We propose a distributed binary arithmetic coder for Slepian-Wolf coding with decoder side information, along with a soft joint decoder. The proposed scheme provides several advantages over existing schemes, and its p...
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We propose a distributed binary arithmetic coder for Slepian-Wolf coding with decoder side information, along with a soft joint decoder. The proposed scheme provides several advantages over existing schemes, and its performance is equal to or better than that of an equivalent scheme based on turbo codes at short and medium block lengths.
In the context of spectrum reusing in Cognitive Radio (CR) networks, spectrum availability should be considered with respect to both space and frequency in making the Power Spectral Density (PSD) map concept. For this...
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In the context of spectrum reusing in Cognitive Radio (CR) networks, spectrum availability should be considered with respect to both space and frequency in making the Power Spectral Density (PSD) map concept. For this reason, the sensed PSDs by the distributed sensors in the area are collected and fused by a fusion center (FC). But, for a given zone, the sensed PSD by neighbor CR sensors may contain a shared common component for a while, and this component can be exploited in the theory of the distributed source coding to compress as much as possible. For this reason, distributed compressive sensing (DCS) methods are used here to exploit the correlation between PSDs and compress them more and reduce this type of overhead traffic. The proposed method can be used to develop a framework when the holding time of the users is large in comparison with the rate of the spectrum sensing.
作者：
Fang, YongNW A&F Univ
Coll Informat Engn Yangling 712100 Shaanxi Peoples R China
distributed Arithmetic coding (DAC) is an effective implementation of Slepian-Wolf coding, especially for short data blocks. However, the properties of DAC codewords have not yet been studied so far. This paper resear...
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distributed Arithmetic coding (DAC) is an effective implementation of Slepian-Wolf coding, especially for short data blocks. However, the properties of DAC codewords have not yet been studied so far. This paper researches DAC codeword distribution over interval [0, 1) for equiprobable binary sources. Some simulation results are given to verify theoretical analyses.
We propose a constructive approach for distributed source coding of correlated binary sources using linear channel codes that can achieve any point of the Slepian-Wolf achievable rate region. Our approach is very intu...
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We propose a constructive approach for distributed source coding of correlated binary sources using linear channel codes that can achieve any point of the Slepian-Wolf achievable rate region. Our approach is very intuitive and can be used with systematic and non-systematic linear codes;Moreover, the proposed coding strategy can easily be extended to the case of more than two sources.
distributed arithmetic coding has been shown to be effective for Slepian-Wolf coding with side information. In this letter, we extend it to rate-compatible coding, which is useful in presence of a feedback channel bet...
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distributed arithmetic coding has been shown to be effective for Slepian-Wolf coding with side information. In this letter, we extend it to rate-compatible coding, which is useful in presence of a feedback channel between encoder and decoder. The performance loss with respect to the original version is negligible.
In the context of distributed source coding, the so-called many-help-one problem comprises a set of auxiliary sources aimed at helping the decoder retrieve a primary source. In this letter, we build on the newly deriv...
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In the context of distributed source coding, the so-called many-help-one problem comprises a set of auxiliary sources aimed at helping the decoder retrieve a primary source. In this letter, we build on the newly derived admissible rate region of a certain binary many-help-one problem to obtain the outage probability (OP) of a decode-and-forward (DF) multirelay system that allows intra-link errors (IE) to be forwarded to the destination. In addition, for comparison, we obtain the OP of a conventional DF multirelay system, which discards any IEs. We show that, the more relays are employed, the more advantageous it is to forward the IEs, as opposed to discarding them.
A setup involving zero-delay sequential transmission of a vector Markov source over a burst erasure channel is studied. A sequence of source vectors is compressed in a causal fashion at the encoder, and the resulting ...
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A setup involving zero-delay sequential transmission of a vector Markov source over a burst erasure channel is studied. A sequence of source vectors is compressed in a causal fashion at the encoder, and the resulting output is transmitted over a burst erasure channel. The destination is required to reconstruct each source vector with zero-delay, but those source sequences that are observed either during the burst erasure, or in the interval of length W following the burst erasure need not be reconstructed. The minimum achievable compression rate is called the rate-recovery function. We assume that each source vector is independent identically distributed (i.i.d.) across the spatial dimension and is sampled from a stationary, first-order Markov process across the temporal dimension. For discrete sources, the case of lossless recovery is considered, and upper and lower bounds on the rate-recovery function are established. Both these bounds can be expressed as the rate for predictive coding, plus a term that decreases at least inversely with the recovery window length W. For Gauss-Markov sources and a quadratic distortion measure, upper and lower bounds on the minimum rate are established when W = 0. These bounds are shown to coincide in the high resolution limit. Finally, another setup involving i.i.d. Gaussian sources is studied and the rate-recovery function is completely characterized in this case.
We consider the network communication scenario, over directed acyclic networks with unit capacity edges in which a number of sources s(i) each holding independent unit-entropy information X-i wish to communicate the s...
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We consider the network communication scenario, over directed acyclic networks with unit capacity edges in which a number of sources s(i) each holding independent unit-entropy information X-i wish to communicate the sum Sigma X-i to a set of terminals t(j). We show that in the case in which there are only two sources or only two terminals, communication is possible if and only if each source terminal pair s(i)/t(j) is connected by at least a single path. For the more general communication problem in which there are three sources and three terminals, we prove that a single path connecting the source terminal pairs does not suffice to communicate Sigma X-i. We then present an efficient encoding scheme which enables the communication of Sigma X-i for the three sources, three terminals case, given that each source terminal pair is connected by two edge disjoint paths.
This correspondence considers the use of punctured quasi-arithmetic (QA) codes for the Slepian-Wolf problem. These entropy codes are defined by finite state machines for memoryless and first-order memory sources. Punc...
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This correspondence considers the use of punctured quasi-arithmetic (QA) codes for the Slepian-Wolf problem. These entropy codes are defined by finite state machines for memoryless and first-order memory sources. Puncturing an entropy coded bit-stream leads to an ambiguity at the decoder side. The decoder makes use of a correlated version of the original message in order to remove this ambiguity. A complete distributed source coding (DSC) scheme based on QA encoding with side information at the decoder is presented, together with iterative structures based on QA codes. The proposed schemes are adapted to memoryless and first-order memory sources. Simulation results reveal that the proposed schemes are efficient in terms of decoding performance for short sequences compared to well-known DSC solutions using channel codes.
In this work, joint decompression and decoding is studied for the uplink of multi-antenna cloud radio access networks. In this system, a set of multi-antenna mobile stations (MSs) wish to communicate with a "clou...
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In this work, joint decompression and decoding is studied for the uplink of multi-antenna cloud radio access networks. In this system, a set of multi-antenna mobile stations (MSs) wish to communicate with a "cloud" decoder through a set of multi-antenna base stations (BSs), which are connected to the cloud decoder through digital backhaul links of limited capacity. The BSs compress the received signal and send it to the cloud decoder, which performs joint decoding of the signals from all MSs. While the conventional solution prescribes that the cloud decoder performs first decompression and then decoding, recent work has shown that potentially larger rates can be achieved with joint decompression and decoding (JDD) at the cloud decoder. The sum-rate maximization problem with JDD, under the assumption of Gaussian test channels, is shown here to be an instance of a class of non-convex problems known as Difference of Convex (DC) problems. Based on this observation, an iterative algorithm based on the Majorization Minimization (MM) approach is proposed that guarantees convergence to a stationary point of the sum-rate maximization problem. Numerical results demonstrate the advantage of the proposed algorithm compared to the conventional approach based on separate decompression and decoding.
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