AI-driven insights into B5G/6G MAC mechanisms: A comprehensive analysis

Abstract

In the 6G wireless communication domain, optimizing the medium access control mechanism is crucial for enhancing the performance of high-speed, over Terabit/sec transmission rate networks. This paper evaluates the adaptive directional antenna protocol for terahertz frequencies technology using the ns-3 simulator, employing different techniques like Shannon entropy, wavelet transform, supervised, unsupervised machine learning, and classical processing methods focusing on the impact of the two used parameters: overlapping ratio and the rotation step. Our approach is to highlight the importance of the Paleo-AI Classical Processing method, which is about incorporating the classical mathematical processing method, with the AI models for better results. The proposed method includes applying some analytical tools like the entropy metrics to understand the dynamic behavior of the radio control frames, particularly in distinguishing between the stable and unstable phases of the communication process and includes the adoption of fractal wavelet analysis for better learning. Additionally, the RNN classification of MAC event sequences into categories supported by transfer learning enhanced the model's efficiency, where we introduced the weighted accuracy to time ratio, a novel approach to assess the competency of various deep learning models. Moreover, different generative AI methods were used to produce synthetic data where the similarity levels were quantified by using six distinct metrics. The overall results of this paper demonstrated the necessity of adapting the MAC protocols to specific environmental conditions, thereby contributing to the development of more resilient B5G/6G communication networks.