Publications

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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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. 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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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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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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Q. Zhou, R. Li, Z. Zhao, C. Peng, and H. Zhang, "Semantic communication with adaptive universal transformer," IEEE 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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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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K. Lu et al., “Rethinking modern communication from semantic coding to semantic communication,” IEEE Wireless Commun., 2022. Cite
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肖柏狄, 李荣鹏, 赵志峰, and 张宏纲, "基于时空特征提取的智能网络切片算法," 无线电通信技术, vol. 48, no. 01, pp. 74–80, 2022, Accessed: Aug. 01, 2022. [Online]. Available: Cite
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吴建军 et al., "任务为中心的6G网络AI架构," 无线电通信技术, vol. 48, no. 04, pp. 599–613, 2022, Accessed: Aug. 01, 2022. [Online]. Available: Cite
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彭程晖 et al., "6G通算融合网络架构," 无线电通信技术, vol. 48, no. 04, pp. 583–591, 2022, Accessed: Aug. 01, 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,” 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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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. 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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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. 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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X. Xu, R. Li, Z. Zhao, and H. Zhang, "Stigmergic independent reinforcement learning for multi-agent collaboration," IEEE Trans. Neural Netw. Learn. Syst., 2021, [Online]. Available: 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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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,” 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
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R. Li, Z. Zhao, C. Qi, and H. Zhang, "Characterizing and learning the mobile data traffic in cellular network," in 5G Networks: Fundamental Requirements, Enabling Technologies, and Operations Management, Wiley-Blackwell, 2018, pp. 453–498. Accessed: Sep. 27, 2018. [Online]. Available: Cite