IEEE Transactions on Cognitive Communications and Networking: Advancing Research

TCCN Committee Meeting IEEE Transactions on Cognitive Communications and Networking (TCCN) ICC 2022, Virtual Conference Ying-Chang Liang Editor-in-Chief IEEE Transactions on Cognitive Communications and Networking 1

IEEE TCCN Journal Scope The IEEE Transactions on Cognitive Communications and Networking (TCCN) is committed to timely publishing of high-quality manuscripts that advance the state-of-the-art of cognitive communications and networking research. The focus of the Transactions will be on cognitive behaviors in all aspects of communications and network control, from the PHY functions (including hardware) through the applications (including architecture), and in all kinds of communication networks and systems regardless of type of traffic, transmission media, operating environment, or capabilities of communicating devices. Cognitive Radio and AI-empowered Communications and Networking 2 2

Subject Areas Area 1: Cognitive Radio Area 2: AI-empowered Communications Area 3: AI-empowered Networking Area 4: AI-empowered Resource Management 3

Topics of Interest Topics of Interest (but not limited to) Machine learning and artificial intelligence for communications and networking Distributed learning, reasoning and optimization for communications and networking Architecture, protocols, cross-layer, and cognition cycle design for intelligent communications and networking Information/communications theory and network science for intelligent communications and networking Ontologies, languages, and knowledge representation for intelligent communications and networking Security and privacy issues in intelligent communications and networking Cognitive radio and dynamic spectrum access Cognitive technologies supporting software-defined radios, systems and networks Emerging services and applications enabled by intelligent communications and networks 4

IEEE TCCN Impact Factor IEEE TCCN is ranked in Q1 TELECOMMUNICATIONS. The impact factor of IEEE TCCN is 4.341 on JCR Report 2020. IEEE Trans. on Cognitive Communications and Networking 4.341 4.574 IEEE Trans. on Communications 5.083 5.646 IEEE Trans. on Wireless Communications 7.016 6.779 IEEE Trans. on Vehicular Technology 5.978 5.379 5 5

TCCN Statistics From First Submission to First Decision (as of Apr. 17, 2022) 6 6

Popular Papers in TCCN T. O Shea and J. Hoydis, "An Introduction to Deep Learning for the Physical Layer," vol. 3, no. 4, pp. 563-575, Dec. 2017 L. Wang, K. Wang, C. Pan, W. Xu, N. Aslam and L. Hanzo, "Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing," vol. 7, no. 1, pp. 73-84, March 2021 M. A. ElMossallamy, H. Zhang, L. Song, K. G. Seddik, Z. Han and G. Y. Li, "Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities," vol. 6, no. 3, pp. 990-1002, Sept. 2020 O. Simeone, "A Very Brief Introduction to Machine Learning With Applications to Communication Systems," vol. 4, no. 4, pp. 648-664, Dec. 2018 A. R. Chiriyath, B. Paul and D. W. Bliss, "Radar-Communications Convergence: Coexistence, Cooperation, and Co-Design," vol. 3, no. 1, pp. 1-12, March 2017 H. Ye, G. Y. Li and B. -H. Juang, "Deep Learning Based End-to-End Wireless Communication Systems Without Pilots," vol. 7, no. 3, pp. 702-714, Sept. 2021 Y. -C. Liang, Q. Zhang, E. G. Larsson and G. Y. Li, "Symbiotic Radio: Cognitive Backscattering Communications for Future Wireless Networks," vol. 6, no. 4, pp. 1242-1255, Dec. 2020 E. Bourtsoulatze, D. Burth Kurka and D. G nd z, "Deep Joint Source-Channel Coding for Wireless Image Transmission," vol. 5, no. 3, pp. 567-579, Sept. 2019 Q. Bai, J. Wang, Y. Zhang and J. Song, "Deep Learning-Based Channel Estimation Algorithm Over Time Selective Fading Channels," vol. 6, no. 1, pp. 125-134, March 2020, doi: 10.1109/TCCN.2019.2943455. Z. Qin, X. Zhou, L. Zhang, Y. Gao, Y. -C. Liang and G. Y. Li, "20 Years of Evolution From Cognitive to Intelligent Communications," vol. 6, no. 1, pp. 6-20, March 2020, M. Vaezi, G. A. Aruma Baduge, Y. Liu, A. Arafa, F. Fang and Z. Ding, "Interplay Between NOMA and Other Emerging Technologies: A Survey," vol. 5, no. 4, pp. 900-919, Dec. 2019 7 7

Special Issues in TCCN Special Issues List Deep Reinforcement Learning for Future Wireless Communication Networks Evolution of Cognitive Radio to AI-enabled Radio and Networks Intelligent Resource Management for 5G and Beyond AI-Based Licensed/Unlicensed Spectrum Interoperability in Future Mobile Wireless System Intelligent Mobile Edge Computing Systems: Challenges and Solutions Machine Learning and Artificial Intelligence for the Physical Layer Intelligent Surfaces for Smart Wireless Communications Convergence of Collaborative Distributed Machine Learning and Edge Computing Towards Intelligent Networking 8

Recommended Special Issue Papers Convergence of Collaborative Distributed Machine Learning and Edge Computing Towards Intelligent Networking Guest Editors: Dusit Niyato; Kaibin Huang; Mehdi Bennis; Miao Pan; Zehui Xiong; Long Bao Le; Dong in Kim; Li-Chun Wang H. Wu and P. Wang, "Fast-Convergent Federated Learning With Adaptive Weighting," in vol. 7, no. 4, pp. 1078-1088, Dec. 2021 Z. Li, C. Jiang and L. Kuang, "Double Auction Mechanism for Resource Allocation in Satellite MEC," in vol. 7, no. 4, pp. 1112-1125, Dec. 2021 I. Nikoloska, J. Holm, A. E. Kal r, P. Popovski and N. Zlatanov, "Inference Over Wireless IoT Links With Importance-Filtered Updates," in vol. 7, no. 4, pp. 1089-1098, Dec. 2021 F. Khoramnejad and M. Erol-Kantarci, "On Joint Offloading and Resource Allocation: A Double Deep Q-Network Approach," in vol. 7, no. 4, pp. 1126-1141, Dec. 2021 T. G. Nguyen, T. V. Phan, D. T. Hoang, T. N. Nguyen and C. So-In, "Federated Deep Reinforcement Learning for Traffic Monitoring in SDN-Based IoT Networks," in vol. 7, no. 4, pp. 1048-1065, Dec. 2021 9

Recommended Special Issue Papers Intelligent Surfaces for Smart Wireless Communications Guest Editors: Chau Yuen, Marco Di Renzo, George C. Alexandropoulos, M rouane Debbah, Xiaojun Yuan S. Li, L. Yang, D. B. d. Costa, M. D. Renzo and M. -S. Alouini, "On the Performance of RIS- Assisted Dual-Hop Mixed RF-UWOC Systems," vol. 7, no. 2, pp. 340-353, June 2021 J. Wang, H. Wang, Y. Han, S. Jin and X. Li, "Joint Transmit Beamforming and Phase Shift Design for Reconfigurable Intelligent Surface Assisted MIMO Systems," vol. 7, no. 2, pp. 354-368, June 2021 S. Huang, S. Wang, R. Wang, M. Wen and K. Huang, "Reconfigurable Intelligent Surface Assisted Mobile Edge Computing With Heterogeneous Learning Tasks," vol. 7, no. 2, pp. 369-382, June 2021 S. Buzzi, C. D Andrea, A. Zappone, M. Fresia, Y. -P. Zhang and S. Feng, "RIS Configuration, Beamformer Design, and Power Control in Single-Cell and Multi-Cell Wireless Networks," vol. 7, no. 2, pp. 398-411, June 2021 Y. Zhang et al., "Beyond Cell-Free MIMO: Energy Efficient Reconfigurable Intelligent Surface Aided Cell-Free MIMO Communications," vol. 7, no. 2, pp. 412-426, June 2021 Y. Sun, C. -X. Wang, J. Huang and J. Wang, "A 3D Non-Stationary Channel Model for 6G Wireless Systems Employing Intelligent Reflecting Surfaces With Practical Phase Shifts," vol. 7, no. 2, pp. 496-510, June 2021 10

Recommended Special Issue Papers Machine Learning and Artificial Intelligence for the Physical Layer Guest Editors: Chunxiao Jiang, Andrea Zanella, Guoru Ding, Oliver Holland, Aly El Gamal, Tim O'Shea Y. Lin, Y. Tu, Z. Dou, L. Chen and S. Mao, "Contour Stella Image and Deep Learning for Signal Recognition in the Physical Layer," vol. 7, no. 1, pp. 34-46, March 2021 L. Wang, K. Wang, C. Pan, W. Xu, N. Aslam and L. Hanzo, "Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing," vol. 7, no. 1, pp. 73-84, March 2021 S. Huang, Y. Ye and M. Xiao, "Learning-Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems," vol. 7, no. 1, pp. 120-132, March 2021 H. Zhang, R. He, B. Ai, S. Cui and H. Zhang, "Measuring Sparsity of Wireless Channels," vol. 7, no. 1, pp. 133-144, March 2021 T. Hu, Y. Huang, Q. Zhu and Q. Wu, "Channel Estimation Enhancement With Generative Adversarial Networks," vol. 7, no. 1, pp. 145-156, March 2021 S. -F. Cheng, L. -C. Wang, C. -H. Hwang, J. -Y. Chen and L. -Y. Cheng, "On-Device Cognitive Spectrum Allocation for Coexisting URLLC and eMBB Users in 5G Systems," vol. 7, no. 1, pp. 171-183, March 2021 11

Recommended Special Issue Papers Intelligent Mobile Edge Computing Systems: Challenges and Solutions Guest Editors: Ning Zhang, Katsuya Suto, Tao Han, Xianbin Wang, Hassan Aboubakr Omar, Qinyu Zhang, Xianfu Chen M. Li, J. Gao, L. Zhao and X. Shen, "Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks," vol. 6, no. 4, pp. 1122-1135, Dec. 2020 C. Wu, Z. Liu, F. Liu, T. Yoshinaga, Y. Ji and J. Li, "Collaborative Learning of Communication Routes in Edge-Enabled Multi-Access Vehicular Environment," vol. 6, no. 4, pp. 1155-1165, Dec. 2020 J. Zhang, Y. Wu, G. Min, F. Hao and L. Cui, "Balancing Energy Consumption and Reputation Gain of UAV Scheduling in Edge Computing," vol. 6, no. 4, pp. 1204-1217, Dec. 2020 S. Gu, Y. Wang, N. Wang and W. Wu, "Intelligent Optimization of Availability and Communication Cost in Satellite-UAV Mobile Edge Caching System With Fault-Tolerant Codes," vol. 6, no. 4, pp. 1230-1241, Dec. 2020. R. Han, Y. Wen, L. Bai, J. Liu and J. Choi, "Rate Splitting on Mobile Edge Computing for UAV-Aided IoT Systems," vol. 6, no. 4, pp. 1193-1203, Dec. 2020. 12

Recommended Special Issue Papers AI-Based Licensed/Unlicensed Spectrum Interoperability in Future Mobile Wireless System Guest Editors: Shahid Mumtaz, Muhammad Ikram Ashraf, Jinming Wen, Shao-Yu Lien, Mohsen Guizani Z. Ullah, F. Al-Turjman and L. Mostarda, "Cognition in UAV-Aided 5G and Beyond Communications: A Survey," vol. 6, no. 3, pp. 872-891, Sept. 2020 Y. Lin, M. Wang, X. Zhou, G. Ding and S. Mao, "Dynamic Spectrum Interaction of UAV Flight Formation Communication With Priority: A Deep Reinforcement Learning Approach," vol. 6, no. 3, pp. 892-903, Sept. 2020 S. Jacob et al., "A Novel Spectrum Sharing Scheme Using Dynamic Long Short-Term Memory With CP-OFDMA in 5G Networks," vol. 6, no. 3, pp. 926-934, Sept. 2020 S. Al-Rubaye and A. Tsourdos, "Airport Connectivity Optimization for 5G Ultra-Dense Networks," vol. 6, no. 3, pp. 980-989, Sept. 2020 B. Ji, Y. Li, S. Chen, C. Han, C. Li and H. Wen, "Secrecy Outage Analysis of UAV Assisted Relay and Antenna Selection for Cognitive Network Under Nakagami- m Channel," vol. 6, no. 3, pp. 904-914, Sept. 2020 13

Recommended Special Issue Papers Intelligent Resource Management for 5G and Beyond Guest Editors: Yulei Wu, Dimitra Simeonidou, Cheng-Xiang Wang, Richard Yu, Sunghyun Choi, Guoliang Xue, Adlen Ksentini M. Chen, Y. Miao, H. Gharavi, L. Hu and I. Humar, "Intelligent Traffic Adaptive Resource Allocation for Edge Computing-Based 5G Networks," vol. 6, no. 2, pp. 499-508, June 2020 X. Zhu, C. Jiang, L. Kuang, Z. Zhao and S. Guo, "Two-Layer Game Based Resource Allocation in Cloud Based Integrated Terrestrial-Satellite Networks," vol. 6, no. 2, pp. 509-522, June 2020 F. Tang, L. Chen, X. Li, L. T. Yang and L. Fu, "Intelligent Spectrum Assignment Based on Dynamical Cooperation for 5G-Satellite Integrated Networks," vol. 6, no. 2, pp. 523-533, June 2020. Y. Zuo, Y. Wu, G. Min, C. Huang and K. Pei, "An Intelligent Anomaly Detection Scheme for Micro-Services Architectures With Temporal and Spatial Data Analysis," vol. 6, no. 2, pp. 548- 561, June 2020. F. Tang, L. Chen, X. Li, L. T. Yang and L. Fu, "Intelligent Spectrum Assignment Based on Dynamical Cooperation for 5G-Satellite Integrated Networks," vol. 6, no. 2, pp. 523-533, June 2020. Y. Li, W. Zhang, C. Wang, J. Sun and Y. Liu, "Deep Reinforcement Learning for Dynamic Spectrum Sensing and Aggregation in Multi-Channel Wireless Networks," vol. 6, no. 2, pp. 464- 475, June 2020. 14

Recommended Special Issue Papers Evolution of Cognitive Radio to AI-Enabled Radio and Networks Guest Editors: Yue Gao, Ekram Hossain, Geoffrey Ye Li, Kevin Sowerby, Carlo Regazzoni, Lin Zhang Z. Qin, X. Zhou, L. Zhang, Y. Gao, Y. Liang and G. Y. Li, "20 Years of Evolution From Cognitive to Intelligent Communications," vol. 6, no. 1, pp. 6-20, March 2020. A. Toma et al., "AI-Based Abnormality Detection at the PHY-Layer of Cognitive Radio by Learning Generative Models," vol. 6, no. 1, pp. 21-34, March 2020. C. Zhong, M. C. Gursoy and S. Velipasalar, "Deep Reinforcement Learning-Based Edge Caching in Wireless Networks," vol. 6, no. 1, pp. 48-61, March 2020. T. Zhang and S. Mao, "Energy-Efficient Power Control in Wireless Networks With Spatial Deep Neural Networks," vol. 6, no. 1, pp. 111-124, March 2020. Q. Bai, J. Wang, Y. Zhang and J. Song, "Deep Learning-Based Channel Estimation Algorithm Over Time Selective Fading Channels," vol. 6, no. 1, pp. 125-134, March 2020. M. A. Qureshi and C. Tekin, "Fast Learning for Dynamic Resource Allocation in AI-Enabled Radio Networks," vol. 6, no. 1, pp. 95-110, March 2020. K. Sankhe et al., "No Radio Left Behind: Radio Fingerprinting Through Deep Learning of Physical-Layer Hardware Impairments," vol. 6, no. 1, pp. 165-178, March 2020 15

Recommended Special Issue Papers Deep Reinforcement Learning for Future Wireless Communication Networks Guest Editors: Shimin Gong, Ahmed El Shafie, Emilio Calvanese Strinati, Dinh Thai Hoang, Antonio De Domenico, Jakob Hoydis, Dusit Niyato A. Sadeghi, G. Wang and G. B. Giannakis, "Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks," vol. 5, no. 4, pp. 1024-1033, Dec. 2019. D. Zhang, F. R. Yu and R. Yang, "Blockchain-Based Distributed Software-Defined Vehicular Networks: A Dueling Deep Q -Learning Approach," vol. 5, no. 4, pp. 1086-1100, Dec. 2019. E. Balevi and J. G. Andrews, "Online Antenna Tuning in Heterogeneous Cellular Networks With Deep Reinforcement Learning," vol. 5, no. 4, pp. 1113-1124, Dec. 2019. M. K. Sharma, A. Zappone, M. Assaad, M. Debbah and S. Vassilaras, "Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach," vol. 5, no. 4, pp. 1140-1154, Dec. 2019. C. Dai, K. Zhu, R. Wang and B. Chen, Contextual Multi-Armed Bandit for Cache-Aware Decoupled Multiple Association in UDNs: A Deep Learning Approach, vol. 5, no. 4, pp. 1046-1059, Dec. 2019. Z. Ning et al., "Deep Reinforcement Learning for Intelligent Internet of Vehicles: An Energy- Efficient Computational Offloading Scheme," vol. 5, no. 4, pp. 1060-1072, Dec. 2019. 16

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