马嫄，博士，365游戏大厅副教授，博士生导师。入选中国科协第六届青年托举人才(2021年)，广东省教育厅青年创新人才、深圳市优秀科技创新人才、“孔雀计划”深圳海外高层次人才C类。马嫄博士于2013年获得北京邮电大学学士学位，2017年获得英国伦敦大学玛丽女王学院工学博士学位。主要研究方向包括压缩感知与稀疏信号恢复、数据驱动信号处理、以及宽带频谱检测与共享等，作为项目负责人主持了包括国家自然科学青年基金、科技部重点研发计划子课题、广东省自然科学基金面上项目等10余项科研项目研究。在本学科国内外著名学术期刊IEEE JSAC、TWC、TVT等以及学术会议上发表论文30余篇，出版英文学术专著1部，授权发明专利4项。相关研究成果获得《通信与信息网络学报》最佳期刊论文奖、英国工程与自然科学研究委员会(EPSRC) GBSense Challenge二等奖、中国通信学会技术发明一等奖等。担任IEEE通信学会认知网络技术委员会(IEEE Technical Committee on Cognitive Networks)副主席 (Asia Pacific)，IET Communications编委等，在2017-2019年连续三年获得IEEE Wireless Communications Letters期刊的模范审稿人称号。

办公室：365游戏大厅致信楼N823

Email:mayuan@szu.edu.cn

电磁空间认知与智能信号处理，主要研究兴趣包括压缩感知与稀疏信号恢复，数据驱动信号处理，毫米波移动通信，以及宽带频谱检测与共享等。

2021年，中国科协第六届青年托举人才

2021年，《通信与信息网络学报》最佳期刊论文奖

2020年，中国通信学会技术发明一等奖

2020年，深圳市优秀科技创新人才

2019年，广东省教育厅青年创新人才

2017-2019年，Exemplary Reviewer for IEEE Wireless Communications Letters

2018年，“孔雀计划”深圳海外高层次人才C类

2013年，College Prize, London University

2013年，北京市优秀毕业生

2013年，北京邮电大学优秀毕业论文

1.国家自然科学基金青年基金项目，2020-2022年；

2.国家科技部重点研发计划子课题，2020-2022年；

3.中国科协青年人才托举工程，2021年-2023年；

4.广东省自然科学基金面上项目，2020-2022年；

5.广东省教育厅青年创新人才项目，2019-2021年；

6.深圳市优秀科技创新人才（博士基础研究）项目，2021-2023年；

7.深圳市海外高层次人才科研启动项目，2020-2022年。

目前担任IEEE通信学会认知网络技术委员会 ( IEEE ComSoc Technical Committee on Cognitive Networks, TCCN) 副主席 (Asia Pacific) (2023-至今)、IET Communications编委、Journal of Communications and Information Networks客座编委等，多次担任IEEE ICC、Globecom、WCNC等通信旗舰会议的技术委员会(TPC)成员，以及IEEE JSAC, TWC, TSP, TCOM, TVT等国际学术期刊的审稿人，在2017-2019年先后三次获得IEEE Wireless Communications Letters期刊的模范审稿人称号。

[J1] X. Zhang, Y. Ma*, Y. Liu, et.al., “Robust DNN-based Recovery of Wideband Spectrum Signals,” IEEE Wireless Communications Letters, vol. 12, no. 10, pp. 1712-1715, Oct. 2023. (IF: 6.3)

[J2] Y. Ma, S. Ren, Z. Quan, and Z. Feng, “Data-Driven Hybrid Beamforming for Uplink Multi-User MIMO in Mobile Millimeter-Wave Systems,” IEEE Transactions on Wireless Communications, vol. 21, no. 11, pp. 9341-9350, Nov. 2022. (中科院1区，IF：10.4)

[J3] Y. Ma, S. Ren, W. Chen, and Z. Quan, “Data-Driven Beam Tracking for Mobile Millimeter-Wave Communication Systems without Channel Estimation,” IEEE Wireless Communications Letters, vol. 10, no. 12, pp. 2747-2751, Dec. 2021. (IF: 5.281)

[J4] R. Liu, Y. Ma*, et.al., “Deep-Learning based Spectrum Sensing in Space-Air-Ground Integrated Networks”, Journal of Communications and Information Networks, Vol. 6, No. 1, pp. 23-31, Mar. 2021.

[J5] O. Elnahas, Y. Ma, Y. Jiang, and Z. Quan, “Clock Synchronization in Wireless Networks using Matrix Completion based Maximum Likelihood Estimation”, IEEE Transactions on Wireless Communications, vol. 19, no. 12, pp. 8220-8231, Dec. 2020. (中科院1区，IF：8.346)

[J6] Y. Ma, et.al., “Optimal Linear Cooperation for Signal Classification in Cognitive Communication Networks,” IEEE Transactions on Wireless Communications, vol. 19, no. 5, pp. 3144-3155, May 2020. (中科院1区，IF：8.346)

[J7] Y. Ma, Z. Quan, D. Li, and B. Zhang, “Minimizing Misclassification for Cooperative Spectrum Sensing using M-ary Hypothesis Testing,” IEEE Transactions on Vehicular Technology, vol. 68, no. 8, pp. 8210-8215, Aug. 2019. (IF: 6.239)

[J8] Y. Ma, Y. Gao, et.al., “TV White Space Spectrum Analysis based on Machine Learning,” Journal of Communications and Information Networks, Vol. 4, No. 2, pp. 23-35, Jun. 2019. (Best Paper Award)

[J9] Y. Ma, X. Wang, Z. Quan, and H. V. Poor, "Data-Driven Measurement of Receiver Sensitivity in Wireless Communication Systems," IEEE Transactions on Communications, vol. 67, no. 5, pp. 3665-3676, May 2019. (IF: 6.166)

[J10] X. Zhang, Y. Ma*, Y. Gao, and W. Zhang, “Autonomous Compressive Sensing Augmented Spectrum Sharing,” IEEE Transactions on Vehicular Technology, vol. 67, no. 8, pp. 6970-6980, Aug. 2018. (IF: 6.239)

[J11] Y. Ma, X. Zhang, and Y. Gao, “Joint Sub-Nyquist Spectrum Sensing Scheme with Geolocation Database over TV White Space,” IEEE Transactions on Vehicular Technology, vol. 67, no. 5, pp. 3998-4007, May 2018. (IF: 6.239)

[J12] X. Zhang, Y. Ma*, Y. Gao, and S. Cui, “Real-time Adaptively Regularized Compressive Sensing in Cognitive Radio Networks,” IEEE Transactions on Vehicular Technology, vol. 67, no. 2, pp. 1146-1157, Feb. 2018. (IF: 6.239)

[J13] X. Zhang, Y. Ma*, H. Qi, and Y. Gao, “Low-Complexity Compressive Spectrum Sensing for Large-Scale Real-Time Processing,” IEEE Wireless Communications Letters, vol. 7, no. 4, pp. 674-677, Aug. 2018.

[J14] X. Zhang, Y. Ma, Y. Gao, et al., "Distributed Compressive Sensing Augmented Wideband Spectrum Sharing for Cognitive IoT," IEEE Internet of Things Journal, vol. 5, no. 4, pp. 3234-3245, Aug. 2018. (中科院1区，IF：9.515)

[J15] Y. Ma, Y. Gao, A. Cavallaro, C. G. Parini, W. Zhang, and Y.-C. Liang, “Sparsity Independent Sub-Nyquist Rate Wideband Spectrum Sensing on Real-time TV White Space,” IEEE Transactions on Vehicular Technology, vol. 66, no. 10, pp. 8784 - 8794, Apr. 2017. (IF: 6.239)

[J16] Y. Ma, Y. Gao, Y.-C. Liang, and S. Cui, “Reliable and Efficient Sub-Nyquist Wideband Spectrum Sensing in Cooperative Cognitive Radio Networks,” IEEE Journal on Selected Areas in Communications, vol. 34, no. 10, pp. 2750-2762, Oct. 2016. (中科院1区，Top期刊，IF: 8.085)