A construction of vector quantizers for noisy channels

Kumazawa, Hiroyuki; Kasahara, Masao; Namekawa, T.

doi:10.1002/ecja.4400670406

Cited by 123 publications

(98 citation statements)

References 9 publications

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“…2. We refer the reader to [1], [34] for a detailed description of the system as well as its design algorithm based on iteratively applying optimality encoder and decoder conditions [7]- [10]. Instead, we focus on illustrating the system's performance over the NBNDC-QB channel.…”

Section: B Channel Optimized Vector Quantization (Covq)mentioning

confidence: 99%

MAP Detection and Robust Lossy Coding Over Soft-Decision Correlated Fading Channels

Shahidi

Alajaji

Linder

2013

IEEE Trans. Veh. Technol.

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Abstract-The joint source-channel coding problem for softdecision demodulated time-correlated fading channels is investigated without the use of channel coding and interleaving. For the purpose of system design, the recently introduced non-binary noise discrete channel with queue based noise (NBNDC-QB) is adopted. This analytically tractable Markovian model has been shown to effectively represent correlated fading channels that are hard to handle analytically. Optimal sequence maximum a posteriori (MAP) detection of a discrete Markov source sent over the NBNDC-QB is first studied. When the Markov source is binary and symmetric, a necessary and sufficient condition under which the MAP decoder is reduced to a simple instantaneous symbol-by-symbol decoder is established. Two robust lossy source coding schemes with low-encoding delay are next proposed for the NBNDC-QB. The first scheme consists of a scalar quantizer, a proper index assignment, and a sequence MAP decoder designed to harness the redundancy left in the quantizer's indices, the channel's soft-decision output and noise correlation. The second scheme is the classical noise resilient vector quantizer known as the channel optimized vector quantizer. It is demonstrated that both systems can successfully exploit the channel's memory and soft-decision information. Signal-to-distortion (SDR) gains of more than 1.7 dB are obtained over hard-decision demodulation by using only 2 bits for soft-decision. Furthermore, gains as high as 4.4 dB can be achieved for a strongly correlated channel, in comparison with systems designed for the ideally interleaved (memoryless) channel. Finally, it is numerically observed that for low coding rates the NBNDC-QB model can accurately approximate discrete fading channels in terms of SDR performance.

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Section: B Channel Optimized Vector Quantization (Covq)mentioning

confidence: 99%

MAP Detection and Robust Lossy Coding Over Soft-Decision Correlated Fading Channels

Shahidi

Alajaji

Linder

2013

IEEE Trans. Veh. Technol.

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“…Our focus here is to investigate the details of the calculation of the specific a posteriori probabilities required, so as to find more efficient solutions. optimum MMSE decoding of (8) are calculated recursively by (14) at the bottom of the page ( , see Appendix for proof), At each time instant the probabilities corresponding to each state are stored to be used in (14) at the next time instant. In addition to the computations required to do this task, the complexity of the AOMMSE decoder is comprised of the cost to perform the multiplications and normalization in (14), as well as the weighted average of the reconstruction rule in (8).…”

Section: ) a Basic Solutionmentioning

confidence: 99%

“…Two classic works in this area are those of Kurtenbach and Wintz [12] on scalar quantization over noisy channels and Chang and Donaldson [13] on the design of a DPCM system for transmission over a discrete memoryless channel. Other works on channel optimized quantization include the works of Kumazawa et al [14] and Farvardin and Vaishampayan [15] on vector quantization over noisy channels as well as the works of Dunham and Gray [16] and Ayanoglu and Gray [17] on joint source-channel trellis coding. Examples of more recent works in this class are present in [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

Efficient Source Decoding Over Memoryless Noisy Channels Using Higher Order Markov Models

Lahouti

Khandani

2004

IEEE Trans. Inform. Theory

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Abstract-Exploiting the residual redundancy in a source coder output stream during the decoding process has been proven to be a bandwidth-efficient way to combat noisy channel degradations. This redundancy can be employed to either assist the channel decoder for improved performance or design better source decoders. In this work, a family of solutions for the asymptotically optimum minimum mean-squared error (MMSE) reconstruction of a source over memoryless noisy channels is presented when the redundancy in the source encoder output stream is exploited in the form of a -order Markov model ( 1) and a delay of 0 is allowed in the decoding process. It is demonstrated that the proposed solutions provide a wealth of tradeoffs between computational complexity and the memory requirements. A simplified MMSE decoder which is optimized to minimize the computational complexity is also presented. Considering the same problem setup, several other maximum a posteriori probability (MAP) symbol and sequence decoders are presented as well. Numerical results are presented which demonstrate the efficiency of the proposed algorithms.Index Terms-Forward-backward recursion, joint sourcechannel coding, maximum a posteriori probability (MAP) detection, Markov sources, minimum mean-squared error (MMSE) estimation, residual redundancies, source decoding.

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“…We assume memoryless and Gauss-Markov sources and an additive white Gaussian noise (AWGN) channel. We demonstrate that within the VQ-based HDA framework, the system can be optimized using an iterative method similar to the traditional channel-optimized VQ (COVQ) design algorithm [4], [7]. Motivated by a broadcast scenario, we then present the performance of a fixed encoder, adaptive decoder system in which the encoder is optimized for a fixed-channel SNR while the decoder adapts to the changing channel SNR.…”

mentioning

confidence: 99%

Design and performance of VQ-based hybrid digital-analog joint source-channel codes

Skoglund

Phamdo

Alajaji

2002

IEEE Trans. Inform. Theory

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Abstract-A joint source-channel hybrid digital-analog (HDA) vector quantization (VQ) system is presented. The main advantage of the new VQ-based HDA system is that it achieves excellent rate-distortion-capacity performance at the design signal-to-noise ratio (SNR) while maintaining a "graceful improvement" characteristic at higher SNRs. It is demonstrated that, within the HDA framework, the parameters of the system can be optimized using an iterative procedure similar to that of channel-optimized vector quantizer design. Comparisons are made with three purely digital systems and one purely analog system. It is found that, at high SNRs, the VQ-based HDA system is superior to the other investigated systems. At low SNRs, the performance of the new scheme can be improved using the optimization procedure and using soft decoding in the digital part of the system. These results demonstrate that the introduced scheme provides an attractive method for terrestrial broadcasting applications.

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A construction of vector quantizers for noisy channels

Cited by 123 publications

References 9 publications

MAP Detection and Robust Lossy Coding Over Soft-Decision Correlated Fading Channels

MAP Detection and Robust Lossy Coding Over Soft-Decision Correlated Fading Channels

Efficient Source Decoding Over Memoryless Noisy Channels Using Higher Order Markov Models

Design and performance of VQ-based hybrid digital-analog joint source-channel codes

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