Artificial Intelligence Techniques for 6G Network Communication: A Comprehensive Review and Performance Analysis

Exponential development in the volume of associated wireless equipment such as telephones, energy-efficient Internet of Things (IoT) products, connected automobiles, etc. will enhance the size of the forthcoming generations wireless networks i.e. 5G advanced, and 6G. Moreover, expected use cases include linked self-driven automobiles, robots, drones, smart residences and urban areas, wireless connectivity of commercial infrastructure, and holographic transmission for 6G networks will require minimal latency and exceptionally high accuracy. Conventional and standard techniques are not appropriate for the efficiency, coordination, and arrangement of such networks to satisfy the needs of the developing applications due to extensive demands, high speed, and a great deal of information produced by devices that are connected. Recently, artificial intelligence (AI) has become expected to be implemented as an innovative approach for the development, operational supervision, and automated operation of subsequent generations mobile communications systems. This paper proposes a transmitter-centric semantic communication framework for 6G networks that integrates large language models with reinforcement learning to enable adaptive, context-aware semantic encoding. By jointly optimizing semantic quality, latency, energy consumption, and transmission overhead, the proposed approach significantly outperforms conventional bit-level and learning-based communication schemes under dynamic network conditions.