Artificial Intelligent in 5G/6G Research: Radio Frequency, Physical Layer, Networking and Security

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 5347

Special Issue Editors

School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: 5G/6G mobile communications; high-speed railway communications; vehicle communication network and wireless localization
Special Issues, Collections and Topics in MDPI journals
National Mobile Communication Research Laboratory, Southeast University, Nanjing 211189, China
Interests: signal processing for wireless communications; large-scale distributed MIMO systems (cell-free massive MIMO)
Special Issues, Collections and Topics in MDPI journals
Department of Engineering, King’s College London, London WC2R 2LS, UK
Interests: molecular communication; wireless AI and edge AI

Special Issue Information

Dear Colleagues,

With the continuous integration of 5G/6G key technologies and artificial intelligence (AI)-enabling technologies, the performance of the communication system can be significantly improved. It is necessary to achieve important breakthroughs in key technologies of the 5G/6G network. In terms of wireless access networks, AI can enable the design of channel coding and can be combined with source channel joint coding to bring new optimization dimensions to coding design. For massive MIMO systems, using AI technology to solve the problems of the multi-antenna signal processing and detection, estimation, and resource scheduling will bring new changes to the design of the multi-antenna system. Meanwhile, antenna design such as beamforming combines the AI technology to increase the received signal strength for target users, which further improves the user’s communication quality. Moreover, the AI-enabling multiple access process can provide new possibilities for improving detection performance and reducing complexity. In the future, AI will develop from 5G application towards 6G endogenous intelligence, through multi-domain integration, intelligent adaptation of frequency spectrum, computing, storage, and other multi-dimensional resources. However, the security boundary brought by multi-domain integration is fuzzy, and network security and privacy issues face new challenges. Using AI to realize network self-optimization and improve network security has become a research hotspot. Furthermore, the combination of intelligent industry application with the internet of things is the main trend for the future network development.

Although some of the work has been combined with AI, AI will continue to deepen in 5G/6G research. This Special Issue aims to provide a platform to academia and industry experts to exchange their ideas and publish the latest research trends and results related to AI in 5G/6G research. We are soliciting original contributions that have not been published and are not currently under consideration by any other journals.

The topics of interest include, but are not limited to:

  • AI Channel Coding Design;
  • Massive MIMO Technologies for 5G/6G Networks;
  • Multiple Access Technologies for 5G/6G Networks;
  • Resource Allocation Algorithms for 5G/6G Networks;
  • Network Security Challenges and Solutions in 5G/6G;
  • RF/Antenna/Filter for 5G/6G;
  • Intelligent Industry Application with 5G/6G;
  • Internet of Thing and Vehicle Networking with 5G/6G.

Technical Program Committee Member:

Associate Professor Qingmiao Zhang East China Jiaotong University

Prof. Dr. Junhui Zhao
Prof. Dr. Dongming Wang
Dr. Yansha Deng
Guest Editors

Manuscript Submission Information

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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

  • channel coding
  • massive MIMO
  • multiple access
  • resource allocation
  • network security
  • radio frequency
  • antenna
  • networking

Published Papers (3 papers)

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Research

19 pages, 736 KiB  
Article
Connectivity Analysis with Co-Channel Interference for Urban Vehicular Ad Hoc Networks
by Shihai Ren, Junhui Zhao, Huan Zhang and Xuan Li
Electronics 2023, 12(9), 2021; https://doi.org/10.3390/electronics12092021 - 27 Apr 2023
Cited by 1 | Viewed by 994
Abstract
In urban vehicular ad hoc networks (VANETs), the complex channel environment and co-channel interference resulted in the uncertain delay of inter-vehicle packet transmission, which causes serious delay jitter. Connectivity is proposed as a key metric to describe this uncertainty. However, existing works lack [...] Read more.
In urban vehicular ad hoc networks (VANETs), the complex channel environment and co-channel interference resulted in the uncertain delay of inter-vehicle packet transmission, which causes serious delay jitter. Connectivity is proposed as a key metric to describe this uncertainty. However, existing works lack a discussion of inter-vehicle connectivity in urban VANETs, particularly with regards to the process of transmitting packets between vehicles. In this paper, we analyze the connectivity probability of urban VANETs under co-channel interference with both complete and incomplete information. When the information is complete, we model the time-varying nature of co-channel interference and channel fading as delay jitter and analyze inter-vehicle connectivity in a time-varying environment. Then, when complete information is unavailable, we estimate the probability distribution of co-channel interference by combining the distribution of multiple parameters with the free space propagation model (Friss) model and Nakagami-m fading model. The expression for the connectivity performance of vehicle-to-vehicle (V2V) links is derived from the signal interference plus noise ratio (SINR) of the destination V2V link. Finally, we analyze the implications of various factors on connectivity, such as the transmit power of the signal, the arrival rate of packets, the number of channels and vehicles, and the distance between the transmitting vehicle and the receiving vehicle. The numerical analysis shows that co-channel interference and signal fading significantly affect inter-vehicle connectivity. Full article
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17 pages, 801 KiB  
Article
A Delay-Optimal Task Scheduling Strategy for Vehicle Edge Computing Based on the Multi-Agent Deep Reinforcement Learning Approach
by Xuefang Nie, Yunhui Yan, Tianqing Zhou, Xingbang Chen and Dingding Zhang
Electronics 2023, 12(7), 1655; https://doi.org/10.3390/electronics12071655 - 31 Mar 2023
Viewed by 1190
Abstract
Cloudlet-based vehicular networks are a promising paradigm to enhance computation services through a distributed computation method, where the vehicle edge computing (VEC) cloudlet are deployed in the vicinity of the vehicle. In order to further improve the computing efficiency and reduce the task [...] Read more.
Cloudlet-based vehicular networks are a promising paradigm to enhance computation services through a distributed computation method, where the vehicle edge computing (VEC) cloudlet are deployed in the vicinity of the vehicle. In order to further improve the computing efficiency and reduce the task processing delay, we present a parallel task scheduling strategy based on the multi-agent deep reinforcement learning (DRL) approach for delay-optimal VEC in vehicular networks, where multiple computation tasks select the target threads in a VEC server to execute the computing tasks. We model the target thread decision of computation tasks as a multi-agent reinforcement learning problem, which is further solved by using a task scheduling algorithm based on multi-agent DRL that is implemented in a distributed manner. The computation tasks, with each selection acting on the target thread acting as an agent, collectively interact with the VEC environment and receive observations with respect to a common reward and learn to reduce the task processing delay by updating the multi-agent deep Q network (MADQN) using the obtained experiences. The experimental results show that the proposed DRL-based scheduling algorithm can achieve significant performance improvement, reducing the task processing delay by 40% and increasing the processing probability of success for computation tasks by more than 30% compared with the traditional task scheduling algorithms. Full article
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16 pages, 3450 KiB  
Article
Effective Resource Allocation Technique to Improve QoS in 5G Wireless Network
by Ramkumar Jayaraman, Baskar Manickam, Suresh Annamalai, Manoj Kumar, Ashutosh Mishra and Rakesh Shrestha
Electronics 2023, 12(2), 451; https://doi.org/10.3390/electronics12020451 - 15 Jan 2023
Cited by 15 | Viewed by 2690
Abstract
A 5G wireless network requires an efficient approach to effectively manage and segment the resource. A Centralized Radio Access Network (CRAN) is used to handle complex distributed networks. Specific to network infrastructure, multicast communication is considered in the performance of data storage and [...] Read more.
A 5G wireless network requires an efficient approach to effectively manage and segment the resource. A Centralized Radio Access Network (CRAN) is used to handle complex distributed networks. Specific to network infrastructure, multicast communication is considered in the performance of data storage and information-based network connectivity. This paper proposes a modified Resource Allocation (RA) scheme for effectively handling the RA problem using a learning-based Resource Segmentation (RS) technique. It uses a modified Random Forest Algorithm (RFA) with Signal Interference and Noise Ratio (SINR) and position coordinates to obtain the position coordinates of end-users. Further, it predicts Modulation and Coding Schemes (MCS) for establishing a connection between the end-user device and the Remote Radio Head (RRH). The proposed algorithm depends on the accuracy of positional coordinates for the correctness of the input parameters, such as SINR, based on the position and orientation of the antenna. The simulation analysis renders the efficiency of the proposed technique in terms of throughput and energy efficiency. Full article
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