Intelligent reed solomon codec algorithm for digital radio and television media braodcasting

Data dependability and error correction are ensured by the widespread usage of Reed-Solomon codes (RSC) in massive data storage and transmission systems. They shine while dealing with burst faults, which are prevalent in many transmission and storage settings and cause corruption of many consecutive bits. Developing technologies for voice communication in the shortwave frequency is an important topic of research. A top priority for these kinds of communication networks is to make them more resistant to noise, which will extend their range, decrease their power consumption, and ultimately make their user devices smaller and lighter. A method that uses voice codec to transform speech into bits and back again is the most effective way to ensure that voice communication lines in the current world are as noise-free as possible. Here, lowering the likelihood of a bit mistake at the output of the error correcting code is the primary means of improving the noise immunity of these communication systems. In order to minimize the likelihood of a bit error for a given signal- to-noise ratio along with channel model variables for Additive White Gaussian Noise (AWGN) and multipath Rayleigh channels, this work aims at creating a methodology for determining the settings of error correcting codes, synchronization sequences, while direct spread sequence spectrum signal codes. So, techniques like Forward Error Correction codes need to be put in place to deal with these problems properly. In order to rectify broadcasts, we propose in this work to use wireless codes on a variety of deployed radio and Television (TV) equipment using Convolutional Neural Networks (CNNs). We assess this approach in comparison to the Reed-Solomon codes and examine the novel RS codes. We show in our simulations that running the RS codes via the MATLAB simulator yields better results.