Fairness-Utilization Trade-off in Wireless Networks with Explainable Kolmogorov-Arnold Networks
This addresses fairness and efficiency challenges in dynamic 6G services, offering a flexible framework for power allocation, though it is incremental as it builds on existing machine learning methods with a new model type.
The paper tackles the problem of transmit power allocation in 6G wireless networks to balance network utilization and user fairness, introducing Kolmogorov-Arnold Networks (KANs) to achieve improved fairness and lower inference costs compared to traditional deep neural networks.
The effective distribution of user transmit powers is essential for the significant advancements that the emergence of 6G wireless networks brings. In recent studies, Deep Neural Networks (DNNs) have been employed to address this challenge. However, these methods frequently encounter issues regarding fairness and computational inefficiency when making decisions, rendering them unsuitable for future dynamic services that depend heavily on the participation of each individual user. To address this gap, this paper focuses on the challenge of transmit power allocation in wireless networks, aiming to optimize $α$-fairness to balance network utilization and user equity. We introduce a novel approach utilizing Kolmogorov-Arnold Networks (KANs), a class of machine learning models that offer low inference costs compared to traditional DNNs through superior explainability. The study provides a comprehensive problem formulation, establishing the NP-hardness of the power allocation problem. Then, two algorithms are proposed for dataset generation and decentralized KAN training, offering a flexible framework for achieving various fairness objectives in dynamic 6G environments. Extensive numerical simulations demonstrate the effectiveness of our approach in terms of fairness and inference cost. The results underscore the potential of KANs to overcome the limitations of existing DNN-based methods, particularly in scenarios that demand rapid adaptation and fairness.