Direct sequential encoding and decoding for discrete sources

Koshelev, V.N.

doi:10.1109/tit.1973.1055006

Cited by 14 publications

(15 citation statements)

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“…Shannon's Lossy Joint Source-Channel Coding Theorem states that, for a given memoryless source and channel pair 5 and for sufficiently large source-block lengths, the source can be transmitted via a source-channel code over the channel at a transmission rate of source symbols/channel symbol and reproduced at the receiver end within an end-to-end distortion given by if the following condition is satisfied [32]: (3) where is the channel capacity and is the source rate-distortion function. For a discrete binary nonuniform i.i.d.…”

Section: B Shannon Limitmentioning

confidence: 99%

“…Blizard [4], Koshelev [5], and Hellman [6] are among the first few who proposed convolutional coding for the joint source-channel coding of sources with natural redundancy, where the source statistics are used at the receiver. Specifically, the convolutional encoding of such sources (Markov and nonuniform sources) over memoryless channels, and their decoding via sequential decoders employing a decoding metric that is dependent on both source and channel distributions, were studied in [4] and [5]. The computational complexity of such sequential decoders is analyzed, and it is shown that for a range of transmission rates, the expected number of computations per decoded bit is finite.…”

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confidence: 99%

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Transmission of Nonuniform Memoryless Sources via Nonsystematic Turbo Codes

Zhu

Alajaji

Bajcsy

et al. 2004

IEEE Trans. Commun.

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Abstract-We investigate the joint source-channel coding problem of transmitting nonuniform memoryless sources over binary phase-shift keying-modulated additive white Gaussian noise and Rayleigh fading channels via turbo codes. In contrast to previous work, recursive nonsystematic convolutional encoders are proposed as the constituent encoders for heavily biased sources. We prove that under certain conditions, and when the length of the input source sequence tends to infinity, the encoder state distribution and the marginal output distribution of each constituent recursive convolutional encoder become asymptotically uniform, regardless of the degree of source nonuniformity. We also give a conjecture (which is empirically validated) on the condition for the higher order distribution of the encoder output to be asymptotically uniform, irrespective of the source distribution. Consequently, these conditions serve as design criteria for the choice of good encoder structures. As a result, the outputs of our selected nonsystematic turbo codes are suitably matched to the channel input, since a uniformly distributed input maximizes the channel mutual information, and hence, achieves capacity. Simulation results show substantial gains by the nonsystematic codes over previously designed systematic turbo codes; furthermore, their performance is within 0.74-1.17 dB from the Shannon limit. Finally, we compare our joint source-channel coding system with two tandem schemes which employ a fourth-order Huffman code (performing near-optimal data compression) and a turbo code that either gives excellent waterfall bit-error rate (BER) performance or good error-floor performance. At the same overall transmission rate, our system offers robust and superior performance at low BERs ( 10 4 ), while its complexity is lower.Index Terms-Additive white Gaussian noise (AWGN) and Rayleigh fading channels, joint source-channel coding, nonsystematic turbo codes, nonuniform independent and identically distributed (i.i.d.) sources, Shannon limit.Paper approved by A. K. Khandani, the Editor for Coding and Information Theory of the IEEE Communications Society.

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Section: B Shannon Limitmentioning

confidence: 99%

mentioning

confidence: 99%

Transmission of Nonuniform Memoryless Sources via Nonsystematic Turbo Codes

Zhu

Alajaji

Bajcsy

et al. 2004

IEEE Trans. Commun.

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“…Sequential decoding is a decoding algorithm for tree codes invented by Wozencraft [18]. The use of sequential decoding in joint source-channel coding systems was proposed by Koshelev [14] and Hellman [12]. The attractive feature of sequential decoding, in this context, is the possibility of generating a -admissible reconstruction sequence, with an average computational complexity that grows only linearly with , the length of the source sequence.…”

Section: Introductionmentioning

confidence: 99%

Joint source-channel coding and guessing with application to sequential decoding

Arıkan

Merhav

1998

IEEE Trans. Inform. Theory

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We extend our earlier work on guessing subject to distortion to the joint source-channel coding context. We consider a system in which there is a source connected to a destination via a channel and the goal is to reconstruct the source output at the destination within a prescribed distortion level with respect to (w.r.t.) some distortion measure. The decoder is a guessing decoder in the sense that it is allowed to generate successive estimates of the source output until the distortion criterion is met. The problem is to design the encoder and the decoder so as to minimize the average number of estimates until successful reconstruction. We derive estimates on nonnegative moments of the number of guesses, which are asymptotically tight as the length of the source block goes to infinity. Using the close relationship between guessing and sequential decoding, we give a tight lower bound to the complexity of sequential decoding in joint source-channel coding systems, complementing earlier works by Koshelev and Hellman. Another topic explored here is the probability of error for list decoders with exponential list sizes for joint source-channel coding systems, for which we obtain tight bounds as well. It is noteworthy that optimal performance w.r.t. the performance measures considered here can be achieved in a manner that separates source coding and channel coding. © 1998 IEEE

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“…More recently, there has been work on ,joint source-channel encoding which is similarly related to syndrome-source-coding. Koshelev [17] has studied the encoding of sources by a convolutional encoder (without the usual pre-encoding to remove source redundancy before channel coding) in which the encoded output is directly transmitted through the channel and the source sequence is recovered by sequential decoding. lie proved that it is possible to obtain arbitrarily small average Hamming distortion whenever the rate R of the code is less than R comp /Hcomps, where RcomP is the usual computational cutoff of the channel and If i is a quantity depending only on the source (11 comp > 11.)…”

Section: Historical Background and Wiarksmentioning

confidence: 99%

Syndrome-source-coding and its universal generalization

Ancheta

1976

IEEE Trans. Inform. Theory

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treated as an error pattern whose syndrome forms the com; rer:sed data. It-i.ishown that syndr^me-s our ce-coding can achieve nrbitrarily small distortion with the number of compressed di&its per source di.gLt arbitrarily close. to the entropy of a binary nlemoryless source. A %uivercal" general i zation of syndr, f -sourcecoding Is formulated which provides robustly-effective, distortionless, coding of source ensembles. Two examples are gi.vcn comparing the performance of noiseless iniversal syndrome-source.--cotli.lig to (1) run-length coding and (2) lynch-D=iva univert;al coding; for nn ensemble of binary memoryl.ess scurces.

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Direct sequential encoding and decoding for discrete sources

Cited by 14 publications

References 1 publication

Transmission of Nonuniform Memoryless Sources via Nonsystematic Turbo Codes

Transmission of Nonuniform Memoryless Sources via Nonsystematic Turbo Codes

Joint source-channel coding and guessing with application to sequential decoding

Syndrome-source-coding and its universal generalization

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