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Information Sciences

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影响因子:
6.0
出版商:
Elsevier
ISSN:
0020-0255
浏览:
129720
关注:
166

征稿

Information Sciences is an academic journal published by Elsevier. (ISSN 0020-0255, impact factor 6.0, CCF B).

Aims & Scope Informatics and Computer Science Intelligent Systems Applications An International Journal Information Sciences will publish original, innovative and creative research results. A smaller number of timely tutorial and surveying contributions will be published from time to time. The journal is designed to serve researchers, developers, managers, strategic planners, graduate students and others interested in state-of-the art research activities in information, knowledge engineering and intelligent systems. Readers are assumed to have a common interest in information science, but with diverse backgrounds in fields such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioural sciences and biochemistry. The journal publishes high-quality, refereed articles. It emphasizes a balanced coverage of both theory and practice. It fully acknowledges and vividly promotes a breadth of the discipline of Informations Sciences. Topics include: Foundations of Information Science: Information Theory, Mathematical Linguistics, Automata Theory, Cognitive Science, Theories of Qualitative Behaviour, Artificial Intelligence, Computational Intelligence, Soft Computing, Semiotics, Computational Biology and Bio-informatics. Implementations and Information Technology: Intelligent Systems, Genetic Algorithms and Modelling, Fuzzy Logic and Approximate Reasoning, Artificial Neural Networks, Expert and Decision Support Systems, Learning and Evolutionary Computing, Expert and Decision Support Systems, Learning and Evolutionary Computing, Biometrics, Moleculoid Nanocomputing, Self-adaptation and Self-organisational Systems, Data Engineering, Data Fusion, Information and Knowledge, Adaptive ad Supervisory Control, Discrete Event Systems, Symbolic / Numeric and Statistical Techniques, Perceptions and Pattern Recognition, Design of Algorithms, Software Design, Computer Systems and Architecture Evaluations and Tools, Human-Computer Interface, Computer Communication Networks and Modelling and Computing with Words Applications: Manufacturing, Automation and Mobile Robots, Virtual Reality, Image Processing and Computer Vision Systems, Photonics Networks, Genomics and Bioinformatics, Brain Mapping, Language and Search Engine Design, User-friendly Man Machine Interface, Data Compression and Text Abstraction and Summarization, Virtual Reality, Finance and Economics Modelling and Optimisation Editors-in-Chief can be reached at the following: Professor Sabrina S. Senatore- [email protected] Professor Zheng Z. Yan- [email protected]
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Special Issues

Special Issue on Information Processing and Intelligent Systems in 6G Networks 截稿日期: 2026-09-15 The sixth-generation (6G) wireless networks are expected to provide ubiquitous, intelligent, and ultra-reliable connectivity for seamless human–machine–thing interactions. Achieving the ambitious goals of extreme data rates, sub-millisecond latency, enhanced energy efficiency, and native integration of sensing and computing will require artificial intelligence (AI) to serve as a foundational enabler of 6G. 6G communication is an information-centric system, for which information science constitutes the theoretical foundation, technological enabler, and paradigm-shaping framework, supporting the evolution of 6G toward intelligent, integrated, and efficient operation. Currently, the integration of AI and 6G has entered a critical phase where theoretical exploration, prototype validation, and early standardization efforts advance in parallel. Data-driven approaches have demonstrated promising potential through early prototypes and trials, yet fundamental challenges remain, including the lack of theoretical or methodological innovations in intelligent systems, principled signal processing and system frameworks, insufficient theoretical modeling of AI-native 6G, and unresolved issues in interpretability, robustness, and trustworthiness of learning-driven communication systems. Guest editors: Dr. Luping Xiang, Nanjing University, ChinaProf. Kezhi Wang, Brunel University of London, UK Dr. Mingzhe Chen, University of Miami, USA Prof. M´erouane Debbah, Khalifa University, UAE Prof. Bo Lei, China Telecom Research Institute, China Special issue information: This Special Issue seeks to gather state-of-the-art research on AI-empowered 6G networks, examining how AI, ranging from machine learning and deep learning to large foundation models and generative AI, can drive innovation in intelligent information processing methods, theoretical or methodological advances in intelligent systems, promote learning-based explainable decision-making, enable native intelligence in 6G, establish intelligence as an intrinsic network attribute, realize intelligent network automation, and unify sensing, communication, and computing. Topics include, but are not limited to: 1. Information-Theoretic Foundations of AI-Native Communication: Modeling semantic, goal-oriented, and learning-driven communication from an information-theoretic perspective. 2. Fundamental Limits and Trade-offs: Investigating the theoretical limits of integrated sensing, communication, and computation, and the trade-offs among reliability, latency, and information content in AI-driven networks. 3. Architecture of AI Agentic 6G: Autonomous AI agent frameworks , information interaction mechanisms between AI agents, agent-based zero-touch network management. 4. Statistical Learning Foundations for AI-Native Systems: Uncertainty-aware learning and decision-making, confidence calibration, out-of-distribution detection, and robust inference under limited or noisy information. 5. 6G for AI Enablement: Communication-Efficient Federated and Distributed Learning, Edge AI, Multi-Agent Collaboration and Network-Aware AI, Privacy-Preserving and Secure AI Techniques. 6. AI-Native Physical Layer: AI-generated waveforms, end-to-end learned transceivers, reinforcement learning for modulation/beamforming, semantic and goal-oriented communication. 7. AI-Enabled Network Intelligence: Radio resource management, mobility optimization, network slicing orchestration, large-model-assisted RAN control. 8. Integrated Sensing and Communication (ISAC): AI-based joint waveform design, radar-communication coexistence, real-time inference optimization. 9. AI for Security and Trustworthiness: Federated learning, privacy-preserving AI, adversarial robustness, explainable AI (XAI) for 6G. 10. Optimization and Control under Information Constraints: Distributed optimization frameworks for limited-information, latency-critical networks, Constrained decision-making, feedback control, and learning with partial observability. 11. Digital Twin and Automation: Predictive resource allocation, zero-touch management, autonomous fault detection. 12. Experimental Prototypes and Testbeds: Hardware-in-the-loop demonstrations, OTA trials, open-source platforms, benchmarking datasets. Manuscript submission information: Important Dates: Submission Open Date: April 15, 2026 Submission Deadline: September 15, 2026 Notification of Acceptance: May 15, 2027 Manuscripts must be submitted via the Information Sciences online submission system (https://submit.elsevier.com/INS). Please select the article type “VSI: Info Processing for 6G” when submitting your manuscript online. Please refer to the Guide for Authors to prepare your manuscript. All submitted papers under this call will undergo the standard review process of the journal. For any further information, the authors may contact the Guest Editors. Keywords: 6G Networks; AI-Native Communication; Information Processing; Intelligent Systems https://www.sciencedirect.com/special-issue/332496/information-processing-and-intelligent-systems-in-6g-networks
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