applsci-logo

Journal Browser

Journal Browser

Application of Reinforcement Learning in Wireless Network

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 3592

Special Issue Editor


E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang 43400, Malaysia
Interests: Internet of Things; iots; cyber physical systems; smart home; human activity recognition; embedded systems

Special Issue Information

Dear Colleagues,

Nowadays, wireless networking will transform from “connecting things” to “connecting intelligence”. Technologies evolve all the time and new communication systems emerge quickly, due to the future demands of IoT/5G communications. This Special Issue aims to exploit the new opportunities of reinforcement learning for future wireless networks by collecting new ideas, the latest findings, state-of-the-art results, and comprehensive surveys of reinforcement learning. The topics in focus include, but are not limited to:

  • 5G
  • mobile communication
  • wireless communications
  • machine learning
  • reinforcement learning
  • wireless network
  • intelligence computing
  • big data 

Dr. Thinagaran Perumal
Guest Editor

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. Applied Sciences 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

  • AI/ML
  • modelling approach
  • 6G networks
  • IoT
  • channel coding

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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

Research

23 pages, 733 KiB  
Article
AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
by Yuxing Wang, Nan Liu, Zhiwen Pan and Xiaohu You
Appl. Sci. 2023, 13(22), 12505; https://doi.org/10.3390/app132212505 - 20 Nov 2023
Cited by 1 | Viewed by 1531
Abstract
Network slicing is a key technology for 5G networks, which divides the traditional physical network into multiple independent logical networks to meet the diverse requirements of end-users. This paper focuses on the resource allocation problem in the scenario where public and non-public network [...] Read more.
Network slicing is a key technology for 5G networks, which divides the traditional physical network into multiple independent logical networks to meet the diverse requirements of end-users. This paper focuses on the resource allocation problem in the scenario where public and non-public network slices coexist. There are two kinds of resources to be allocated: one is the resource blocks (RBs) allocated to the users in the radio access network, and the other is the server resources in the core network. We first formulate the above resource allocation problem as a nonlinear integer programming problem by maximizing the operator profit as the objective function. Then, a combination of deep reinforcement learning (DRL) and machine learning (ML) algorithms are used to solve this problem. DRL, more specifically, independent proximal policy optimization (IPPO), is employed to provide the RB allocation scheme that makes the objective function as large as possible. ML, more specifically, random forest (RF), assists DRL agents in receiving fast reward feedback by determining whether the allocation scheme is feasible. The simulation results show that the IPPO-RF algorithm has good performance, i.e., not only are all the constraints satisfied, but the requirements of the non-public network slices are ensured. Full article
(This article belongs to the Special Issue Application of Reinforcement Learning in Wireless Network)
Show Figures

Figure 1

18 pages, 21994 KiB  
Article
Joint Optimization of Service Fairness and Energy Consumption for 3D Trajectory Planning in Multiple Solar-Powered UAV Systems
by Shuhan Cai and Junbin Liang
Appl. Sci. 2023, 13(8), 5136; https://doi.org/10.3390/app13085136 - 20 Apr 2023
Viewed by 1438
Abstract
In this paper, we study the three-dimensional (3D) trajectory optimization problems of unmanned aerial vehicles (UAV) with a solar energy supply, aiming to provide communication coverage for mobile users on the ground. In general, the higher UAVs fly, the more solar energy they [...] Read more.
In this paper, we study the three-dimensional (3D) trajectory optimization problems of unmanned aerial vehicles (UAV) with a solar energy supply, aiming to provide communication coverage for mobile users on the ground. In general, the higher UAVs fly, the more solar energy they collect, but the smaller the range of coverage they could achieve, and vice versa. How to plan optimal trajectories for UAVs so that more users can be encompassed, while allowing UAVs to collect enough solar energy, is a challenging issue. Moreover, we also consider how geographically fair coverage for each ground user can be achieved. To solve these problems, we designed a multiple solar-powered UAV (SP-UAV) energy consumption model and a fairness model, while designed an observation space, state space, action space, and reward function. Then, we proposed a multiple SP-UAV 3D trajectory optimization algorithm based on deep reinforcement learning (DRL). Our algorithm is able to balance the energy consumption of UAVs to extend the system’s lifetime, while avoiding both collisions and flying out of communication range. Finally, we trained our model through simulation experiments and conducted comparative experiments and analysis based on real network topology data. The results show that our algorithm is superior to the existing typical algorithms in coverage, fairness, and lifetime. Full article
(This article belongs to the Special Issue Application of Reinforcement Learning in Wireless Network)
Show Figures

Figure 1

Back to TopTop