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, "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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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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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, 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 Download
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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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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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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. 2021. 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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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 Download
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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