Multiuser MIMO

Multiple User MIMO Communication

In a multiuser MIMO (MU-MIMO) system, a base station communicates with multiple users. On the downlink, known as the MIMO broadcast channel, the base station sends different information streams to the users. On the uplink, the base station receives different information from the users. Other variations of MU-MIMO involve full or partial multi-cast of data. Note that while MU-MIMO is often discussed in the context of cellular communication, it could conceivably be used in wireless local area networks or in wireless ad hoc networks.

MU-MIMO on the downlink is especially interesting because the MIMO sum capacity can scale with the minimum of the number of base station antennas and the sum of the number of users times the number of antennas per user. This means that MU-MIMO can achieve MIMO capacity gains with a multiple antenna base station and a bunch of single antenna mobile users! This is of particular interest since real estate for multiple antennas is limited on small handheld devices. MU-MIMO has been discussed extensively in 3GPP LTE Advanced.

With my research group the WSIL and collaborators, I have developed and analyzed a variety of different multiple user MIMO transmission techniques. Much of our work has focused on the downlink, specifically on developing low complexity alternatives to dirty paper coding transmission. Here I describe some of the research directions that my research group has taken in the general area of multiple user MIMO communication, with some select publications, and a brief summary of our key results. Some specialized topics like multiple user limited feedback communication are described in separate pages. Only select journal publications are mentioned here; more journal publications and conference papers may be found on my CV.

Overview Papers

D. Gesbert, M. Kountouris, R. W. Heath, Jr., C. B. Chae,  and  T. Salzer, “From Single user to Multiuser Communications: Shifting the MIMO paradigm,” IEEE Signal Processing Magazine, Vol. 24, No. 5, pp. 36-46, Oct., 2007.

This paper provides an overview of the benefits and challenges of multiple user MIMO communication, as compared with single user MIMO systems. It discusses the potential improvements in sum capacity and some of the specific requirements of multiple user MIMO, like high quality channel state information.

J. G. Andrews, W. Choi,  and  R. W. Heath, Jr., “Overcoming Interference in Spatial Multiplexing MIMO Cellular Networks,” IEEE Trans. on Wireless, vol. 14, no. 6, pp. 95-104, Dec. 2007.

These papers provide a nice overview of the impact of interference on spatial multiplexing MIMO systems. It sets the stage for coordination techniques like network MIMO.

Multiple Mode Adaptation

J. Zhang, R. W. Heath, Jr., M. Kountouris, and  J. G. Andrews, “Multi-mode Transmission for the MIMO Broadcast Channel with Imperfect Channel State Information,” to appear in IEEE Trans. on Communications.

C. Lee, C. B. Chae, S. Vishwanath, and R. W. Heath, Jr., “Adaptive Mode Switching in Correlated Multiple Antenna Cellular NetworksJournal of Communications and Networks, Special Issue on Wireless Cooperative Transmission and Its Applications, vol. 11, no. 3, pp. 279-286, June 2009.

R. Chen, Z. Shen, J. G. Andrews and  R. W. Heath, Jr., “Multi-mode Transmission for Multiuser MIMO Systems with Block Diagonalization,” IEEE Trans. on Signal Processing, vol. 56, no. 7, part 2, pp. 3294-3302, July 2008.

In single user MIMO systems, the optimum number of spatial multiplexing streams, or spatial modes, varies with the channel quality. In multiple user MIMO systems a similar phenomenon is observed, but is more complex. For example, the optimum number of streams per user and the number of users may also vary depending on the performance objective. For example, with four transmit antennas and four users, each with a single receive antenna, there may be times where the sum rate is highest if only two of the four users are served. We have developed an analyzed mode switching algorithms that allow us to adjust the number of streams and the number of users for different performance objectives.

Block Diagonalization

C. B. Chae, S. Shim,  and  R. W. Heath, Jr., “Block Diagonalized Vector Perturbation for Multi-user MIMO Systems,” IEEE Trans. on Wireless, vol. 7, no. 11, pp. 4051-4057, Nov. 2008.

R. Chen, R. W. Heath, Jr.,  and  J. G. Andrews, “Transmit Selection Diversity for Unitary Precoded Multiuser Spatial Multiplexing Systems with Linear Receivers,” IEEE Trans. on Signal Processing, vol. 55, no. 3, pp. 1159-1171, March 2007.

Z. Shen, J. G. Andrews, R. W. Heath, Jr., and  B. L. Evans, “Low Complexity User Selection Algorithms for Multiuser MIMO Systems with Block Diagonalization,” IEEE Trans. on Signal Processing, vol. 54, no. 9, pp. 3658-3663, Sep. 2006.

Block diagonalization is a multiple user MIMO transmission strategy where the base station sends multiple data streams to multiple users. It combines the benefits of spatial multiplexing and multiple user MIMO by providing both high per-user rates and high sum rates by taking advantage of multiple receive antennas. Essentially block diagonalization is an extension of zero forcing transmission where the interference created from one users’s transmission on another user’s is zero. We have developed several different techniques for systems with block diagonalization including nonlinear vector perturbation to improve performance, multiple user antenna subset selection, and low complexity user scheduling algorithms.

Coordinated Beamforming

C. B. Chae, D. Mazzarese, T. Inoue and  R. W. Heath, Jr., “Coordinated Beamforming for the Multiuser MIMO Broadcast Channel with Limited Feedforward,” IEEE Trans. on Signal Processing, vol. 56, no. 12, pp. 6044-6056, Dec. 2008.

C. B. Chae and  R. W. Heath, Jr., “On the Optimality of Linear Multiuser MIMO Beamforming with Many Receive Antennas,” IEEE Signal Processing Letters, vol. 16, no. 2, pp. 117-120, Feb. 2009.

Coordinated beamforming is a transmission technique similar to block diagonalization, but supports more receive antennas than data streams. Coordinated beamforming requires a joint design of transmit precoders and receive filters, unlike block diagonalization where the transmit precoders and the receive filters are designed independently. We have developed a limited feedforward coordinated beamforming strategy. The idea is that the base station computes then quantizes the optimum receiver filters and sends them to the users through a low bandwidth feedforward control channel. We have also shown how coordinated beamforming is optimum under certain assumptions with many receive antennas.

Multiuser MIMO in CDMA Systems

W. Choi, J. G. Andrews,  and  R. W. Heath, Jr., “Multiuser Antenna Partitioning for MIMO-CDMA Systems,” IEEE Trans. on Veh. Tech., vol. 56, no. 5, part 1, pp. 2448-2456, Sept. 2007.

In this paper we proposed an antenna partitioning technique for MIMO-CDMA systems. The idea is to partition users into groups that are served by different transmit antennas. This requires a very small amount of feedback yet achieves high performance by exploiting the decorrelation among the transmit antennas.


We have been fortunate to have several government sponsors of our work including the National Science Foundation and the Office of Naval Research. We have also had several industrial sponsors in the past including Motorola, Samsung, and Freescale. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the sponsors.

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