ANALYSIS OF INFORMATION THEORY FOR SIGNAL PROCESSING AND COMMUNICATION APPLICATIONS

The fundamentals of information theory have revolutionised the fields of signal processing and communication. In addition to effectively representing and processing digital signals, information theory also offers a quantitative framework for comprehending the underlying capabilities and constraints of communication systems. The main ideas of information theory are examined in this abstract in relation to signal processing and communication applications. The abstract first explores the idea of entropy, which quantifies the typical amount of information contained in a random variable. Understanding the capacity of communication channels and the best encoding and decoding techniques requires an understanding of entropy as a fundamental parameter. Additionally, the idea of mutual information is covered, which measures the amount of information that two random variables share with one another. It investigates the field of information theory known as coding theory, which focuses on error detection and correction codes. It explores several coding techniques such block codes, convolutional codes, and turbo codes and emphasises the need of error-correcting codes in guaranteeing reliable communication over noisy channels. The channel capacity, or maximum rate at which data can be sent across a specific communication channel, is another topic covered in this paper. The also covers source coding, which deals with effective data representation and compression. In order to achieve data compression, it presents the idea of entropy coding, which includes methods like Huffman coding and arithmetic coding