| 職稱：副教授/博導 | ||||||||||||||
辦公室：南京市江甯區秣周東路9号中國無線谷A5-215室 | |||||||||||||||
辦公電話： | |||||||||||||||
Email： | |||||||||||||||
學習經曆： | |||||||||||||||
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工作經曆: | |||||||||||||||
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教授課程： | |||||||||||||||
《無源雷達探測中的信号處理(雙語研讨)》本科三年級(春季) 專業選修課（2020至今） 《現代數字信号處理》研究生一年級(秋季) 專業必修課（2020至今） 《數字通信原理與系統》研究生一年級(春季) 專業必修課（2019、2020） | |||||||||||||||
研究方向： | |||||||||||||||
● 智能無線通信與探測 ● 雷達信号處理 ● 信号處理理論 * 更多信息詳見個人主頁：https://sites.google.com/site/shenghengliu/ | |||||||||||||||
獲獎情況： | |||||||||||||||
● 2017年度中國通信學會優秀博士學位論文獎 ● 2020年度南京市留學人員科技創新項目擇優資助 ● 2021年江蘇省高等學校優秀科技創新團隊成員 ● 2022年beat365正版唯一第29屆青年教師授課競賽三等獎 | |||||||||||||||
招生信息： | |||||||||||||||
● 每年招生固定名額為：南京校區（學碩專碩均收，以推免生為主）4人、無錫校區（專碩）2人、蘇州校區（專碩）1人。 ● 歡迎數理及（信号或通信）專業課基礎好、學習研究主動性強、性格積極樂觀的、有志于學術研究的同學們報考。 ● 當前課題組主要推進以下三個方向的研究工作（以算法、理論研究為主線）：(1) 雷達信号處理； (2) 智能無線通信網絡；(3) 通信感知一體化。 ● 課題組軟硬件條件充足，曆年來報考競争激烈，已畢業及在讀研究生均能在國際頂級學術會議或期刊發表研究成果。 | |||||||||||||||
論文著作： | |||||||||||||||
近三年代表性期刊論文（标注*為通訊作者）： [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.
近三年代表性會議論文（均為通訊作者）： [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. | |||||||||||||||
科研項目： | |||||||||||||||
項目名稱 | 項目類别 | 項目時間 | 工作類别 | 項目金額 | |||||||||||
雲邊端協同賦能的移動VR視頻智能傳輸理論與方法 | 國家自然科學基金項目 | 2021.01-2023.12 | 項目負責人 | 24萬元 | |||||||||||
汽車雷達探測關鍵算法研發 | 橫向預研項目 | 2022.05-2023.06 | 項目負責人 | 128萬元 | |||||||||||
基于多維特征學習的電阻抗層析成像與識别研究 | 江蘇省自然科學基金項目 | 2019.07-2022.06 | 項目負責人 | 20萬元 | |||||||||||
數據與模型協同驅動的智能邊緣傳輸網絡 | 國家重點研發計劃項目子課題 | 2019/07-2023/06 | 主要參與人 | 573萬 | |||||||||||
6G試驗驗證與技術評估研究 | 國家重點研發計劃項目子課題 | 2020/12-2023/11 | 主要參與人 | 104萬 | |||||||||||
5G場景下波束設計和特定信号探測 | 國家自然科學基金項目 | 2020.01-2023.12 | 主要參與人 | 260萬 | |||||||||||
近三年來部分發明專利： | |||||||||||||||
專利号 | 專利名稱 | 專利類型 | |||||||||||||
202210226277.0 | 定位參數估計方法、裝置、設備、存儲介質和程序産品 | 發明專利 | |||||||||||||
202210045023.9 | 目标空間位置參數估計方法及裝置 | 發明專利 | |||||||||||||
202111024093.8 | 信号到達角估計方法、裝置、電子設備及存儲介質 | 發明專利 | |||||||||||||
202111503756.4 | 一種基于随機聚合波束成形的用戶設備選擇方法 | 發明專利 | |||||||||||||
202110953367.5 | 一種基于變換域最大比合并的OTFS 調制信号檢測方法 | 發明專利 | |||||||||||||
202110920003.7 | 相位偏差校正方法、裝置、計算機設備和存儲介質 | 發明專利 | |||||||||||||
202110803841.6 | OTFS 信号處理方法、裝置、設備及存儲介質 | 發明專利 | |||||||||||||
202110502499.6 | 一種基線校準方法、裝置、網絡側設備及存儲介質 | 發明專利 | |||||||||||||
202110144931.9 | 定位方法、裝置、計算機設備和存儲介質 | 發明專利 | |||||||||||||
202110170759.4 | 目标角度和距離定位方法、裝置、雷達和存儲介質 | 發明專利 | |||||||||||||
202110073349.8 | 一種基于秩最小化Toeplitz重構的互質陣波達方向估計 | 發明專利 | |||||||||||||
202110616233.4 | 移動邊緣計算網絡中基于分布式強化學習的隐私保護動态邊緣緩存設計方法 | 發明專利 | |||||||||||||
202110047037.X | 一種基于貝葉斯學習的OTFS雷達目标參數估計方法 | 發明專利 | |||||||||||||
PCT/CN2020/125255 | 終端定位方法、裝置、計算機設備和存儲介質 | PCT國際專利 | |||||||||||||
PCT/CN2021/124026 | 基于頻率分集的陣列天線的波束控制方法、系統及控制器 | PCT國際專利 | |||||||||||||
PCT/CN2021/143475 | 目标角度和距離确定方法、裝置、雷達和存儲介質， | PCT國際專利 | |||||||||||||
