摘要：With the rapid growth of wireless traffic and applications, the abilities to efficiently allocate spectral resources and secure data transmission to adapt to the diverse user demands and behaviors mark the future of wireless communications. To improve spectrum utilization, cognitive radio technology has been introduced to enable secondary use of licensed spectrum. However, it is a challenging task to incentivize both licensed and secondary users. This talk will focus on incentive-driven approaches for spectrum sharing. In particular, geographic considerations unique to such a system will be emphasized. Similarly, incentive-driven approaches can be used to enhance wireless security. Novel physical-layer security schemes exploiting the cooperation and competition among the entire network from the game-theoretic perspective will be highlighted. In the end, future extension of the work and potential application of machine learning to intelligent wireless communications will be discussed.

简介：

周向炜：美国路易斯安那州立大学电子与计算机工程系助理教授，博士生导师。2011年于美国佐治亚理工大学获得电子与计算机工程博士学位，同年起在Marvell Semiconductor任高级系统工程师，2013年至2015年在美国南伊利诺伊大学任助理教授。主要工作领域涉及无线通信、统计信号处理、跨层优化，目前主要研究方向为频谱与能量高效及安全通信、无线系统共存、机器学习与智能通信等。承担美国国家科学基金“Enhancing Access to the Radio Spectrum (EARS)”计划的项目研究，已在IEEE JSAC、TWC、TCOM等权威期刊发表20余篇论文以及ICC、GLOBECOM、ICASSP等通信与信号处理领域国际旗舰会议发表30余篇论文。现为IEEE高级会员（Senior Member），担任IEEE TWC (Transactions on Wireless Communications) 编委（Editor），曾任IEEE VTC 2016-Spring讨论分会共同主席（Track Co-Chair）。