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International Journal of Antennas and Propagation
Volume 2013 (2013), Article ID 956756, 11 pages
Research Article

A Low Complexity Near-Optimal MIMO Antenna Subset Selection Algorithm for Capacity Maximisation

1Department of ECE, JCT College of Engineering and Technology, Coimbatore 641105, India
2Department of ECE, Dr. Mahalingam College of Engineering and Technology, Pollachi 642003, India

Received 26 May 2013; Revised 13 September 2013; Accepted 16 September 2013

Academic Editor: Christoph F. Mecklenbräuker

Copyright © 2013 Ayyem Pillai Vasudevan and R. Sudhakar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Multiple input multiple output (MIMO) wireless systems employ a scheme called antenna subset selection for maximising the data rate or reliability for the prevailing channel conditions with the available or affordable number of radio frequency (RF) chains. In this paper, a low-complexity, and near-optimal performance fast algorithm is formulated and the detailed algorithm statements are stated with the exact complexity involved for capacity-maximising receive-only selection. The complexities of other receive-only selection comparable algorithms are calculated. Complexities have been stated in terms of both complex-complex flops and real-real flops. Significantly, all the algorithms are seen in the perspective of linear increase of capacity with the number of selected antennas up to one less than the total number of receive antennas. It is shown that our algorithm will be a good choice in terms of both performance and complexity for systems, which look for linear increase in capacity with the number of selected antennas up to one less than the total receive antennas. Our algorithm complexity is much less dependent on the number of transmit antennas and is not dependent on the number of selected antennas and it strikes a good tradeoff between performance and speed, which is very important for practical implementations.