Massive MIMO Systems
Massive MIMO Systems
|Siva Prasad Var Chiluvuri|
Massive MIMO (M-MIMO), which consists of a large number of antennas at the access point, is a promising technology to meet the high data rate and quality of service requirements of 5G wireless systems. In this project, CWC faculty addresses several challenges in M- MIMO system design. The topics include channel modeling and estimation, energy efficient techniques and hardware architectures. In particular, this project studies several key aspects of massive MIMO systems: channel estimation in different environments for frequency division duplex (FDD) systems, data-aided channel estimation for time division duplex (TDD) systems, and distributed massive MIMO design tradeoffs. In particular, the project considers a novel dictionary learning perspective to enable low dimensional representations of the channel and compressive channel learning that can significantly reduce the feedback overhead for FDD systems. Also part of the research plan includes the development of semi-blind channel estimation methods to improve channel estimation well beyond what is possible with pilot-only training as well as to address the pilot contamination problem. Low complexity receiver design that leverages the M-MIMO spatial degrees of freedom and physical layer techniques with low complexity hardware, i.e. reduced RF chains are also addressed in this research. Also of interest is energy efficiency at all levels; system, hardware, and processing. Examining the main components that contribute to the base station (BS) power consumption, the project aims to adapt the number of active RF chains, a significant factor in the power budget, to reduce the total BS power consumption while satisfying the QoS requirements. A MIMO testbed is being planned to better understand the channel modeling and scalability issues.
Publications by year
W. Ni, P-H. Chiang, and S. Dey, “Energy Efficient Hybrid Beamforming in Massive MU-MIMO Systems via Eigenmode Selection”, to appear in Proceedings of IEEE International Conference on Green Computing and Communications (GreenCom – 2017), October 2017
- Choi JW, Shim B, Ding Y, Rao B, Kim DI, "Compressed sensing for wireless communications: Useful tips and tricks," IEEE Communications Surveys & Tutorials. 2017 Feb 6.
- Yacong Ding and Bhaskar D. Rao,"Dictionary Learning Based Sparse Channel Representation and Estimation for FDD Massive MIMO Systems", submitted to IEEE Transactions on Wireless Communications.