Literature DB >> 35957333

Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications.

Antoni Ivanov1, Krasimir Tonchev1, Vladimir Poulkov1, Agata Manolova1, Nikolay N Neshov1.   

Abstract

Resource allocation (RA) has always had a prominent place in wireless communications research due to its significance for network throughput maximization, and its inherent complexity. Concurrently, graph-based solutions for RA have also grown in importance, providing opportunities for higher throughput and efficiency due to their representational capabilities, as well as challenges for realizing scalable algorithms. This article presents a comprehensive review and analysis of graph-based RA methods in three major wireless network types: cellular homogeneous and heterogeneous, device-to-device, and cognitive radio networks. The main design characteristics, as well as directions for future research, are provided for each of these categories. On the basis of this review, the concept of Graph-based Resource allocation for Integrated Space and Terrestrial communications (GRIST) is proposed. It describes the inter-connectivity and coexistence of various terrestrial and non-terrestrial networks via a hypergraph and its attributes. In addition, the implementation challenges of GRIST are explained in detail. Finally, to complement GRIST, a scheme for determining the appropriate balance between different design considerations is introduced. It is described via a simplified complete graph-based design process for resource management algorithms.

Entities:  

Keywords:  6G; cellular networks; cognitive radio; device-to-device networks; graph; heterogeneous networks; resource allocation; wireless communications

Mesh:

Year:  2022        PMID: 35957333      PMCID: PMC9371046          DOI: 10.3390/s22155778

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.847


  3 in total

1.  A Graph Convolutional Network-Based Deep Reinforcement Learning Approach for Resource Allocation in a Cognitive Radio Network.

Authors:  Di Zhao; Hao Qin; Bin Song; Beichen Han; Xiaojiang Du; Mohsen Guizani
Journal:  Sensors (Basel)       Date:  2020-09-13       Impact factor: 3.576

2.  The PRISMA 2020 statement: an updated guideline for reporting systematic reviews.

Authors:  Matthew J Page; Joanne E McKenzie; Patrick M Bossuyt; Isabelle Boutron; Tammy C Hoffmann; Cynthia D Mulrow; Larissa Shamseer; Jennifer M Tetzlaff; Elie A Akl; Sue E Brennan; Roger Chou; Julie Glanville; Jeremy M Grimshaw; Asbjørn Hróbjartsson; Manoj M Lalu; Tianjing Li; Elizabeth W Loder; Evan Mayo-Wilson; Steve McDonald; Luke A McGuinness; Lesley A Stewart; James Thomas; Andrea C Tricco; Vivian A Welch; Penny Whiting; David Moher
Journal:  Syst Rev       Date:  2021-03-29

Review 3.  A Comprehensive Survey on Graph Neural Networks.

Authors:  Zonghan Wu; Shirui Pan; Fengwen Chen; Guodong Long; Chengqi Zhang; Philip S Yu
Journal:  IEEE Trans Neural Netw Learn Syst       Date:  2021-01-04       Impact factor: 10.451

  3 in total

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