6G Wireless Communication Systems: Applications, Opportunities and Challenges, Volume III

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 3786

Special Issue Editors


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Guest Editor

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Guest Editor
Faculty of Engineering and Information Technology, An-Najah National University, Nablus 44859, Palestine
Interests: space-time coded orthogonal frequency division multiplexing; propagation channel modeling; 5G Mobile Communications; novel localization applications
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Guest Editor
School of Engineering, Computing and Design, University of Chichester, Chichester PO19 6PE, UK
Interests: intelligent reflecting surfaces (IRS); smart signal processing; massive MIMO; 5G and beyond; machine learning; optimization; Internet of Things (IoT); smart energy cities
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Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK
Interests: energy-efficient front-end design; radio frequency; energy harvesting; communications systems; 5G communications; sensor design; localisation-based services; signal processing; optimisation process; MIMO system design; health hazards; propagations, antennas and electromagnetic computational techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The next generation of wireless networks, i.e., enhanced 5G, 6G and beyond, is designed with the aim of handling Gb/s data and billions of users, devices, and connections reliably and with very low latency. This future wireless network is a heterogeneous network with a variety of frequency bands, ranging from below 6 GHz to almost 100 GHz. Indeed, a new wireless millimeter-length wave communication network will be an appropriate medium for supporting a huge increase in outdoor and indoor high-speed wireless activities including watching HD videos and TV, playing HD video games, using virtual reality (VR) and augmented reality (AR) technology, and employing advanced telepresence applications. To boost these developments, industry and academia will play key roles in defining the framework of this new generation of wireless communication networks, its services, breakthrough technologies, and sustainability approaches.

The objective of this Special Issue is to provide a platform for all researchers, from both industry and academia, to present their original scientific contributions, with a particular emphasis on radically new concepts and ideas for next-generation wireless communication systems. 

Dr. Chan Hwang Hwang See
Prof. Dr. Simeon Keates
Dr. Yousef Dama
Dr. Kelvin Anoh
Prof. Dr. Raed A. Abd-Alhameed
Guest Editors

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Keywords

  • next-generation communication systems
  • 5G/6G
  • wireless sensor networks

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Published Papers (2 papers)

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18 pages, 2664 KiB  
Article
Power-Efficient Resource Allocation for Active STAR-RIS-Aided SWIPT Communication Systems
by Chuanzhe Gao, Shidang Li, Yixuan Wu, Siyi Duan, Mingsheng Wei and Bencheng Yu
Future Internet 2024, 16(8), 266; https://doi.org/10.3390/fi16080266 - 25 Jul 2024
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Abstract
Simultaneous wireless information and power transfer (SWIPT) has emerged as a pivotal technology in 6G, offering an efficient means of delivering energy to a large quantity of low-power devices while transmitting data concurrently. To address the challenges of obstructions, high path loss, and [...] Read more.
Simultaneous wireless information and power transfer (SWIPT) has emerged as a pivotal technology in 6G, offering an efficient means of delivering energy to a large quantity of low-power devices while transmitting data concurrently. To address the challenges of obstructions, high path loss, and significant energy consumption associated with long-distance communication, this work introduces a novel alternating iterative optimization strategy. The proposed approach combines active simultaneous transmission and reflection of reconfigurable intelligent surfaces (STAR-RIS) with SWIPT to maximize spectrum efficiency and reduce overall system energy consumption. This method addresses the considerable energy demands inherent in SWIPT systems by focusing on reducing the power output from the base station (BS) while meeting key constraints: the communication rate for information receivers (IRs) and minimum energy levels for energy receivers (ERs). Given complex interactions between variables, the solution involves an alternating iterative optimization process. In the first stage of this approach, the passive beamforming variables are kept constant, enabling the use of semi-definite relaxation (SDR) and successive convex approximation (SCA) algorithms to optimize active beamforming variables. In the next stage, with active beamforming variables fixed, penalty-based algorithms are applied to fine-tune the passive beamforming variables. This iterative process continues, alternating between active and passive beamforming optimization, until the system converges on a stable solution. The simulation results indicated that the proposed system configuration, which leverages active STAR-RIS, achieves lower energy consumption and demonstrates improved performance compared to configurations utilizing passive RIS, active RIS, and passive STAR-RIS. This evidence suggests that the proposed approach can significantly contribute to advancing energy efficiency in 6G systems. Full article
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Review

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29 pages, 1178 KiB  
Review
Machine Learning Strategies for Reconfigurable Intelligent Surface-Assisted Communication Systems—A Review
by Roilhi F. Ibarra-Hernández, Francisco R. Castillo-Soria, Carlos A. Gutiérrez, Abel García-Barrientos, Luis Alberto Vásquez-Toledo and J. Alberto Del-Puerto-Flores
Future Internet 2024, 16(5), 173; https://doi.org/10.3390/fi16050173 - 17 May 2024
Cited by 1 | Viewed by 2167
Abstract
Machine learning (ML) algorithms have been widely used to improve the performance of telecommunications systems, including reconfigurable intelligent surface (RIS)-assisted wireless communication systems. The RIS can be considered a key part of the backbone of sixth-generation (6G) communication mainly due to its electromagnetic [...] Read more.
Machine learning (ML) algorithms have been widely used to improve the performance of telecommunications systems, including reconfigurable intelligent surface (RIS)-assisted wireless communication systems. The RIS can be considered a key part of the backbone of sixth-generation (6G) communication mainly due to its electromagnetic properties for controlling the propagation of the signals in the wireless channel. The ML-optimized (RIS)-assisted wireless communication systems can be an effective alternative to mitigate the degradation suffered by the signal in the wireless channel, providing significant advantages in the system’s performance. However, the variety of approaches, system configurations, and channel conditions make it difficult to determine the best technique or group of techniques for effectively implementing an optimal solution. This paper presents a comprehensive review of the reported frameworks in the literature that apply ML and RISs to improve the overall performance of the wireless communication system. This paper compares the ML strategies that can be used to address the RIS-assisted system design. The systems are classified according to the ML method, the databases used, the implementation complexity, and the reported performance gains. Finally, we shed light on the challenges and opportunities in designing and implementing future RIS-assisted wireless communication systems based on ML strategies. Full article
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