The objective of this research is to develop the critical analytical framework for Multiple Input Multiple Output (MIMO) communication in complex channels. Existing MIMO capacity calculation software will be enhanced by including complex channel models for vehicles, lossy biological channels, and highly noisy channels. We will also integrate detector models with the electromagnetic and communication MIMO models, and verify the models via measurement.
Intellectual Merit: Today's MIMO models do not include an accurate representation of non-Gaussian, ultra-reflective, depolarizing, and highly lossy channels seen in many personal communication channels, body-worn or implanted medical communication channels, highly reflective and lossy ('Hyper-Raleigh') channels typical of intra-vehicular communication for sensor networks inside aircraft, cars, buses, trains, ships, etc., most wireless ad-hoc network environments, or the human body scattering channel for medical imaging. This research program will provide more advanced channel models for MIMO. This will enable specialized MIMO design for each application, providing a far greater probability of initial success for the deployed systems.
Broader Impacts: This research addresses unmet communication demand for future wireless devices. In addition to the significant societal, medical, and financial aspects of that potential advancement, the research will be directly integrated into coursework at the University of Utah.
Multiple Input Multiple Output (MIMO) communication systems are well known for their ability to turn the echos and reflections in our environment that normally interfere with communication into an advantage. By increasing the number of antennas and using specialized signal processing algorithms, we can increase the communication channel to allow more and better data to be sent in otherwise very difficult channel environments. The inside of metallic vehicles (aircraft, helicopters, cars, buses, trains, etc.) have channels with a lot of echo and multipath reflection. New sensors that are being developed for safety and diagnosis of vehicle health (fire, vibration, heat, engine health, electrical fault location, etc.) often require communication within this very difficult channel. Because communication can be very difficult in these channels, many base stations may be needed in order to prevent dropping signals in these channels. MIMO may provide a better way. In this project we have explored MIMO communication for the intravehicular channels in aircraft, helicopters, buses, and other enclosed spaces (stairways and tunnels). The MIMO system clearly performs better than traditional communication systems in these channels, and may therefore provide a very good option when communicating between sensors inside these vehicles. We developed software for simulating these complex environments and evaluating the MIMO channel capacity, thus enabling site-specific design for sensor systems requiring wireless communication within the vehicle. We also evaluated a wide variety of traditional antennas (wire and microstrip antennas) as well as several newer antennas (planar inverted F (PIFA) antennas, genetic algorithm (GA) antennas, etc.) and found that using multiple antennas of different types, particularly with strongly variant radiation patterns and polarizations, gave the best performance. This project has provided a good set of design tools and some specific design options for communication with sensors in vehicles. These sensors may then be used to improve the safety and efficiency of vehicular transportation.
