Iterative Algorithm for Channel Capacity C

The iterative algorithm for channel capacity C calculation employs the Blahut-Arimoto method to converge to optimal values. The core function [CC, Paa] = ChannelCap(P, k) operates as follows: - Input parameter P: Forward transition probability matrix representing conditional probabilities P(output|input) - Input parameter k: Iteration precision threshold determining convergence criteria - Output CC: Optimized channel capacity value in bits per channel use - Output Paa: Optimized input probability distribution achieving capacity Algorithm implementation details: 1. Initialize Pa with uniform distribution over r input symbols 2. Compute Pb (output probabilities) through matrix multiplication: Pb = Pa * P 3. Calculate initial capacity C using mutual information formula 4. Iteratively update Pa using divergence minimization until |ΔC| < k 5. Key operations include logarithmic calculations for mutual information and probability normalization The iterative process continues until the capacity change between consecutive iterations falls below the specified precision k, ensuring mathematical convergence to the true channel capacity. The implementation handles dimension validation for P matrix (r×s dimensions) and includes error checking for probability normalization constraints.