Fairness-Utilization Trade-off in Wireless Networks with Explainable Kolmogorov-Arnold Networks

TL;DR

This paper introduces Kolmogorov-Arnold Networks (KANs) for power allocation in 6G wireless networks, balancing fairness and utilization with explainability and low inference costs, outperforming traditional deep learning methods.

Contribution

The paper proposes a novel KAN-based framework for fair power allocation in wireless networks, addressing fairness, efficiency, and explainability challenges of existing DNN approaches.

Findings

KANs achieve lower inference costs than traditional DNNs.

The approach effectively balances fairness and network utilization.

Numerical simulations validate the method's superiority in dynamic environments.

Abstract

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 $\alpha$-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.