Transmit Correlation Diversity: Generalization, New Techniques, and Improved Bounds
Artikel i vetenskaplig tidskrift, 2022

When the users in a MIMO broadcast channel experience different spatial transmit correlation matrices, a class of gains is produced that is denoted transmit correlation diversity. This idea was conceived for channels in which transmit correlation matrices have mutually exclusive eigenspaces, allowing non-interfering training and transmission. This paper broadens the scope of transmit correlation diversity to the case of partially and fully overlapping eigenspaces and introduces techniques to harvest these generalized gains. For the two-user MIMO broadcast channel, we derive achievable degrees of freedom (DoF) and achievable rate regions with/without channel state information at the receiver (CSIR). When CSIR is available, the proposed achievable DoF region is tight in some configurations of the number of receive antennas and the channel correlation ranks. We then extend the DoF results to the K-user case by analyzing the interference graph that characterizes the overlapping structure of the eigenspaces. Our achievability results employ a combination of product superposition in the common part of the eigenspaces, and pre-beamforming (rate splitting) to create multiple data streams in non-overlapping parts of the eigenspaces. Massive MIMO is a natural example in which spatially correlated link gains are likely to occur. We study the achievable downlink sum rate for a frequency-division duplex massive MIMO system under transmit correlation diversity.

Transmitting antennas

rate splitting

Signal to noise ratio

Fading channels

channel state information


spatial correlation

product superposition

MIMO broadcast channels

Channel state information

Massive MIMO



Fan Zhang

University of Texas at Dallas

Khac-Hoang Ngo

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Sheng Yang

Université Paris-Saclay

Aria Nosratinia

University of Texas at Dallas

IEEE Transactions on Information Theory

0018-9448 (ISSN)

Vol. In Press







Mer information

Senast uppdaterat