Weighted Performance Function for ( r , s ) - Entropy of Discrete Memoryless Communication Channel under Single Constraint

In almost every engineering and technology fields, information channel capacity is the tightest upper bound on the amount of information that can be reliably transmitted over a communications channel. Brijpaul and Sharma [1] gave a direct method of computing the performance function of a discrete memoryless communication channels. They obtained the performance function for discrete channels by using Shannon entropy which is additive in nature, but in some situations additivity does not hold well. In such situations, nonadditivity prevails. Arimoto [2] and Blahut [3] proposed an iterative method to compute the channel capacity of a discrete memoryless channel. In this paper we present an algorithm for computing performance function for useful (r, s) – entropy under single and multiple constraints by defining mutual information in terms of Sharma and Mittal [4] entropy of order r and degree s which is non-additive in nature.