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Rudiger Urbanke

Publications and source records attributed to Rudiger Urbanke.

22 records · Page 2Linked to original sources

The Generalized Area Theorem and Some of its Consequences

There is a fundamental relationship between belief propagation and maximum a posteriori decoding. The case of transmission over the binary erasure channel was investigated in detail in a companion paper. This paper investigates the extension to general memoryless channels (paying special attention to the binary case). An area theorem for transmission over general memoryless channels is introduced and some of its many consequences are discussed. We show that this area theorem gives rise to an upper-bound on the maximum a posteriori threshold for sparse graph codes. In situations where this bound is tight, the extrinsic soft bit estimates delivered by the belief propagation decoder coincide with the correct a posteriori probabilities above the maximum a posteriori threshold. More generally, it is conjectured that the fundamental relationship between the maximum a posteriori and the belief propagation decoder which was observed for transmission over the binary erasure channel carries over to the general case. We finally demonstrate that in order for the design rate of an ensemble to approach the capacity under belief propagation decoding the component codes have to be perfectly matched, a statement which is well known for the special case of transmission over the binary erasure channel.

cs.IT↗

Why We Can Not Surpass Capacity: The Matching Condition

We show that iterative coding systems can not surpass capacity using only quantities which naturally appear in density evolution. Although the result in itself is trivial, the method which we apply shows that in order to achieve capacity the various components in an iterative coding system have to be perfectly matched. This generalizes the perfect matching condition which was previously known for the case of transmission over the binary erasure channel to the general class of binary-input memoryless output-symmetric channels. Potential applications of this perfect matching condition are the construction of capacity-achieving degree distributions and the determination of the number required iterations as a function of the multiplicative gap to capacity.

cs.IT↗

Life Above Threshold: From List Decoding to Area Theorem and MSE

We consider communication over memoryless channels using low-density parity-check code ensembles above the iterative (belief propagation) threshold. What is the computational complexity of decoding (i.e., of reconstructing all the typical input codewords for a given channel output) in this regime? We define an algorithm accomplishing this task and analyze its typical performance. The behavior of the new algorithm can be expressed in purely information-theoretical terms. Its analysis provides an alternative proof of the area theorem for the binary erasure channel. Finally, we explain how the area theorem is generalized to arbitrary memoryless channels. We note that the recently discovered relation between mutual information and minimal square error is an instance of the area theorem in the setting of Gaussian channels.

cs.IT↗

Finite-Length Scaling and Finite-Length Shift for Low-Density Parity-Check Codes

Consider communication over the binary erasure channel BEC using random low-density parity-check codes with finite-blocklength n from `standard' ensembles. We show that large error events is conveniently described within a scaling theory, and explain how to estimate heuristically their effect. Among other quantities, we consider the finite length threshold e(n), defined by requiring a block error probability P_B = 1/2. For ensembles with minimum variable degree larger than two, the following expression is argued to hold e(n) = e -e_1 n^{-2/3} +Θ(n^{-1}) with a calculable shift} parameter e_1>0.

cs.IT↗