The Experts below are selected from a list of 9672 Experts worldwide ranked by ideXlab platform
Baodian Wei - One of the best experts on this subject based on the ideXlab platform.
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statistical learning aided decoding of bmst tail biting convolutional code
International Symposium on Information Theory, 2019Co-Authors: Wenchao Lin, Suihua Cai, Baodian WeiAbstract:This paper is concerned with block Markov superposition transmission (BMST) of tail-biting convolutional code (TBCC). We propose a new decoding Algorithm for BMST-TBCC, which integrates a serial list Viterbi Algorithm (SLVA) with a soft check instead of conventional cyclic redundancy check (CRC). The basic idea is that, compared with an erroneous candidate codeword, the correct candidate codeword for the first sub-frame has less influence on the output of Viterbi Algorithm for the second sub-frame. The threshold is then determined by statistical learning based on the introduced empirical divergence function. The numerical results illustrate that, under the constraint of equivalent decoding delay, the BMST-TBCC has comparable performance with the polar codes. As a result, BMST-TBCCs may find applications in the scenarios of the streaming ultra-reliable and low latency communication (URLLC) data services.
Andrea Goldsmith - One of the best experts on this subject based on the ideXlab platform.
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Viterbinet a deep learning based Viterbi Algorithm for symbol detection
IEEE Transactions on Wireless Communications, 2020Co-Authors: Nir Shlezinger, Nariman Farsad, Yonina C Eldar, Andrea GoldsmithAbstract:Symbol detection plays an important role in the implementation of digital receivers. In this work, we propose ViterbiNet, which is a data-driven symbol detector that does not require channel state information (CSI). ViterbiNet is obtained by integrating deep neural networks (DNNs) into the Viterbi Algorithm. We identify the specific parts of the Viterbi Algorithm that depend on the channel model, and design a DNN to implement only those computations, leaving the rest of the Algorithm structure intact. We then propose a meta-learning based approach to train ViterbiNet online based on recent decisions, allowing the receiver to track dynamic channel conditions without requiring new training samples for every coherence block. Our numerical evaluations demonstrate that the performance of ViterbiNet, which is ignorant of the CSI, approaches that of the CSI-based Viterbi Algorithm, and is capable of tracking time-varying channels without needing instantaneous CSI or additional training data. Moreover, unlike conventional Viterbi detection, ViterbiNet is robust to CSI uncertainty, and it can be reliably implemented in complex channel models with constrained computational burden. More broadly, our results demonstrate the conceptual benefit of designing communication systems that integrate DNNs into established Algorithms.
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Viterbinet a deep learning based Viterbi Algorithm for symbol detection
arXiv: Learning, 2019Co-Authors: Nir Shlezinger, Nariman Farsad, Yonina C Eldar, Andrea GoldsmithAbstract:Symbol detection plays an important role in the implementation of digital receivers. In this work, we propose ViterbiNet, which is a data-driven symbol detector that does not require channel state information (CSI). ViterbiNet is obtained by integrating deep neural networks (DNNs) into the Viterbi Algorithm. We identify the specific parts of the Viterbi Algorithm that are channel-model-based, and design a DNN to implement only those computations, leaving the rest of the Algorithm structure intact. We then propose a meta-learning based approach to train ViterbiNet online based on recent decisions, allowing the receiver to track dynamic channel conditions without requiring new training samples for every coherence block. Our numerical evaluations demonstrate that the performance of ViterbiNet, which is ignorant of the CSI, approaches that of the CSI-based Viterbi Algorithm, and is capable of tracking time-varying channels without needing instantaneous CSI or additional training data. Moreover, unlike conventional Viterbi detection, ViterbiNet is robust to CSI uncertainty, and it can be reliably implemented in complex channel models with constrained computational burden. More broadly, our results demonstrate the conceptual benefit of designing communication systems to that integrate DNNs into established Algorithms.
Wenchao Lin - One of the best experts on this subject based on the ideXlab platform.
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statistical learning aided decoding of bmst tail biting convolutional code
International Symposium on Information Theory, 2019Co-Authors: Wenchao Lin, Suihua Cai, Baodian WeiAbstract:This paper is concerned with block Markov superposition transmission (BMST) of tail-biting convolutional code (TBCC). We propose a new decoding Algorithm for BMST-TBCC, which integrates a serial list Viterbi Algorithm (SLVA) with a soft check instead of conventional cyclic redundancy check (CRC). The basic idea is that, compared with an erroneous candidate codeword, the correct candidate codeword for the first sub-frame has less influence on the output of Viterbi Algorithm for the second sub-frame. The threshold is then determined by statistical learning based on the introduced empirical divergence function. The numerical results illustrate that, under the constraint of equivalent decoding delay, the BMST-TBCC has comparable performance with the polar codes. As a result, BMST-TBCCs may find applications in the scenarios of the streaming ultra-reliable and low latency communication (URLLC) data services.
Nir Shlezinger - One of the best experts on this subject based on the ideXlab platform.
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Viterbinet a deep learning based Viterbi Algorithm for symbol detection
IEEE Transactions on Wireless Communications, 2020Co-Authors: Nir Shlezinger, Nariman Farsad, Yonina C Eldar, Andrea GoldsmithAbstract:Symbol detection plays an important role in the implementation of digital receivers. In this work, we propose ViterbiNet, which is a data-driven symbol detector that does not require channel state information (CSI). ViterbiNet is obtained by integrating deep neural networks (DNNs) into the Viterbi Algorithm. We identify the specific parts of the Viterbi Algorithm that depend on the channel model, and design a DNN to implement only those computations, leaving the rest of the Algorithm structure intact. We then propose a meta-learning based approach to train ViterbiNet online based on recent decisions, allowing the receiver to track dynamic channel conditions without requiring new training samples for every coherence block. Our numerical evaluations demonstrate that the performance of ViterbiNet, which is ignorant of the CSI, approaches that of the CSI-based Viterbi Algorithm, and is capable of tracking time-varying channels without needing instantaneous CSI or additional training data. Moreover, unlike conventional Viterbi detection, ViterbiNet is robust to CSI uncertainty, and it can be reliably implemented in complex channel models with constrained computational burden. More broadly, our results demonstrate the conceptual benefit of designing communication systems that integrate DNNs into established Algorithms.
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Viterbinet a deep learning based Viterbi Algorithm for symbol detection
arXiv: Learning, 2019Co-Authors: Nir Shlezinger, Nariman Farsad, Yonina C Eldar, Andrea GoldsmithAbstract:Symbol detection plays an important role in the implementation of digital receivers. In this work, we propose ViterbiNet, which is a data-driven symbol detector that does not require channel state information (CSI). ViterbiNet is obtained by integrating deep neural networks (DNNs) into the Viterbi Algorithm. We identify the specific parts of the Viterbi Algorithm that are channel-model-based, and design a DNN to implement only those computations, leaving the rest of the Algorithm structure intact. We then propose a meta-learning based approach to train ViterbiNet online based on recent decisions, allowing the receiver to track dynamic channel conditions without requiring new training samples for every coherence block. Our numerical evaluations demonstrate that the performance of ViterbiNet, which is ignorant of the CSI, approaches that of the CSI-based Viterbi Algorithm, and is capable of tracking time-varying channels without needing instantaneous CSI or additional training data. Moreover, unlike conventional Viterbi detection, ViterbiNet is robust to CSI uncertainty, and it can be reliably implemented in complex channel models with constrained computational burden. More broadly, our results demonstrate the conceptual benefit of designing communication systems to that integrate DNNs into established Algorithms.
L Papke - One of the best experts on this subject based on the ideXlab platform.
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decoding turbo codes with the soft output Viterbi Algorithm sova
International Symposium on Information Theory, 1994Co-Authors: J Hagenauer, L PapkeAbstract:Iterative decoding of two dimensional systematic convolutional codes has been termed "turbo"-(de)coding. It is shown that the simple soft output Viterbi Algorithm (SOVA) meets all the requirements for iterative decoding if an a priori term is added. With simple 4 and 16 state codes surprisingly good performance is achieved for the Gaussian and Rayleigh channel with a very small degradation relative to the complicated MAP Algorithm. >
