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: 30 June 2024 | Viewed by 1990

Special Issue 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

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Keywords

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

Published Papers (2 papers)

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Research

23 pages, 733 KiB  
Article
AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
Appl. Sci. 2023, 13(22), 12505; https://doi.org/10.3390/app132212505 - 20 Nov 2023
Viewed by 621
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)
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18 pages, 21994 KiB  
Article
Joint Optimization of Service Fairness and Energy Consumption for 3D Trajectory Planning in Multiple Solar-Powered UAV Systems
Appl. Sci. 2023, 13(8), 5136; https://doi.org/10.3390/app13085136 - 20 Apr 2023
Viewed by 999
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)
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