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Edge AI for 6G and Internet of Things

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 6980

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Special Issue Editors

Dr. Xin Hu

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Guest Editor

School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: 6G; artificial intelligence; Internet of Things

Dr. Ziwei Liu

E-Mail Website
Guest Editor

School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Interests: satellite communication; Internet of Things; artificial intelligence

Dr. Lexi Xu

E-Mail Website
Guest Editor

Research Institute, China Unicom, Beijing 100048, China
Interests: big data; artificial intelligence; satellite network; mobile communication
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Special Issue Information

Dear Colleagues,

As artificial intelligence (AI) permeates wireless applications, some data-driven and computing-intensive services are emerging, such as mobile high-definition AR/VR and real-time fingertip interactions. Moreover, application scenarios have been extended to penetrate the vertical industry, which have multi-dimension and diverse quality of service (QoS) requirements. Recently, there has been much research and development of 6G and Internet of Things, and much higher QoS requirements of data rate, latency, and connectivity will be identified. To support the user experience of these services in future 6G and Internet of Things, the procedures of data transmissions and service implementations should be coupled tightly. It requires the fusion of AI and big data to enable network intelligence, especially at the edge of wireless networks. Therefore, the concept of edge AI is raised to support AI-driven services and to implement intelligent network management and signal processing.

The aim of this Special Issue of Electronics is to present state-of-the-art investigations in various Edge AI technologies for future 6G and Internet of Things applications. 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:

The deployment of AI in radio access network;
Dynamic resource allocation in wireless communications;
AI-based computation offloading;
Intelligent mobile edge computing;
Big Data-assisted 5G/6G private network;
AI-based 5G/6G MEC optimization and operation;
Big Data and AI for 5G/6G cellular network optimization;
Edge computation-based network structure for space-terrestrial integrated network;
Semantic communication for 6G and Internet of Thing network;
Decentralized ID and blockchain for user identification in 6G edge computation network.

We look forward to receiving your contributions.

Dr. Xin Hu
Dr. Ziwei Liu
Dr. Lexi Xu
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

6G
edge AI
Internet of Things
satellite communications

Published Papers (6 papers)

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Research

18 pages, 4354 KiB

Open AccessArticle

CrossTLNet: A Multitask-Learning-Empowered Neural Network with Temporal Convolutional Network–Long Short-Term Memory for Automatic Modulation Classification

by Gujiuxiang Gao, Xin Hu, Boyan Li, Weidong Wang and Fadhel M. Ghannouchi

Electronics 2023, 12(22), 4668; https://doi.org/10.3390/electronics12224668 - 16 Nov 2023

Viewed by 652

Abstract

Amidst the evolving landscape of non-cooperative communication, automatic modulation classification (AMC) stands as an essential pillar, enabling adaptive and reliable signal processing. Due to the advancement of deep learning (DL) technology, neural networks have found application in AMC. However, the previous DL models face the inter-class confusion problem in high-order modulations. To address this issue, we propose a multitask-learning-empowered hybrid neural network, named CrossTLNet. Specifically, after the signal enters the model, it is first transformed into two task components: in-phase/quadrature (I/Q) form and amplitude/phase (A/P) form. For each task, we design a method that combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network to effectively capture long-term dependency features in high-order modulations. To enable interaction between these two different dimensional features, we innovatively introduce a cross-attention method, thereby further enhancing the model’s ability to distinguish signal features. Moreover, we also design a simple and efficient knowledge distillation method to reduce the size of CrossTLNet, making it easier to deploy in real-time or resource-limited scenarios. The experimental results indicate that the suggested method exhibits exceptional performance in AMC on public benchmarks, especially in high-order modulations. Full article

(This article belongs to the Special Issue Edge AI for 6G and Internet of Things)

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20 pages, 4239 KiB

Open AccessArticle

Rigid–Flexible Coupled System Attitude–Orbit Integration Fixed-Time Control

by Yinghui Zhang, Chen Ma, Songjing Ma, Junfeng Pan, Xiaohong Sui, Boxuan Lin and Mengjie Shi

Electronics 2023, 12(15), 3329; https://doi.org/10.3390/electronics12153329 - 03 Aug 2023

Cited by 2 | Viewed by 646

Abstract

A diffractive imaging system consisting of two satellites is analyzed in view of dynamics. The mathematical model of rigid and flexion couples is studied to describe the relative motion of diffractive satellites and imaging satellites. Based on an integrated dynamics model with dual quaternion, a fixed-time non-singular terminal sliding mode controller is designed to meet the requirements of Earth observation. Finally, introducing the non-singular terminal sliding mode as the control group, a comparative simulation of relative motion and control is implemented to verify the controller and dynamics model. Full article

(This article belongs to the Special Issue Edge AI for 6G and Internet of Things)

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18 pages, 1029 KiB

Open AccessFeature PaperArticle

Virtual Network Function Migration Considering Load Balance and SFC Delay in 6G Mobile Edge Computing Networks

by Yi Yue, Xiongyan Tang, Zhiyan Zhang, Xuebei Zhang and Wencong Yang

Electronics 2023, 12(12), 2753; https://doi.org/10.3390/electronics12122753 - 20 Jun 2023

Cited by 1 | Viewed by 1384

Abstract

With the emergence of Network Function Virtualization (NFV) and Software-Defined Networks (SDN), Service Function Chaining (SFC) has evolved into a popular paradigm for carrying and fulfilling network services. However, the implementation of Mobile Edge Computing (MEC) in sixth-generation (6G) mobile networks requires efficient resource allocation mechanisms to migrate virtual network functions (VNFs). Deep learning is a promising approach to address this problem. Currently, research on VNF migration mainly focuses on how to migrate a single VNF while ignoring the VNF sharing and concurrent migration. Moreover, most existing VNF migration algorithms are complex, unscalable, and time-inefficient. This paper assumes that each placed VNF can serve multiple SFCs. We focus on selecting the best migration location for concurrently migrating VNF instances based on actual network conditions. First, we formulate the VNF migration problem as an optimization model whose goal is to minimize the end-to-end delay of all influenced SFCs while guaranteeing network load balance after migration. Next, we design a Deep Learning-based Two-Stage Algorithm (DLTSA) to solve the VNF migration problem. Finally, we combine previous experimental data to generate realistic VNF traffic patterns and evaluate the algorithm. Simulation results show that the SFC delay after migration calculated by DLTSA is close to the optimal results and much lower than the benchmarks. In addition, it effectively guarantees the load balancing of the network after migration. Full article

(This article belongs to the Special Issue Edge AI for 6G and Internet of Things)

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15 pages, 3385 KiB

Open AccessArticle

An AI-Enhanced Strategy of Service Offloading for IoV in Mobile Edge Computing

by Hongyu Peng, Xiaosong Zhang, Hongwu Li, Lexi Xu and Xiaodong Wang

Electronics 2023, 12(12), 2719; https://doi.org/10.3390/electronics12122719 - 17 Jun 2023

Cited by 2 | Viewed by 1164

Abstract

A full connected world is expected to be introduced in the sixth generation mobile network (6G). As a typical fully connected scenario, the internet of vehicle (IoV) enables intelligent vehicle operations via artificial intelligence (AI) and edge computing technologies. Thus, integrating intelligence into edge computing is, no doubt, a promising development trend. In the future of vehicular networks, a massive variety of services need powerful computing resources and higher quality of service (QoS). Existing computing resources are insufficient to match those increasing requirements. Most works on this problem focused on finding the power-delay’s trade-off, ignoring QoS and stable load balance. In this study, we found that the computing power and redundancy of vehicles’ in IoV is increasing. So, those redundant computing resources are possible to be used to solve the shortage of computing resource. CNN is a typical AI technique. This technology is very suitable for solving the problems in this article. So, we adopted CNN technique of AI to design and algorithm of convolutional long short-term memory (CN_LSTM) based traffic prediction (ACLBTP). ACLBTP was designed to gain the predicted number of vehicles belonging to the edge node. Secondly, according to the problem of insufficient computing resources on remote servers, we found that a large amount of redundant computing resources exist in edge nodes. So, we used edge computing technique to solve the problem of insufficient computing resources on remote servers. ASOBCL was designed to distribute computing tasks to edge nodes. Meanwhile, an intelligent service offloading framework was provided in this article. Based on the framework, an algorithm of improved gradient descent (AIGD) was created to accelerate the speed of iteration. So, the ACLBTP’s convergence of convolutional neural network (CNN) based on AIGD was able to be accelerated too. In ASOBCL, a sorting technique was adopted to speed up the offloading work. Simulation results demonstrated the fact that the prediction strategy designed in this paper had high accuracy. The low offloading time and maintaining stable load balance were gained via running ASOBCL. Low offloading time means short response time. Additionally, the QoS was guaranteed. So, these strategies designed in this paper were effective and valuable. Full article

(This article belongs to the Special Issue Edge AI for 6G and Internet of Things)

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18 pages, 4094 KiB

Open AccessArticle

A 6G-Enabled Lightweight Framework for Person Re-Identification on Distributed Edges

by Xiting Peng, Yichao Wang, Xiaoyu Zhang, Haibo Yang, Xiongyan Tang and Shi Bai

Electronics 2023, 12(10), 2266; https://doi.org/10.3390/electronics12102266 - 17 May 2023

Cited by 2 | Viewed by 977

Abstract

In the upcoming 6G era, edge artificial intelligence (AI), as a key technology, will be able to deliver AI processes anytime and anywhere by the deploying of AI models on edge devices. As a hot issue in public safety, person re-identification (Re-ID) also needs its models to be urgently deployed on edge devices to realize real-time and accurate recognition. However, due to complex scenarios and other practical reasons, the performance of the re-identification model is poor in practice. This is especially the case in public places, where most people have similar characteristics, and there are environmental differences, as well other such characteristics that cause problems for identification, and which make it difficult to search for suspicious persons. Therefore, a novel end-to-end suspicious person re-identification framework deployed on edge devices that focuses on real public scenarios is proposed in this paper. In our framework, the video data are cut images and are input into the You only look once (YOLOv5) detector to obtain the pedestrian position information. An omni-scale network (OSNet) is applied through which to conduct the pedestrian attribute recognition and re-identification. Broad learning systems (BLSs) and cycle-consistent adversarial networks (CycleGAN) are used to remove the noise data and unify the style of some of the data obtained under different shooting environments, thus improving the re-identification model performance. In addition, a real-world dataset of the railway station and actual problem requirements are provided as our experimental targets. The HUAWEI Atlas 500 was used as the edge equipment for the testing phase. The experimental results indicate that our framework is effective and lightweight, can be deployed on edge devices, and it can be applied for suspicious person re-identification in public places. Full article

(This article belongs to the Special Issue Edge AI for 6G and Internet of Things)

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16 pages, 5770 KiB

Open AccessArticle

A CSAR 3D Imaging Method Suitable for Edge Computation

by Lina Chu, Yanheng Ma, Zhisong Hao, Bingxuan Li, Yuanping Shi and Wei Li

Electronics 2023, 12(9), 2092; https://doi.org/10.3390/electronics12092092 - 04 May 2023

Viewed by 974

Abstract

Due to the large amount of CSAR echo data carried by UAVs, either the original echo data need to be transmitted to the ground for processing or post-processing must be implemented after the flight. Therefore, it is difficult to use edge computing power such as a UAV onboard computer to implement image processing. The commonly used back projection (BP) algorithm and corresponding improved imaging algorithms require a large amount of computation and have slow imaging speed, which further limits the realization of CSAR 3D imaging on edge nodes. To improve the speed of CSAR 3D imaging, this paper proposes a CSAR 3D imaging method suitable for edge computation. Firstly, the improved Crazy Climber algorithm extracts sine track ridges that represent the amplitude changes in the range-compressed echo. Secondly, two-dimensional (2D) profiles of CSAR with different heights are obtained via inverse Radon transform (IRT). Thirdly, the Hough transform is used to extract the intersection points of the defocused circle along the heights in the X and Y directions. Finally, 3D point cloud extraction is completed through voting screening. In this paper, image detection methods such as ridge extraction, IRT, and Hough transform replace the phase compensation processing of the traditional BP 3D imaging method, which significantly reduces the time of CSAR 3D imaging. The correctness and effectiveness of the proposed method are verified by the 3D imaging results for the simulated data of ideal targets and X-band CSAR outfield flight raw data carried by a small rotor unmanned aerial vehicle (SRUAV). The proposed method provides a new direction for the fast 3D imaging of edge nodes, such as aircraft and small ground terminals. The image can be directly transmitted, which can improve the information transmission efficiency of the Internet of Things (IoT). Full article

(This article belongs to the Special Issue Edge AI for 6G and Internet of Things)

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