Beyond 5G and 6G Communication Systems

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 (20 February 2024) | Viewed by 3328

Special Issue Editors

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
Interests: collaborative distributed learning paradigms in future wireless networks; deep learning/machine learning-enabled data transmissions; 5G-advanced/6G network architecture; fog/cloud computing-based wireless networks; integration of communication and sensing; performance analysis and software simulation evaluations of 5G-NR systems; advanced network control and resource management in future wireless networks
Dr. Xiaoshi Song
E-Mail Website
Guest Editor
College of Computer Science and Engineering, Northeastern University, Shenyang, China
Interests: 5G and B5G wireless communication systems

Special Issue Information

Dear Colleagues,

As artificial intelligence (AI) use becomes more prevalent in wireless applications, data-driven and computing-intensive services emerge, involving much higher quality-of-service (QoS) requirements for data rate, latency, and connectivity. To provide optimal user experiences in diverse application scenarios, continuous evolution from transmission techniques to network architectures and beyond 5G (B5G)/6G communication systems is necessary, a subject which has garned significant interest from both academia and the industry.

The 3rd Generation Partner Project—Release 18 (3GPP-R18) is another topic of recent interest. It has triggered the development of 5G-A systems and the explorative study of 6G systems, necessitating a platform for the exchange of knowledge between academia and the industry, bridge the gap between these two institutions, and has received support from many renowned international institutions and experts.

This Special Issue will focus on recent advances in fundamental theory, key techniques, and the standardization process for B5G/6G communication systems. Topics of interest include, but are not limited to:

  • Information, computation, and learning theory concerning B5G/6G communication systems.
  • Network architecture for B5G/6G communication systems.
  • Network intelligence for B5G/6G communication systems.
  • Advanced physical-layer signal processing techniques, and air interface design for B5G/6G communication systems.
  • Radio resource management for B5G/6G communication systems.
  • Integration of communication, computation, and sensing for B5G/6G communication systems.
  • Digital economy considering B5G/6G communication systems.
  • Recent advances in standardization in B5G/6G communication systems.
  • Advanced evaluation tool development and data set construction for B5G/6G communication systems.
  • Security and privacy in B5G/6G communication systems.
  • Prototype, demo, test-bed, and proof-of-concept for 6G systems.

Dr. Zhongyuan Zhao
Prof. Dr. Xiaoshi Song
Guest Editors

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

  • information, computation, and learning theory concerning B5G/6G communication systems
  • network architecture for B5G/6G communication systems
  • network intelligence for B5G/6G communication systems
  • advanced physical-layer signal processing techniques, and air interface design for B5G/6G communication systems
  • radio resource management for B5G/6G communication systems
  • integration of communication, computation, and sensing for B5G/6G communication systems
  • digital economy considering B5G/6G communication systems
  • recent advances in standardization in B5G/6G communication systems
  • advanced evaluation tool development and data set construction for B5G/6G communication systems
  • security and privacy in B5G/6G communication systems
  • prototype, demo, test-bed, and proof-of-concept for 6G systems

Published Papers (3 papers)

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

Research

19 pages, 5187 KiB  
Article
Intelligent Computation Offloading Based on Digital Twin-Enabled 6G Industrial IoT
Appl. Sci. 2024, 14(3), 1035; https://doi.org/10.3390/app14031035 - 25 Jan 2024
Viewed by 431
Abstract
Digital twin (DT) technology, which can provide larger and more accurate amounts of data, combined with the additional computility brought by virtual environments, can support more complex connected industrial applications. Simultaneously, the development and maturity of 6G technology has driven the development of [...] Read more.
Digital twin (DT) technology, which can provide larger and more accurate amounts of data, combined with the additional computility brought by virtual environments, can support more complex connected industrial applications. Simultaneously, the development and maturity of 6G technology has driven the development of industrial manufacturing and greatly improved the operational efficiency of the industrial internet of things (IIoT). Nevertheless, massive data, heterogeneous IoT device attributes, and the deterministic and bounded latency for delay sensitive applications are major barriers to improving the quality of services (QoS) in the IIoT. In this article, we first construct a new DT-enabled network architecture and computation offloading delay model in the IIoT. Then, the computation offloading problem is formulated with the goal of minimizing the overall task completion delay and achieving resource allocation. Since the formulation is a joint optimization problem, we use deep reinforcement learning (DRL) to solve the original problem, which can be described by a Markov decision process (MDP). Numerical results show that our proposed scheme is able to improve the task success rate and reduce the task processing end-to-end delay compared to the benchmark schemes. Full article
(This article belongs to the Special Issue Beyond 5G and 6G Communication Systems)
Show Figures

Figure 1

22 pages, 539 KiB  
Article
Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications
Appl. Sci. 2023, 13(19), 10745; https://doi.org/10.3390/app131910745 - 27 Sep 2023
Viewed by 802
Abstract
With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging [...] Read more.
With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve highly accurate evaluation results using the conventional statistical methods due to the limitations of data quality. In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly detection and forecasting scheme are designed. Second, the network structure and the training strategy are introduced to fully explore the potential of the GAN model. Finally, the experimental results of our proposed GAN model are provided based on the practical unlabeled network operation data in various 5G vertical scenarios, which show that our proposed scheme can achieve significant performance gains for network anomaly detection and forecasting. Full article
(This article belongs to the Special Issue Beyond 5G and 6G Communication Systems)
Show Figures

Figure 1

14 pages, 1315 KiB  
Article
A Joint Channel Estimation and Compression Method Based on GAN in 6G Communication Systems
Appl. Sci. 2023, 13(4), 2319; https://doi.org/10.3390/app13042319 - 10 Feb 2023
Cited by 1 | Viewed by 1218
Abstract
Due to the increasing popularity of communication devices and vehicles, the channel environment becomes more and more complex, which makes conventional channel estimation methods further increase the pilot overhead to maintain estimation performance. However, it declines the throughput of communication networks. In this [...] Read more.
Due to the increasing popularity of communication devices and vehicles, the channel environment becomes more and more complex, which makes conventional channel estimation methods further increase the pilot overhead to maintain estimation performance. However, it declines the throughput of communication networks. In this paper, we provide a novel two-stage based channel estimation method by using generative adversarial networks (GANs) to handle this problem in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the first stage aims to learn the mapping from a low-dimensional latent variable to the real channel sample. During the second stage, an iterative algorithm method is designed to find the optimal latent variable by matching the pilot channels of a real channel and generated channel. Then, the data channels are recovered based on the learned mapping relationship between the latent variable and the real channel sample. The simulation results show that our proposed method can achieve a performance gain of more than 2 dB with a pilot reduction by 75% when SNR is 10 dB, by comparing with the widely used Wiener filter interpolation method. In addition, as the low-dimensional latent variable can be obtained simultaneously, it can also be used for reducing the feedback overhead. Full article
(This article belongs to the Special Issue Beyond 5G and 6G Communication Systems)
Show Figures

Figure 1

Back to TopTop