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STARS Assisted Semi-Grant-Free NOMA Communications

IEEE Transactions on Wireless Communications 2025年

作者： Xie, Jin Yue, Xinwei Zhao, Jingjing Zou, Yixuan Liu, Yuanwei Liu, Rongke Ding, Zhiguo Key Laboratory of Information and Communication Systems China Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100101 China Beihang University School of Electronic and Information Engineering Beijing100191 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates

This paper investigates the performance of simultaneously transmitting and reflecting surface (STARS) assisted semi-grant-free non-orthogonal multiple access network with randomly distributed users. By deploying STARS, the transmit signals of grant-based user (GBU) and grant-free users (GFUs) can be exquisitely adjusted to reduce interference. We propose a maximum channel scheduling (MCS) protocol that allows a GFU to access GBU’s channel with the assistance of STARS. In particular, the impacts of perfect/imperfect successive interference cancellation (pSIC/ipSIC) on MCS protocol are taken into account. To characterize the performance of STARS aided MCS (STARS-MCS) network, we derive the expressions of outage probability for GBU and GFU with pSIC/ipSIC. By applying convolution theorem and Laplace transform, the asymptotic expressions of outage probability and diversity orders for GBU and GFU are attained. We further design a STARS-based power control (SPC) strategy to eliminate the outage probability error floor and improve the outage performance. Numerical results show that: 1) The performance of STARS-MCS outperforms the existing benchmarks in terms of outage probability and system throughput;2) The SPC strategy can effectively improve the performance of the STARS-MCS network and eliminate the outage probability error floor at high signal-to-noise ratios;and 3) By adjusting reflection and transmission coefficients of STARS, the outage performance of GBU and GFU can be greatly enhanced. © 2025 IEEE. All rights reserved.

关键词： Laplace transforms

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A Digital Twin-Based Intelligent Network Architecture for Underwater Acoustic Sensor Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Song, Shanshan Huangfu, Bingwen Guo, Jiani Liu, Jun Cui, Junhong Shen, Xuemin Beihang University School of Electronic and Information Engineering Beijing100191 China Jilin University College of Computer Science and Technology Changchun130012 China UESTC Shenzhen Institute for Advanced Study Shenzhen China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Underwater acoustic sensor networks (UASNs) drive toward strong environmental adaptability, intelligence, and multifunctionality. However, due to unique UASN characteristics, such as long propagation delay, dynamic channel quality, and high attenuation, existing studies present untimeliness, inefficiency, and inflexibility in real practice. Digital twin (DT) technology is promising for UASNs to break the above bottlenecks by providing high-fidelity status prediction and exploring optimal schemes. In this article, we propose a Digital Twin-based Network Architecture (DTNA), enhancing UASNs' environmental adaptability, intelligence, and multifunctionality. By extracting real UASN information from local (node) and global (network) levels, we first design a layered architecture to improve the DT replica fidelity and UASN control flexibility. In local DT, we develop a resource allocation paradigm (RAPD), which rapidly perceives performance variations and iteratively optimizes allocation schemes to improve real-time environmental adaptability of resource allocation algorithms. In global DT, we aggregate decentralized local DT data and propose a collaborative Multi-agent reinforcement learning framework (CMFD) and a task-oriented network slicing (TNSD). CMFD patches scarce real data and provides extensive DT data to accelerate AI model training. TNSD unifies heterogeneous tasks' demand extraction and efficiently provides comprehensive network status, improving the flexibility of multi-task scheduling algorithms. Finally, practical and simulation experiments verify the high fidelity of DT. Compared with the original UASN architecture, experiment results demonstrate that DTNA can: (i) improve the timeliness and robustness of resource allocation;(ii) greatly reduce the training time of AI algorithms;(iii) more rapidly obtain network status for multi-task scheduling at a low cost. © 2002-2012 IEEE.

关键词： Resource allocation

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6G Communication New Paradigm: The Integration of Unmanned Aerial Vehicles and Intelligent Reflecting Surfaces

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IEEE Communications Surveys and Tutorials 2025年

作者： Ning, Zhaolong Li, Tengfeng Wu, Yu Wang, Xiaojie Wu, Qingqing Yu, Fei Richard Guo, Song Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Chongqing University of Posts and Telecommunications School of Cyber Security and Information Law Chongqing400065 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai Shanghai200240 China Carleton University School of Information Technology OttawaONK1S 5B6 Canada The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

With the continuous development of Intelligent Reflecting Surfaces (IRSs) and Unmanned Aerial Vehicles (UAVs), their combination has become foundational technologies to complement the terrestrial network by providing communication enhancement services for large-scale users. This article provides a comprehensive overview of IRS-assisted UAV communications for 6th-Generation (6G) networks. First, the applications supported by IRS-assisted UAV communications for 6G networks are introduced, and key issues originated from applications supported by IRSs and UAVs for 6G networks are summarized and analyzed. Then, prototypes and main technologies related to the integration of IRSs and UAVs are introduced. Driven by applications and technologies of IRS-assisted UAV communications, existing solutions in the realms of energy-constrained communications, secure communications, and enhanced communications are summarized, and corresponding empirical lessons are provided. Finally, we discuss some research challenges and open issues in IRS-assisted UAV communications, offering directions for the future development. © 1998-2012 IEEE.

关键词： Aircraft communication

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Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

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IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory Control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

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MULGONET: An interpretable neural network framework to integrate multi-omics data for cancer recurrence prediction and biomarker discovery

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Fundamental Research 2025年

作者： Lan, Wei Tang, Zhentao Liao, Haibo Chen, Qingfeng Chen, Yi-Ping Phoebe Zhang, Zhaolei Wang, Jianxin Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronic and Information Guangxi University Guangxi 530004 China Department of Computer Science and Information Technology La Trobe University 3086 VIC Australia Donnelly Centre Department of Computer Science and Department of Molecular Genetics University of Toronto M5S 1A1 ON Canada School of Computer Science and Engineering Central South University Hunan 410083 China

Multi-omics cancer data provides complementary views of tumorigenesis and progression. Technical challenges exist in integrating these heterogeneous data into deep learning models to better understand tumorigenesis and predict cancer recurrence. We herein propose a novel end-to-end deep learning method (MULGONET) for cancer recurrence prediction and biomarker discovery. First, MULGONET can effectively solve the curse of dimensionality and the lack of model interpretability in multi-omics data integration. Second, it explores interactions and regulatory relationships between genes and GO terms, thus providing biological insights. Benchmark results show that MULGONET outperforms other contemporary classification methods. It achieves AUPRs of 0.774 ± 0.015, 0.873 ± 0.003 and 0.702 ± 0.011 on the bladder, pancreatic and stomach cancer datasets, respectively. We also show that MULGONET can effectively identify prognostic genes and GO terms associated with cancer recurrence. © 2025

关键词： Biomarker discovery Cancer recurrence Deep learning Interpretability Multi-omics data

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QoE-Guaranteed Optimization in MEC-Enabled Metaverse: An Active Inference Deep Reinforcement Learning Approach

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Du, Jianbo Gong, Jie Chu, Xiaoli Xiong, Zehui Chen, Xianfu Dong, Mianxiong Yu, F. Richard Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi'an710121 China The University of Sheffield Mappin Street Department of Electronic and Electrical Engineering SheffieldS1 3JD United Kingdom Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore Singapore Shenzhen CyberAray Network Technology Company Ltd Shenzhen518000 China Muroran Institute of Technology Department of Sciences and Informatics Muroran050-8585 Japan Carleton University Systems and Computer Engineering OttawaON Canada

In this paper, we consider a MEC-enabled metaverse scenario which consists of a remote metaverse server and an edge server that cooperates to provide services to mobile users. The edge server is deployed at the base station (BS), serves a dual role: augmenting computational capabilities for user equipment (UE) and pre-caching a portion of the metaverse service contents before each time slot. Moreover, the foreground information and the requested contents generated by the UEs can also be cached to the BS. We formulate a problem to maximize the cache hit number by jointly optimizing contents pre-caching and resource allocation at the BS while considering UEs preference and reducing the UEs total energy consumption, essential for the efficient delivery of services in dynamic MEC environments. To solve this problem, we reformulate it as a partially observable markov decision process and propose an active inference enabled deep reinforcement learning algorithm, which combines active inference with deep reinforcement learning to select the optimal strategy by minimizing the expected free energy. Simulations show that the proposed algorithm can effectively improve the total quality of experience and the cache hit number of UEs, while minimizing the UEs total energy consumption compared with other baseline algorithms © 2015 IEEE.

关键词： Active learning

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Blind Joint Multi-DFO Estimation for High-Mobility OFDM Communication Systems

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IEEE Transactions on Vehicular technology 2025年

作者： Feng, Yunqi Zhu, Zhehua Lu, Weidang Zhao, Nan Nallanathan, Arumugam Zhejiang University of Technology College of Information Engineering Key Laboratory of Communication Networks and Applications of Zhejiang Province Hangzhou310023 China Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

In high-mobility communication systems, the relative motion between the transceivers will cause severe Doppler shifts, resulting in the doubly-selective fading channel. In this correspondence, a blind joint multiple Doppler frequency offsets (DFOs) estimator is proposed for high-mobility communication with orthogonal frequency division multiplexing (OFDM). Both the receive beamforming and multi-beamformer redundancy are employed to estimate and compensate multiple DFOs, thereby effectively mitigating the channel time-variation. Moreover, the inter-channel interference (ICI) has been derived to further analyze the impact of residual DFOs due to the estimation error on the system performance. Numerical results verify that the proposed blind estimator substantially outperforms existing ones. © 1967-2012 IEEE.

关键词： Frequency allocation

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Resource and Trajectory Optimization for Secure Enhancement in IRS-Assisted UAV-MEC Systems

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IEEE Transactions on Vehicular technology 2025年

作者： Zhang, Fuyuqi Ding, Yu Cao, Mingfeng Wu, Mengru Lu, Weidang Nallanathan, Arumugam Zhejiang University of Technology College of Information Engineering Hangzhou310023 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggido Yongin-si17104 Korea Republic of

Intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle-mobile edge computing (UAV-MEC) systems have brought great advancements in future networks. However, due to the line-of-sight transmission, confidential task information is highly susceptible to interception by malicious UAV eavesdroppers, posing a significant security challenge. To overcome this challenge, we aim to design a joint resource and trajectory optimization scheme to enhance the secure performance of the IRS-assisted UAV-MEC system. Specifically, the minimum secrecy capacity of users is maximized by optimizing time allocation, transmit power, local computation CPU frequency, IRS phase shifts, and UAV trajectory while satisfying users' task processing capacity requirements. To address the non-convex optimization problem, we first employ mathematical techniques to simplify it into a more tractable form, and subsequently decompose it into several sub-problems. These sub-problems can be solved iteratively through combining phase alignment with successive convex approximation. Simulation results verify that our proposed scheme can improve the secrecy capacity performance of the considered IRS-assisted UAV-MEC system compared with benchmarks. © 1967-2012 IEEE.

关键词： Convex optimization

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Facial age estimation by group-centric feature learning

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Expert Systems with Applications 2025年 285卷

作者： Zhao, Qilu Li, Yante College of Computer Science and Technology Qingdao University 308 Ningxia Road Qingdao266000 China Department of Information Technology and Electronic Engineering University of Oulu Pentti Kaiteran katu 1 Oulu90570 Finland

Predicting age from facial images presents a significant challenge due to the intricate nature of the facial aging process. Human faces undergo gradual changes as they age, causing faces to appear very similar at adjacent ages. However, notable age differences result in significant appearance changes, as the human face matures in different ways at different ages, e.g., bone growth in childhood and skin wrinkles in adulthood. In a word, the process of facial aging is continuous but also non-stationary. To address these challenges, our approach is grounded in the insight that the non-stationary aging process can be decomposed into a series of stationary subprocesses. Using a divide-and-conquer strategy, we divide ages into several overlapping groups to facilitate group-centric feature learning. Within each age group, where the aging dynamics are relatively stable, we employ a specialized deep neural network, termed an "expert", to extract group-centric ordinal features. The overlapping of age groups allows each expert to capture the continuity between adjacent groups. The group-centric features are then fused into universal representations, with an attention layer employed to selectively emphasize the most salient feature pertinent to the final age estimation task. For the age estimator, we leverage a hybrid method involving expectation regression to alleviate the inconsistency between training and evaluation stages found in traditional label distribution learning estimators. Extensive experiments involving several famous datasets (MORPH2, CACD, and AFAD) for age estimation have demonstrated the superior performance of our proposed model over several existing state-of-the-art methods. © 2025 Elsevier Ltd

关键词： Self-supervised learning

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Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks

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IEEE Transactions on Communications 2025年

作者： Yang, Li Naser, Shimaa Shami, Abdallah Muhaidat, Sami Ong, Lyndon Debbah, Merouane Ontario Tech University Faculty of Business and Information Technology OshawaONL1G 0C5 Canada Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates Western University Department of Electrical and Computer Engineering LondonONN6A 3K7 Canada Ciena HanoverMD21076 United States

The transition from fifth-generation (5G) to sixth-generation (6G) mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), driven by Artificial Intelligence (AI) and Machine Learning (ML), are designed to automate the entire lifecycle of network operations with minimal human intervention, presenting a promising solution for enhancing automation in 5G/6G networks. However, the implementation of ZTNs brings forth the need for autonomous and robust cybersecurity solutions, as ZTNs rely heavily on automation. AI/ML algorithms are widely used to develop cybersecurity mechanisms, but require substantial specialized expertise and encounter model drift issues, posing significant challenges in developing autonomous cybersecurity measures. Therefore, this paper proposes an automated security framework targeting Physical Layer Authentication (PLA) and Cross-Layer Intrusion Detection Systems (CLIDS) to address security concerns at multiple Internet protocol layers. The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments. Experimental results illustrate that the proposed framework achieves high performance on the public Radio Frequency (RF) fingerprinting and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017) datasets, showcasing its effectiveness in addressing PLA and CLIDS tasks within dynamic and complex networking environments. Furthermore, the paper explores open challenges and research directions in the 5G/6G cybersecurity domain. This framework represents a significant advancement towards fully autonomous and secure 6G networks, paving the way for future innovations in network automation and cybersecurity. © 1972-2012 IEEE.

关键词： Cybersecurity

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