In this paper the analysis of packet loss prediction for Gilbert-model with loss rate uplink is performed. Based on the loss rate uplinks, an iterative algorithm has been derived to compute a new loss probability func...
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In this paper the analysis of packet loss prediction for Gilbert-model with loss rate uplink is performed. Based on the loss rate uplinks, an iterative algorithm has been derived to compute a new loss probability function, which can adapt to the channel dynamics and predict packet loss rate more accurately. The results in this work are helpful for any joint source-channel coding system for which FEC protection with loss rate uplink is suitable. (C) 2004 Elsevier B.V. All rights reserved.
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.
A single memoryless Gaussian source is observed by many terminals, subject to independent Gaussian observation noises. The terminals are linked to a fusion center via a standard Gaussian multiple-access channel. The f...
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A single memoryless Gaussian source is observed by many terminals, subject to independent Gaussian observation noises. The terminals are linked to a fusion center via a standard Gaussian multiple-access channel. The fusion center needs to recover the underlying Gaussian source with respect to mean-squared error. In this correspondence, a theorem of Witsenhausen is shown to imply that an optimal communication strategy is uncoded transmission, i.e., each terminal's channel input is merely a scaled version of its noisy observation.
In this letter, based on deep joint source-channel coding (DeepJSCC), we propose a channel adaptive scheme based on entropy model and a subchannel matching method with entropy indication to minimize reconstruction dis...
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In this letter, based on deep joint source-channel coding (DeepJSCC), we propose a channel adaptive scheme based on entropy model and a subchannel matching method with entropy indication to minimize reconstruction distortion for wireless image transmission over orthogonal frequency division multiplexing (OFDM) channels. Specifically, after an image is compressed and packaged into several OFDM packets, the more critical OFDM packets are mapped to subchannels with higher quality based on estimated channel state information (CSI). In addition, after analyzing the effect of channel signal-to-noise ratio (CSNR) on the parameters of our network model, we achieve the adaptation of a single model to various CSNRs simply by adapting the training strategy, without the need to input CSNR into additionally introduced network. Extensive numerical experiments show that our method achieves state-of-the-art performance among existing DeepJSCC schemes over OFDM channels.
In this letter, we consider two problems of sending a bivariate Gaussian source through a two-user Gaussian non-orthogonal multiple-access channel. We provide a new distortion outer bound for the considered joint sour...
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In this letter, we consider two problems of sending a bivariate Gaussian source through a two-user Gaussian non-orthogonal multiple-access channel. We provide a new distortion outer bound for the considered joint source-channel coding problems, which significantly reduces the gap between inner and outer bounds of the distortion for correlation coefficients below a threshold. Furthermore, numerical results show, that under some conditions, our proposed outer bound matches with the inner bound obtained via the separate source-channelcoding scheme, which proves the optimality of this scheme.
A robust soft-decision channel-optimized vector quantization (COVQ) scheme for Turbo-coded additive white Gaussian noise (AWGN) and Rayleigh fading channels is proposed. The log likelihood ratio (LLR) generated by the...
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A robust soft-decision channel-optimized vector quantization (COVQ) scheme for Turbo-coded additive white Gaussian noise (AWGN) and Rayleigh fading channels is proposed. The log likelihood ratio (LLR) generated by the Turbo decoder is exploited via the use of a q-bit scaler soft-decision demodulator, The concatenation of the Turbo encoder, modulator, AWGN channel or Rayleigh fading channel, Turbo decoder, and q-bit soft-decision demodulator is modeled as an expanded discrete memoryless channel (DMC), A COVQ scheme for this expanded discrete channel is designed. Numerical results indicate substantial performance improvements over traditional tandem coding systems, COVQ schemes designed for hard-decision demodulated Turbo-coded channels(q = 1), as well as performance gains over a recent soft decoding COVQ scheme by Ho.
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.
In this letter, we study the joint source-channel coding problem of communicating the linear functions of correlated Gaussian sources over a Gaussian multiple access channel. In this setup, each transmitter observes o...
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In this letter, we study the joint source-channel coding problem of communicating the linear functions of correlated Gaussian sources over a Gaussian multiple access channel. In this setup, each transmitter observes one of two correlated sources. The receiver aims to reconstruct a linear combination of both correlated Gaussian sources within an average distortion level. For this setup, we first obtain a new lower bound on the achievable distortion and then, we propose a novel lattice-based hybrid transmission scheme and its achievable distortion is derived. We show that our proposed scheme can achieve the optimal distortion under some conditions.
Linear programming (LP) decoding of low-density parity-check codes over discrete memoryless symmetric channels was introduced by Feldman et al in [1]. Here, we extend the LP decoding paradigm by applying it to two add...
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Linear programming (LP) decoding of low-density parity-check codes over discrete memoryless symmetric channels was introduced by Feldman et al in [1]. Here, we extend the LP decoding paradigm by applying it to two additional scenarios: jointsource-channel (JSC) coding and decoding over the infinite-memory non-ergodic binary Polya-contagion channel. Simulation results indicate that the JSC LP decoder yields significant gains over the standard LP decoder for non-uniform sources. Simulations also show that the LP decoder for the Polya channel performs moderately well in relation to the epsilon-capacity limit.
Messages coded and transmitted over a channel usually contain some redundancy which is not utilized by channel decoding techniques, especially if its governing statistical parameters are unknown. We propose to integra...
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Messages coded and transmitted over a channel usually contain some redundancy which is not utilized by channel decoding techniques, especially if its governing statistical parameters are unknown. We propose to integrate universal lossless sourcecoding techniques into channel decoding of redundant sequences with unknown statistics to improve performance of Viterbi and turbo decoding. Simulation results demonstrate that we achieve identical bit error rate (BER) performance to nonuniversal techniques that utilize prior knowledge of the message statistics, and that if redundancy exists even in an a-priori unknown form, we can improve the code performance over standard techniques.
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