Simulation of Viterbi Decoding for Convolutional Codes

This simulation of Viterbi decoding for convolutional codes aims to evaluate the performance of convolutional codes in communication channels. The simulation process involves implementing encoding operations at the transmitter side to process original data, followed by transmission of encoded data through simulated channel conditions. At the receiver end, the Viterbi decoding algorithm is implemented to decode received data while tracking bit error rates across varying signal-to-noise ratio conditions. The core implementation typically includes convolution encoding using generator polynomials, branch metric calculations based on Hamming or Euclidean distance, path metric accumulation through Add-Compare-Select operations, and survivor path traceback mechanisms. Through this simulation experiment, we can analyze convolutional code performance under different channel conditions, providing valuable insights for optimizing convolutional code design parameters such as constraint length and generator polynomial selection. The simulation framework may incorporate Monte Carlo methods for statistical reliability, with key functions handling state transitions, metric computations, and decoding latency analysis.