Decentralized Channel Management in WLANs with Graph Neural Networks

• URL: http://arxiv.org/abs/2210.16949v1
• Date: Sun, 30 Oct 2022 21:14:45 GMT
• Title: Decentralized Channel Management in WLANs with Graph Neural Networks
• Authors: Zhan Gao and Yulin Shao and Deniz Gunduz and Amanda Prorok
• Abstract summary: Wireless local area networks (WLANs) manage multiple access points (APs) and assign radio frequency to APs for satisfying traffic demands. This paper puts forth a learning-based solution that can be implemented in a decentralized manner.
• Score: 17.464353263281907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wireless local area networks (WLANs) manage multiple access points (APs) and assign scarce radio frequency resources to APs for satisfying traffic demands of associated user devices. This paper considers the channel allocation problem in WLANs that minimizes the mutual interference among APs, and puts forth a learning-based solution that can be implemented in a decentralized manner. We formulate the channel allocation problem as an unsupervised learning problem, parameterize the control policy of radio channels with graph neural networks (GNNs), and train GNNs with the policy gradient method in a model-free manner. The proposed approach allows for a decentralized implementation due to the distributed nature of GNNs and is equivariant to network permutations. The former provides an efficient and scalable solution for large network scenarios, and the latter renders our algorithm independent of the AP reordering. Empirical results are presented to evaluate the proposed approach and corroborate theoretical findings.

Related papers

• Unsupervised Graph-based Learning Method for Sub-band Allocation in 6G Subnetworks [2.0583251142940377]
  We present an unsupervised approach for frequency sub-band allocation in wireless networks using graph-based learning. We model the subnetwork deployment as a conflict graph and propose an unsupervised learning approach inspired by the graph colouring and the Potts model to optimize the sub-band allocation.
  arXiv  Detail & Related papers  (2023-12-13T12:57:55Z)
• Decentralized Learning over Wireless Networks: The Effect of Broadcast with Random Access [56.91063444859008]
  We investigate the impact of broadcast transmission and probabilistic random access policy on the convergence performance of D-SGD. Our results demonstrate that optimizing the access probability to maximize the expected number of successful links is a highly effective strategy for accelerating the system convergence.
  arXiv  Detail & Related papers  (2023-05-12T10:32:26Z)
• State-Augmented Learnable Algorithms for Resource Management in Wireless Networks [124.89036526192268]
  We propose a state-augmented algorithm for solving resource management problems in wireless networks. We show that the proposed algorithm leads to feasible and near-optimal RRM decisions.
  arXiv  Detail & Related papers  (2022-07-05T18:02:54Z)
• 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)
• Low Complexity Channel estimation with Neural Network Solutions [1.0499453838486013]
  We deploy a general residual convolutional neural network to achieve channel estimation in a downlink scenario. Compared with other deep learning methods for channel estimation, our results suggest improved mean squared error computation.
  arXiv  Detail & Related papers  (2022-01-24T19:55:10Z)
• 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)
• 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)
• Decentralized Learning for Channel Allocation in IoT Networks over Unlicensed Bandwidth as a Contextual Multi-player Multi-armed Bandit Game [134.88020946767404]
  We study a decentralized channel allocation problem in an ad-hoc Internet of Things network underlaying on the spectrum licensed to a primary cellular network. Our study maps this problem into a contextual multi-player, multi-armed bandit game, and proposes a purely decentralized, three-stage policy learning algorithm through trial-and-error.
  arXiv  Detail & Related papers  (2020-03-30T10:05:35Z)

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.