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Applied Sciences
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Artificial Intelligence for Wireless Communications in Networks of Autonomous Agents
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Artificial Intelligence for Wireless Communications in Networks of Autonomous Agents

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 5532

Special Issue Editors

Dr. Francesco Guidi

E-Mail Website
Guest Editor
Institute of Electronics, Computer and Telecommunication Engineering IEIIT, National Research Council of Italy (CNR-IEIIT), Viale Risorgimento 2, 40136 Bologna, Italy
Interests: positioning; UWB; millimeter-wave; RFID; radar; wireless communications
Special Issues, Collections and Topics in MDPI journals
Dr. Anna Guerra

E-Mail Website
Guest Editor
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Viale dell’Università 50, 47522 Cesena, Italy
Interests: wireless communications; localization; autonomous navigation; unmanned aerial vehicles
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Barbara Mavì Masini

E-Mail Website
Guest Editor
1. Institute of Electronics Information Engineering and Telecommunications, National Research Council, Bologna, Italy
2. Department of Electrical, University of Bologna, 40136 Bologna, Italy
Interests: connected vehicles; Internet of vehicles (IoV); relay-assisted communications; visible light communication (VLC); 5G
Special Issues, Collections and Topics in MDPI journals
Dr. Davide Dardari

E-Mail Website
Guest Editor
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Viale dell’Università 50, 47522 Cesena, Italy
Interests: wireless communications; localization; distributed signal processing; RFID
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) for improving wireless communications has recently attracted an explosive interest within academic, research and industry communities. Indeed, AI-based solutions represent a promising tool to face the ever-increasing demand of capacity, coverage, latency and efficiency required by next 6G scenarios.

This becomes even more important in networks of autonomous agents demanded to accomplish challenging tasks, e.g. cooperative perception in connected and automated driving, while guaranteeing ultra-reliable and ultra-low latency communications. In this way, AI can be used to address radio resource management (e.g., the interaction with the edge) or to augment the surrounding radio ambient awareness, thus facilitating the cooperation among agents and the interference management.

This Special Issue will host papers dealing with the adoption of AI for improving wireless communications when networks of autonomous agents, e.g. vehicles or moving robots (including UAVs), are employed. Submissions are expected to focus on new proposed solutions, practical schemes, experimentation or to review a recent trend in one of the diverse aspects of machine learning for wireless communications, discussing the potentialities and the existing open challenges.

Potential topics include, but are not limited to:

  • Intelligent communication and coordination protocols
  • Machine learning for communication in dynamic networks (e.g., vehicle-to-everything (V2X), unmanned aerial vehicle-to-everything (U2X))
  • Machine learning for edge intelligence
  • AI techniques for ultra-reliable and low latency communications (URLLC)
  • AI techniques for massive machine type communication (mMTC)
  • Enhanced connectivity in distributed networks of mobile agents
  • PHY and MAC advanced cooperative techniques for autonomous driving or navigation
  • Radio resource management
  • Interference management
  • Self-formation and control for swarms of robots (e.g., UAVs)
  • Radio environment awareness enhanced by AI
  • Safety, security and privacy in networks of autonomous agents
  • Trials, test-beds and experimentation

Dr. Francesco Guidi
Dr. Anna Guerra
Prof. Dr. Barbara Mavì Masini
Prof. Davide Dardari
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

  • Machine learning
  • Wireless communications
  • Networks of Autonomous Agents
  • Autonomous navigation/driving
  • Radio environment awareness
  • 6G
  • Edge intelligence
  • Cooperative perception scenarios

Published Papers (2 papers)

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35 pages, 1140 KiB  
Article
An Intelligent Cluster-Based Routing Scheme in 5G Flying Ad Hoc Networks
by Muhammad Fahad Khan, Kok-Lim Alvin Yau, Mee Hong Ling, Muhammad Ali Imran and Yung-Wey Chong
Appl. Sci. 2022, 12(7), 3665; https://0-doi-org.brum.beds.ac.uk/10.3390/app12073665 - 06 Apr 2022
Cited by 19 | Viewed by 2267
Abstract
Flying ad hoc network (FANET) is an application of 5G access network, which consists of unmanned aerial vehicles or flying nodes with scarce resources and high mobility rates. This paper proposes a deep Q-network (DQN)-based vertical routing scheme to select routes with higher [...] Read more.
Flying ad hoc network (FANET) is an application of 5G access network, which consists of unmanned aerial vehicles or flying nodes with scarce resources and high mobility rates. This paper proposes a deep Q-network (DQN)-based vertical routing scheme to select routes with higher residual energy levels and lower mobility rates across network planes (i.e., macro-plane, pico-plane, and femto-plane), which has not been investigated in the literature. The main motivation behind this work is to address frequent link disconnections and network partitions in order to enhance network performance. The 5G access network has a central controller (CC) and distributed controllers (DCs) in different network planes. The proposed scheme is a hybrid approach that allows CC and DCs to exchange information among themselves, and handle global and local information, respectively. The proposed scheme is suitable for highly dynamic ad hoc FANETs, and it enables data communication between UAVs in various applications, such as monitoring and performing surveillance of borders, and targeted-based operations (e.g., object tracking). Vertical routing is performed over a clustered network, in which clusters are formed across different network planes to provide inter-plane and inter-cluster communications. This helps to offload data traffic across different network planes to enhance network lifetime. Compared to the traditional reinforcement learning approach, the proposed DQN-based vertical routing scheme has shown to increase network lifetime by up to 60%, reduce energy consumption by up to 20%, and reduce the rate of link breakages by up to 50%. Full article
(This article belongs to the Special Issue Artificial Intelligence for Wireless Communications in Networks of Autonomous Agents)
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19 pages, 1650 KiB  
Article
Energy-Efficient Power Allocation and User Association in Heterogeneous Networks with Deep Reinforcement Learning
by Chi-Kai Hsieh, Kun-Lin Chan and Feng-Tsun Chien
Appl. Sci. 2021, 11(9), 4135; https://0-doi-org.brum.beds.ac.uk/10.3390/app11094135 - 30 Apr 2021
Cited by 23 | Viewed by 2369
Abstract
This paper studies the problem of joint power allocation and user association in wireless heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based approach. This is a challenging problem since the action space is hybrid, consisting of continuous actions (power allocation) and discrete [...] Read more.
This paper studies the problem of joint power allocation and user association in wireless heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based approach. This is a challenging problem since the action space is hybrid, consisting of continuous actions (power allocation) and discrete actions (device association). Instead of quantizing the continuous space (i.e., possible values of powers) into a set of discrete alternatives and applying traditional deep reinforcement approaches such as deep Q learning, we propose working on the hybrid space directly by using the novel parameterized deep Q-network (P-DQN) to update the learning policy and maximize the average cumulative reward. Furthermore, we incorporate the constraints of limited wireless backhaul capacity and the quality-of-service (QoS) of each user equipment (UE) into the learning process. Simulation results show that the proposed P-DQN outperforms the traditional approaches, such as the DQN and distance-based association, in terms of energy efficiency while satisfying the QoS and backhaul capacity constraints. The improvement in the energy efficiency of the proposed P-DQN on average may reach 77.6% and 140.6% over the traditional DQN and distance-based association approaches, respectively, in a HetNet with three SBS and five UEs. Full article
(This article belongs to the Special Issue Artificial Intelligence for Wireless Communications in Networks of Autonomous Agents)
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