Skip to main content
Article thumbnail
Location of Repository

Different Multiple Input Multiple Output Systems

By Mohammed Alamgir


The use of multiple antennas at the transmitter as well as at the receiver can greatly improve the capacity of a wireless link when operating in a rich scattering environment. In such an arrangement all transmitting antennas radiate in the same frequency band so the overall spectral efciency becomes very high. Such a multiple antenna scheme, popularly known as Multiple Input Multiple Output (MIMO) has potential application in wireless local area networks (WLAN) and cellular micro-cells. One reason is that the WLANs and other short range wireless systems often operate in an indoor environment, which offers rich scattering. The other reason is the demand for higher data rates in cellular and WLAN systems to cater for multimedia services. Recently researchers have proposed di erent architectures for materializing the potential of the MIMO scheme. VBLAST (Vertical-Bell Labs Space Time) is a popular architecture that will play an important role in future standardizations. Furthermore, different decoding methods have been proposed for VBLAST. The SVD (Singular Value Decomposition) based system is envisioned as a highly effective MIMO technique in a TDD (Time Division Duplex) framework. Such a system operates by adapting the constellation size across different subchannels. In this work we study the VBLAST and SVD architectures and compare the perfor- mance and computing power requirement of these architectures. Also in this study a new effcient decoding method for the VBLAST architecture is proposed. The original VBLAST decoding method relies on the repetitive computation of the pseudoinverse of the channel matrix. Alternatively, there are methods based on the QR decomposi- tion, the matrix square root etc. Our new decoding method is based on a relatively less known matrix decomposition, the Polar Decomposition. The new method requires less computation and has several other advantages like the possibility of incremental updates, channel rank tracking, etc. We consider three different types of channels: IID random, slow fading and measured channels. The entire work is simulated in the MATLAB environment. The main contribution of this work includes: a comparative study and a head to head comparison of the VBLAST and SVD-based MIMO systems. The application of adaptive modulation in the SVD-based system and the introduction of a new effcient decoding method for the VBLAST system are also included. Simulation results are reported with comments and conclusions

Topics: 290000 Engineering and Technology, School of Engineering and Science, transmitter, receiver, wireless link, antenna, local area network
Year: 2003
OAI identifier:

Suggested articles


  1. (1990). Algorithms for the Polar Decomposition,”
  2. (1999). An Optimum Iteration for the Matrix Polar Decomposition,” Electronic Transaction on Numerical Computing,
  3. (2002). Analysis of Singular Value Decomposition Applied to Wireless Communications,”
  4. Aneta Pavlic and Andreas Semmler,“Improving BLAST Performance Using Space Time Block Codes and
  5. (2002). Attainable Throughput of an Interference-Limited
  6. (1995). Capacity of Multi-antenna Gaussian Channels,”
  7. (1986). Computing the Polar Decomposition-with Applications,”
  8. (2000). Digital Communications,”
  9. (1974). Effects on Correlation Between Two Mobile Radio Base Station Antennas,”
  10. (1990). Fast Polar Decomposition of an Arbitrary Matrix,”
  11. (2000). Iterative QR Detection for BLAST,”
  12. (1974). Microwave Mobile Communications,”
  13. (2001). MIMO Transmission over a Time Varying
  14. (2002). MIMO Transmission over a Time Varying TDD Channel Using SVD,”
  15. (1998). On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas,”
  16. Personal contact with Professor Mike Faulkner,
  17. (1996). Press, 3rd edition,
  18. (1996). Probability and Information - An Integrated Approach,”
  19. Valenzuela,“VBLAST:An Architecture for Realizing Very High Data Rates Over the Rich-Scattering Wireless Channel,” Bell Labs, Lucent Tech., available online at
  20. (1997). Variable Rate Variable Power MQAM for Fading Channels ,”
  21. (2002). Waterfilling methods for MIMO systems,” in
  22. (1996). Wireless Communications - Principles and Practice,” Prentice Hall, Upper Saddle River,
  23. (1999). Wolniansky,“Detection Algorithm and Initial Results using

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.