Binary Data Correction Simulation Using Convolutional Code on Additive White Gaussian Noise Channel

Access to digital communications in remote areas requires a mechanism to increase the robustness of the transmitted data. Many areas in Indonesia still have difficulty accessing the Internet. This is because the location of the settlement is remote from the signal transmitter Convolutional codes are a technique to improve the reliability of data transmission. This article contains a simulation process of Convolutional Code from an application that we developed using Java. The basic difference between block codes and convolution codes in designing and evaluating is that Block codes are based on algebraic techniques or a combination whereas ConvCode based on construction techniques. Some of the excellent features of this application are demo encoding, modulation, noise generation on white Gaussian noise channels, and decoding using the Viterbi algorithm. The error correcting code process begins by checking the bit similarity (hamming distance) in the code word with the trellis diagram which will produce a path with weights depending on the hamming distance. With the Viterbi Algorithm, we will decode the codeword into the initial code by finding the highest probability (Maximum Likelihood) based on the Hamming distance from each state. Experiments show that the application successfully demonstrates the system's reliability to recover information signals damaged by noise.