Research on multiple-input, multiple-output (MIMO) communications has shown that using multiple antennas can dramatically improve the capacity of wireless channels. Since wireless devices are limited in size, using multiple antennas often requires close spacing which leads to coupling among the antennas. Coupling can profoundly impact the received power, diversity and system capacity. Moreover, this impact depends not only on the antennas and how they are arranged in space; it also depends on detailed aspects of the wireless transceiver, such as the impedance matching networks that connect the antennas to the rest of the receiver front end. Current approaches to designing these networks for coupled MIMO systems are intrinsically narrowband and exhibit poor performance under broadband conditions.
This project seeks to develop a unified theoretical framework for the design of broadband wireless transceivers. The main idea is that Fano's broadband matching theory provides a characterization of physically-realizable matching networks, while Shannon's information theory provides a way to evaluate how each network could be used to communicate in the best possible way. By combining these theories, this project considers how antennas, matching networks and communications algorithms interact to determine overall system performance, and how best to jointly optimize these components.
Three main issues are addressed: new information- and decision-theoretic bounds on the performance of broadband single-antenna systems; extensions to broadband MIMO systems; and information-theoretic design criteria to jointly optimize the antennas, matching networks and communications algorithms. This work has the potential to significantly advance science and engineering by providing a more unified view of the RF front end and by developing new communications and matching techniques that may significantly improve wireless performance.
This project developed a new information-theoretic approach to the design of single- and multi-antenna transceivers for wireless communications. The main idea is that Fano's broadband matching theory characterizes the ability of each physically-realizable matching network to convey power from the antennas to the rest of the front end, while Shannon's information theory provides a way to evaluate how each network could be used to communicate in the best possible way. By combining these theories, we have developed design metrics that reflect how antennas, matching networks, amplifiers and communications algorithms interact to determine overall wireless system performance. These metrics also provide a way to jointly optimize these interacting components. Three main issues were addressed: (1) new information- and decision-theoretic bounds on the performance of broadband single-antenna wireless systems; (2) extensions to broadband multi-antenna systems; and (3) information-theoretic design criteria to help jointly optimize the antenna array, matching networks, amplifiers and communications algorithms. This project also contained an educational component that included four undergraduate research projects and several class projects related to this effort. The results of this project have the potential to reduce the power and bandwidth requirements of mobile wireless communications. This project provided a more unified view of the RF front end and developed new models, communications algorithms, antennas and matching techniques which can substantially improve the error probability, data rate and capacity of cellular and wireless local area networks. These benefits may enable the use of larger constellations to increase the effective data rate, or smaller reuse factors in a multi-cell environment to increase system capacity, or a larger range or coverage area to decrease the density of base stations, or lower power requirements in order to reduce the cost and complexity of the cellular handsets. All of these benefits contribute to the public welfare by increasing capacity and reducing the costs of the nation’s public communications infrastructure.
