Wireless communication networks continue to get more complex. Communication over unused TV bands (cognitive radios), self-organizing ad-hoc networks for military and emergency applications, or the heterogeneous networks envisioned in the next generation cellular (LTE advanced) systems all are clear evidence of this trend. As consumer demands, dependence on wireless services, and the complexity of networks continue to grow, there is a need to manage the resources efficiently without overburdening the network owner and end user. To address these challenges, this research involves significant and novel enhancements to the building blocks of modern communication systems.

The modeling, feedback, and cognitive techniques being studied in this project are essential for the development of robust and efficient wireless systems. The research project involves the following three parts: 1) Development of novel channel modeling methods that decompose the channel into a specular component and a diffuse component. A key consideration in this work is developing a rigorous framework for channel prediction and utilizing the insight to develop robust feedback based Multiple Input Multiple Output (MIMO) systems that degrade gracefully. 2) The development and analysis of feedback based multi-user MIMO-OFDM systems. This includes novel channel estimation and representations schemes, novel schemes for encoding sparsity, and performance analysis of reduced feedback MIMO-OFDM systems. 3) The development of advanced cognition at the physical layer. This includes waveform design in cognitive radios based on timing consideration and more comprehensive models for channels to provide awareness based on location, learning and memory.

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University of California San Diego
La Jolla
United States
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