This dissertation was presented to the Faculty of the Graduate School of The University of Texas at Austin in partial fulfillment of the requirements for the degree of
Ph.D. in Electrical Engineering
Radio Frequency Interference Modeling and Mitigation in Wireless Receivers
Kapil Gulati, Ph.D.E.E.
The University of Texas at Austin, August 2011
Prof. Brian L. Evans
Dissertation - Defense Slides - Software
In wireless communication systems, receivers have generally been designed under the assumption that the additive noise in system is Gaussian. Wireless receivers, however, are affected by radio frequency interference (RFI) generated from various sources such as other wireless users, switching electronics, and computational platforms. RFI is well modeled using non-Gaussian impulsive statistics and can severely degrade the communication performance of wireless receivers designed under the assumption of additive Gaussian noise.
Methods to avoid, cancel, or reduce RFI have been an active area of research over the past three decades. In practice, RFI cannot be completely avoided or canceled at the receiver. Methods to reduce the intensity of RFI at the receiver are acceptable as long as the degradation in communication performance caused by the residual RFI is tolerable. Intensity of residual RFI, however, is rapidly increasing as the reuse of available radio spectrum increases, sources of electromagnetic radiation increase, and the form factor of computational platform decreases. To this end, this dissertation derives the statistics of the residual RFI and utilizes them to analyze and improve the communication performance of wireless receivers.
Prior work in statistical modeling of RFI is limited by the spatial distribution of the sources of RFI considered. This dissertation derives closed-form instantaneous statistics of RFI in a broad range of interferer topologies, with applications to wireless ad hoc, cellular, local area, and femtocell networks.
This dissertation then extends the RFI statistics to include the temporal dimension. The network model adopted in this dissertation spans the extremes of temporal independence to long-term temporal dependence. The joint temporal statistics of RFI are utilized to derive closed-form expressions for various performance measures for single hop communications in decentralized wireless networks, unveiling 2x potential improvement in network throughput by optimizing certain medium access control layer parameters.
Finally, the knowledge of joint temporal statistics of RFI is used to derive pre-filtering methods, amenable to real-time implementation, for mitigating the residual RFI. This dissertation uses a recently proposed non-linear measure of distance that yields improved robustness and improves the link spectral efficiency, for example, by an additional 1-6 bits/s/Hz per communication link in a decentralized wireless network.
For more information contact: Kapil Gulati <email@example.com>