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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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. 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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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, Z. Zhao, J. Crowcroft, Z. Zhao, and H. Zhang, "DEMO: The Implementation of Stigmergy in Network-assisted Multi-agent System," presented at the ACM Mobicom 2020 (Demos Session), Los Cabos, Mexico, 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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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., 2020, doi: 10.1109/TCOMM.2020.3019534. 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., 2020, doi: 10.1109/TMC.2020.2975783. 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 (Demos Session), 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 Communications Letters, 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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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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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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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. 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. Cite
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R. Li, Z. Zhao, C. Yang, C. Wu, and H. Zhang, “Wireless big data in cellular networks: The cornerstone of smart cities,” IET Commun., vol. 12, no. 13, pp. 1517–1523, Aug. 2018. Cite
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Y. Chen, R. Li, Z. Zhao, and H. Zhang, “On the capacity of D2D social networks with fractal communications,” presented at the IEEE ICT 2018, Saint-Malo, France, Jun. 2018. Cite
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C. Kan, G. Ding, Q. Wu, R. Li, and F. Song, “Robust relative fingerprinting-based passive source localization via data cleansing,” IEEE Access, vol. 6, pp. 19255–19269, Mar. 2018. Cite
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J. Li, Z. Zhao, and R. Li, "Machine learning-based IDS for software-defined 5G network," IET Netw., vol. 7, no. 2, pp. 53–60, Mar. 2018, [Online]. Available: Cite
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Y. Hua, Z. Zhao, Z. Liu, X. Chen, R. Li, and H. Zhang, “Traffic prediction based on random connectivity in deep learning with long short-term memory,” presented at the IEEE VTC 2018 Fall, Chicago, Illinois, USA, 2018. Cite
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Z. Zhao, M. Li, and R. Li, "Temporal-spatial distribution nature of traffic and base stations in cellular networks," IET Commun., vol. 11, no. 16, pp. 2410 – 2416, Nov. 2017, [Online]. Available: Cite
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H. Wang, R. Li, L. Fan, and H. Zhang, “Joint computation offloading and data caching with delay optimization in mobile-edge computing systems,” presented at the WCSP 2017, Nanjing, China, Oct. 2017. Cite
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X. Meng, Z. Zhao, R. Li, and H. Zhang, “An intelligent honeynet architecture based on software defined security,” presented at the WCSP 2017, Nanjing, China, Oct. 2017. Cite
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X. Xu, Z. Zhao, R. Li, and H. Zhang, “A resource scheduling scheme based on utility function in CoMP environment,” presented at the WCSP 2017, Nanjing, China, Oct. 2017. Cite
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J. Sun, L. Shen, G. Ding, R. Li, and Q. Wu, "Predictability analysis of spectrum state evolution: Performance bounds and real-world data analytics," IEEE Access, vol. 5, pp. 22760–22774, Oct. 2017, [Online]. Available: Cite
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Z. Zhao, F. Hong, and R. Li, "SDN based VxLAN optimization in cloud computing networks," IEEE Access, vol. 5, pp. 23312–23319, Oct. 2017, [Online]. Available: Cite
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Y. Zhou, Z. Zhao, R. Li, H. Zhang, and Y. Louet, "Cooperation based probabilistic caching strategy in clustered cellular networks," IEEE Commun. Lett., vol. 21, no. 9, pp. 2029–2032, Sep. 2017, [Online]. Available: Cite
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Y. Jin, X.-G. Xia, Y. Chen, and R. Li, "Full duplex delay diversity relay transmission using bit-interleaved coded OFDM," IEEE Trans. Commun., vol. 65, no. 8, pp. 3250–3258, Aug. 2017, [Online]. Available: Cite
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C. Yuan, Z. Zhao, R. Li, M. Li, and H. Zhang, “On the emerging of scaling law, fractality and small-world in cellular networks,” presented at the IEEE VTC Spring, Sydney, Australia, Jun. 2017. Cite
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R. Li, W. Quan, Y. Chen, and Y. Wu, “A tomography of full-duplex cellular networks,” presented at the IEEE VTC Spring 2017, Sydney, Australia, Jun. 2017. Cite
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R. Li et al., "The learning and prediction of application-level traffic data in cellular networks," IEEE Trans. Wireless Commun., vol. 16, no. 6, pp. 3899–3912, Jun. 2017, [Online]. Available: Cite
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R. Li, Z. Zhao, H. Zhang, and X. Zhong, "The characteristics study of mobile instantaneous messaging traffic in cellular network," SCIENTIA SINICA Informationis, vol. 47, no. 5, p. 637, May 2017, [Online]. Available: Cite
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L. Fan, Z. Zhao, C. Qi, R. Li, and H. Zhang, “A revisiting to queueing theory for mobile instant messaging with keep-alive mechanism in cellular networks,” presented at the IEEE ICC 2017, Paris, France, May 2017. Cite
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R. Li, Y. Chen, G. Y. Li, and G. Liu, "Full-duplex cellular networks," IEEE Commun. Mag., vol. 56, no. 4, pp. 184–191, Apr. 2017, [Online]. Available: Cite
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C. Yuan, Z. Zhao, R. Li, M. Li, and H. Zhang, "The emergence of scaling law, fractal patterns and small-world in wireless networks," IEEE Access, vol. 5, no. 1, pp. 3121–3130, Mar. 2017, [Online]. Available: Cite
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D. Wen, G. Yu, R. Li, Y. Chen, and G. Y. Li, "Results on energy- and spectral- efficiency tradeoff in cellular networks with full-duplex enabled base stations," IEEE Trans. Wireless Commun., vol. 16, no. 3, pp. 1494–1507, Mar. 2017, [Online]. Available: Cite