Chinese Journal of Electronics

Multi-Dimensional QoS Evaluation and Optimization of Mobile Edge Computing for IoT: A Survey

HUANG Jiwei, LIU Fangzheng, ZHANG Jianbing

2024, 33(4): 859-874. doi: 10.23919/cje.2023.00.264

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Abstract:
With the evolvement of the Internet of things (IoT), mobile edge computing (MEC) has emerged as a promising computing paradigm to support IoT data analysis and processing. In MEC for IoT, the differentiated requirements on quality of service (QoS) have been growing rapidly, making QoS a multi-dimensional concept including several attributes, such as performance, dependability, energy efficiency, and economic factors. To guarantee the QoS of IoT applications, theories and techniques of multi-dimensional QoS evaluation and optimization have become important theoretical foundations and supporting technologies for the research and application of MEC for IoT, which have attracted significant attention from both academia and industry. This paper aims to survey the existing studies on multi-dimensional QoS evaluation and optimization of MEC for IoT, and provide insights and guidance for future research in this field. This paper summarizes the multi-dimensional and multi-attribute QoS metrics in IoT scenarios, and then several QoS evaluation methods are presented. For QoS optimization, the main research problems in this field are summarized, and optimization models as well as their corresponding solutions are elaborated. We take notice of the booming of edge intelligence in artificial intelligence-empowered IoT scenarios, and illustrate the new research topics and the state-of-the-art approaches related to QoS evaluation and optimization. We discuss the challenges and future research directions.

QoS-Aware Computation Offloading in LEO Satellite Edge Computing for IoT: A Game-Theoretical Approach

CHEN Ying, HU Jintao, ZHAO Jie, MIN Geyong

2024, 33(4): 875-885. doi: 10.23919/cje.2022.00.412

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Low earth orbit (LEO) satellite edge computing can overcome communication difficulties in harsh environments, which lack the support of terrestrial communication infrastructure. It is an indispensable option for achieving worldwide wireless communication coverage in the future. To improve the quality-of-service (QoS) for Internet-of-things (IoT) devices, we combine LEO satellite edge computing and ground communication systems to provide network services for IoT devices in harsh environments. We study the QoS-aware computation offloading (QCO) problem for IoT devices in LEO satellite edge computing. Then we investigate the computation offloading strategy for IoT devices that can minimize the total QoS cost of all devices while satisfying multiple constraints, such as the computing resource constraint, delay constraint, and energy consumption constraint. We formulate the QoS-aware computation offloading problem as a game model named QCO game based on the non-cooperative competition game among IoT devices. We analyze the finite improvement property of the QCO game and prove that there is a Nash equilibrium for the QCO game. We propose a distributed QoS-aware computation offloading (DQCO) algorithm for the QCO game. Experimental results show that the DQCO algorithm can effectively reduce the total QoS cost of IoT devices.

Blockchain Meets Generative Behavior Steganography: A Novel Covert Communication Framework for Secure IoT Edge Computing

CAO Yuanlong, LI Junjie, CHAO Kailin, XIAO Jianmao, LEI Gang

2024, 33(4): 886-898. doi: 10.23919/cje.2023.00.382

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The rapid development of Internet of things (IoT) and edge computing technologies has brought forth numerous possibilities for the intelligent and digital future. The frequent communication and interaction between devices inevitably generate a large amount of sensitive information. Deploying a blockchain network to store sensitive data is crucial for ensuring privacy and security. The openness and synchronicity of blockchain networks give rise to challenges such as transaction privacy and storage capacity issues, significantly impeding their development in the context of edge computing and IoT. This paper proposes a reliable fog computing service solution based on a blockchain fog architecture. This paper stores data files in the inter planetary file system (IPFS) and encrypts the file hash values used for retrieving data files with stream cipher encryption. It employs a steganographic transmission technique leveraging AlphaZero’s Gomoku algorithm to discretely transmit the stream cipher key across the blockchain network without a carrier, thus achieving dual encryption. This approach aims to mitigate the storage burden on the blockchain network while ensuring the security of transaction data. Experimental results demonstrate that the model enhances the transmission capacity of confidential information from kilobytes (KB) to megabytes (MB) and exhibits high levels of covert and security features.

A Deep Deterministic Policy Gradient-Based Method for Enforcing Service Fault-Tolerance in MEC

LONG Tingyan, CHEN Peng, XIA Yunni, MA Yong, SUN Xiaoning, ZHAO Jiale, LYU Yifei

2024, 33(4): 899-909. doi: 10.23919/cje.2023.00.105

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Mobile edge computing (MEC) provides edge services to users in a distributed and on-demand way. Due to the heterogeneity of edge applications, deploying latency and resource-intensive applications on resource-constrained devices is a key challenge for service providers. This is especially true when underlying edge infrastructures are fault and error-prone. In this paper, we propose a fault tolerance approach named DFGP, for enforcing mobile service fault-tolerance in MEC. It synthesizes a generative optimization network (GON) model for predicting resource failure and a deep deterministic policy gradient (DDPG) model for yielding preemptive migration decisions. We show through extensive simulation experiments that DFGP is more effective in fault detection and guaranteeing quality of service, in terms of fault detection accuracy, migration efficiency, task migration time, task scheduling time, and energy consumption than other existing methods.

An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

YAO Liang, XU Xiaolong, DOU Wanchun, Bilal Muhammad

2024, 33(4): 910-919. doi: 10.23919/cje.2023.00.111

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As a pivotal enabler of intelligent transportation system (ITS), Internet of vehicles (IoV) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive, and privacy-aware vehicular applications in IoV result in the transformation from cloud computing to edge computing, which enables tasks to be offloaded to edge nodes (ENs) closer to vehicles for efficient execution. In ITS environment, however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.

A Review of Intelligent Configuration and Its Security for Complex Networks

ZHAO Yue, YANG Bin, TENG Fei, NIU Xianhua, HU Ning, TIAN Bo

2024, 33(4): 920-947. doi: 10.23919/cje.2023.00.001

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Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

DeepLogic: Priority Testing of Deep Learning Through Interpretable Logic Units

LIN Chenhao, ZHANG Xingliang, SHEN Chao

2024, 33(4): 948-964. doi: 10.23919/cje.2022.00.451

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Abstract:
With the increasing deployment of deep learning-based systems in various scenes, it is becoming important to conduct sufficient testing and evaluation of deep learning models to improve their interpretability and robustness. Recent studies have proposed different criteria and strategies for deep neural network (DNN) testing. However, they rarely conduct effective testing on the robustness of DNN models and lack interpretability. This paper proposes a new priority testing criterion, called DeepLogic, to analyze the robustness of the DNN models from the perspective of model interpretability. We first define the neural units in DNN with the highest average activation probability as “interpretable logic units”. We analyze the changes in these units to evaluate the model’s robustness by conducting adversarial attacks. After that, the interpretable logic units of the inputs are taken as context attributes, and the probability distribution of the softmax layer in the model is taken as internal attributes to establish a comprehensive test prioritization framework. The weight fusion of context and internal factors is carried out, and the test cases are sorted according to this priority. The experimental results on four popular DNN models using eight testing metrics show that our DeepLogic significantly outperforms existing state-of-the-art methods.

Lightweight Steganography Detection Method Based on Multiple Residual Structures and Transformer

LI Hao, ZHANG Yi, WANG Jinwei, ZHANG Weiming, LUO Xiangyang

2024, 33(4): 965-978. doi: 10.23919/cje.2022.00.452

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Existing deep learning-based steganography detection methods utilize convolution to automatically capture and learn steganographic features, yielding higher detection efficiency compared to manually designed steganography detection methods. Detection methods based on convolutional neural network frameworks can extract global features by increasing the network’s depth and width. These frameworks are not highly sensitive to global features and can lead to significant resource consumption. This manuscript proposes a lightweight steganography detection method based on multiple residual structures and Transformer (ResFormer). A multi-residuals block based on channel rearrangement is designed in the preprocessing layer. Multiple residuals are used to enrich the residual features and channel shuffle is used to enhance the feature representation capability. A lightweight convolutional and Transformer feature extraction backbone is constructed, which reduces the computational and parameter complexity of the network by employing depth-wise separable convolutions. This backbone integrates local and global image features through the fusion of convolutional layers and Transformer, enhancing the network’s ability to learn global features and effectively enriching feature diversity. An effective weighted loss function is introduced for learning both local and global features, BiasLoss loss function is used to give full play to the role of feature diversity in classification, and cross-entropy loss function and contrast loss function are organically combined to enhance the expression ability of features. Based on BossBase-1.01, BOWS2 and ALASKA#2, extensive experiments are conducted on the stego images generated by spatial and JPEG domain adaptive steganographic algorithms, employing both classical and state-of-the-art steganalysis techniques. The experimental results demonstrate that compared to the SRM, SRNet, SiaStegNet, CSANet, LWENet, and SiaIRNet methods, the proposed ResFormer method achieves the highest reduction in the parameter, up to 91.82%. It achieves the highest improvement in detection accuracy, up to 5.10%. Compared to the SRNet and EWNet methods, the proposed ResFormer method achieves an improvement in detection accuracy for the J-UNIWARD algorithm by 5.78% and 6.24%, respectively.

Efficient Untargeted White-Box Adversarial Attacks Based on Simple Initialization

ZHOU Yunyi, GAO Haichang, HE Jianping, ZHANG Shudong, WU Zihui

2024, 33(4): 979-988. doi: 10.23919/cje.2022.00.449

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Adversarial examples (AEs) are an additive amalgamation of clean examples and artificially malicious perturbations. Attackers often leverage random noise and multiple random restarts to initialize perturbation starting points, thereby increasing the diversity of AEs. Given the non-convex nature of the loss function, employing randomness to augment the attack’s success rate may lead to considerable computational overhead. To overcome this challenge, we introduce the one-hot mean square error loss to guide the initialization. This loss is combined with the strongest first-order attack, the projected gradient descent, alongside a dynamic attack step size adjustment strategy to form a comprehensive attack process. Through experimental validation, we demonstrate that our method outperforms baseline attacks in constrained attack budget scenarios and regular experimental settings. This establishes it as a reliable measure for assessing the robustness of deep learning models. We explore the broader application of this initialization strategy in enhancing the defense impact of few-shot classification models. We aspire to provide valuable insights for the community in designing attack and defense mechanisms.

An Integrated External Archive Local Disturbance Mechanism for Multi-Objective Snake Optimizer

GAO Leifu, LIU Zheng

2024, 33(4): 989-996. doi: 10.23919/cje.2023.00.023

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It is an interesting research direction to develop new multi-objective optimization algorithms based on meta-heuristics. Both the convergence accuracy and population diversity of existing methods are not satisfactory. This paper proposes an integrated external archive local disturbance mechanism for multi-objective snake optimizer (IMOSO) to overcome the above shortcomings. There are two improved strategies. The adaptive mating between subpopulations strategy introduces the special mating behavior of snakes with multiple husbands and wives into the original snake optimizer. Some positions are updated according to the dominated relationships between the newly created individuals and the original individuals. The external archive local disturbance mechanism is used to re-search partial non-inferior solutions with poor diversities. The perturbed solutions are non-dominated sorting with the generated solutions by the next iteration to update the next external archive. The main purpose of this mechanism is to make full use of the non-inferior solution information to better guide the population evolution. The comparison results of the IMOSO and 7 state-of-the-art algorithms on WFG benchmark functions show that IMOSO has better convergence and population diversity.

Lightweight Object Detection Networks for UAV Aerial Images Based on YOLO

LI Yanshan, WANG Jiarong, ZHANG Kunhua, YI Jiawei, WEI Miaomiao, ZHENG Lirong, XIE Weixin

2024, 33(4): 997-1009. doi: 10.23919/cje.2022.00.300

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Existing high-precision object detection algorithms for UAV (unmanned aerial vehicle) aerial images often have a large number of parameters and heavy weight, which makes it difficult to be applied to mobile devices. We propose three YOLO-based lightweight object detection networks for UAVs, named YOLO-L, YOLO-S, and YOLO-M, respectively. In YOLO-L, we adopt a deconvolution approach to explore suitable upsampling rules during training to improve the detection accuracy. The convolution-batch normalization-SiLU activation function (CBS) structure is replaced with Ghost CBS to reduce the number of parameters and weight, meanwhile Maxpool maximum pooling operation is proposed to replace the CBS structure to avoid generating parameters and weight. YOLO-S greatly reduces the weight of the network by directly introducing CSPGhostNeck residual structures, so that the parameters and weight are respectively decreased by about 15% at the expense of 2.4% mAP. And YOLO-M adopts the CSPGhostNeck residual structure and deconvolution to reduce parameters by 5.6% and weight by 5.7%, while mAP only by 1.8%. The results show that the three lightweight detection networks proposed in this paper have good performance in UAV aerial image object detection task.

Multi-Scale Binocular Stereo Matching Based on Semantic Association

ZHENG Jin, JIANG Botao, PENG Wei, ZHANG Qiaohui

2024, 33(4): 1010-1022. doi: 10.23919/cje.2022.00.338

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Abstract:
Aiming at the low accuracy of existing binocular stereo matching and depth estimation methods, this paper proposes a multi-scale binocular stereo matching network based on semantic association. A semantic association module is designed to construct the contextual semantic association relationship among the pixels through semantic category and attention mechanism. The disparity of those regions where the disparity is easily estimated can be used to assist the disparity estimation of relatively difficult regions, so as to improve the accuracy of disparity estimation of the whole image. Simultaneously, a multi-scale cost volume computation module is proposed. Unlike the existing methods, which use a single cost volume, the proposed multi-scale cost volume computation module designs multiple cost volumes for features of different scales. The semantic association feature and multi-scale cost volume are aggregated, which fuses the high-level semantic information and the low-level local detailed information to enhance the feature representation for accurate stereo matching. We demonstrate the effectiveness of the proposed solutions on the KITTI2015 binocular stereo matching dataset, and our model achieves comparable or higher matching performance, compared to other seven classic binocular stereo matching algorithms.

FGM-SPCL: Open-Set Recognition Network for Medical Images Based on Fine-Grained Data Mixture and Spatial Position Constraint Loss

ZHANG Ruru, E Haihong, YUAN Lifei, WANG Yanhui, WANG Lifei, SONG Meina

2024, 33(4): 1023-1033. doi: 10.23919/cje.2023.00.081

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Abstract:
The current intelligent auxiliary diagnosis models all follow the closed-set recognition setting. After the model is deployed online, the input data is often not completely controlled. Diagnosing an untrained disease as a known category would lead to serious medical malpractice. Therefore, realizing the open-set recognition is significant to the safe operation of the intelligent auxiliary diagnosis model. Currently, most open-set recognition models are studied for natural images, and it is very challenging to obtain clear and concise decision boundaries between known and unknown classes when applied to fine-grained medical images. We propose an open-set recognition network for medical images based on fine-grained data mixture and spatial position constraint loss (FGM-SPCL) in this work. Considering the fine graininess of medical images and the diversity of unknown samples, we propose a fine-grained data mixture (FGM) method to simulate unknown data by performing a mixing operation on known data to expand the coverage of unknown data difficulty levels. In order to obtain a concise and clear decision boundary, we propose a spatial position constraint loss (SPCL) to control the position distribution of prototypes and samples in the feature space and maximize the distance between known classes and unknown classes. We validate on a private ophthalmic OCT dataset, and extensive experiments and analyses demonstrate that FGM-SPCL outperforms state-of-the-art models.

YOLO-Drone: A Scale-Aware Detector for Drone Vision

LI Yutong, MA Miao, LIU Shichang, YAO Chao, GUO Longjiang

2024, 33(4): 1034-1045. doi: 10.23919/cje.2023.00.254

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Object detection is an important task in drone vision. Since the number of objects and their scales always vary greatly in the drone-captured video, small object-oriented feature becomes the bottleneck of model performance, and most existing object detectors tend to underperform in drone-vision scenes. To solve these problems, we propose a novel detector named YOLO-Drone. In the proposed detector, the backbone of YOLO is firstly replaced with ConvNeXt, which is the state-of-the-art one to extract more discriminative features. Then, a novel scale-aware attention (SAA) module is designed in detection head to solve the large disparity scale problem. A scale-sensitive loss (SSL) is also introduced to put more emphasis on object scale to enhance the discriminative ability of the proposed detector. Experimental results on the latest VisDrone 2022 test-challenge dataset (detection track) show that our detector can achieve average precision (AP) of 39.43%, which is tied with the previous state-of-the-art, meanwhile, reducing 39.8% of the computational cost.

A General Authentication and Key Agreement Framework for Industrial Control System

GAO Shan, CHEN Junjie, ZHANG Bingsheng, REN Kui, YE Xiaohua, SHEN Yongsheng

2024, 33(4): 1046-1062. doi: 10.23919/cje.2023.00.192

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In modern industrial control systems (ICSs), when user retrieving the data stored in field device like smart sensor, there exists two main problems: one is lack of the verification for identification of user and field device; the other is that user and field device need exchange a key to encrypt sensitive data transmitted over the network. We propose a comprehensive authentication and key agreement framework that enables all connected devices in an ICS to mutually authenticate each other and establish a peer-to-peer session key. The framework combines two types of protocols for authentication and session key agreement: The first one is an asymmetric cryptographic key agreement protocol based on transport layer security handshake protocol used for Internet access, while the second one is a newly designed lightweight symmetric cryptographic key agreement protocol specifically for field devices. This proposed lightweight protocol imposes very light computational load and merely employs simple operations like one-way hash function and exclusive-or (XOR) operation. In comparison to other lightweight protocols, our protocol requires the field device to perform fewer computational operations during the authentication phase. The simulation results obtained using OpenSSL demonstrates that each authentication and key agreement process in the lightweight protocol requires only 0.005 ms. Our lightweight key agreement protocol satisfies several essential security features, including session key secrecy, identity anonymity, untraceability, integrity, forward secrecy, and mutual authentication. It is capable of resisting impersonation, man-in-the-middle, and replay attacks. We have employed the Gong-Needham-Yahalom (GNY) logic and automated validation of Internet security protocols and application tool to verify the security of our symmetric cryptographic key agreement protocol.

A Distributed Self-Tallying Electronic Voting System Using the Smart Contract

YAO Jingyu, YANG Bo, WANG Tao, ZHANG Wenzheng

2024, 33(4): 1063-1076. doi: 10.23919/cje.2023.00.233

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For electronic voting (e-voting) with a trusted authority, the ballots may be discarded or tampered, so it is attractive to eliminate the dependence on the trusted party. An e-voting protocol, where the final voting result can be calculated by any entity, is known as self-tallying e-voting protocol. To the best of our knowledge, addressing both abortive issue and adaptive issue simultaneously is still an open problem in self-tallying e-voting protocols. Combining Ethereum blockchain with cryptographic technologies, we present a decentralized self-tallying e-voting protocol. We solve the above problem efficiently by utilizing optimized Group Encryption Scheme and standard Exponential ElGamal Cryptosystem. We use zero-knowledge proof and homomorphic encryption to protect votes’ secrecy and achieve self-tallying. All ballots can be verified by anyone and the final voting result can be calculated by any entity. In addition, using the paradigm of score voting and “1-out-of-k” proof, our e-voting system is suitable for a wide range of application scenarios. Experiments show that our protocol is more competitive and more suitable for large-scale voting.

BAD-FM: Backdoor Attacks Against Factorization-Machine Based Neural Network for Tabular Data Prediction

MENG Lingshuo, GONG Xueluan, CHEN Yanjiao

2024, 33(4): 1077-1092. doi: 10.23919/cje.2023.00.041

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Backdoor attacks pose great threats to deep neural network models. All existing backdoor attacks are designed for unstructured data (image, voice, and text), but not structured tabular data, which has wide real-world applications, e.g., recommendation systems, fraud detection, and click-through rate prediction. To bridge this research gap, we make the first attempt to design a backdoor attack framework, named BAD-FM, for tabular data prediction models. Unlike images or voice samples composed of homogeneous pixels or signals with continuous values, tabular data samples contain well-defined heterogeneous fields that are usually sparse and discrete. Tabular data prediction models do not solely rely on deep networks but combine shallow components (e.g., factorization machine, FM) with deep components to capture sophisticated feature interactions among fields. To tailor the backdoor attack framework to tabular data models, we carefully design field selection and trigger formation algorithms to intensify the influence of the trigger on the backdoored model. We evaluate BAD-FM with extensive experiments on four datasets, i.e., HUAWEI, Criteo, Avazu, and KDD. The results show that BAD-FM can achieve an attack success rate as high as 100% at a poisoning ratio of 0.001%, outperforming baselines adapted from existing backdoor attacks against unstructured data models. As tabular data prediction models are widely adopted in finance and commerce, our work may raise alarms on the potential risks of these models and spur future research on defenses.

Joint Communication-Caching-Computing Resource Allocation for Bidirectional Data Computation in IRS-Assisted Hybrid UAV-Terrestrial Network

LIAO Yangzhe, LIU Lin, SONG Yuanyan, XU Ning

2024, 33(4): 1093-1103. doi: 10.23919/cje.2023.00.089

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Joint communication-caching-computing resource allocation in wireless inland waterway communications enables resource-constrained unmanned surface vehicles (USVs) to provision computation-intensive and latency-sensitive tasks forward beyond fifth-generation (B5G) and sixth-generation (6G) era. The power of such resource allocation cannot be fully studied unless bidirectional data computation is properly managed. A novel intelligent reflecting surface (IRS)-assisted hybrid UAV-terrestrial network architecture is proposed with bidirectional tasks. The sum of uplink and downlink bandwidth minimization problem is formulated by jointly considering link quality, task execution mode selection, UAVs trajectory, and task execution latency constraints. A heuristic algorithm is proposed to solve the formulated challenging problem. We divide the original challenging problem into two subproblems, i.e., the joint optimization problem of USVs offloading decision, caching decision and task execution mode selection, and the joint optimization problem of UAVs trajectory and IRS phase shift-vector design. The Karush–Kuhn–Tucker conditions are utilized to solve the first subproblem and the enhanced differential evolution algorithm is proposed to solve the latter one. The results show that the proposed solution can significantly decrease bandwidth consumption in comparison with the selected advanced algorithms. The results also prove that the sum of bandwidth can be remarkably decreased by implementing a higher number of IRS elements.

Cellular V2X-Based Integrated Sensing and Communication System: Feasibility and Performance Analysis

LI Yibo, ZHAO Junhui, LIAO Jieyu, HU Fajin

2024, 33(4): 1104-1116. doi: 10.23919/cje.2022.00.340

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Communication and sensing are basically required in intelligent transportation. The combination of two functions can provide a viable way in alleviating concerns about resource limitations. To achieve this, we propose an integrated sensing and communication (ISAC) system based on cellular vehicle-to-everything (C-V2X). We first analyze the feasibility of new radio (NR) waveform for ISAC system. We discuss the possibility of reusing NR waveform for sensing based on current NR-V2X standards. Ambiguity function is calculated to investigate the sensing performance limitation of NR waveform. A C-V2X-based ISAC system is then designed to realize the two tasks in vehicular network simultaneously. We formulate an integrated framework of vehicular communication and automotive sensing using the already-existing NR-V2X network. Based on the proposed ISAC framework, we develop a receiver algorithm for target detection/estimation and communication with minor modifications. We evaluate the performance of the proposed ISAC system with communication throughput, detection probability, and range/velocity estimation accuracy. Simulations show that the proposed system achieves high reliability communication with 99.9999% throughput and high accuracy sensing with errors below 1 m and 1 m/s in vehicle scenarios.

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2024, Volume 33, Issue 4

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