Joint user pairing and precoding in MU-MIMO broadcast channel with limited feedback

Authors:
Xin Xia;Shu Fang;Gang Wu;Shaoqian Li
Affiliations:
National Key Laboratory of Science and Technology on Communications of China, Chengdu, China;National Key Laboratory of Science and Technology on Communications of China, Chengdu, China;National Key Laboratory of Science and Technology on Communications of China, Chengdu, China;National Key Laboratory of Science and Technology on Communications of China, Chengdu, China
Venue:
IEEE Communications Letters
Year:
2010

Citing 8
Cited 0

Improved multiuser MIMO unitary precoding using partial channel state information and insights from the Riemannian manifold

IEEE Transactions on Wireless Communications
Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels

IEEE Transactions on Signal Processing
Fast transfer of channel state information in wireless systems

IEEE Transactions on Signal Processing
Grassmannian beamforming for multiple-input multiple-output wireless systems

IEEE Transactions on Information Theory
MIMO Broadcast Channels With Finite-Rate Feedback

IEEE Transactions on Information Theory
On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming

IEEE Journal on Selected Areas in Communications
Fundamental Limits in MIMO Broadcast Channels

IEEE Journal on Selected Areas in Communications
On transceiver design and channel quantization for downlink multiuser MIMO systems with limited feedback

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a joint user pairing and precoding scheme in MU-MIMO broadcast channel with limited feedback. In the proposed scheme, each user feeds back its preferable precoding vector index and two scalars denoting quantization error and the largest singular value of channel matrix, respectively. Base station (BS) selects the first user who has highest estimated SINR, and generates a unitary precoding matrix based on the first user's preferred precoding vector. Then, BS selects users one by one. In each step, BS calculates the estimated SINR for each un-selected user, using the generated precoding vector, and then chooses the user with largest estimated SINR. Numerical results verify its effectiveness.