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.
Abstract (translated)
有效的用户传输功率分配对于实现6G无线网络带来的重大进展至关重要。在最近的研究中，深度神经网络（DNN）已被用来解决这一挑战。然而，这些方法在做决策时经常遇到公平性和计算效率低下的问题，这使得它们不适合未来依赖每个个体用户参与的动态服务。为了解决这个差距，本文专注于无线网络中的传输功率分配挑战，旨在通过优化$\alpha$-公平性来平衡网络利用率和用户体验公平性。我们提出了一种新颖的方法，利用Kolmogorov-Arnold网络（KANs），这是一类机器学习模型，与传统的DNN相比，它们提供了更低的推理成本，并具有更好的可解释性。该研究提供了一个全面的问题定义，确立了功率分配问题的NP-hard性质。接着，提出了两个算法用于数据集生成和分散式KAN训练，为在动态6G环境中实现多种公平目标提供了一个灵活框架。大量的数值模拟证明了我们在公平性和推理成本方面的有效性。结果强调了KANs克服现有DNN方法限制的潜力，特别是在需要快速适应和保证公平性的场景中。
URL
https://arxiv.org/abs/2411.01924
