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

• URL: http://arxiv.org/abs/2411.01924v1
• Date: Mon, 04 Nov 2024 09:40:47 GMT
• Title: Fairness-Utilization Trade-off in Wireless Networks with Explainable Kolmogorov-Arnold Networks
• Authors: Masoud Shokrnezhad, Hamidreza Mazandarani, Tarik Taleb,
• Abstract summary: 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. Two algorithms are proposed for dataset generation and decentralized KAN training, offering a flexible framework for achieving various fairness objectives.
• Score: 14.51946231794179
• License:
• 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.

Related papers

• The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning [6.9569682335746235]
  Spiking Neural Networks (SNNs) have recently gained significant interest in on-chip learning in embedded devices. In this paper, we explore the inherent robustness of SNNs under noisy communication in Federated Learning. We propose a novel Federated Learning with TopK Sparsification algorithm to reduce the bandwidth usage for FL training.
  arXiv  Detail & Related papers  (2024-09-19T13:37:18Z)
• GNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless Networks [9.031738020845586]
  This article proposes a GNN-based algorithm to address the joint resource allocation problem in heterogeneous wireless networks. Our proposed algorithm achieves satisfactory performance but with higher computational efficiency compared to traditional optimisation algorithms.
  arXiv  Detail & Related papers  (2024-07-28T04:51:00Z)
• Graph Neural Networks-Based User Pairing in Wireless Communication Systems [0.34410212782758043]
  We propose an unsupervised graph neural network (GNN) approach to efficiently solve the user pairing problem. At 20 dB SNR, our proposed approach achieves a 49% better sum rate than k-means and a staggering 95% better sum rate than SUS.
  arXiv  Detail & Related papers  (2023-05-14T11:57:42Z)
• Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes [10.150768420975155]
  Due to mutual interference between users, power allocation problems in wireless networks are often non-trivial. Graph Graph neural networks (GNNs) have recently emerged as a promising approach tackling these problems and an approach exploits underlying topology of wireless networks.
  arXiv  Detail & Related papers  (2023-03-27T10:59:09Z)
• Adaptive Target-Condition Neural Network: DNN-Aided Load Balancing for Hybrid LiFi and WiFi Networks [19.483289519348315]
  Machine learning has the potential to provide a complexity-friendly load balancing solution. The state-of-the-art (SOTA) learning-aided LB methods need retraining when the network environment changes. A novel deep neural network (DNN) structure named adaptive target-condition neural network (A-TCNN) is proposed.
  arXiv  Detail & Related papers  (2022-08-09T20:46:13Z)
• Learning Resilient Radio Resource Management Policies with Graph Neural Networks [124.89036526192268]
  We formulate a resilient radio resource management problem with per-user minimum-capacity constraints. We show that we can parameterize the user selection and power control policies using a finite set of parameters. Thanks to such adaptation, our proposed method achieves a superior tradeoff between the average rate and the 5th percentile rate.
  arXiv  Detail & Related papers  (2022-03-07T19:40:39Z)
• Learning Autonomy in Management of Wireless Random Networks [102.02142856863563]
  This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes. We develop a flexible deep neural network formalism termed distributed message-passing neural network (DMPNN) with forward and backward computations independent of the network topology.
  arXiv  Detail & Related papers  (2021-06-15T09:03:28Z)
• Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks [52.32646357164739]
  We propose a deep neural network (DNN) to solve the solutions of the optimal power flow (ACOPF) The proposed SIDNN is compatible with a broad range of OPF schemes. It can be seamlessly integrated in other learning-to-OPF schemes.
  arXiv  Detail & Related papers  (2021-03-27T00:45:23Z)
• Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks [119.81868223344173]
  This paper investigates the optimal resource allocation in free space optical (FSO) fronthaul networks. We consider the graph neural network (GNN) for the policy parameterization to exploit the FSO network structure. The primal-dual learning algorithm is developed to train the GNN in a model-free manner, where the knowledge of system models is not required.
  arXiv  Detail & Related papers  (2020-06-26T14:20:48Z)
• Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G [53.23237216769839]
  We develop a deep learning framework to approximate the optimal resource allocation policy for base stations. We find that a fully-connected neural network (NN) cannot fully guarantee the requirements due to the approximation errors and quantization errors of the numbers of subcarriers. Considering that the distribution of wireless channels and the types of services in the wireless networks are non-stationary, we apply deep transfer learning to update NNs in non-stationary wireless networks.
  arXiv  Detail & Related papers  (2020-03-29T04:48:22Z)
• Wireless Power Control via Counterfactual Optimization of Graph Neural Networks [124.89036526192268]
  We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating over a single shared wireless medium. To mitigate the interference among concurrent transmissions, we leverage the network topology to create a graph neural network architecture. We then use an unsupervised primal-dual counterfactual optimization approach to learn optimal power allocation decisions.
  arXiv  Detail & Related papers  (2020-02-17T07:54:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.