Intelligent Resource Allocation Methods for Next Generation Wireless Cellular Systems

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: 15 July 2024 | Viewed by 600

Special Issue Editors


E-Mail Website
Guest Editor
Department of Electronic Engineering and Computer Science, Hong Kong Metropolitan University, Hong Kong SAR 999077, China
Interests: intelligent wireless communications; digital twin; MEC; IoT

E-Mail Website
Guest Editor
School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Interests: broadband wireless communications; including resource management; non-orthogonal multiple access; reconfigurable intelligent surface; small cells; interference management

E-Mail Website
Guest Editor
School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519000, China
Interests: URLLC; semantic communications; integrated sensing and communication; hybrid automatic repeat request; non-orthogonal multiple access; short-packet communications; intelligent reflecting surface; Internet of Things

E-Mail Website
Guest Editor
Department of Electronic Engineering and Computer Science, Hong Kong Metropolitan University, Hong Kong SAR 999077, China
Interests: IoT; uav networks; space-air-ground integrated networks

Special Issue Information

Dear Colleagues,

As wireless cellular networks continue to evolve and embrace new technologies, the efficient allocation of resources becomes not only crucial but also necessary to meet the ever-increasing demands for higher data rates, improved quality of service, and ultra-high reliable connectivity. The effective management and allocation of wireless cellular network resources, including spectrum, power, and computational resources, are essential for meeting these demands and ensuring optimal network performance.

This Special Issue on "Intelligent Resource Allocation Methods for Next-Generation Wireless Cellular Networks" aims to address this critical need by exploring novel resource allocation methods that leverage intelligent techniques. By harnessing the power of artificial intelligence, machine learning, optimization algorithms, and other intelligent approaches, researchers and practitioners can optimize resource allocation in next-generation wireless cellular networks.

The articles featured in this Special Issue cover a wide range of topics, including but not limited to:

  • Machine learning-based resource allocation algorithms;
  • Optimization models for resource allocation;
  • Dynamic spectrum allocation techniques;
  • Energy-efficient resource allocation strategies;
  • Joint resource allocation schemes for heterogeneous networks;
  • Resource management for advanced wireless techniques like intelligent reflecting surface (IRS), massive MIMO, non-orthogonal multiple access (NOMA), rate-splitting multiple access (RSMA), unmanned aerial vehicle (UAV), multi-access computing (MEC), and wireless edge caching;
  • Resource allocation for wireless digital twin networks;
  • Resource management for the sixth-generation wireless communication networks (6G);
  • Resource allocation for generative artificial intelligence-enabled 6G networks.

Dr. Yaru Fu
Dr. Hong Wang
Dr. Zheng Shi
Dr. Yalin Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital twin
  • Internet-of-Things
  • intelligent reflecting surface (IRS)
  • massive MIMO
  • non-orthogonal multiple access (NOMA)
  • rate-splitting multiple access (RSMA)
  • sixth-generation wireless communication networks (6G)
  • unmanned aerial vehicle (UAV)

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

19 pages, 859 KiB  
Article
A Joint Optimization Algorithm for UAV Location and Offloading Decision Based on Wireless Power Supply
by Shuo Sun and Qi Zhu
Electronics 2024, 13(12), 2320; https://doi.org/10.3390/electronics13122320 - 13 Jun 2024
Viewed by 241
Abstract
In this paper, a joint optimization algorithm of offloading decision, energy harvesting time, and unmanned aerial vehicle (UAV) location is proposed for user equipment (UEs)’s task completion latency problem in a communication–sensing–computing integration scenario with wireless energy supply. Under the constraints of causality [...] Read more.
In this paper, a joint optimization algorithm of offloading decision, energy harvesting time, and unmanned aerial vehicle (UAV) location is proposed for user equipment (UEs)’s task completion latency problem in a communication–sensing–computing integration scenario with wireless energy supply. Under the constraints of causality of energy harvesting consumption by the UEs and conditional mutual information, the total latency minimization problem of the UEs is established. Firstly, the optimization variables of the problem are transformed from three variables of offloading decision, energy harvesting time, and UAV location to two variables of offloading decision and UAV location by means of the derived closed expression, and then the transformed optimization problem is decomposed into the offloading decision optimization sub-problem and the UAV location optimization sub-problem to be solved alternately and iteratively. The genetic algorithm is employed to tackle the optimization sub-problem of offloading decisions, and the successive convex approximation algorithm is applied to the drone positioning optimization sub-problem. Simulation results show that the proposed algorithm in this paper reduces the average task completion latency by 35 percent and 15 percent, respectively, compared to the two baseline algorithms for different numbers of UEs. Full article
Back to TopTop