Black-Box and Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks

• URL: http://arxiv.org/abs/2108.13178v1
• Date: Wed, 4 Aug 2021 13:06:36 GMT
• Title: Black-Box and Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks
• Authors: Ivana Nikoloska and Osvaldo Simeone
• Abstract summary: We consider the problem of power control for a wireless network with an arbitrarily time-varying topology. A data-driven design methodology that leverages graph neural networks (GNNs) is adopted. We propose both black-box and modular meta-learning techniques.
• Score: 39.59987601426039
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we consider the problem of power control for a wireless network with an arbitrarily time-varying topology, including the possible addition or removal of nodes. A data-driven design methodology that leverages graph neural networks (GNNs) is adopted in order to efficiently parametrize the power control policy mapping the channel state information (CSI) to transmit powers. The specific GNN architecture, known as random edge GNN (REGNN), defines a non-linear graph convolutional filter whose spatial weights are tied to the channel coefficients. While prior work assumed a joint training approach whereby the REGNN-based policy is shared across all topologies, this paper targets adaptation of the power control policy based on limited CSI data regarding the current topology. To this end, we propose both black-box and modular meta-learning techniques. Black-box meta-learning optimizes a general-purpose adaptation procedure via (stochastic) gradient descent, while modular meta-learning finds a set of reusable modules that can form components of a solution for any new network topology. Numerical results validate the benefits of meta-learning for power control problems over joint training schemes, and demonstrate the advantages of modular meta-learning when data availability is extremely limited.

Related papers

• Learning State-Augmented Policies for Information Routing in Communication Networks [92.59624401684083]
  We develop a novel State Augmentation (SA) strategy to maximize the aggregate information at source nodes using graph neural network (GNN) architectures. We leverage an unsupervised learning procedure to convert the output of the GNN architecture to optimal information routing strategies. In the experiments, we perform the evaluation on real-time network topologies to validate our algorithms.
  arXiv  Detail & Related papers  (2023-09-30T04:34:25Z)
• Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach [36.32772317151467]
  Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. The dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalability and high training costs. In this paper, a novel link-output GNN (LOGNN)-based resource management approach is proposed to flexibly optimize the resource allocation in MEC.
  arXiv  Detail & Related papers  (2023-06-15T08:21:41Z)
• Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks [27.600081147252155]
  We develop a novel graph sumable framework to maximize energy efficiency in wireless communication networks. We show the permutation training which is a desirable property for models of wireless network data. Results demonstrate its generalizability across different network topologies.
  arXiv  Detail & Related papers  (2022-01-27T20:23:24Z)
• Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph Learning [65.08818785032719]
  Graph Networks (GNNs) have achieved great success in processing graph data by extracting and propagating structure-aware features. Recently the field has advanced from local propagation schemes that focus on local neighbors towards extended propagation schemes that can directly deal with extended neighbors consisting of both local and high-order neighbors. Despite the impressive performance, existing approaches are still insufficient to build an efficient and learnable extended propagation scheme that can adaptively adjust the influence of local and high-order neighbors.
  arXiv  Detail & Related papers  (2021-12-02T10:35:33Z)
• 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)
• Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks [39.59987601426039]
  This paper studies the higher-level problem of enabling fast adaption of the power control policy to time-varying topologies. We apply first-order meta-learning on data from multiple topologies with the aim of optimizing for a few-shot adaptation to new network configurations.
  arXiv  Detail & Related papers  (2021-05-02T12:43:10Z)
• Learning Power Control for Cellular Systems with Heterogeneous Graph Neural Network [37.060397377445504]
  We show that the power control policy has a combination of different PI and PE properties, and existing HetGNN does not satisfy these properties. We design a parameter sharing scheme for HetGNN such that the learned relationship satisfies the desired properties.
  arXiv  Detail & Related papers  (2020-11-06T02:41:38Z)
• Policy-GNN: Aggregation Optimization for Graph Neural Networks [60.50932472042379]
  Graph neural networks (GNNs) aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors. It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features. We propose Policy-GNN, a meta-policy framework that models the sampling procedure and message passing of GNNs into a combined learning process.
  arXiv  Detail & Related papers  (2020-06-26T17:03:06Z)
• 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)

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.