Hindawi Publishing Corporation International Journal of Antennas and Propagation Volume 2013, Article ID 956756, 11 pages http://dx.doi.org/10.1155/2013/956756 Research Article A Low Complexity Near-Optimal MIMO Antenna Subset Selection Algorithm for Capacity Maximisation Ayyem Pillai Vasudevan 1 and R. Sudhakar 2 1 Department of ECE, JCT College of Engineering and Technology, Coimbatore 641105, India 2 Department of ECE, Dr. Mahalingam College of Engineering and Technology, Pollachi 642003, India Correspondence should be addressed to Ayyem Pillai Vasudevan; ayyempillai@yahoo.com Received 26 May 2013; Revised 13 September 2013; Accepted 16 September 2013 Academic Editor: Christoph F. Mecklenbr¨ auker Copyright © 2013 A. P. Vasudevan and R. Sudhakar. Tis 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 afordable 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. Te complexities of other receive- only selection comparable algorithms are calculated. Complexities have been stated in terms of both complex-complex fops and real-real fops. Signifcantly, 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 tradeof between performance and speed, which is very important for practical implementations. 1. Introduction Multiple input and multiple output (MIMO) wireless systems can be used for increasing Shannon capacity, or decreasing bit error rate through, respectively, spatial multiplexing or diversity. Te more the number of antennas, the more will be the capacity and diversity order. But, regardless of spatial multiplexing or diversity concepts, important difculty in using a MIMO system is an increased complexity and hence cost due to the need of increased radio frequency (RF) chains, which consist of power amplifers, low noise amplifers, downconverters, upconverters, and so forth. Tis paper focuses on maximising the capacity. Because of the high cost burden involved in RF chains, it is necessary to have less number of RF chains, yet maximise the capacity. Tis is done by having a larger number of space links at our disposal and selecting the best as many number of links as equal to the number of the RF chains. Selecting the best subset links out of a larger number of links is obviously done by having a larger number of antennas and selecting the best subset of antennas corresponding to the best links. Te antenna subset selection can be at the transmit side or at the receive side or at both sides. Tis paper is concentrating on the selection at the receive side. For a system, which has   total receive antennas and   total transmit antennas, the optimal way to select a subset of   antennas for maximizing capacity is to carry out determinant calculation [     ] times as required by the capacity formula given by Telatar [1] and then arrive at the highest capacity-giving antenna subset. Such an exhaustive search method was used in [2] for diversity reception. Similar argument is applicable for transmit side also. Surely, [     ] computations of determinants will become prohibitively large. To solve this complexity problem with minimal loss on the capacity performance, suboptimal algorithms have been developed. Various capacity-based single-sided antenna selection problems have been discussed in the literature [3–13]. In [3],