Optimal Code Length Estimates From Dependent Samples With Bounds on the Estimation Error

Modern communication networks require robust and adaptive communication protocols to optimize various performance metrics, such as latency and energy efficiency. Adaptive communication protocols require real-time estimation of critical system parameters, amongst which the average length of an optimal source code is of key importance, when data compression is involved. In this letter, we prove finite-sample estimates-along with their confidence levels-of the worst case average length of an optimal source code over a channel transmitting dependent data. More specifically, this is achieved by establishing a concentration inequality for the estimation of entropy of the source. Data dependence is modeled through stationary mixing. Evaluation of the proposed bounds is computationally efficient and can be used for real-time estimation.