Online Learning Aided Solutions for 6G Wireless Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 3724

Special Issue Editors


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Guest Editor
Computational learning theory team, RIKEN-Advanced Intelligence Center, Fukuoka 819-0395 Japan
Interests: wireless communications; machine learning; online learning; 5G, B5G, and 6G systems; image processing; millimeter waves; RIS systems; the Internet of things
Special Issues, Collections and Topics in MDPI journals

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Co-Guest Editor
Department of Electrical Engineering, Prince Sattam bin Abdulaziz University, Wadi Addwasir 11991, Saudi Arabia
Interests: 5G, 5G+; 6G wireless communication system; artificial intelligence application in wireless communications; millimeter wave communications; MIMO systems; underwater communications; optical communications
Special Issues, Collections and Topics in MDPI journals

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Co-Guest Editor
Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
Interests: cybersecurity; artificial intelligence (AI); internet of things (IoT); smart grids; 5G/6G networks; vehicular networks; communication networks; image processing; signal processing; smart healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The evolving Sixth Generation (6G) wireless networks will provide 100 to 1000 Gbps rates and ultra-low latency of 1 millisecond using native, embedded Artificial Intelligence (AI) capability to support myriad services, such as Holographic Type Communication (HTC), tactile Internet, remote surgery, etc. However, these services demand ultra-reliability, which is highly impacted by the dynamically changing environment of 6G heterogeneous tiny cells, whereby static AI-based solutions fitting all scenarios and devices are impractical. Therefore, online learning-based solutions are proper ones for future 6G networks due to their self-decision making and adaptability to the time-varying wireless communication environment. This can be done by smartly leveraging online-based algorithms such as statistical learning theory, online convex optimization (OCO), multi-armed Bandits (MABs) with their different categories, game theory, and online prediction techniques, to carefully handle different 6G challenges. This talented low cost/fast converging learning methodology motivates researchers and practitioners to apply it and bound its performance in various future wireless communication systems, including Millimeter-Wave/TeraHertz (mmWave/THz) communications, D2D communications, NOMA based systems, Physical Layer Security (PLS), Unmanned Aerial Vehicles (UAV) communications, Cognitive Radio (CR) systems, Reconfigurable Intelligent Systems (RIS), Wireless Power Transfer, etc. Online learning-based algorithms, including online supervised learning, online unsupervised learning, reinforcement learning like MABs, meta-learning, transfer learning, federated learning, and adaptive learning can provide smart/low complexity solutions for learning the 6G channel, optimizing the latency, finding the best neighbor for relaying the BS signal, finding the best device in D2D system, optimizing the trajectory of the UAV, optimizing the performance of RIS-aided wireless communications, optimizing beam directions in mmWave/THz system, improving wireless power transfer, etc. Thus, the focus of this SI is to show the positivity of online learning-based solutions for the challenges of future 6G wireless communication networks by attracting a lot of high-quality submissions.

This Special Issue (SI) is soliciting original technical papers addressing the main research challenges in the direction of applying online learning aided solutions for handling future 6G networks, including, but not being limited to the following topics:

  • Online learning-based solutions for mmWave/THz channel estimation.
  • Online learning-based solutions for UAV -based wireless communications.
  • Online learning-based solutions for device-to-device (D2D) communications.
  • Online learning-based solutions for cognitive radio systems.
  • Online learning-based solutions for 6G enhanced IoT networks.
  • Online learning-based solutions for wireless power transfer systems.
  • Online learning-based solutions for mobile/edge computing.
  • Online learning-based solutions for V2V, V2I, and V2X scenarios.
  • Online learning-based solutions for RIS-aided wireless communication networks.
  • Online-based wireless resource allocation and mobility management in 6G networks.
  • Online aided energy harvesting, power control, and wireless power transfer channel estimation and prediction solutions.
  • Self-learning-based multi-access and modulation (NOMA, OTFS, SCMA, etc.).
  • Online learning solutions for 6G ultra-high reliability and low-latency communications (URLLC).
  • Online learning-based solutions for Interference avoidance, management, and cancellation techniques in 6G networks.
  • Self-learning aided new communications and network technologies in 6G, such as visible light communication (VLC), optical networks, and aerial access networks.
  • Online learning solutions for efficient computation offloading technologies in 6G networks.
  • Online learning-based solutions for wireless spectrum sensing, localization, and signal processing in 6G networks.
  • Communication-efficient online learning techniques (such as transfer learning, federated learning, online un/supervised learning, reinforcement learning, and meta-learning).
  • Online enhanced security and privacy issues in 6G communication networks.

Dr. Sherief Hashima
Dr. Ehab Mahmoud Mohamed
Dr. Mostafa Fouda
Guest Editors

Manuscript Submission Information

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Keywords

  • 6G
  • online learning
  • MAB
  • D2D communications
  • CR
  • UAV communications
  • RIS
  • NOMA
  • V2X communications

Published Papers (2 papers)

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Research

13 pages, 377 KiB  
Article
Predicting Path Loss of an Indoor Environment Using Artificial Intelligence in the 28-GHz Band
by Saud Alhajaj Aldossari
Electronics 2023, 12(3), 497; https://doi.org/10.3390/electronics12030497 - 18 Jan 2023
Cited by 4 | Viewed by 1268
Abstract
The propagation of signal and its strength in an indoor area have become crucial in the era of fifth-generation (5G) and beyond-5G communication systems, which use high bandwidth. High millimeter wave (mmWave) frequencies present a high signal loss and low signal strength, particularly [...] Read more.
The propagation of signal and its strength in an indoor area have become crucial in the era of fifth-generation (5G) and beyond-5G communication systems, which use high bandwidth. High millimeter wave (mmWave) frequencies present a high signal loss and low signal strength, particularly during signal propagation in indoor areas. It is considerably difficult to design indoor wireless communication systems through deterministic modeling owing to the complex nature of the construction materials and environmental changes caused by human interactions. This study presents a methodology of data-driven techniques that will be applied to predict path loss using artificial intelligence. The proposed methodology enables the prediction of signal loss in an indoor environment with an accuracy of 97.4%. Full article
(This article belongs to the Special Issue Online Learning Aided Solutions for 6G Wireless Networks)
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18 pages, 1072 KiB  
Article
Distribution of Multi MmWave UAV Mounted RIS Using Budget Constraint Multi-Player MAB
by Ehab Mahmoud Mohamed, Mohammad Alnakhli, Sherief Hashima and Mohamed Abdel-Nasser
Electronics 2023, 12(1), 12; https://doi.org/10.3390/electronics12010012 - 20 Dec 2022
Cited by 10 | Viewed by 1447
Abstract
Millimeter wave (mmWave), reconfigurable intelligent surface (RIS), and unmanned aerial vehicles (UAVs) are considered vital technologies of future six-generation (6G) communication networks. In this paper, various UAV mounted RIS are distributed to support mmWave coverage over several hotspots where numerous users exist in [...] Read more.
Millimeter wave (mmWave), reconfigurable intelligent surface (RIS), and unmanned aerial vehicles (UAVs) are considered vital technologies of future six-generation (6G) communication networks. In this paper, various UAV mounted RIS are distributed to support mmWave coverage over several hotspots where numerous users exist in harsh blockage environment. UAVs should be spread among the hotspots to maximize their average achievable data rates while minimizing their hovering and flying energy consumptions. To efficiently address this non-polynomial time (NP) problem, it will be formulated as a centralized budget constraint multi-player multi-armed bandit (BCMP-MAB) game. In this formulation, UAVs will act as the players, the hotspots as the arms, and the achievable sum rates of the hotspots as the profit of the MAB game. This formulated MAB problem is different from the traditional one due to the added constraints of the limited budget of UAVs batteries as well as collision avoidance among UAVs, i.e., a hotspot should be covered by only one UAV at a time. Numerical analysis of different scenarios confirm the superior performance of the proposed BCMP-MAB algorithm over other benchmark schemes in terms of average sum rate and energy efficiency with comparable computational complexity and convergence rate. Full article
(This article belongs to the Special Issue Online Learning Aided Solutions for 6G Wireless Networks)
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