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Intelligent integrated sensing and communication:a survey

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Science China(information Sciences) 2025年第03期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of information and communication engineering Dalian University of Technology College of information engineering Zhejiang University of Technology School of information and Electronics Beijing Institute of Technology department of Electrical and Computer engineering The University of Texas at Dallas department of Electrical and Computer engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

Electrocardiogram Signal Denoising Using Optimized Adaptive Hybrid Filter with Empirical Wavelet Transform

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 66-80页

作者： BALASUBRAMANIAN S NARUKA Mahaveer Singh TEWARI Gaurav department of Electronics and communication engineering Maharishi University of Information TechnologyMaharishi Vidya MandirLucknow226013India department of Electronics and communication engineering IEC College of Engineering and TechnologyGreater Noida201310India department of Electrical communication engineering School of Information and Communication TechnologyGautam Buddha UniversityGreater Noida201310India

Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for *** electrocardiogram(ECG)signal is a comprehensive non-invasive method for determining cardiac *** health practitioners use the ECG signal to ascertain critical information about the human *** this article,swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms(EWTs).At first,the white Gaussian noise is added to the input ECG signal and then applied to the *** ECG signals are denoised by the proposed adaptive hybrid *** honey badge optimization(HBO)algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight *** proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian,electromyogram and electrode motion artifact noises.A comparison of the HBO approach with recursive least square-based adaptive filter,multichannel least means square,and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid *** experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising.

关键词： electrocardiogram(ECG)signal denoising empirical wavelet transform(EWT) honey badge optimization(HBO) adaptive hybrid filter window function

Energy-Efficient D2D-Aided Dual UAV Data Collection

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Chinese Journal of Electronics 2025年第1期34卷 176-185页

作者： Qiulei Huang Wei Wang Zhaohui Song Nan Zhao School of information and communication engineering Dalian University of Technology Space information Research Institute Hangzhou Dianzi University

Aided by device-to-device(D2D) connections, unmanned aerial vehicle(UAV) can significantly enhance the coverage of wireless communications. In this paper, we consider a data collection system with the assistance of D2D, where two fixed-wing UAVs as aerial base stations cooperatively serve the ground devices. To accommodate more devices, we propose two effective algorithms to establish the multi-hop D2D connections. Then, the user scheduling, UAV trajectory, and transmit power are jointly optimized to maximize the energy efficiency, which is a non-convex problem. Accordingly, we decompose it into three subproblems. The scheduling optimization is first converted into a linear programming. Then, the trajectory design and the transmit power optimization are reformulated as two convex problems by the Dinkelbach method. Finally, an iterative algorithm is proposed to effectively solve the original problem. Simulation results are presented to verify the effectiveness of the proposed scheme.

关键词： Wireless communication Simulation Data collection Autonomous aerial vehicles Linear programming Energy efficiency Trajectory Device-to-device communication Iterative methods Optimization

Link-16 anti-jamming performance evaluation based on grey relational analysis and cloud model

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Journal of Systems engineering and Electronics 2025年第1期36卷 62-72页

作者： NING Xiaoyan WANG Ying WANG Zhenduo SUN Zhiguo College of information and communication engineering Harbin Engineering UniversityHarbin 150001China

Anti-jamming performance evaluation has recently received significant attention. For Link-16, the anti-jamming performance evaluation and selection of the optimal anti-jamming technologies are urgent problems to be solved. A comprehensive evaluation method is proposed, which combines grey relational analysis (GRA) and cloud model, to evaluate the anti-jamming performances of Link-16. Firstly, on the basis of establishing the anti-jamming performance evaluation indicator system of Link-16, the linear combination of analytic hierarchy process(AHP) and entropy weight method (EWM) are used to calculate the combined weight. Secondly, the qualitative and quantitative concept transformation model, i.e., the cloud model, is introduced to evaluate the anti-jamming abilities of Link-16 under each jamming scheme. In addition, GRA calculates the correlation degree between evaluation indicators and the anti-jamming performance of Link-16, and assesses the best anti-jamming technology. Finally, simulation results prove that the proposed evaluation model can achieve the objective of feasible and practical evaluation, which opens up a novel way for the research of anti-jamming performance evaluations of Link-16.

关键词： Link-16 anti-jamming grey relational analysis(GRA) cloud model combination weights

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Loss Aware Feature Attention Mechanism for Class and Feature Imbalance Issue

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Computers, Materials & Continua 2025年第1期82卷 751-775页

作者： Yuewei Wu Ruiling Fu Tongtong Xing Fulian Yin College of information and communication engineering Communication University of ChinaBeijing100024China State Key Laboratory of Media Convergence and communication Communication University of ChinaBeijing100024China

In the Internet era,recommendation systems play a crucial role in helping users find relevant information from large *** imbalance is known to severely affect data quality,and therefore reduce the performance of recommendation *** to the imbalance,machine learning algorithms tend to classify inputs into the positive(majority)class every time to achieve high prediction *** can be categorized such as by features and classes,but most studies consider only class *** this paper,we propose a recommendation system that can integrate multiple networks to adapt to a large number of imbalanced features and can deal with highly skewed and imbalanced datasets through a loss *** propose a loss aware feature attention mechanism(LAFAM)to solve the issue of feature *** network incorporates an attention mechanism and uses multiple sub-networks to classify and learn *** better results,the network can learn the weights of sub-networks and assign higher weights to important *** propose suppression loss to address class imbalance,which favors negative loss by penalizing positive loss,and pays more attention to sample points near the decision *** on two large-scale datasets verify that the performance of the proposed system is greatly improved compared to baseline methods.

关键词： Imbalanced data deep learning e-commerce recommendation loss function big data analysis

Enhanced detection of obfuscated malware in memory dumps:a machine learning approach for advanced cybersecurity

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Cybersecurity 2025年第1期8卷 103-125页

作者： Md.Alamgir Hossain Md.Saiful Islam Institute of information and communication Technology(IICT) Bangladesh University of Engineering and Technology(BUET)DhakaBangladesh

In the realm of cybersecurity,the detection and analysis of obfuscated malware remain a critical challenge,especially in the context of memory *** research paper presents a novel machine learning-based framework designed to enhance the detection and analytical capabilities against such elusive threats for binary and multi type’s *** approach leverages a comprehensive dataset comprising benign and malicious memory dumps,encompassing a wide array of obfuscated malware types including Spyware,Ransomware,and Trojan Horses with their *** begin by employing rigorous data preprocessing methods,including the normalization of memory dumps and encoding of categorical *** tackle the issue of class imbalance,a Synthetic Minority Over-sampling Technique is utilized,ensuring a balanced representation of various malware *** selection is meticulously conducted through Chi-Square tests,mutual information,and correlation analyses,refning the model’s focus on the most indicative attributes of obfuscated *** heart of our framework lies in the deployment of an Ensemble-based Classifer,chosen for its robustness and efectiveness in handling complex data *** model’s performance is rigorously evaluated using a suite of metrics,including accuracy,precision,recall,F1-score,and the area under the ROC curve(AUC)with other evaluation metrics to assess the model’s *** proposed model demonstrates a detection accuracy exceeding 99%across all cases,surpassing the performance of all existing models in the realm of malware detection.

关键词： Obfuscated malware detection Memory dump analysis Advanced malware analytics Malware behavioral patterns Advanced malware analytics Machine learning in cybersecurity

Situation-adaptive neural network for fast pre-computing image enhancement

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Science China(information Sciences) 2025年第2期68卷 380-382页

作者： Xinyue LI Huiyu DUAN Jia WANG Xiaohong LIU Yitong CHEN Guangtao ZHAI Institute of Image communication and Network engineering Department of Electronic EngineeringShanghai Jiao Tong University

As intelligent vision tasks become more widespread, enhancing image quality before further computational analysis is crucial. Recently, deep learning has shown potential for automated pre-computing enhancement, but it typically requires substantial computational resources and is hard to adapt to in multiple situations without *** practice, image enhancement often demands flexible adjustments based on different situations and subsequent computation devices, such as optical computing.

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An integrated PHM framework for radar systems through system structural decomposition

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Journal of Systems engineering and Electronics 2025年第1期36卷 95-107页

作者： WANG Hong KULEVOME Delanyo Kwame Bensah ZHAO Zi’an School of information and communication engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Implementing an efficient real-time prognostics and health management (PHM) framework improves safety and reduces maintenance costs in complex engineering ***, research on PHM framework development for radar systems is limited. Furthermore, typical PHM approaches are centralized, do not scale well, and are challenging to *** paper proposes an integrated PHM framework for radar systems based on system structural decomposition to enhance reliability and support maintenance actions. The complexity challenge associated with implementing PHM at the system level is addressed by dividing the radar system into subsystems. Subsequently, optimal measurement point selection and sensor placement algorithms are formulated for effective data acquisition. Local modules are developed for each subsystem health assessment, fault diagnosis, and fault prediction without a centralized controller. Maintenance decisions are based on each local module’s fault diagnosis and prediction results. To further improve the effectiveness of the prognostics stage, the feasibility of integrating deep learning (DL) models is also *** experiments with different degradation patterns are performed to evaluate the effectiveness of the framework’s DLbased prognostics model. The proposed framework facilitates transitioning from traditional reactive maintenance practices to a predictive maintenance approach, thereby reducing downtime and improving the overall availability of radar systems.

关键词： deep learning prognostics and health management(PHM) radar systems remaining useful life(RUL)

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Preamble slice orderly queue access scheme in cell-free dense communication systems

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Digital communications and Networks 2025年第1期11卷 126-135页

作者： Jun Sun Mengzhu Guo Jian Liu School of Telecommunications and information engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Computer and communication engineering University of Science and TechnologyBeijing 100083China

High reliability applications in dense access scenarios have become one of the main goals of 6G *** solve the access collision of dense Machine Type communication(MTC)devices in cell-free communication systems,an intelligent cooperative secure access scheme based on multi-agent reinforcement learning and federated learning is proposed,that is,the Preamble Slice Orderly Queue Access(PSOQA)*** this scheme,the preamble arrangement is combined with the access *** preamble arrangement is realized by preamble slices which is from the virtual preamble *** access devices learn to queue orderly by deep reinforcement *** orderly queue weakens the random and avoids collision.A preamble slice is assigned to an orderly access queue at each access *** orderly queue is determined by interaction information among multiple *** the federated reinforcement learning framework,the PSOQA scheme is implemented to guarantee the privacy and security of ***,the access performance of PSOQA is compared with other random contention schemes in different load *** results show that PSOQA can not only improve the access success rate but also guarantee low-latency tolerant performances.

关键词： Machine type communication Random access Reinforcement learning Access success probability Federated learning

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