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Electronics
Special Issues
Mobile Networking: Latest Advances and Prospects
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Mobile Networking: Latest Advances and Prospects

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 2785

Special Issue Editors

Dr. Lexi Xu

E-Mail Website
Guest Editor
Research Institute, China Unicom, Beijing 100048, China
Interests: big data; self-organizing network; satellite system; radio resource management in mobile network
Dr. Gaofeng Cui

E-Mail Website
Guest Editor
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: satellite communication; 6G; radio resource management; edge computing; IoT
Dr. Chaowei Wang

E-Mail Website
Guest Editor
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: next-generation mobile communication; wireless sensor; IoT
Dr. Feng Yin

E-Mail Website
Guest Editor
School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Longxiang BLVD. 2001, Longgang District, Shenzhen 518172, China
Interests: Bayesian learning and optimization; big data; distributed algorithms; data-driven wireless system; wireless localization and tracking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of smart terminals and infrastructures, as well as diversified applications with colorful demands (e.g., autonomous driving, virtual and augmented reality, space‒air‒ground integrated network), mobile networks address the challenges from novel applications and services. To address the above challenges and meet eMBB, uRLLC, and mMTC applications/services requirements, both industry and academia make great efforts on improving mobile networks. Artificial intelligence (AI) and edge intelligence will empower the learning and prediction capability for 5G-A/6G mobile networks. In addition, 5G-A/6G mobile networks employ a series of new technologies to achieve deep integration, efficient resource management and flexible network operation, for example, software-defined networking (SDN), network function virtualization (NFV), network slicing and system coexistence, small cells, reconfigurable intelligent surface (RIS), and semantic communications.

The aim of this Special Issue of Electronics is to present state-of-the-art investigations in the latest advances and prospects of mobile networking. We invite researchers to contribute original and unique articles, as well as sophisticated review articles. Topics include, but are not limited to, the following areas:

  • Software-defined networking;
  • Network function virtualization;
  • Cross-layer design and optimization;
  • Network slicing and system coexistence;
  • Small cells for heterogeneous mobile network;
  • Reconfigurable intelligent surface empowered 5G-A/6G mobile network;
  • Semantic communications for 5G-A/6G mobile network;
  • Edge intelligence enabled 5G-A/6G mobile network architecture;
  • Artificial-intelligence-empowered 5G-A/6G mobile network;
  • Big-data-assisted private 5G-A/6G mobile network;
  • Edge-computation-based network structure for space‒air‒ground-integrated network;
  • Service and applications for 5G-A/6G mobile network.

Dr. Lexi Xu
Dr. Gaofeng Cui
Dr. Chaowei Wang
Dr. Feng Yin
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. 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

  • 5G-A
  • 6G
  • mobile network
  • artificial intelligence

Published Papers (4 papers)

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Research

13 pages, 1004 KiB  
Article
Shared Knowledge Distillation Network for Object Detection
by Zhen Guo, Pengzhou Zhang and Peng Liang
Electronics 2024, 13(8), 1595; https://doi.org/10.3390/electronics13081595 - 22 Apr 2024
Viewed by 270
Abstract
Object detection based on Knowledge Distillation can enhance the capabilities and performance of 5G and 6G networks in various domains, such as autonomous vehicles, smart surveillance, and augmented reality. The integration of object detection with Knowledge Distillation techniques is expected to play a [...] Read more.
Object detection based on Knowledge Distillation can enhance the capabilities and performance of 5G and 6G networks in various domains, such as autonomous vehicles, smart surveillance, and augmented reality. The integration of object detection with Knowledge Distillation techniques is expected to play a pivotal role in realizing the full potential of these networks. This study presents Shared Knowledge Distillation (Shared-KD) as a solution to overcome optimization challenges caused by disparities in cross-layer features between teacher–student networks. The significant gaps in intermediate-level features between teachers and students present a considerable obstacle to the efficacy of distillation. To tackle this issue, we draw inspiration from collaborative learning in real-world education, where teachers work together to prepare lessons and students engage in peer learning. Building upon this concept, our innovative contributions in model construction are highlighted as follows: (1) A teacher knowledge augmentation module: this module is proposed to combine lower-level teacher features, facilitating the knowledge transfer from the teacher to the student. (2) A student mutual learning module is introduced to enable students to learn from each other, mimicking the peer learning concept in collaborative learning. (3) The Teacher Share Module combines lower-level teacher features: the specific functionality of the teacher knowledge augmentation module is described, which involves combining lower-level teacher features. (4) The multi-step transfer process can be easily optimized due to the minimal gap between the features: the proposed approach breaks down the knowledge transfer process into multiple steps, which can be easily optimized due to the minimal gap between the features involved in each step. Shared-KD uses simple feature losses without additional weights in transformation, resulting in an efficient distillation process that can be easily combined with other methods for further improvement. The effectiveness of our approach is validated through experiments on popular tasks such as object detection and instance segmentation. Full article
(This article belongs to the Special Issue Mobile Networking: Latest Advances and Prospects)
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18 pages, 904 KiB  
Article
Interference Situational Aware Beam Pointing Optimization for Dense LEO Satellite Communication System
by Mengmin He, Gaofeng Cui, Weidong Wang, Xinzhou Cheng and Lexi Xu
Electronics 2024, 13(6), 1096; https://doi.org/10.3390/electronics13061096 - 16 Mar 2024
Viewed by 466
Abstract
Recently, the low earth orbit (LEO) mega-constellation faces serious time-varying interferences due to spectrum sharing, dense deployment, and high mobility. Therefore, it is important to study the interference avoidance techniques for the dense LEO satellite system. In this paper, the interference situational aware [...] Read more.
Recently, the low earth orbit (LEO) mega-constellation faces serious time-varying interferences due to spectrum sharing, dense deployment, and high mobility. Therefore, it is important to study the interference avoidance techniques for the dense LEO satellite system. In this paper, the interference situational aware beam pointing optimization technique is proposed. Firstly, the angle of departure (AoD) and angle of arrival (AoA) of the interfering links are obtained to represent the time-varying interference. Then, the interference avoidance problem for dense LEO satellite systems is modeled as a non-convex optimization problem, and a particle swarm optimization (PSO) based method is proposed to obtain the optimal beam pointing of the user terminal (UT). Simulations show that the relative error of the mean signal-to-interference plus noise ratio (SINR) obtained by the proposed method is 0.51%, so the co-channel interference can be effectively mitigated for the dense LEO satellite communication system. Full article
(This article belongs to the Special Issue Mobile Networking: Latest Advances and Prospects)
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23 pages, 3672 KiB  
Article
UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering
by Yudi Chen, Xiangyu Liu, Changqing Li, Jiao Zhu, Min Wu and Xiang Su
Electronics 2024, 13(6), 1054; https://doi.org/10.3390/electronics13061054 - 12 Mar 2024
Viewed by 535
Abstract
When an unmanned aerial vehicles (UAV) swarm is used for edge computing, and high-speed data transmission is required, accurate tracking of the UAV swarm’s centroid is of great significance for the acquisition and synchronization of signal demodulation. Accurate centroid tracking can also be [...] Read more.
When an unmanned aerial vehicles (UAV) swarm is used for edge computing, and high-speed data transmission is required, accurate tracking of the UAV swarm’s centroid is of great significance for the acquisition and synchronization of signal demodulation. Accurate centroid tracking can also be applied to accurate communication beamforming and angle tracking, bringing about a reception gain. Group target tracking (GTT) offers a suitable framework for tracking the centroids of UAV swarms. GTT typically involves accurate modeling of target maneuvering behavior and effective state filtering. However, conventional coordinate-uncoupled maneuver models and multi-model filtering methods encounter difficulties in accurately tracking highly maneuverable UAVs. To address this, an innovative approach known as 3DCDM-based GRU-MM is introduced for tracking the maneuvering centroid of a UAV swarm. This method employs a multi-model filtering technique assisted by a gated recurrent unit (GRU) network based on a suitable 3D coordinate-coupled dynamic model. The proposed dynamic model represents the centroid’s tangential load, normal load, and roll angle as random processes, from which a nine-dimensional unscented Kalman filter is derived. A GRU is utilized to update the model weights of the multi-model filtering. Additionally, a smoothing-differencing module is presented to extract the maneuvering features from position observations affected by measurement noise. The resulting GRU-MM method achieved a classification accuracy of 99.73%, surpassing that of the traditional IMM algorithm based on the same model. Furthermore, our proposed 3DCDM-based GRU-MM method outperformed the Singer-KF and 3DCDM-based IMM-EKF in terms of the RMSE for position estimation, which provides a basis for further edge computing. Full article
(This article belongs to the Special Issue Mobile Networking: Latest Advances and Prospects)
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22 pages, 2000 KiB  
Article
Trajectory and Phase Shift Optimization for RIS-Equipped UAV in FSO Communications with Atmospheric and Pointing Error Loss
by Haocheng Jia, Gaojie Chen, Chong Huang, Shuping Dang and Jonathon A. Chambers
Electronics 2023, 12(20), 4275; https://doi.org/10.3390/electronics12204275 - 16 Oct 2023
Viewed by 780
Abstract
This paper proposes a new framework for reconfigurable intelligent surface (RIS)-equipped unmanned aerial vehicles (UAVs) in free-space optical (FSO) communication. To ensure practicality, we consider atmospheric loss caused by fog, which leads to an inhomogeneous medium for laser propagation. In addition, we incorporate [...] Read more.
This paper proposes a new framework for reconfigurable intelligent surface (RIS)-equipped unmanned aerial vehicles (UAVs) in free-space optical (FSO) communication. To ensure practicality, we consider atmospheric loss caused by fog, which leads to an inhomogeneous medium for laser propagation. In addition, we incorporate the pointing error loss caused by the power fraction on the photodetector (PD) into the system and derive a closed-form expression for the elliptical beam footprint in the pointing error loss. We then propose a leading angle assisted particle swarm optimization (PSO) method to efficiently optimize the numerical results of pointing error loss. Furthermore, after obtaining these numerical results as a precondition, the UAV trajectory is optimized using the proximal policy optimization (PPO) method to achieve the maximum average capacity. Numerical simulations demonstrate that the proposed optimization method achieves greater efficiency and accuracy compared to the decode-and-forward (DF) relay and deep Q-learning (DQN) methods. Full article
(This article belongs to the Special Issue Mobile Networking: Latest Advances and Prospects)
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