A GNN-Based Supervised Learning Framework for Resource Allocation in Wireless IoT Networks
TL;DR: In this paper , a graph neural network (GNN)-based framework is proposed to address the problem of resource allocation for D2D resource allocation in the Internet of Things (IoT).
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Abstract: The Internet of Things (IoT) allows physical devices to be connected over the wireless networks. Although device-to-device (D2D) communication has emerged as a promising technology for IoT, the conventional solutions for D2D resource allocation are usually computationally complex and time consuming. The high complexity poses a significant challenge to the practical implementation of wireless IoT networks. A graph neural network (GNN)-based framework is proposed to address this challenge in a supervised manner. Specifically, the wireless network is modeled as a directed graph, where the desirable communication links are modeled as nodes and the harmful interference links are modeled as edges. The effectiveness of the proposed framework is verified via two case studies, namely the link scheduling in D2D networks and the joint channel and power allocation in D2D underlaid cellular networks. Simulation results demonstrate that the proposed framework outperforms the benchmark schemes in terms of the average sum rate and the sample efficiency. In addition, the proposed GNN approach shows potential generalizability to different system settings and robustness to the corrupted input features. It also accelerates the D2D resource optimization by reducing the execution time to only a few milliseconds.
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References
•Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
- 01 Jan 2015
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
138.5K
The Graph Neural Network Model
TL;DR: A new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains, and implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space.
•Proceedings Article
How Powerful are Graph Neural Networks
Keyulu Xu,Weihua Hu,Jure Leskovec,Stefanie Jegelka +3 more
- 01 Oct 2018
TL;DR: In this paper, the expressive power of GNNs to capture different graph structures is analyzed and a simple architecture for graph representation learning is proposed. But the results characterize the discriminative power of popular GNN variants and show that they cannot learn to distinguish certain simple graph structures.
A Tutorial on the Cross-Entropy Method
TL;DR: This tutorial presents the CE methodology, the basic algorithm and its modifications, and discusses applications in combinatorial optimization and machine learning.
A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks
TL;DR: In this paper, a joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm.
1.2K