Joint Spatial Division and Multiplexing: Opportunistic Beamforming, User Grouping and Simplified Downlink Scheduling

Joint Spatial Division and Multiplexing (JSDM) is a downlink multiuser MIMO scheme recently proposed by the authors in order to enable "massive MIMO" gains and simplified system operations for Frequency Division Duplexing (FDD) systems. The key idea lies in partitioning the users into grou...

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Bibliographic Details
Published in:IEEE journal of selected topics in signal processing 2014-10, Vol.8 (5), p.876-890
Main Authors: Junyoung Nam, Adhikary, Ansuman, Jae-Young Ahn, Caire, Giuseppe
Format: Article
Language:English
Subjects:
Algorithms
Antennas
Array signal processing
Channels
Communication channels
Covariance matrices
Division
Downlink
Feedback
Interference
JSDM
Multiplexing
opportunistic beamforming
Partitioning
probabilistic scheduling
Scheduling
Signal to noise ratio
user grouping
Vectors
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Summary:Joint Spatial Division and Multiplexing (JSDM) is a downlink multiuser MIMO scheme recently proposed by the authors in order to enable "massive MIMO" gains and simplified system operations for Frequency Division Duplexing (FDD) systems. The key idea lies in partitioning the users into groups with approximately similar channel covariance eigenvectors and serving these groups by using two-stage downlink precoding scheme obtained as the concatenation of a pre-beamforming matrix, that depends only on the channel second-order statistics, with a multiuser MIMO linear precoding matrix, which is a function of the effective channels including pre-beamforming. The role of pre-beamforming is to reduce the dimensionality of the effective channel by exploiting the near-orthogonality of the eigenspaces of the channel covariances of the different user groups. This paper is an extension of our initial work on JSDM, and addresses some important practical issues. First, we focus on the regime of finite number of antennas and large number of users and show that JSDM with simple opportunistic user selection is able to achieve the same scaling law of the system capacity with full channel state information. Next, we consider the large-system regime (both antennas and users growing large) and propose a simple scheme for user grouping in a realistic setting where users have different angles of arrival and angular spreads. Finally, we propose a low-overhead probabilistic scheduling algorithm that selects the users at random with probabilities derived from large-system random matrix analysis. Since only the pre-selected users are required to feedback their channel state information, the proposed scheme realizes important savings in the CSIT feedback.
ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2014.2313808