1,824 research outputs found
Improved Modeling of the Correlation Between Continuous-Valued Sources in LDPC-Based DSC
Accurate modeling of the correlation between the sources plays a crucial role
in the efficiency of distributed source coding (DSC) systems. This correlation
is commonly modeled in the binary domain by using a single binary symmetric
channel (BSC), both for binary and continuous-valued sources. We show that
"one" BSC cannot accurately capture the correlation between continuous-valued
sources; a more accurate model requires "multiple" BSCs, as many as the number
of bits used to represent each sample. We incorporate this new model into the
DSC system that uses low-density parity-check (LDPC) codes for compression. The
standard Slepian-Wolf LDPC decoder requires a slight modification so that the
parameters of all BSCs are integrated in the log-likelihood ratios (LLRs).
Further, using an interleaver the data belonging to different bit-planes are
shuffled to introduce randomness in the binary domain. The new system has the
same complexity and delay as the standard one. Simulation results prove the
effectiveness of the proposed model and system.Comment: 5 Pages, 4 figures; presented at the Asilomar Conference on Signals,
Systems, and Computers, Pacific Grove, CA, November 201
Unequal Error Protection Querying Policies for the Noisy 20 Questions Problem
In this paper, we propose an open-loop unequal-error-protection querying
policy based on superposition coding for the noisy 20 questions problem. In
this problem, a player wishes to successively refine an estimate of the value
of a continuous random variable by posing binary queries and receiving noisy
responses. When the queries are designed non-adaptively as a single block and
the noisy responses are modeled as the output of a binary symmetric channel the
20 questions problem can be mapped to an equivalent problem of channel coding
with unequal error protection (UEP). A new non-adaptive querying strategy based
on UEP superposition coding is introduced whose estimation error decreases with
an exponential rate of convergence that is significantly better than that of
the UEP repetition coding introduced by Variani et al. (2015). With the
proposed querying strategy, the rate of exponential decrease in the number of
queries matches the rate of a closed-loop adaptive scheme where queries are
sequentially designed with the benefit of feedback. Furthermore, the achievable
error exponent is significantly better than that of random block codes
employing equal error protection.Comment: To appear in IEEE Transactions on Information Theor
Parallel vs. Sequential Belief Propagation Decoding of LDPC Codes over GF(q) and Markov Sources
A sequential updating scheme (SUS) for belief propagation (BP) decoding of
LDPC codes over Galois fields, , and correlated Markov sources is
proposed, and compared with the standard parallel updating scheme (PUS). A
thorough experimental study of various transmission settings indicates that the
convergence rate, in iterations, of the BP algorithm (and subsequently its
complexity) for the SUS is about one half of that for the PUS, independent of
the finite field size . Moreover, this 1/2 factor appears regardless of the
correlations of the source and the channel's noise model, while the error
correction performance remains unchanged. These results may imply on the
'universality' of the one half convergence speed-up of SUS decoding
Channel combining and splitting for cutoff rate improvement
The cutoff rate of a discrete memoryless channel (DMC) is often
used as a figure of merit, alongside the channel capacity . Given a
channel consisting of two possibly correlated subchannels , , the
capacity function always satisfies , while there are
examples for which . This fact that cutoff rate can
be ``created'' by channel splitting was noticed by Massey in his study of an
optical modulation system modeled as a 'ary erasure channel. This paper
demonstrates that similar gains in cutoff rate can be achieved for general
DMC's by methods of channel combining and splitting. Relation of the proposed
method to Pinsker's early work on cutoff rate improvement and to Imai-Hirakawa
multi-level coding are also discussed.Comment: 5 pages, 7 figures, 2005 IEEE International Symposium on Information
Theory, Adelaide, Sept. 4-9, 200
On the Complexity of Exact Maximum-Likelihood Decoding for Asymptotically Good Low Density Parity Check Codes: A New Perspective
The problem of exact maximum-likelihood (ML) decoding of general linear codes is well-known to be NP-hard. In this paper, we show that exact ML decoding of a class of asymptotically good low density parity check codes — expander codes — over binary symmetric channels (BSCs) is possible with an average-case polynomial complexity. This offers a new way of looking at the complexity issue of exact ML decoding for communication systems where the randomness in channel plays a fundamental central role. More precisely, for any bit-flipping probability p in a nontrivial range, there exists a rate region of non-zero support and a family of asymptotically good codes which achieve error probability exponentially decaying in coding length n while admitting exact ML decoding in average-case polynomial time. As p approaches zero, this rate region approaches the Shannon channel capacity region. Similar results can be extended to AWGN channels, suggesting it may be feasible to eliminate the error floor phenomenon associated with belief-propagation decoding of LDPC codes in the high SNR regime. The derivations are based on a hierarchy of ML certificate decoding algorithms adaptive to the channel realization. In this process, we propose an efficient O(n^2) new ML certificate algorithm based on the max-flow algorithm. Moreover, exact ML decoding of the considered class of codes constructed from LDPC codes with regular left degree, of which the considered expander codes are a special case, remains NP-hard; thus giving an interesting contrast between the worst-case and average-case complexities
The price of certainty: "waterslide curves" and the gap to capacity
The classical problem of reliable point-to-point digital communication is to
achieve a low probability of error while keeping the rate high and the total
power consumption small. Traditional information-theoretic analysis uses
`waterfall' curves to convey the revolutionary idea that unboundedly low
probabilities of bit-error are attainable using only finite transmit power.
However, practitioners have long observed that the decoder complexity, and
hence the total power consumption, goes up when attempting to use sophisticated
codes that operate close to the waterfall curve.
This paper gives an explicit model for power consumption at an idealized
decoder that allows for extreme parallelism in implementation. The decoder
architecture is in the spirit of message passing and iterative decoding for
sparse-graph codes. Generalized sphere-packing arguments are used to derive
lower bounds on the decoding power needed for any possible code given only the
gap from the Shannon limit and the desired probability of error. As the gap
goes to zero, the energy per bit spent in decoding is shown to go to infinity.
This suggests that to optimize total power, the transmitter should operate at a
power that is strictly above the minimum demanded by the Shannon capacity.
The lower bound is plotted to show an unavoidable tradeoff between the
average bit-error probability and the total power used in transmission and
decoding. In the spirit of conventional waterfall curves, we call these
`waterslide' curves.Comment: 37 pages, 13 figures. Submitted to IEEE Transactions on Information
Theory. This version corrects a subtle bug in the proofs of the original
submission and improves the bounds significantl
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