Title :
Evolutionary Algorithm-Based Approximation of the Capacity of Full-Surface Channels
Author :
Iqba, M.A. ; Weeks, William, IV ; Panagos, Adam
Author_Institution :
Univ. of Missouri-Rolla, Rolla
Abstract :
Ideas of Evolutionary algorithms can be used to improve the Maximum Likelihood (ML) estimate of the full-surface data, this improved estimate is used to compute the channel capacity of a full-surface communication channel, i.e. a noisy two-dimensional ISI channel. Channel capacity is computed as a function of the entropy rate. Using a Shannon-McMillan-Breimann theorem, the problem is further reduced to the computation of the probability associated with the output. This density function is estimated by using the improved ML data obtained.
Keywords :
channel capacity; entropy; evolutionary computation; maximum likelihood estimation; ML data; Shannon-McMillan-Breimann theorem; density function; entropy rate; evolutionary algorithm based approximation; full surface channels capacity; full-surface communication channel; full-surface data; maximum likelihood estimation; Channel capacity; Data storage systems; Entropy; Evolutionary computation; Greedy algorithms; Intersymbol interference; Maximum likelihood estimation; Mutual information; Optical noise; Optical recording;
Conference_Titel :
Region 5 Technical Conference, 2007 IEEE
Conference_Location :
Fayetteville, AR
Print_ISBN :
978-1-4244-1280-8
Electronic_ISBN :
978-1-4244-1280-8
DOI :
10.1109/TPSD.2007.4380392
