Wireless network optimization for cognitive radio

To meet the ever-increasing demand for wireless data, systems that efficiently utilize available spectrum such as cognitive radio networks are envisioned. As such, this dissertation focuses on practical and theoretical aspects on how cognitive radio can enhance a network. The networks considered in this research consist of many terminals between which information is transferred. Of particular interest are networks that utilize a relay to connect users that otherwise cannot communicate due to distance or obstruction. Physical layer network coding improves the efficiency of network information transfer and is therefore important to addressing data demands. In this dissertation, the practical application of asynchronous physical layer network coding to a relay network constructed with inexpensive terminals as an Internet-of-Things is investigated. The main result of this work derives estimation and decoding techniques for a K-user system. Furthermore, a specific case of the theory is validated using USRP software-defined radios. Distributed methods are also applied to a relay network to define a relay that consists of many disconnected terminals that perform a limited set of functions such as quantization. For this relay, maximum likelihood and zero-forcing decoding algorithms are derived. A critical function of cognitive radio networks is interference classification and mitigation. In this dissertation, a distributed method based on error control coding to classify interference scenarios is proposed. Using this method, performance can be compared to theoretical bounds from finite blocklength coding. Practical aspects of interference mitigation were also investigated during the DARPA Spectrum Challenge. As such, a synchronization algorithm implemented for the challenge to overcome jamming is described