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It has recently become popular to use simulation-based algorithms to empirically estimate achievable information rates over intersymbol interference (ISI) channels with inputs from specific input constellations. Such algorithms are guaranteed to converge by invoking the Shannon-McMillan-Brieman theorem provided that the output sequence is stationary and ergodic. In this note, we establish a central limit theorem result on the rate of convergence, and show that the variance of the estimates decreases like 1/N (where N is the sequence length employed) as N goes to infinity. This result indicates that it is possible to achieve estimation accuracy with any desired level by simply increasing the number of samples appropriately.
Information Theory and Applications Workshop, 2008
Date of Conference: Jan. 27 2008-Feb. 1 2008