theroleofspecializationinldpccodes

theroleofspecializationinldpccodes内容摘要：

|1()|0( evPevP Decision Rule  After sufficiently many iterations, return the likelihood ratio:  o t h e r w i s e ,10)( if ,0ˆ)1(|,ivcvcyx mBxvvTheorem about MP Algorithm  If the algorithm stops after r iterations, then the algorithm returns the maximum a posteriori probability estimate of xv given y within radius r of v.  However, the variables within a radius r of v must be dependent only by the equations within radius r of v, v r ... ... ... Analysis of Message Passing Decoding (Density Evolution)  in Density Evolution we keep track of message densities, rather than the densities themselves.  At each iteration, we average over all of the edges which are connected by a permutation.  We assume that the allzeros codeword was transmitted (which requires that the channel be symmetric). . Update Rule  The update rule for Density Evolution is defined in the additive domain of each type of node.  Whereas in , we add (log) messages:  In , we convolve message densities: vcvicvivc mBm39。 |)(,39。 )(, )(39。 vcvicvivc mDBmD39。 |)(,39。 )(, ))((39。 *)(Familiar Example:  If one die has density function given by:  The density function for the sum of two dice is given by the convolution: 1 3 6 5 4 2 2 4 7 6 5 3 8 10 12 11 9 . Threshold  Fixing the channel message densities, the message densities will either converge to minus infinity, or they won39。 t.  For the gaussian chan。