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Artificial Intelligence (AI)-Enabled 6G Communications
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Artificial Intelligence (AI)-Enabled 6G Communications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 6648

Special Issue Editors

Dr. Changchuan Yin

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: wireless communications; networking; statistical signal processing; machine learning
Dr. Mingzhe Chen

E-Mail Website
Guest Editor
Electrical Engineering Department, Princeton University, Princeton, NJ 08544, USA
Interests: machine learning; virtual reality; caching; unmanned aerial vehicles
Dr. Zhaohui Yang

E-Mail
Guest Editor
Department of Engineering, King's College London, London WC2R 2LS, UK
Interests: UAV; NOMA; intelligent communication; distributed algorithm

Special Issue Information

Dear Colleagues,

Recently, the use of artificial intelligence (AI) has been investigated by both academia and industry as a new paradigm of future 6G networks. AI-enabled 6G is expected to further explore the inherent limitations of 5G and to drive the intelligent development of communication networks. Therefore, AI will play an important role in 6G networking, signal processing, resource management, and security.

In consequence, this Special Issue seeks innovative works on a wide range of research topics, spanning both theoretical and system research on AI-enabled 6G communications, including results from academia and industry, related but not restricted to the following topics:

  • Self-organizing communication networks;
  • Massive access and grant-free transmission;
  • Protocol design and optimization;
  • Wireless network resource management;
  • Reliable and robust network operation;
  • AI-driven edge/fog computing;
  • Data privacy and security;
  • Intelligent sensing, learning, and decision making;
  • Signal processing for AI-enabled communication networks;
  • AI applications in 6G networks.

Dr. Changchuan Yin
Dr. Mingzhe Chen
Dr. Zhaohui Yang
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. Sensors 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 2600 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

  • Artificial intelligence (AI)
  • 6G
  • networking
  • signal processing
  • resource management
  • security

Published Papers (3 papers)

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Research

18 pages, 535 KiB  
Article
Biological Intelligence Inspired Trajectory Design for Energy Harvesting UAV Networks
by Xuanlin Liu, Sihua Wang and Changchuan Yin
Sensors 2023, 23(2), 863; https://0-doi-org.brum.beds.ac.uk/10.3390/s23020863 - 11 Jan 2023
Viewed by 1851
Abstract
In this paper, the problem of trajectory design for energy harvesting unmanned aerial vehicles (UAVs) is studied. In the considered model, the UAV acts as a moving base station to serve the ground users, while collecting energy from the charging stations located at [...] Read more.
In this paper, the problem of trajectory design for energy harvesting unmanned aerial vehicles (UAVs) is studied. In the considered model, the UAV acts as a moving base station to serve the ground users, while collecting energy from the charging stations located at the center of a user group. For this purpose, the UAV must be examined and repaired regularly. In consequence, it is necessary to optimize the trajectory design of the UAV while jointly considering the maintenance costs, the reward of serving users, the energy management, and the user service time. To capture the relationship among these factors, we first model the completion of service and the harvested energy as the reward, and the energy consumption during the deployment as the cost. Then, the deployment profitability is defined as the ratio of the reward to the cost of the UAV trajectory. Based on this definition, the trajectory design problem is formulated as an optimization problem whose goal is to maximize the deployment profitability of the UAV. To solve this problem, a foraging-based algorithm is proposed to find the optimal trajectory so as to maximize the deployment profitability and minimize the average user service time. The proposed algorithm can find the optimal trajectory for the UAV with low time complexity at the level of polynomial. Fundamental analysis shows that the proposed algorithm achieves the maximal deployment profitability. Simulation results show that, compared to Q-learning algorithm, the proposed algorithm effectively reduces the operation time and the average user service time while achieving the maximal deployment profitability. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Enabled 6G Communications)
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21 pages, 757 KiB  
Article
Precoder and Decoder Co-Designs for Radar and Communication Spectrum Sharing
by Yuanhao Cui, Visa Koivunen and Xiaojun Jing
Sensors 2022, 22(7), 2619; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072619 - 29 Mar 2022
Cited by 2 | Viewed by 2199
Abstract
The coexistence of radar and communication systems is necessary to facilitate new wireless systems and services due to the shortage of the useful radio spectrum. Moreover, changes in spectrum regulation will be introduced in which the spectrum is allocated in larger chunks and [...] Read more.
The coexistence of radar and communication systems is necessary to facilitate new wireless systems and services due to the shortage of the useful radio spectrum. Moreover, changes in spectrum regulation will be introduced in which the spectrum is allocated in larger chunks and different radio systems need to share the spectrum. For example, 5G NR, LTE and Wi-Fi systems have to share the spectrum with S-band radars. Managing interference is a key task in coexistence scenarios. Cognitive radio and radar technologies facilitate using the spectrum in a flexible manner and sharing channel awareness between the two subsystems. In this paper, we propose a nullspace-based joint precoder–decoder design for coexisting multicarrier radar and multiuser multicarrier communication systems. The maximizing signal interference noise ratio (max-SINR) criterion and interference alignment (IA) constraints are employed in finding the precoder and decoder. By taking advantage of IA theory, a maximum degree of freedom upper bound for the K+1-radar-communication-user interference channel can be achieved. Our simulation studies demonstrate that interference can be practically fully canceled in both communication and radar systems. This leads to improved detection performance in radar and a higher rate in communication subsystems. A significant performance gain over a nullspace-based precoder-only design is also obtained. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Enabled 6G Communications)
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23 pages, 943 KiB  
Article
Joint Power and Subchannel Allocation for Distributed Storage in Cellular-D2D Underlays
by Fengxia Han, Hao Deng, Jianfeng Shi and Hao Jiang
Sensors 2021, 21(23), 8059; https://0-doi-org.brum.beds.ac.uk/10.3390/s21238059 - 02 Dec 2021
Viewed by 1344
Abstract
Wireless distributed storage is beneficial in the provision of reliable content storage and offloading of cellular traffic. In this paper, we consider a cellular device-to-device (D2D) underlay-based wireless distributed storage system, in which the minimum storage regenerating (MSR) coding combined with the partial [...] Read more.
Wireless distributed storage is beneficial in the provision of reliable content storage and offloading of cellular traffic. In this paper, we consider a cellular device-to-device (D2D) underlay-based wireless distributed storage system, in which the minimum storage regenerating (MSR) coding combined with the partial downloading scheme is employed. To alleviate burdens on insufficient cellular resources and improve spectral efficiency in densely deployed networks, multiple storage devices can simultaneously use the same uplink cellular subchannel under the non-orthogonal multiple access (NOMA) protocol. Our objective is to minimize the total transmission power for content reconstruction, while guaranteeing the signal-to-interference-plus-noise ratio (SINR) constraints for cellular users by jointly optimizing power and subchannel allocation. To tackle the non-convex combinational program, we decouple the original problem into two subproblems and propose two low-complexity algorithms to efficiently solve them, followed by a joint optimization, implemented by alternately updating the solutions to each subproblem. The numerical results illustrate that our proposed algorithms are capable of performing an exhaustive search with lower computation complexity, and the NOMA-enhanced scheme provides more transmission opportunities for neighbor storage devices, thus significantly reducing the total power consumption. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Enabled 6G Communications)
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