| 职称：副教授/博导 | ||||||||||||||
办公室：南京市江宁区秣周东路9号中国无线谷A5-215室 | |||||||||||||||
办公电话： | |||||||||||||||
Email： | |||||||||||||||
学习经历： | |||||||||||||||
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工作经历: | |||||||||||||||
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教授课程： | |||||||||||||||
《无源雷达探测中的信号处理(双语研讨)》本科三年级(春季) 专业选修课（2020至今） 《现代数字信号处理》研究生一年级(秋季) 专业必修课（2020至今） 《数字通信原理与系统》研究生一年级(春季) 专业必修课（2019、2020） | |||||||||||||||
研究方向： | |||||||||||||||
● 智能无线通信与探测 ● 雷达信号处理 ● 信号处理理论 * 更多信息详见个人主页：https://sites.google.com/site/shenghengliu/ | |||||||||||||||
获奖情况： | |||||||||||||||
● 2017年度中国通信学会优秀博士学位论文奖 ● 2020年度南京市留学人员科技创新项目择优资助 ● 2021年江苏省高等学校优秀科技创新团队成员 ● 2022年yl23455永利官网第29届青年教师授课竞赛三等奖 | |||||||||||||||
招生信息： | |||||||||||||||
● 每年招生固定名额为：南京校区（学硕专硕均收，以推免生为主）4人、无锡校区（专硕）2人、苏州校区（专硕）1人。 ● 欢迎数理及（信号或通信）专业课基础好、学习研究主动性强、性格积极乐观的、有志于学术研究的同学们报考。 ● 当前课题组主要推进以下三个方向的研究工作（以算法、理论研究为主线）：(1) 雷达信号处理； (2) 智能无线通信网络；(3) 通信感知一体化。 ● 课题组软硬件条件充足，历年来报考竞争激烈，已毕业及在读研究生均能在国际顶级学术会议或期刊发表研究成果。 | |||||||||||||||
论文著作： | |||||||||||||||
近三年代表性期刊论文（标注*为通讯作者）： [J1] Zheng, C., Liu, S.*, Huang, Y.*, Zhang, W., Yang, L., Unsupervised recurrent federated learning for edge popularity prediction in privacy-preserving mobile edge computing networks, IEEE Internet of Things Journal, vol. 9, no. 23, in press, (DOI: 10.1109/JIOT.2022.3189055). [J2] Pan, M., Liu, P.*, Liu, S., et al., Efficient joint DOA and TOA estimation for indoor positioning with 5G picocell base stations, IEEE Transactions on Instrumentation and Measurement, vol. 71, art. no. 8005219, Aug. 2022. [J3] Mao, Z., Liu, S.*, Zhang, Y.D., Han, L., Huang, Y.*, Joint DoA-range estimation using space-frequency virtual difference coarray, IEEE Transactions on Signal Processing, vol. 70, pp. 2576–2592, May 2022. [J4] Liu, S., Zheng, C., Huang, Y.*, Quek, T.Q.S., Distributed reinforcement learning for privacy-preserving dynamic edge caching, IEEE Journal on Selected Areas in Communications, vol. 40, no. 3, pp. 749–760, Mar. 2022. [J5] Meng, F., Liu, S., Huang, Y.*, Lu*, Z., Learning-aided beam prediction in mmWave MU-MIMO systems for high-speed railway, IEEE Transactions on Communications, vol. 70, no. 1, pp. 693–706, Jan. 2022. [J6] Xu, C., Liu, S.*, Yang, Z., Huang, Y.*, Wong, K.K., Learning rate optimization for federated learning exploiting over-the-air computation, IEEE Journal on Selected Areas in Communications, vol. 39, no. 12, pp. 3742–3756, Dec. 2021. [J7] Zheng, C., Liu, S.*, Huang, Y.*, Yang, L., Hybrid policy learning for energy-latency tradeoff in MEC-assisted VR video service, IEEE Transactions on Vehicular Technology, vol. 70, no. 9, pp. 9006–9021, Sept. 2021. [J8] Liu, S., Mao, Z., Zhang, Y.D., Huang, Y.*, Rank minimization-based Toeplitz reconstruction for DoA estimation using coprime array, IEEE Communications Letters, vol. 25, no. 7, pp. 2265–2269, July 2021. [J9] Xu, C., Liu, S., Zhang, C., Huang, Y.*, Lu, Z.*, Yang, L., Multi-agent reinforcement learning based distributed transmission in collaborative cloud-edge systems, IEEE Transactions on Vehicular Technology, vol. 70, no. 2, pp. 1658–1672, Feb. 2021. [J10] Zhang, H., Shan, T., Liu, S.*, Tao, R., Performance evaluation and parameter optimization of sparse Fourier transform, Signal Processing, vol. 179, art. no. 107823, Feb. 2021. [J11] Liu, S.*, Huang, Y.*, Wu, H., Tan, C., Jia, J., Efficient multi-task structure-aware sparse Bayesian learning for frequency-difference electrical impedance tomography, IEEE Transactions on Industrial Informatics, vol. 17, no. 1, pp. 463–472, Jan. 2021. (ESI高被引) [J12] Liu, S.*, Cao, R., Huang, Y.*, Ouypornkochagorn, T., Jia, J., Time sequence learning for electrical impedance tomography using Bayesian spatiotemporal priors, IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 9, pp. 6045–6057, Sept. 2020. [J13] Zhang, H., Shan, T., Liu, S.*, Tao, R., Optimized sparse fractional Fourier transform: Principle and performance analysis, Signal Processing, vol. 174, art. no. 107646, Sept. 2020.
近三年代表性会议论文（均为通讯作者）： [C1] Su, J., Meng, F.,Liu, S., Huang, Y., Lu, Z., ‘Learning to predict and optimize imperfect MIMO system performance: Framework and application’, in Proceedings of the 2022 IEEE Global Communications Conference (GLOBECOM): Mobile & Wireless Networks Symposium, pp. 1–6, Rio de Janeiro, Brazil, December 4–8, 2022. [C2] He, Z.,Liu, S., Miao, X., Huang, Y., ‘Two-dimensional adaptive beamforming based on atomic-norm minimization’, in Proceedings of the 2022 IEEE International Symposium on Phased Array Systems and Technology (PAST), pp. 1–5, Waltham, MA, USA, October 11–14, 2022. [C3] Su, J.,Liu, S., Huang, Y., Yuan, J., ‘Peak-to-average power ratio reduction via symbol precoding in OTFS modulation’, in Proceedings of the 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring), pp. 1–5, Helsinki, Finland, June 19–22, 2022. [C4] Gong, Z.,Liu, S., Huang, Y., ‘Doppler diversity reception for OTFS modulation’, in Proceedings of the 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring), pp. 1–5, Helsinki, Finland, June 19–22, 2022. [C5] Liu, L.,Liu, S., Huang, Y., Amin, M.G., ‘Joint DoA-range estimation using moving time-modulated frequency diverse coprime array’, in Proceedings of the 2022 IEEE Radar Conference, pp. 1–5, New York City, NY, USA, March 21–25, 2022. [C6] Ni, T.,Liu, S., Mao, Z., Huang, Y., ‘Information-theoretic target localization with compressed measurement using FDA radar’, in Proceedings of the 2022 IEEE Radar Conference, pp. 1–5, New York City, NY, USA, March 21–25, 2022. [C7] Zheng, C.,Liu, S.,Huang, Y.,Quek, T.Q.S., ‘Privacy-preserving federated reinforcement learning for popularity-assisted edge caching’, in Proceedings of the 2021 IEEE Global Communications Conference (GLOBECOM), pp. 1–6, Madrid, Spain, December 7–11, 2021. [C8] Zhang, Y.,Liu, S., Lu, Z., Meng, F., Huang, Y., ‘Learning-aided beam management for mmWave high-speed railway networks’, in Proceedings of the 2021 IEEE Global Communications Conference (GLOBECOM), pp. 1–6, Madrid, Spain, December 7–11, 2021. [C9] Xu, C.,Liu, S., Huang, Y., Huang, C., Zhang, Z., ‘Over-the-air learning rate optimization for federated learning’, in Proceedings of the IEEE International Conference on Communications (ICC 2021), Montreal, QC, Canada, June 14–23, 2021. [C10] Cao, R., Liu, S., Mao, Z., Huang, Y., ‘Doubly-Toeplitz-based interpolation for joint DOA-range estimation using coprime FDA’, in Proceedings of the 2021 IEEE Radar Conference, Atlanta, GA, USA, May 10–14, 2021. [C11] Ni, T., Liu, S., Mao, Z., Huang, Y., ‘Range-dependent beamforming using space-frequency virtual difference coarray’,in Proceedings of the 2021 IEEE Radar Conference, Atlanta, GA, USA, May 10–14, 2021. [C12] Liu, C., Liu, S., Mao, Z., Huang, Y., Wang, H., ‘Low-complexity parameter learning for OTFS modulation based automotive radar’, in Proceedings of the 46th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada, June 6–11, 2021. [C13] Liu, C., Liu, S., Zhang, C., Huang, Y., Wang, H., ‘Multipath propagation analysis and ghost target removal for FMCW automotive radars’, in Proceedings of the 2020 IET International Radar Conference (IRC), Chongqing, China, November 4–6, 2020. [C14] Xu, C.,Liu, S., Zhang, C., Huang, Y., Yang, L., ‘Joint user scheduling and beam selection in mmWave networks based on multi-agent reinforcement learning’, in Proceedings of the 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), Hangzhou, China, June 8–11, 2020. [C15] Zheng, C., Liu, S., Huang, Y., Yang, L., ‘MEC-enabled wireless VR video service: A learning-based mixed strategy for energy-latency tradeoff’, in Proceedings of the 2020 IEEE Wireless Communications and Networking Conference (WCNC), Seoul, South Korea, April 6–9, 2020. | |||||||||||||||
科研项目： | |||||||||||||||
项目名称 | 项目类别 | 项目时间 | 工作类别 | 项目金额 | |||||||||||
云边端协同赋能的移动VR视频智能传输理论与方法 | 国家自然科学基金项目 | 2021.01-2023.12 | 项目负责人 | 24万元 | |||||||||||
汽车雷达探测关键算法研发 | 横向预研项目 | 2022.05-2023.06 | 项目负责人 | 128万元 | |||||||||||
基于多维特征学习的电阻抗层析成像与识别研究 | 江苏省自然科学基金项目 | 2019.07-2022.06 | 项目负责人 | 20万元 | |||||||||||
数据与模型协同驱动的智能边缘传输网络 | 国家重点研发计划项目子课题 | 2019/07-2023/06 | 主要参与人 | 573万 | |||||||||||
6G试验验证与技术评估研究 | 国家重点研发计划项目子课题 | 2020/12-2023/11 | 主要参与人 | 104万 | |||||||||||
5G场景下波束设计和特定信号探测 | 国家自然科学基金项目 | 2020.01-2023.12 | 主要参与人 | 260万 | |||||||||||
近三年来部分发明专利： | |||||||||||||||
专利号 | 专利名称 | 专利类型 | |||||||||||||
202210226277.0 | 定位参数估计方法、装置、设备、存储介质和程序产品 | 发明专利 | |||||||||||||
202210045023.9 | 目标空间位置参数估计方法及装置 | 发明专利 | |||||||||||||
202111024093.8 | 信号到达角估计方法、装置、电子设备及存储介质 | 发明专利 | |||||||||||||
202111503756.4 | 一种基于随机聚合波束成形的用户设备选择方法 | 发明专利 | |||||||||||||
202110953367.5 | 一种基于变换域最大比合并的OTFS 调制信号检测方法 | 发明专利 | |||||||||||||
202110920003.7 | 相位偏差校正方法、装置、计算机设备和存储介质 | 发明专利 | |||||||||||||
202110803841.6 | OTFS 信号处理方法、装置、设备及存储介质 | 发明专利 | |||||||||||||
202110502499.6 | 一种基线校准方法、装置、网络侧设备及存储介质 | 发明专利 | |||||||||||||
202110144931.9 | 定位方法、装置、计算机设备和存储介质 | 发明专利 | |||||||||||||
202110170759.4 | 目标角度和距离定位方法、装置、雷达和存储介质 | 发明专利 | |||||||||||||
202110073349.8 | 一种基于秩最小化Toeplitz重构的互质阵波达方向估计 | 发明专利 | |||||||||||||
202110616233.4 | 移动边缘计算网络中基于分布式强化学习的隐私保护动态边缘缓存设计方法 | 发明专利 | |||||||||||||
202110047037.X | 一种基于贝叶斯学习的OTFS雷达目标参数估计方法 | 发明专利 | |||||||||||||
PCT/CN2020/125255 | 终端定位方法、装置、计算机设备和存储介质 | PCT国际专利 | |||||||||||||
PCT/CN2021/124026 | 基于频率分集的阵列天线的波束控制方法、系统及控制器 | PCT国际专利 | |||||||||||||
PCT/CN2021/143475 | 目标角度和距离确定方法、装置、雷达和存储介质， | PCT国际专利 | |||||||||||||
