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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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. 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. 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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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, 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., 2020. 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. 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., 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,” in Proc. IEEE Globecom 2019, Big Island, Hawaii, USA, 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, 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 (Demos Session), Los Cabos, Mexico, 2019. 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. 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. 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. 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,” in Proc. ACM TURC 2019, Chengdu, China, 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. 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. 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. 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. 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. 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,” in Proc. IEEE ICT 2018, Saint-Malo, France, 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. 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,” in Proc. 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. 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,” in Proc. WCSP 2017, Nanjing, China, 2017. Cite
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X. Meng, Z. Zhao, R. Li, and H. Zhang, “An intelligent honeynet architecture based on software defined security,” in Proc. WCSP 2017, Nanjing, China, 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,” in Proc. WCSP 2017, Nanjing, China, 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. 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. 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. 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. Cite
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R. Li, W. Quan, Y. Chen, and Y. Wu, “A tomography of full-duplex cellular networks,” in Proc. IEEE VTC Spring 2017, Sydney, Australia, 2017. 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,” in Proc. IEEE VTC 2017 Spring, Sydney, Australia, 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. 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. 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,” in Proc. IEEE ICC 2017, Paris, France, 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. 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. 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. Cite
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R. Li et al., "Intelligent 5G: When cellular networks meet artificial intelligence," IEEE Wireless Commun., vol. 24, no. 5, pp. 175–183, 2017. Cite
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D. Wen, G. Yu, R. Li, Y. Chen, and G. Y. Li, “Energy- and Spectral- Efficiency Tradeoff in Full-Duplex Communications,” in Proc. IEEE Globecom 2016 Workshops, Washington, DC, USA, 2016. Cite
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R. Li, Y. Chen, and Y. Wu, "Binary power control for full-duplex cellular networks," in Proc. IEEE PIMRC 2016, Valencia, Spain, 2016. Cite
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X. Zhou, R. Li, T. Chen, and H. Zhang, "Network slicing as a service: Enable industries own software-defined cellular networks," IEEE Commun. Mag., vol. 54, no. 7, pp. 146–153, Jul. 2016. Cite
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J. Zheng, H. Zhang, Y. Cai, R. Li, and A. Anpalagan, "Game-theoretic multi-channel multi-access in energy harvesting wireless sensor networks," IEEE Sensor J., vol. 16, no. 11, pp. 4587–4594, Jun. 2016. Cite
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C. Qi, Z. Zhao, R. Li, and H. Zhang, “Characterizing and modeling social mobile data traffic in cellular networks,” in Proc. IEEE VTC 2016-Spring, Nanjing, China, 2016. Cite