Symbol-based iterative decoding is proposed for the transmission of classical Markov source signals over a quantum channel using a three-stage serial concatenation of a convolutional code (CC), a unity-rate code and a...
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Symbol-based iterative decoding is proposed for the transmission of classical Markov source signals over a quantum channel using a three-stage serial concatenation of a convolutional code (CC), a unity-rate code and a two-qubit superdense (SD) protocol. A modified symbol-based maximum a posteriori algorithm is employed for CC decoding to exploit the Markov source statistics during the iterative decoding process. Extrinsic information transfer chart analysis is performed to evaluate the benefit of the extrinsic mutual information gleaned from the CC decoder for sources with different correlations. We evaluate the bit error rate performance of the proposed coding scheme and compare it with the relevant benchmark schemes, including the turbo coding-based SD scheme. We demonstrate that a near-capacity performance can be achieved using the proposed scheme and when utilizing sources having a high correlation coefficient of rho = 0.9, the proposed coding scheme performs within 0.53 dB from the entanglement-assisted classical capacity.
We propose a novel joint source-channel coding (JSCC) framework that jointly optimizes encoding modes of macroblocks and unequal error protection (UEP) of packets for error-resilient video transmission, and address th...
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ISBN:
(纸本)9781457713033
We propose a novel joint source-channel coding (JSCC) framework that jointly optimizes encoding modes of macroblocks and unequal error protection (UEP) of packets for error-resilient video transmission, and address the problem of source packet loss due to bit errors incurred in error-prone channels. In the proposed framework, we consider the source packet loss probability as a function of the encoding configuration of a slice. The task of optimization is complicated by the inherent interdependency between macroblocks in the same slice, since the encoding mode optimization for each macroblock requires the encoding configuration of entire slice to be known in advance. We resolve such interdependency via an iterative method that alternates between the optimization of the slice size and the associated encoding modes, and we also utilize adaptive quantization for coding residual for source rate control in different channel conditions. Simulation results show that our framework outperforms existing approaches.
In the context of distributed joint source-channel coding, we conceive a joint decoding and estimation scheme for binary Markov sources exhibiting spatio-temporal correlation. The proposed scheme is designed based on ...
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In the context of distributed joint source-channel coding, we conceive a joint decoding and estimation scheme for binary Markov sources exhibiting spatio-temporal correlation. The proposed scheme is designed based on the serial concatenation of a trellis coded modulation (TCM) scheme and a unity-rate code. The symbol-based maximum a posteriori algorithm employed for TCM. decoding is modified in order to exploit the source correlation. The estimation of both the spatial and temporal correlation parameters is performed jointly with the iterative decoding, hence allowing the estimated parameters to he updated after each iteration. Our simulation results reveal that when both the spatial and temporal correlation parameters are unknown, the proposed joint decoding and estimation scheme approaches the performance to the ideal system relying on perfectly known correlation parameters, therefore, demonstrating the superiority of the proposed scheme.
In this letter an algorithm is proposed for optimal rate allocation constructed over a wide set of granular channel rates obtained from rateless codes to provide an ideal adjustment for an unequal error protection sch...
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In this letter an algorithm is proposed for optimal rate allocation constructed over a wide set of granular channel rates obtained from rateless codes to provide an ideal adjustment for an unequal error protection scheme. This algorithm is based on dynamic programming for resource allocations with capacity restrictions solved as a knapsack problem;formulated to find among each rate arrangement for a progressive bit stream the one that minimizes distortion and maximizes throughput, that represents the adaptation parameters to optimize the rate-distortion performance by balancing resources. Computational results have shown gains in distortion [peak signal-to-noise ratio (PSNR)] of approximately 20 dB over the maximum admissible SNR variation in comparison to the equivalent equal error protection rate. Enough protection levels have been defined to establish the best capacity convergence and algorithm efficiency.
As an extension of source polarization and channel polarization, this paper considers jointsource-channel polarization, which results in a joint source-channel coding (JSCC) scheme using a quasi uniform systematic po...
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As an extension of source polarization and channel polarization, this paper considers jointsource-channel polarization, which results in a joint source-channel coding (JSCC) scheme using a quasi uniform systematic polar code (SPC). In this JSCC scheme, the source with side information is encoded as a systematic polar codeword and only parity bits are transmitted through the channel. The indices of systematic bits are quasi-uniform, which enable the source and the channel to be jointly polarized to either a high entropy part or a low entropy part. The analysis reveals that the quasi-uniform SPC cannot be constructed via original polar coding. To solve this problem, additional bit-swap coding is introduced to modify original polar coding and construct this kind of SPCs. The proposed JSCC scheme can asymptotically approach the information-theoretical limit. For the noiseless channel, the proposed scheme is degraded into classic Slepain-Wolf coding or lossless sourcecoding based on parity approach.
A new analog-digital hybrid coding architecture for joint source-channel coding is proposed. The encoder generates a channel input by a symbol-by-symbol mapping of the observed (analog) source and its (digital) compre...
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ISBN:
(纸本)9781457705953
A new analog-digital hybrid coding architecture for joint source-channel coding is proposed. The encoder generates a channel input by a symbol-by-symbol mapping of the observed (analog) source and its (digital) compression codeword, while the decoder reconstructs the source by a symbol-by-symbol mapping of the (analog) channel output and the decoded (digital) compression codeword from it. When applied to the problem of lossy communication of sources over the two-user discrete memoryless interference channel, this hybrid coding scheme achieves the best known performance and recovers as special cases several previous results on lossless and lossy communication over single hop networks.
We propose an efficient tree search algorithm for determining the free distance of variable-length error-correcting codes (VLECs). A main idea behind the algorithm is to structure all pairs of code word-concatenated s...
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We propose an efficient tree search algorithm for determining the free distance of variable-length error-correcting codes (VLECs). A main idea behind the algorithm is to structure all pairs of code word-concatenated sequences as a tree, in which we seek the pair of sequences that determine the free distance. In order to speed up the algorithm, we establish constraints that do not compromise optimality in determining the free distance. Experimental results on VLECs algorithmically constructed for the English alphabet show that our algorithm requires a considerably smaller number of bitwise distance computations and covers a much smaller number of tree nodes than Dijkstra's algorithm operating over the pairwise distance graph while being a hundred times faster in terms of execution time.
Remote sensing satellites allow continuous information acquisition from large areas of the earth and have been intensively applied in a number of applications, from agriculture to defense. A major challenge in remote ...
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Remote sensing satellites allow continuous information acquisition from large areas of the earth and have been intensively applied in a number of applications, from agriculture to defense. A major challenge in remote sensing is that satellite communication systems present bandwidth restrictions and several issues typical of time-variant channels, which justifies the need for signal coding techniques. In that sense, this letter proposes an unequal error protection method for aerospace applications using the recommendations for source and channelcoding created by the Consultative Committee for Space Data System (CCSDS) as frameworks. The proposed method makes use of the CCSDS-recommended convolutional code to ensure a channelcoding step as low complex as possible, which allows implementation in a wide range of embedded platforms. This letter exploits the natural data division delivered by the compressor to unequally protect the information. The proposed method, which relies on a multiobjective optimization problem, allows one to find rate arrangements that minimize the distortion of the received image for a given value of an average coding rate within a granular range. The system performance is evaluated over an additive white Gaussian noise channel model. The obtained results show that the proposed method presents several advantages over an equal error protection strategy, and paves the way for scenarios with stringent energy and bandwidth constraints.
A hybrid digital-analog wireless image transmission scheme, called SparseCast, is introduced, which provides graceful degradation with channel quality. SparseCast achieves improved end-to-end reconstruction quality wh...
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A hybrid digital-analog wireless image transmission scheme, called SparseCast, is introduced, which provides graceful degradation with channel quality. SparseCast achieves improved end-to-end reconstruction quality while reducing the bandwidth requirement by exploiting frequency-domain sparsity through compressed sensing. The proposed algorithm produces a linear relationship between the channel signal-to-noise ratio and peak signal-to-noise ratio (PSNR) without requiring the channel state knowledge at the transmitter. This is particularly attractive when transmitting to multiple receivers or over unknown time-varying channels, as the receiver PSNR depends on the experienced channel quality and is not bottlenecked by the worst channel. SparseCast is benchmarked against two alternative algorithms: SoftCast and block CS-smooth projected Landweber (BCS-SPL). Our findings show that the proposed algorithm outperforms SoftCast by approximately 3.5 dB and BCS-SPL by 15.2 dB.
In this paper, we study the joint source-channel coding problem of transmitting a discrete-time analog source over an additive white Gaussian noise (AWGN) channel with interference known at transmitter. We consider th...
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ISBN:
(纸本)9781457705953
In this paper, we study the joint source-channel coding problem of transmitting a discrete-time analog source over an additive white Gaussian noise (AWGN) channel with interference known at transmitter. We consider the case when the source and the interference are correlated. We first derive an outer bound on the achievable distortion and then, we propose two joint source-channel coding schemes to make use of the correlation between the source and the interference. The first scheme is the superposition of the uncoded signal and a digital part which is the concatenation of a Wyner-Ziv encoder and a dirty paper encoder. In the second scheme, the digital part is replaced by a hybrid digital and analog scheme so that the proposed scheme can provide graceful degradation in the presence of (signal-to-noise ratio) SNR mismatch. Interestingly, unlike the independent interference setup, we show that neither of both schemes outperform the other universally in the presence of SNR mismatch.
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