Academic Journal
Q1IEEE Transactions on Cognitive Communications and NetworkingIEEE Transactions on Cognitive Communications and Networking
About IEEE Transactions on Cognitive Communications and NetworkingIEEE Transactions on Cognitive Communications and Networking
IEEE Transactions on Cognitive Communications and NetworkingIEEE Transactions on Cognitive Communications and Networking is a scholarly journal published by Institute of Electrical and Electronics Engineers Inc.. SCImago 2025 places it in Q1 with an SJR of 2.359 and an H-index of 75.
Its listed coverage is 2015-2026 and its research categories include Artificial Intelligence (Q1); Computer Networks and Communications (Q1); Hardware and Architecture (Q1). The 2025 dataset reports 327 documents and 3936 citations across the latest three-year reporting window.
In the era of 5G, 6G, and beyond, network intelligence has become more critical than ever. As spectrum resources become increasingly scarce, and data traffic continues to grow, cognitive technologies offer promising solutions. By enabling smarter spectrum management, minimizing interference, and optimizing resource allocation, cognitive communications can significantly improve the efficiency and reliability of wireless networks.
IEEE TCCN plays a crucial role in sharing the latest breakthroughs in this space. It helps bridge the gap between academic research and real-world applications, fostering innovation that can shape the future of wireless and cognitive networks.
Key Features of the Journal
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High-Quality Peer-Reviewed Articles: Each submission goes through a rigorous peer-review process ensuring technical accuracy, novelty, and relevance.
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Quarterly Publication: Published four times a year, providing timely insights into emerging trends.
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Interdisciplinary Focus: Combines aspects of electrical engineering, computer science, machine learning, and wireless communications.
Who Should Read or Submit to IEEE TCCN?
IEEE TCCN is an essential resource for:
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Academic researchers and students in communications and networking
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Industry professionals working in telecom, AI, and IoT
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Policymakers and regulators interested in dynamic spectrum management
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Startups and innovators focusing on smart wireless technologies
If you are conducting cutting-edge research on cognitive radios, spectrum sensing, or intelligent network architectures, submitting your work to IEEE TCCN can increase your visibility and impact in the global research community.
Journal Metrics
Metrics can change by reporting year. Verify time-sensitive values with the publisher or indexing service.
Aims & Scope
The IEEE Transactions on Cognitive Communications and Networking (TCCN) is a leading peer-reviewed journal published by the IEEE Communications Society. It serves as a premier platform for researchers, engineers, and academics who are exploring the next frontier in intelligent and adaptive communication systems. This journal focuses on the growing need for smart, self-learning networks that can dynamically adjust to complex and changing environments — a field known as cognitive communications.
What is Cognitive Communications and Networking?
Cognitive communications and networking refer to systems that can sense, learn, and adapt based on the surrounding radio environment. These systems use artificial intelligence, machine learning, and statistical modeling to optimize spectrum use, enhance signal transmission, and manage network resources effectively. As wireless technology continues to evolve with the rise of 5G, 6G, IoT, and edge computing, cognitive networking has become increasingly critical for maintaining efficiency and performance.
Core Topics Covered by IEEE TCCN
The IEEE Transactions on Cognitive Communications and Networking covers a wide range of topics, including but not limited to:
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Cognitive Radio Networks (CRNs): Technologies enabling radios to sense the spectrum and dynamically select frequencies to avoid interference.
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Dynamic Spectrum Access: Techniques that allow more efficient use of the radio spectrum by enabling secondary users to utilize unused licensed bands.
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Machine Learning in Communications: Integration of AI and deep learning methods into cognitive networks for better decision-making and automation.
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Resource Allocation and Network Optimization: Algorithms for improving network throughput, latency, and energy efficiency.
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Security and Privacy in Cognitive Networks: Addressing new challenges introduced by adaptive and decentralized networks.
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Cooperative and Distributed Sensing: Enhancing spectrum awareness using multiple sensing nodes for improved reliability.
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Standardization and Applications: Emerging standards and real-world implementations in vehicular networks, smart cities, and military communications.
Why Publish in IEEE TCCN?
Publishing in IEEE TCCN offers high visibility in the academic and industrial research communities. It is indexed in major databases like Scopus, Web of Science, and IEEE Xplore, ensuring your work reaches a global audience. The journal is known for its rigorous peer-review process, technical depth, and contributions from leading researchers in the field.
Recent Research Articles
Latest publications matched automatically by ISSN.
A Local-Global Feature Extraction Self-Supervision Network for Gridless DOA Estimation With Arbitrary Sparse Arrays
Liping Teng, Hongjun Li, Hua Chen, Wei Liu et al.
2026 · DOI: 10.1109/tccn.2025.3637056Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs
Haifeng Wen, Hong Xing, Osvaldo Simeone
2026 · DOI: 10.1109/tccn.2025.3640114Improved Conditional GAN-Based Channel Estimation for MIMO-OFDM Systems
Ming Ye, Xiao Liang, Cunhua Pan, Yinfei Xu et al.
2026 · DOI: 10.1109/tccn.2025.3597694Large Multimodal Model-Aided Scheduling for 6G Autonomous Communications
Sunwoo Kim, Byonghyo Shim
2026 · DOI: 10.1109/tccn.2025.3633741Enhancing Backscatter Communication via Time-Reversal Filtering: Scheme Design and Performance Analysis
Weijia Lei, Hang Yang, Hong Tang, Hongjiang Lei et al.
2026 · DOI: 10.1109/tccn.2026.3676028Machine Learning-Based Optimization for Metamaterial Internet of Things With ISAC
Jukuri Sandeep, Abhinav Singh Parihar, Keshav Singh, Kapal Dev et al.
2026 · DOI: 10.1109/tccn.2026.3688523Resilient Wireless Sensor Actor Networks Through Multi-Objective Self-Adaptation
Ruben Gomes, Noélia Correia
2026 · DOI: 10.1109/tccn.2026.3656393FedSTGAT: Federated Spatio-Temporal Graph Attention Network for Root Alarm Identification in Optical Transport Network
Weijie Yang, Chunyu Zhang, Cheng Xing, Min Zhang et al.
2026 · DOI: 10.1109/tccn.2026.3694892Unified Near/Far-Field Precoding With Regression-Domain Codebook
Haochen Wu, Liyang Lu, Zhaocheng Wang
2026 · DOI: 10.1109/tccn.2026.3685421DOI: A Systematic Framework for Incremental Identification of Specific Emitters in Open-Set Scenarios
Fei Teng, Wenqiang Shi, Yingke Lei, Hu Jin et al.
2026 · DOI: 10.1109/tccn.2025.3569554Task Offloading on Dense Electric Intelligent Vehicular Networks: A Mean Field Deep Reinforcement Learning Approach
Huixian Gu, Liqiang Zhao, Kai Liang, Hong Xing et al.
2026 · DOI: 10.1109/tccn.2025.3638769Collaborative Task Offloading in Space Computing Power Network: A World Model-Based Multi-Agent Reinforcement Learning Approach
Yuqi Cong, Zhiwei Wei, Jiarui Chen, Bing Li et al.
2026 · DOI: 10.1109/tccn.2026.3717925Hyperbolic Spatio-Temporal Graph Learning With Agent Reasoning for Root Cause Localization in Cloud-Edge Microservices
Yechen He, Yang Yang, Lanlan Rui, Celimuge Wu et al.
2026 · DOI: 10.1109/tccn.2026.3715900Predictive Beamforming for OTFS-Enabled URLLC in High-Mobility Vehicular Networks
Jianzhe Xue, Tiankai Jiang, Zhanxi Ma, Yunting Xu et al.
2026 · DOI: 10.1109/tccn.2025.3587126Communication Efficient Robotic Mixed Reality With Gaussian Splatting Cross-Layer Optimization
Chenxuan Liu, He Li, Zongze Li, Shuai Wang et al.
2026 · DOI: 10.1109/tccn.2025.3599522Diffusion-DDPG: An Edge Intelligence Offloading Algorithm Empowering Agentic AI for Autonomous Decision-Making
Xiaoming Yuan, Songyu Wang, Aiwen Wang, Ning Zhang et al.
2026 · DOI: 10.1109/tccn.2026.3697300A Multimodal Agentic AI Framework for Environmental Reconstruction and Semantic Channel Modeling
Guangzheng Jing, Yixiao Tong, José Rodríguez-Piñeiro, Jingxiang Hong et al.
2026 · DOI: 10.1109/tccn.2026.3689820Paying Deformable Attention to Sparse Spatial Observations for Deep Radio Map Estimation
Kangjun Liu, Chunyan Qiu, Ke Chen, Qingfang Zheng et al.
2026 · DOI: 10.1109/tccn.2025.3613520Realistic Cooperative Strategies Based on Dynamic Spectrum Sharing for Integrated Satellite-Terrestrial Networks
Zhiqiang Li, Shuai Han, Weixiao Meng, Cheng Li et al.
2026 · DOI: 10.1109/tccn.2025.3554017Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks via Auction and Diffusion-Based MARL
Jiahao You, Ziye Jia, Can Cui, Chao Dong et al.
2026 · DOI: 10.1109/tccn.2025.3641588Reviews
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April 22, 2025 at 1:29 pm
April 22, 2025