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[学术报告]Massive Device Connectivity with Massive MIMO
2017-02-22 21:27   审核人:

时间:20173215:00-16:00

地点:工学一号馆301

报告人:刘亮 博士(加拿大多伦多大学)

报告题目:Massive Device Connectivity with Massive MIMO

 

个人简介:

Liang Liu received the B. Eng degree from the Tianjin University, China, in 2010, and the Ph. D degree from the National University of Singapore in 2014. He is currently a Postdoctoral Fellow in the Department of Electrical and Computer Engineering at University of Toronto. His research interests include convex optimization, resource allocation in interference channel, energy harvesting, and 5G.

Dr. Liu has published more than 20 papers in IEEE journals and conferences within 5 years, whichhave received more than1200 citations in total.Among them, 7 journal papers have been listed as the ESI highly cited paper, and 6 of them have each received more than 100 citations. He was the recipient of a Best Paper Award from IEEE WCSP 2011.

 

报告摘要:

This talk studies a single-cell uplink massive device communication scenario in which a large number of single antenna devices are connected to the base station (BS), but user traffic is sporadic so that at a given coherence interval, only a subset of users are active. For such a system, active user detection and channel estimation are key issues. To accommodate such a large number of active users, this talk studies the asymptotic regime where the BS is equipped with a large number of antennas. A grant-free two-phase access scheme is adopted where user activity detection and channel estimation are performed in the first phase, and data is transmitted in the second phase. Our main contributions are as follows. First, we show that despite the non-orthogonality of pilot sequences (which is necessary for accommodating a large number of potential devices), in the asymptotic massive multiple-input multiple-output (MIMO) regime, both the missed detection and false alarm probabilities can be made to go to zero by utilizing compressed sensing techniques that exploit sparsity in user activities. Further, we show that despite the guaranteed success in user activity detection, the non-orthogonality of pilot sequences nevertheless can cause significantly larger channel estimation error than that in conventional massive MIMO system, thus lowering the overall achievable transmission rate. This talk quantifies the cost due to device detection and channel estimation and illustrates its effect on the optimal pilot length for massive device connectivity

 

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