-vilarAbstract:This paper studies an almost-lossless source-Channel Coding scheme in which source messages are assigned to different classes and encoded with a Channel code that depends on the class index. The code performance is analyzed by means of random-Coding error exponents and validated by simulation of a low-complexity implementation using existing source and Channel codes. While each class code can be seen as a concatenation of a source code and a Channel code, the overall performance improves on that of separate source-Channel Coding and approaches that of joint source-Channel Coding when the number of classes increases.
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ISIT - Source-Channel Coding with Multiple Classes
2014 IEEE International Symposium on Information Theory, 2014Co-Authors: Irina E. Bocharova, Adrià Tauste Campo, Albert Guillen I Fabregas, Alfonso Martinez, Boris D. Kudryashov, Gonzalo Vazquez-vilarAbstract:We study a source-Channel Coding scheme in which source messages are assigned to classes and encoded using a Channel code that depends on the class index. While each class code can be seen as a concatenation of a source code and a Channel code, the overall performance improves on that of separate source-Channel Coding and approaches that of joint source-Channel Coding as the number of classes increases. The performance of this scheme is studied by means of random-Coding bounds and validated by simulation of a low-complexity implementation using existing source and Channel codes. © 2014 IEEE.
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Allerton Conference - Converse bounds for finite-length joint source-Channel Coding
2012 50th Annual Allerton Conference on Communication Control and Computing (Allerton), 2012Co-Authors: Adrià Tauste Campo, Gonzalo Vazquez-vilar, Albert Guillen I Fabregas, Alfonso MartinezAbstract:Based on the hypothesis-testing method, we derive lower bounds on the average error probability of finite-length joint source-Channel Coding. The extension of the meta-converse bound of Channel Coding to joint source-Channel Coding depends on the codebook and the deCoding rule and thus, it is a priori computationally challenging. Weaker versions of this general bound recover known converses in the literature and provide computationally feasible expressions.
Hessam Mahdavifar - One of the best experts on this subject based on the ideXlab platform.
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ITW - Channel Coding at Low Capacity
2019 IEEE Information Theory Workshop (ITW), 2019Co-Authors: Mohammad Fereydounian, Hamed Hassani, Mohammad Vahid Jamali, Hessam MahdavifarAbstract:Low-capacity scenarios have become increasingly important in the technology of Internet of Things (IoT) and the next generation of mobile networks. Such scenarios require efficient and reliable transmission of information over Channels with an extremely small capacity. Within these constraints, the performance of state-of-the-art Coding techniques is far from optimal in terms of either rate or complexity. Moreover, the current non-asymptotic laws of optimal Channel Coding provide inaccurate predictions for Coding in the low-capacity regime. In this paper, we provide the first comprehensive study of Channel Coding in the low-capacity regime. We will investigate the fundamental non-asymptotic limits for Channel Coding as well as challenges that must be overcome for efficient code design in low-capacity scenarios.
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Channel Coding at Low Capacity
arXiv: Information Theory, 2018Co-Authors: Mohammad Fereydounian, Hamed Hassani, Mohammad Vahid Jamali, Hessam MahdavifarAbstract:Low-capacity scenarios have become increasingly important in the technology of the Internet of Things (IoT) and the next generation of mobile networks. Such scenarios require efficient and reliable transmission of information over Channels with an extremely small capacity. Within these constraints, the performance of state-of-the-art Coding techniques is far from optimal in terms of either rate or complexity. Moreover, the current non-asymptotic laws of optimal Channel Coding provide inaccurate predictions for Coding in the low-capacity regime. In this paper, we provide the first comprehensive study of Channel Coding in the low-capacity regime. We will investigate the fundamental non-asymptotic limits for Channel Coding as well as challenges that must be overcome for efficient code design in low-capacity scenarios.
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Channel Coding at Low Capacity.
arXiv: Information Theory, 2018Co-Authors: Mohammad Fereydounian, Hamed Hassani, Mohammad Vahid Jamali, Hessam MahdavifarAbstract:Low-capacity scenarios have become increasingly important in the technology of Internet of Things (IoT) and next generation of mobile networks. Such scenarios require efficient, reliable transmission of information over Channels with extremely small capacity. Within these constraints, the performance of state-of-the-art Coding techniques is far from optimal in terms of either rate or complexity. Moreover, the current non-asymptotic laws of optimal Channel Coding provide inaccurate predictions for Coding in the low-capacity regime. In this paper, we provide the first comprehensive study of Channel Coding in the low-capacity regime. We will investigate the fundamental non-asymptotic limits for Channel Coding as well as challenges that must be overcome for efficient code design in low-capacity scenarios.
Andrea Goldsmith - One of the best experts on this subject based on the ideXlab platform.
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Deep Learning for Joint Source-Channel Coding of Text
arXiv: Information Theory, 2018Co-Authors: Nariman Farsad, Milind Rao, Andrea GoldsmithAbstract:We consider the problem of joint source and Channel Coding of structured data such as natural language over a noisy Channel. The typical approach to this problem in both theory and practice involves performing source Coding to first compress the text and then Channel Coding to add robustness for the transmission across the Channel. This approach is optimal in terms of minimizing end-to-end distortion with arbitrarily large block lengths of both the source and Channel codes when transmission is over discrete memoryless Channels. However, the optimality of this approach is no longer ensured for documents of finite length and limitations on the length of the enCoding. We will show in this scenario that we can achieve lower word error rates by developing a deep learning based encoder and decoder. While the approach of separate source and Channel Coding would minimize bit error rates, our approach preserves semantic information of sentences by first embedding sentences in a semantic space where sentences closer in meaning are located closer together, and then performing joint source and Channel Coding on these embeddings.
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ICASSP - Deep Learning for Joint Source-Channel Coding of Text
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Nariman Farsad, Milind Rao, Andrea GoldsmithAbstract:We consider the problem of joint source and Channel Coding of structured data such as natural language over a noisy Channel. The typical approach to this problem in both theory and practice involves performing source Coding to first compress the text and then Channel Coding to add robustness for the transmission across the Channel. This approach is optimal in terms of minimizing end-to-end distortion with arbitrarily large block lengths of both the source and Channel codes when transmission is over discrete memoryless Channels. However, the optimality of this approach is no longer ensured for documents of finite length and limitations on the length of the enCoding. We will show in this scenario that we can achieve lower word error rates by developing a deep learning based encoder and decoder. While the approach of separate source and Channel Coding would minimize bit error rates, our approach preserves semantic information of sentences by first embedding sentences in a semantic space where sentences closer in meaning are located closer together, and then performing joint source and Channel Coding on these em-beddings.
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ISIT - Dynamic joint source-Channel Coding with feedback
2013 IEEE International Symposium on Information Theory, 2013Co-Authors: Tara Javidi, Andrea GoldsmithAbstract:This paper considers real time joint source-Channel Coding of a Markov source over a discrete memoryless Channel with noiseless feedback. The encoder incurs a cost which is minimized along with a real-time end-to-end distortion. The problem is mapped to a partially observable Markov decision problem and the corresponding optimality equations, in the form of dynamic programming equations, are derived. As a consequence of the dynamic programming formulation, basic structural properties of the optimal enCoding and deCoding strategies are established. In addition, the problem formulation and solution obtained for dynamic joint source-Channel Coding with noiseless feedback is shown to encompass a much broader class of problems including that of information acquisition and real time tracking.
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Joint source/Channel Coding for wireless Channels
1995 IEEE 45th Vehicular Technology Conference. Countdown to the Wireless Twenty-First Century, 1Co-Authors: Andrea GoldsmithAbstract:Shannon's (1948) fundamental theorem showing that source Coding and Channel Coding can be separated without any loss of optimality does not apply to general time-varying Channels. Since the distortion by the source encoder decreases with the data rate, while the Channel errors increase with the data rate, the joint source/Channel Coding problem reduces to allocating bits in an optimal way between the source and Channel encoders as the source and Channel vary. The author introduces two additional degrees of freedom by allowing both the transmit power and the data rate to vary, subject to an average power constraint. Under these varying power and rate conditions, he first obtains an expression to minimize the end-to-end distortion of general joint source/Channel codes for fading Channels. He then proposes an adaptive joint source/Channel coded modulation technique. The Channel code adapts both the transmission rate and power using variable-rate coded MQAM (on a Rayleigh fading Channel). He analytically derives the minimum end-to-end distortion of our joint Coding scheme. The solution cannot be obtained in closed form, and therefore requires computer search methods. He also obtains a simple upper bound on the distortion by holding the Channel error rate constant. Numerical results for this distortion upper bound as a function of the Channel Coding gain and error rate are obtained. The optimal power control which achieve this bound is also determined.
Alfonso Martinez - One of the best experts on this subject based on the ideXlab platform.
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Multi-Class Source-Channel Coding
IEEE Transactions on Information Theory, 2016Co-Authors: Irina E. Bocharova, Adrià Tauste Campo, Albert Guillen I Fabregas, Alfonso Martinez, Boris D. Kudryashov, Gonzalo Vazquez-vilarAbstract:This paper studies an almost-lossless source-Channel Coding scheme in which source messages are assigned to different classes and encoded with a Channel code that depends on the class index. The code performance is analyzed by means of random-Coding error exponents and validated by simulation of a low-complexity implementation using existing source and Channel codes. While each class code can be seen as a concatenation of a source code and a Channel code, the overall performance improves on that of separate source-Channel Coding and approaches that of joint source-Channel Coding when the number of classes increases.
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ISIT - Source-Channel Coding with Multiple Classes
2014 IEEE International Symposium on Information Theory, 2014Co-Authors: Irina E. Bocharova, Adrià Tauste Campo, Albert Guillen I Fabregas, Alfonso Martinez, Boris D. Kudryashov, Gonzalo Vazquez-vilarAbstract:We study a source-Channel Coding scheme in which source messages are assigned to classes and encoded using a Channel code that depends on the class index. While each class code can be seen as a concatenation of a source code and a Channel code, the overall performance improves on that of separate source-Channel Coding and approaches that of joint source-Channel Coding as the number of classes increases. The performance of this scheme is studied by means of random-Coding bounds and validated by simulation of a low-complexity implementation using existing source and Channel codes. © 2014 IEEE.
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Allerton Conference - Converse bounds for finite-length joint source-Channel Coding
2012 50th Annual Allerton Conference on Communication Control and Computing (Allerton), 2012Co-Authors: Adrià Tauste Campo, Gonzalo Vazquez-vilar, Albert Guillen I Fabregas, Alfonso MartinezAbstract:Based on the hypothesis-testing method, we derive lower bounds on the average error probability of finite-length joint source-Channel Coding. The extension of the meta-converse bound of Channel Coding to joint source-Channel Coding depends on the codebook and the deCoding rule and thus, it is a priori computationally challenging. Weaker versions of this general bound recover known converses in the literature and provide computationally feasible expressions.
Mohammad Fereydounian - One of the best experts on this subject based on the ideXlab platform.
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ITW - Channel Coding at Low Capacity
2019 IEEE Information Theory Workshop (ITW), 2019Co-Authors: Mohammad Fereydounian, Hamed Hassani, Mohammad Vahid Jamali, Hessam MahdavifarAbstract:Low-capacity scenarios have become increasingly important in the technology of Internet of Things (IoT) and the next generation of mobile networks. Such scenarios require efficient and reliable transmission of information over Channels with an extremely small capacity. Within these constraints, the performance of state-of-the-art Coding techniques is far from optimal in terms of either rate or complexity. Moreover, the current non-asymptotic laws of optimal Channel Coding provide inaccurate predictions for Coding in the low-capacity regime. In this paper, we provide the first comprehensive study of Channel Coding in the low-capacity regime. We will investigate the fundamental non-asymptotic limits for Channel Coding as well as challenges that must be overcome for efficient code design in low-capacity scenarios.
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Channel Coding at Low Capacity
arXiv: Information Theory, 2018Co-Authors: Mohammad Fereydounian, Hamed Hassani, Mohammad Vahid Jamali, Hessam MahdavifarAbstract:Low-capacity scenarios have become increasingly important in the technology of the Internet of Things (IoT) and the next generation of mobile networks. Such scenarios require efficient and reliable transmission of information over Channels with an extremely small capacity. Within these constraints, the performance of state-of-the-art Coding techniques is far from optimal in terms of either rate or complexity. Moreover, the current non-asymptotic laws of optimal Channel Coding provide inaccurate predictions for Coding in the low-capacity regime. In this paper, we provide the first comprehensive study of Channel Coding in the low-capacity regime. We will investigate the fundamental non-asymptotic limits for Channel Coding as well as challenges that must be overcome for efficient code design in low-capacity scenarios.
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Channel Coding at Low Capacity.
arXiv: Information Theory, 2018Co-Authors: Mohammad Fereydounian, Hamed Hassani, Mohammad Vahid Jamali, Hessam MahdavifarAbstract:Low-capacity scenarios have become increasingly important in the technology of Internet of Things (IoT) and next generation of mobile networks. Such scenarios require efficient, reliable transmission of information over Channels with extremely small capacity. Within these constraints, the performance of state-of-the-art Coding techniques is far from optimal in terms of either rate or complexity. Moreover, the current non-asymptotic laws of optimal Channel Coding provide inaccurate predictions for Coding in the low-capacity regime. In this paper, we provide the first comprehensive study of Channel Coding in the low-capacity regime. We will investigate the fundamental non-asymptotic limits for Channel Coding as well as challenges that must be overcome for efficient code design in low-capacity scenarios.
