主讲人简介：

Dr. Zhijin Qin is a Lecturer (Assistant Professor) with the School of Electronic Engineering and Computer Science at Queen Mary University of London (QMUL), UK. She is also an Honorary Research Fellow in Imperial College London, UK. She was with Lancaster University as a Lecturer from 2017 to 2018. Before that, she was with Imperial College London as a Postdoctoral Research Associate from 2016 to 2017. She received her Ph.D. degree on Electronic Engineering from QMUL in 2016, and the B.S. degree from Beijing University of Posts and Telecommunications in 2012. Her research interest includes machine learning and compressive sensing in wireless communications, Internet of Things (IoT) networks and non-orthogonal multiple access for 5G networks. She is an associate editor of IEEE Communication Letter. She received the Best Paper Award from IEEE GLOBECOM 2017.

报告摘要：        

Sparse representation can efficiently model signals in different applications to facilitate processing. In this talk, the sparse representation in wireless communications will be discussed, with focus on the most recent compressive sensing enabled approaches. With the sparsity property, compressive sensing can enhance the spectrum efficiency and energy efficiency for the wireless communications and IoT networks. This talk starts from a comprehensive overview of compressive sensing principles and different sparse domains potentially used in 5G and IoT networks. Then recent work on applying compressive sensing to address the major opportunities and challenges in wireless communications and IoT networks is introduced, with wideband spectrum sensing in cognitive radio networks as an example. Moreover, other potential applications and research challenges on sparse representation for wireless communications and IoT networks are identified.