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).
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
•Posted Content
Graph-based Deep Learning for Communication Networks: A Survey.
TL;DR: A recent survey of graph-based deep learning methods for communication networks is presented in this article. But the focus of this survey is not on the application of deep learning in communication networks.
170
AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions
Sundas Iftikhar,Sukhpal Singh Gill,Chenghao Song,Minxian Xu,Mohammad Sadegh Aslanpour,Adel Nadjaran Toosi,Junhui Du,Huaming Wu,Shreya Ghosh,Deepraj Chowdhury,Muhammed Golec,Mohit Kumar,Ahmed M. Abdelmoniem,Felix Cuadrado,Blesson Varghese,Omer Rana,Schahram Dustdar,Steve Uhlig +17 more
TL;DR: In this article , the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments are analyzed using a systematic literature review (SLR).
Five Facets of 6G: Research Challenges and Opportunities
TL;DR: In this paper , the authors provide a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, and applications, as well as designs, and advocate a further evolutionary step toward multi-component Pareto optimization.
Innovative Trends in the 6G Era: A Comprehensive Survey of Architecture, Applications, Technologies, and Challenges
01 Jan 2023
TL;DR: In this article , the authors identify a complete picture of changes in architectures, technologies, and challenges that will shape the 6G network, and they hope the research results will provide indications for further studies on 6G ecosystems.
74
Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions
TL;DR: In this paper , the authors presented the state-of-the-art in quantum computing and provided comprehensive overviews through machine learning approaches for their applications in the 6G networks.
References
A Graph-coloring based resource allocation algorithm for D2D communication in cellular networks
Xuejia Cai,Jun Zheng,Yuan Zhang +2 more
- 08 Jun 2015
TL;DR: Simulation results show that the proposed GOAL algorithm can significantly improve the system capacity and accommodate more D2D users.
77
Optimal power allocation for two-cell sum rate maximization under minimum rate constraints
Chung Shue Chen,Geir E. Oien +1 more
- 30 Dec 2008
TL;DR: The proposed scheme outperforms in both the outage probability and sum rate performance generally and provides a generalization of the binary power control (BPC) proposed in this paper.
34
Accelerating Generalized Benders Decomposition for Wireless Resource Allocation
TL;DR: This work proposes to leverage machine learning (ML) techniques to accelerate GBD aiming at decreasing the complexity of the master problem, and utilizes two different ML techniques, classification and regression, to deal with this acceleration task.
32
Near-Optimal Joint Antenna Selection for Amplify-and-Forward Relay Networks
TL;DR: A low-complexity near-optimal joint antenna selection algorithm based on a constrained cross entropy optimization (CCEO) method to maximize the achievable rate and the convergence is guaranteed and it is illustrated that the proposed CCEO algorithm can always achieve near-Optimal results regardless of the number of selected antennas, outage probabilities and the signal-to-noise ratios at the terminals.
21