Grassmannian Predictive Coding
for Limited Feedback Multiuser MIMO Systems
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Prague, Czech Republic, May 22-27, 2011.
Grassmannian beamforming with limited feedback provides diversity gain using quantized channel state information at the transmitter in single user and multiuser multiple-input multiple-output wireless systems. Unfortunately, the multiuser systems require larger codebooks since the quantization error creates interference that limits the sum rate performance. To reduce the feedback requirements in multiuser systems, we propose a Grassmannian predictive coding technique that exploits the temporal channel correlation. The proposed algorithm is derived by using the differential geometric structure of the Grassmann manifold. We show that with practical feedback rates and a fixed pair of codebooks, significant sum rate improvement can be obtained as a function of the channel correlation.