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Y. Liu, D. Li, R. Li, Z. Zhao, Y. Zhu, and H. Zhang, “Secure and Efficient Stigmergy-Empowered Blockchain Framework for Heterogeneous Collaborative Services in the Internet of Vehicles,” IEEE Commun. Mag., 2023. Cite
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H. Wei, C. Wang, R. Li, and M. Zhao, “Mean-field MARL-based priority-aware CSMA/CA strategy in large-scale manets,” presented at the IEEE Globecom 2022, Rio de Janeiro, Brazil, Dec. 2022. Cite
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K. Lu, R. Li, and H. Zhang, “Contrastive monotonic pixel-level modulation,” presented at the ECCV 2022, Tel-Aviv, Oct. 2022. Cite
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X. Yi, R. Li, C. Peng, J. Wu, and Z. Zhao, “HFedMTL: Hierarchical federated multi-task learning,” presented at the IEEE PIMRC 2022, Virtual Edition, Sep. 2022. Cite
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X. Xu, R. Li, Z. Zhao, and H. Zhang, "Stigmergic independent reinforcement learning for multi-agent collaboration," IEEE Trans. Neural Netw. Learn. Syst., vol. 33, no. 9, pp. 4285–4299, Sep. 2022, [Online]. Available: Cite
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X. Xu, R. Li, Z. Zhao, and H. Zhang, “Communication-efficient consensus mechanism for federated reinforcement learning,” presented at the IEEE ICC 2022, Seoul, South Korea, May 2022. Cite
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B. Xiao, Y. Shao, R. Li, Z. Zhao, and H. Zhang, "DEMO: Deep reinforcement learning for resource management in cellular network slicing," presented at the IEEE INFOCOM 2022, Virtual Edition, May 2022. [Online]. Available: Cite
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X. Xu, R. Li, Z. Zhao, and H. Zhang, "Trustable policy collaboration scheme for multi-agent stigmergic reinforcement learning," IEEE Commun. Lett., vol. 26, no. 4, pp. 823–827, Apr. 2022, [Online]. Available: Cite
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J. Zhu, R. Li, Z. Zhao, and H. Zhang, “AoI-based temporal attention graph neural network for popularity prediction in ICN,” presented at the IEEE WCNC 2022, Austin, TX, USA, Apr. 2022. Cite
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Q. Zhou, R. Li, Z. Zhao, C. Peng, and H. Zhang, "Semantic communication with adaptive universal transformer," IEEE Wirel. Commun. Lett., vol. 11, no. 3, pp. 453–457, Mar. 2022, [Online]. Available: Cite
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M. Chen, R. Li, J. Crowcroft, J. Wu, Z. Zhao, and H. Zhang, "RAN information-assisted TCP congestion control using deep reinforcement learning with reward redistribution," IEEE Trans. Commun., vol. 70, no. 1, pp. 215–230, Jan. 2022, [Online]. Available: Cite
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K. Lu et al., “Rethinking modern communication from semantic coding to semantic communication,” IEEE Wireless Commun., 2022. Cite
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Q. Zhou, R. Li, Z. Zhao, Y. Xiao, and H. Zhang, "Adaptive bit rate control in semantic communication with incremental knowledge-based HARQ," IEEE Open J. Commun. Soc., vol. 3, pp. 1076–1089, 2022, [Online]. Available: Cite
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J. Zhu et al., “AoI-based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching,” IEEE Trans. Cog. Commun. Netw., 2022. Cite
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C. Qi et al., "An efficient pruning scheme of deep neural networks for Internet of things applications," EURASIP J. Adv. Signal Process., vol. 2021, no. 1, pp. 1–21, Dec. 2021, Accessed: Nov. 10, 2021. [Online]. Available: Cite
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R. Li, W. Liang, C. Peng, X. An, Z. Zhao, and H. Zhang, “Network AI management & orchestration: A federated multi-task learning case,” presented at the IEEE Globecom 2021 (EL5GMNB Workshop), Madrid, Spain, Dec. 2021. Cite
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Z. Liu et al., “Persistent homology-based topological analysis on the gestalt patterns during human brain cognition process,” J. Healthcare Eng., vol. 2021, p. e2334332, Nov. 2021. Cite
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Y. Shao, R. Li, B. Hu, Y. Wu, Z. Zhao, and H. Zhang, "Graph attention network-based multi-agent reinforcement learning for slicing resource management in dense cellular network," IEEE Trans. Veh. Tech., vol. 70, no. 10, pp. 10792–10803, Oct. 2021, [Online]. Available: Cite
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J. Pan, H. Shan, R. Li, Y. Wu, W. Wu, and T. Q. S. Quek, “Channel estimation based on deep learning in vehicle-to-everything environments,” IEEE Commun. Lett., vol. 25, no. 6, pp. 1891–1895, Jun. 2021. Cite
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Y. Chen, R. Li, Z. Zhao, and H. Zhang, "On the capacity of fractal D2D social networks with hierarchical communications," IEEE Trans. Mob. Comput., vol. 20, no. 6, pp. 2254–2268, Jun. 2021, [Online]. Available: Cite
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M. Chen, R. Li, Z. Zhao, and H. Zhang, “RAN information-assisted TCP congestion control via DRL with reward redistribution,” presented at the IEEE ICC 2021 (DDINS Workshop), Montreal, QC, Canada, Jun. 2021. Cite
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Y. Shao, R. Li, Z. Zhao, and H. Zhang, “Graph attention network-based DRL for network slicing management in dense cellular networks,” presented at the IEEE WCNC 2021, Nanjing, China, Apr. 2021. Cite
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S. Shen, R. Li, Z. Zhao, H. Zhang, and Y. Zhou, “Learning to prune in training via dynamic channel propagation,” presented at the ICPR 2021, Milan, Italy, Jan. 2021. Cite
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S. W. H. Shah, R. Li, M. M. U. Rahman, A. N. Mian, W. Aman, and J. Crowcroft, “Statistical QoS guarantees of a device-to-device link assisted by a full-duplex relay,” Trans. Emerg. Telecommun. Technol. (ETT), 2021. Cite
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S. Shen, R. Li, Z. Zhao, Q. Liu, J. Liang, and H. Zhang, “Efficient deep structure learning for resource-limited IoT devices,” presented at the IEEE Globecom 2020, Taipei, China, Dec. 2020. Cite
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G. Wang, Y. Zhong, R. Li, X. Ge, T. Q. S. Quek, and G. Mao, “Effect of spatial and temporal traffic statistics on the performance of wireless networks,” IEEE Trans. Commun., vol. 68, no. 11, pp. 7083–7097, Nov. 2020. Cite
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A. Dai, R. Li, Z. Zhao, and H. Zhang, “Graph convolutional multi-agent reinforcement learning for uav coverage control,” presented at the WCSP 2020, Nanjing, China, Oct. 2020. Cite
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K. Chen, R. Li, Z. Zhao, and H. Zhang, “The implementation of asynchronous advantage actor-critic with stigmergy in network-assisted multi-agent system,” presented at the WCSP 2020, Nanjing, China, Oct. 2020. Cite
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B. Su, R. Li, and H. Zhang, “Evolving deep convolutional neural network for intrusion detection based on neat,” presented at the WPMC 2020, Virutal Edition, Oct. 2020. Cite
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R. Li, C. Wang, R. Guo, Z. Zhao, and H. Zhang, “The LSTM-based advantage actor-critic learning for resource management in network slicing with user mobility,” IEEE Commun. Lett., vol. 24, no. 9, pp. 2005–2009, Sep. 2020. Cite
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K. Chen, R. Li, J. Crowcroft, Z. Zhao, and H. Zhang, "DEMO: The implementation of stigmergy in network-assisted multi-agent system," presented at the ACM Mobicom 2020, London, UK, Sep. 2020. [Online]. Available: Cite
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R. Li, Z. Zhao, X. Xu, F. Ni, and H. Zhang, “The collective advantage for advancing communications and intelligence,” IEEE Wireless Commun., vol. 27, no. 4, pp. 96–102, Aug. 2020. Cite
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Y. Chen, R. Li, Z. Zhao, and H. Zhang, "Study on base station topology in national cellular networks: Take advantage of alpha shapes, betti numbers, and euler characteristics," IEEE Systems J., vol. 14, no. 2, pp. 2202–2213, Jun. 2020, [Online]. Available: Cite
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Y. Shao, Z. Zhao, R. Li, and Y. Zhou, “Target detection for multi-UAVs via digital pheromones and navigation algorithm in unknown environments,” Front. Inform. Technol. Electron. Eng., vol. 21, no. 5, pp. 796–808, May 2020. Cite
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Y. Hua, R. Li, Z. Zhao, X. Chen, and H. Zhang, "GAN-powered deep distributional reinforcement learning for resource management in network slicing," IEEE J. Sel. Area. Comm., vol. 38, no. 2, pp. 334–349, Feb. 2020, [Online]. Available: Cite
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A. Dai, Z. Zhao, R. Li, H. Zhang, and Y. Zhou, “Evaluation mechanism of collective intelligence for heterogeneous agents group,” IEEE Access, vol. 8, pp. 28385–28394, Feb. 2020. Cite
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Y. Hua, R. Li, Z. Zhao, H. Zhang, and X. Chen, “GAN-based deep distributional reinforcement learning for resource management in network slicing,” presented at the IEEE Globecom 2019, Big Island, Hawaii, USA, Dec. 2019. Cite
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C. Wang, R. Li, Z. Zhao, and H. Zhang, “Statistics-enhanced destination prediction model for multi-users based on deep learning,” presented at the IEEE ICCT 2019, Xi’an, China, Oct. 2019. Cite
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S. Zhang, F. Yang, S. Song, R. Li, Z. Zhao, and H. Zhang, "DEMO: The design and implementation of intelligent software defined security framework," presented at the ACM Mobicom 2019, Los Cabos, Mexico, Oct. 2019. [Online]. Available: Cite
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C. Qi, Y. Hua, R. Li, Z. Zhao, and H. Zhang, "Deep reinforcement learning with discrete normalized advantage functions for resource management in network slicing," IEEE Commun. Lett., vol. 23, no. 6, pp. 1337–1341, Aug. 2019, [Online]. Available: Cite
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C. Wang, L. Ma, R. Li, T. S. Durrani, and H. Zhang, "Exploring trajectory prediction through machine learning methods," IEEE Access, vol. 7, pp. 101441–101452, Jul. 2019, [Online]. Available: Cite
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Y. Hua, Z. Zhao, R. Li, X. Chen, Z. Liu, and H. Zhang, "Deep learning with long short-term memory for time series prediction," IEEE Commun. Mag., vol. 57, no. 6, pp. 114–119, Jun. 2019, [Online]. Available: Cite
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F. Yang, S. Zhang, S. Song, R. Li, Z. Zhao, and H. Zhang, “A testbed for intelligent software defined security framework,” presented at the ACM TURC 2019, Chengdu, China, May 2019. Cite
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Y. Zhou et al., "Multicast scheduling for delay-energy trade-off under bursty request arrivals in cellular networks," IET Commun., vol. 13, no. 11, pp. 1696–1701, Apr. 2019, [Online]. Available: Cite
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J. Li, Z. Zhao, R. Li, and H. Zhang, “AI-based two-stage intrusion detection for software defined IoT networks,” IEEE Internet Things J., vol. 6, no. 2, pp. 2093–2102, Apr. 2019. Cite
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Y. Chen, R. Li, Z. Zhao, and H. Zhang, "Fundamentals on base stations in urban cellular networks: From the perspective of algebraic topology," IEEE Wireless Commun. Lett., vol. 8, no. 2, pp. 612–615, Apr. 2019, [Online]. Available: Cite
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X. Xu, Z. Zhao, R. Li, and H. Zhang, "Brain-inspired stigmergy learning," IEEE Access, vol. 7, pp. 54410–54424, Apr. 2019, [Online]. Available: Cite
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R. Li, Z. Zhao, Y. Zhong, C. Qi, and H. Zhang, "The stochastic geometry analyses of cellular networks with ALPHA-stable self-similarity," IEEE Trans. Commun., vol. 67, no. 3, pp. 2487–2503, Mar. 2019, [Online]. Available: Cite
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H. Zhang, Y. Hua, C. Wang, R. Li, and Z. Zhao, "Deep learning based traffic and mobility prediction," in Machine Learning for Future Wireless Communications, John Wiley & Sons, Ltd, 2019, pp. 119–136. doi: 10.1002/9781119562306.ch7. Cite
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R. Li et al., "Deep reinforcement learning for resource management in network slicing," IEEE Access, vol. 6, pp. 74429–74441, Nov. 2018, [Online]. Available: Cite