Single User MIMO

Single User MIMO Communication

MIMO Single User Concept

MIMO Single User Concept

Single user MIMO communication systems exploit multiple transmit and receive antennas to improve capacity, reliability, and resistance to interference. Here I describe some of the research directions that my research group has taken in the general area of single user MIMO communication, with some select publications, and a brief summary of our key results. Some specialized topics like 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.

Antenna Subset Selection

R. W. Heath, Jr. and D. J. Love, “Multi-Mode Antenna Selection for Spatial Multiplexing with Linear Receivers,” IEEE Trans. on Signal Processing, vol. 53, no. 8, part 2, pp. 3042-3056, August 2005.

D. A. Gore, R. W. Heath, Jr.,  and A. J. Paulraj, “Transmit Selection in Spatial Multiplexing Systems,” IEEE Communication Letters, vol. 6, no. 11, pp. 491-493, November 2002.

R. W. Heath, Jr., S. Sandhu, and A. J. PaulrajAntenna selection for spatial multiplexing systems with linear receivers, IEEE Communication Letters, vol. 5, no. 4, pp. 142-144, April 2001.

In spatial multiplexing communication systems, it may be advantageous to transmit on a subset of the available transmit antennas. For example, it may be advantageous to have fewer RF chains than antennas due to cost, complexity, or power consumption. We developed algorithms for selecting subsets of antennas and showed that antenna subset selection can improve the diversity performance of spatial multiplexing MIMO systems. Antenna subset selection can be viewed as a special case of limited feedback precoding.

Adaptive Modulation

R. Daniels, C. Caramanis, and R. W. Heath, Jr., “Adaptation in Convolutionally-Coded MIMO-OFDM Wireless Systems through Supervised Learning and SNR Ordering,” IEEE Trans. on Veh. Tech., vol. 59, no. 1, 2010, pp. 114-126.

C. B. Chae, A. Forenza, R. W. Heath, Jr., M. R. McKay, and  I. B. Collings, “Adaptive MIMO Transmission Techniques for Broadband Wireless Communication Systems,” IEEE Communications Magazine, May 2010, pp. 112-118.

A. Forenza, M. R. McKay, A. Pandharipande,  R. W. Heath, Jr.,  and  I. B. Collings, “Adaptive MIMO Transmission for Exploiting the Capacity of Spatially Correlated Channels,” IEEE Trans. on Veh. Tech., vol. 56, no. 2, pp. 619-630, March 2007.

S. Catreux, V. Erceg, D. Gesbert,  and R. W. Heath, Jr., “Adaptive Modulation and MIMO Coding for Broadband Wireless Data Networks,” IEEE Communications Magazine, pp.108-115, June 2002.

In MIMO communication systems, link adaptation is more challenging because of the presence of a spatial dimension in the channel. This increases the space of possible actions now that the number of MIMO data streams needs to be selected in addition to code rate and modulation order among other parameters. It becomes more challenging to select the optimum mode in the presence of convolutional coding and interleaving. We developed powerful techniques for link adaptation in coded MIMO systems. Some of our approaches select the optimum number of streams and code rate based on the instantaneous channel state information; other approaches use statistical information to select the optimum number of streams and then instantaneous channel state information to choose the rate. Our most recent work uses machine learning to implement flexible online adaptive learning strategies.

MIMO Propagation Channels

R. Bhagavatula, C. Oesteges, and  R. W. Heath, Jr., “A New Double Directional Channel Model Including Antenna Patterns, Array Orientation and Depolarization,” IEEE Trans. on Veh. Tech., vol. 59, no. 5, pp. 2219-2231, December 2008.

R. Bhagavatula, R. W. Heath, Jr., and  K. Linehan, “Performance Evaluation of MIMO Base Station Antenna Designs,” Antenna Systems & Technology, vol. 11, no. 6, pp. 14-17, Nov. / Dec. 2008.

R. Bhagavatula, R. W. Heath, Jr., A. Forenza, and  S. Vishwanath, “Sizing up MIMO Arrays,” IEEE Vehicular Technology Magazine, vol. 3, no. 4, pp. 31-38, Dec. 2008.

A. Forenza, D. J. Love,  and  R. W. Heath, Jr., “Simplified Spatial Correlation Models for Clustered MIMO Channels with Different Array Configurations,” IEEE Trans. on Veh. Tech., vol. 56, no. 4, part 2, pp. 1924-1934, July 2007.

The performance of a MIMO communication system depends to a large extent on the assumptions about the propagation environment. Propagation channel models are used in algorithm development and benchmarking, and of course in estimating real-world system performance. We have  developed new propagation channel models that better model channels with co-polar antenna patterns, low complexity channel models, and we have used these models to study the performance of MIMO communication systems in cellular systems, and for multimedia distribution in aircraft.

MIMO Antenna Design

R. Bhagavatula, R. W. Heath, Jr., A. Forenza, N. J. Kirsch, and K. R. Dandekar, “Impact of Mutual Coupling on Adaptive Switching Between MIMO Transmission Strategies and Antenna Configurations,” Wireless Personal Communications Journal, DOI: 10.1007/s11277-008-9513-2, May, 2008.

A. Forenza and  R. W. Heath, Jr., “Optimization Methodology for Designing 2-CPAs Exploiting Pattern Diversity in Clustered MIMO Channels”, IEEE Trans. on Communications, Vol. 56, no. 10, pp. 1748 -1759, Oct. 2008.

D. Piazza, N. J. Kirsch, A. Forenza, R. W. Heath, Jr., and K. R. Dandekar, “Design and Evaluation of a Reconfigurable Antenna Array for MIMO Systems,” IEEE Transactions on Antennas and Propagation, vol. 56, no. 3, pp. 869-881, March 2008.

A. Forenza and  R. W. Heath, Jr., “Benefit of Pattern Diversity Via 2-element Array of Circular Patch Antennas in Indoor Clustered MIMO Channels”, IEEE Trans. on Communications, vol. 54, no. 5, pp. 943-954, May 2006.

L. Dong, H. Choo, H. Ling,  and R. W. Heath, Jr., “MIMO Wireless Handheld Terminals Using Antenna Pattern Diversity,” IEEE Trans. on Wireless, vol. 4, no. 4, pp. 1869-1873, July 2005.

Essential to MIMO communication are the antennas. The characteristics of the antennas, the antenna patterns, and the spacing of antennas, influence how the signals are coupled into the propagation channel. This in turn impacts the performance of the MIMO communication link. We have studied the impact of mutual coupling on MIMO link performance, have optimized MIMO array architectures to provide high capacity, and have developed antenna array designs that exploit pattern diversity.

Receiver Design for MIMO and MIMO-OFDM Systems

J. Kim, R. W. Heath, Jr.,  and E. J. Powers, “Reduced Complexity Signal Detection for OFDM System with Transmit Diversity,” Journal of Communication Networks, vol. 9, no. 1, pp.75-83, Mar. 2007.

T. Tang and R. W. Heath, Jr., “A Space-Time Receiver with Joint Synchronization and Interference Cancellation in Asynchronous MIMO-OFDM Systems,” IEEE Trans. on Veh. Tech., vol. 57, no. 5, pp. 2991-3005, Sept. 2008.

R. Samanta, R. W. Heath, Jr., and  B. L. EvansJoint Interference Cancellation and Channel Shortening in Multi-User MIMO Systems,” IEEE Trans. on Veh. Tech., vol. 56, no. 2, pp. 652-660, Mar. 2007.

T. Tang and R. W. Heath, Jr., “Space-Time Interference Cancellation in MIMO-OFDM Systems,” IEEE Trans. on Veh. Tech., vol. 54, no. 5, pp. 1802-1816, September 2005.

D. J. Love, S. Hosur, A. Batra,  and R. W. Heath, Jr., “Space-Time Chase Decoding,” IEEE Trans. on Wireless, vol. 4, no. 5, pp. 2035-2039, September 2005.

J. Kim, R. W. Heath, Jr.,  and E. J. Powers, “Receiver Designs for Alamouti Coded OFDM Systems in Fast Fading Channels,” IEEE Trans. on Wireless, vol. 4, no. 2, pp. 550-559, March 2005.

Obtaining the benefits of MIMO communication requires developing efficient receiver algorithms. This becomes more challenging in the presence of time-varying channels, frequency offsets, and co-channel interference. We have developed several different receiver algorithms. Some of our work has focused on low complexity near-optimal detectors. Our other work has considered joint interference cancellation and equalization in MIMO or MIMO-OFDM systems.

Space-Time Codes

Hoojin Lee, J. G. Andrews, R. W. Heath, Jr., and  E. J. Powers, “The Performance of Space-Time Block Codes from Coordinate Interleaved Orthogonal Designs over Nakagami-m Fading Channels,” IEEE Trans. on Communications, vol. 57, no. 3,pp. 653-664, March 2009.

Kyung Seung Ahn, R. W. Heath, Jr., and H. K. Baik, “Shannon Capacity and Symbol Error Rate of Space-Time Block Codes in MIMO Rayleigh Channels with Channel Estimation Error,” IEEE Trans. on Wireless, vol. 7, no. 1, pp. 324-333, Jan. 2008.

R. W. Heath, Jr. and A. J. Paulraj, “Linear Dispersion Codes for MIMO Systems Based on Frame Theory,” IEEE Trans. on Signal Processing, vol. 50, no. 10, pp.2429-2441, October 2002.

R. W. Heath, Jr. and A. J. Paulraj, “Capacity Maximizing Linear Space-Time Codes,” IEICE Trans. Electron. vol. E85-C, no.3, pp. 428-35, March 2002.

Space-time coding is a transmission technique that involves spreading information across the transmit antennas. The usual objective of space-time coding is to achieve good spatial diversity, which involves careful design of the space-time codewords. We have developed new space-time coding techniques and we have analyzed the performance of existing techniques in presence of channel estimation error and in non-Rayeligh channels.

Sponsors

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.