Work

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

Year: 2025

Type: article

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, supervised and unsupervised machine learning, focusing on the impact of the two used parameters: overlapping ratio and the rotation step. Our approach includes applying 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.

Source: Internet of Things

DOI: 10.1016/j.iot.2025.101571

Authors: Djamila Talbi, Zoltan Gal

Institution: University of Debrecen

Language: English

Topic: Wireless Networks and Protocols

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,690