The objective of this research is to develop methods to assess non-ideal realizable Multiple Input Multiple Output (MIMO) capacity and optimize antennas and detectors for realistic MIMO systems. MIMO wireless systems provide additional capacity, to enable more users, additional services, and new applications for new markets. While in theory, MIMO systems promise virtually limitless increases in throughput by using many different antennas with independent communication paths, as MIMO matures its claims of boundless improvements have been tempered by real-world measurements. The approaches utilized in this project are (1) To adapt existing capacity calculations to incorporate antenna design data, and to use genetic algorithms and / or linearized inversion techniques to develop the best possible realistic MIMO antennas, (2) To use Markov chain Monte-Carlo (MCMC) methods to improve on existing detection algorithms and adapt them to realistic channels, and (3) To verify the predicted performance of the antennas and detection system.
The broader impacts of this research are to enable the continued propagation of personal communication system technologies into broader band (higher capacity) applications for entertainment, remote health care, remote security monitoring, etc. Another significant impact of this project will be felt in the NSF-sponsored Hands-on Integrated Project-Based (HIP) curriculum development effort at the University of Utah, and in a Software Radio textbook currently being written by one of the PIs. An abbreviated version of the system will serve as a multi-course design project that can combine antennas, communication, signal processing, microwave engineering, and more.
