AI-Driven Error Correction Mechanisms in Next-Generation Wireless Networks

Communication reliability becomes crucial in ever changing and challenging environments in next generation wireless networks; hence, efficient error correction mechanisms are highly sought after. The paper presents an AI driven method of coding Error Correction Codes (ECC) based on advanced machine learning techniques like the Reinforcement Learning (RL) and genetic algorithms. Preprocessing is the first step in the process where channel conditions and signal parameters are prepared for AI models. Handling data normalization, cleaning and organizing those relevant features like SNR and packet loss patterns. After finding these features, we use them to extract feature, so that AI models will focus on the most important feature which has impact on the network performance. The core of the proposed approach is in the classifier, where we let AI models optimize the construction of ECC parameters (block length and redundancy) to optimize performance. Experimental results show the superiority of AI driven error correction in terms of performance in the face of difficult Wi-Fi transmission, thereby improving reliability of the future networks.