Fast antenna subset selection in MIMO systems

• Published in: IEEE transactions on signal processing Vol. 52; no. 2; pp. 339 - 347
• Main Authors: Gharavi-Alkhansari, M., Gershman, A.B.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.2004; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Antennas; Applied sciences; Computation; Computational complexity; Computational efficiency; Exact sciences and technology; Fading; Hardware; High performance computing; MIMO; Optimization; Receiving antennas; Studies; System performance; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Transceivers; Transmission and modulation (techniques and equipments); Wireless communication; Wireless communication systems; Performance evaluation; MIMO system; Selection problem; Channel capacity; Simulation; Wireless telecommunication; optimal antenna subset selection; Algorithm; MIMO wireless systems; Optimization; Antenna
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2003.821099

Multiple antenna wireless communication systems have recently attracted significant attention due to their higher capacity and better immunity to fading as compared to systems that employ a single-sensor transceiver. Increasing the number of transmit and receive antennas enables to improve system performance at the price of higher hardware costs and computational burden. For systems with a large number of antennas, there is a strong motivation to develop techniques with reduced hardware and computational costs. An efficient approach to achieve this goal is the optimal antenna subset selection. In this paper, we propose a fast antenna selection algorithm for wireless multiple-input multiple-output (MIMO) systems. Our algorithm achieves almost the same outage capacity as the optimal selection technique while having lower computational complexity than the existing nearly optimal antenna selection methods. The optimality of the proposed technique is established in several important specific cases. A QR decomposition-based interpretation of our algorithm is provided that sheds a new light on the optimal antenna selection problem.