Artificial intelligence driven channel coding and resource optimization for wireless networks: A systematic survey

This study investigates integrating artificial intelligence (AI) methodologies to improve current wireless networks' channel coding and resource management. The study also analyzes several AI models, including, but not limited to, machine learning and deep learning, and their ability to increase the performance and the flexibility of communication systems. The study will focus on the intelligent development of error-correcting codes, adaptive changes in modulation and coding, and dynamic resource allocation (power control, spectrum control, and interference mitigation). This study will drive AI-based methods to try to improve the limitations of traditional approaches in complex and dynamic wireless contexts with varying traffic and strict quality-of-service (QoS). This will include literature reviews, identification

of AI models, and development of a simulation framework to evaluate the performance of intelligent models. The study will help to design wireless communication systems which can be more effective and robust by using AI, which in turn will provide more effective use of the spectrum, less time delay, more reliable and adjustable resources in the next generation of wireless systems.

Message: Hi FE, I need help with “Artificial intelligence driven channel coding and resource optimization for wireless networks: A systematic survey” in “Electronics & Communication Engineering”