We respect the reproduction of our works and believe it will help to motivate us to run faster. All manuscripts within my website are subject to the copyright law. Paper downloading is limited to academic research only. For more information, read the Copyright Notice. Meanwhile, we are gradually releasing our simulation codes on Github.
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X. Yu et al., “Communication-efficient cooperative multi-agent PPO via regulated segment mixture in internet of vehicles,” in Proc. IEEE Globecom 2023, Kuala Lumpur, Malaysia, Dec. 2023. Cite
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Y. Xiang, S. Li, R. Li, Z. Zhao, and H. Zhang, “Decentralized adaptive formation via consensus-oriented multi-agent communication,” in Proc. WCSP 2023, Hangzhou, China, Nov. 2023. Cite
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W. Geng, B. Xiao, R. Li, N. Wei, Z. Zhao, and H. Zhang, “Decomposition-based multi-agent distributional reinforcement learning for task-oriented UAV collaboration with noisy rewards,” in Proc. WCSP 2023, Hangzhou, China, Nov. 2023. Cite
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F. Ni, Z. Zhao, R. Li, and H. Zhang, “EEG signal-assisted algebraic topological feature-enhanced deep neural networks for gestalt illusory contour perception,” IEEE Access, vol. 11, pp. 96029–96042, Sep. 2023. Cite
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S. Tong, X. Yu, R. Li, K. Lu, Z. Zhao, and H. Zhang, “Alternate learning based sparse semantic communications for visual transmission,” in Proc. IEEE PIMRC 2023, Toronto, Canada, Sep. 2023. Cite
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B. Wang, R. Li, J. Zhu, Z. Zhao, and H. Zhang, “Knowledge enhanced semantic communication receiver,” IEEE Commun. Lett., vol. 27, no. 7, pp. 1794–1798, Jul. 2023. Cite
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J. Zhu, R. Li, X. Chen, S. Mao, J. Wu, and Z. Zhao, “Semantics-enhanced temporal graph networks for content caching and energy saving,” in Proc. IEEE ICC 2023, Rome, Italy, Jun. 2023. Cite
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J. Fang, C. Wang, R. Li, H. Wei, and M. Zhao, “Matrix factorization and deep autoencoder based clustering scheme for large-scale UAV networks,” in Proc. IEEE VTC 2023-Spring, Florence, Italy, Jun. 2023. Cite
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B. Xiao et al., “Stochastic graph neural network-based value decomposition for multi-agent reinforcement learning in urban traffic control,” in Proc. IEEE VTC 2023-Spring, Florence, Italy, Jun. 2023. 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., vol. 9, no. 2, pp. 345–358, Apr. 2023. Cite
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K. Lu et al., “Rethinking modern communication from semantic coding to semantic communication,” IEEE Wireless Commun., vol. 30, no. 1, pp. 158–164, Feb. 2023. Cite
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X. Xu, R. Li, Z. Zhao, and H. Zhang, “The gradient convergence bound of federated multi-agent reinforcement learning with efficient communication,” IEEE Trans. Wireless Commun., 2023. Cite
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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,” in Proc. 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,” in Proc. ECCV, in ECCV ’22. 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,” in Proc. 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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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, Jul. 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,” in Proc. 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," in Proc. IEEE INFOCOM 2022, in INFOCOM '22. 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,” in Proc. IEEE WCNC 2022, in WCNC ’22. 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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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,” in Proc. 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,” in Proc. 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,” in Proc. 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,” in Proc. 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,” in Proc. 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,” in Proc. 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,” in Proc. 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,” in Proc. WPMC 2020, Virtual 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," in Proc. 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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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, 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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Y. Hua, R. Li, Z. Zhao, H. Zhang, and X. Chen, “GAN-based deep distributional reinforcement learning for resource management in network slicing,” in Proc. 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,” in Proc. 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," in Proc. 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