研究期間：10108~10207;According to studies sponsored by Federal Communications Commission (FCC), over 70% of the allocated spectrum is not in use at any time even in a crowded area where the spectrum usage is intensive. The remaining portion of the unlicensed spectrum is being exhausted by emerging wireless services and applications, leading to the so-called spectrum scarcity problem. Cognitive radio (CR) techniques provide a solution to this problem. A CR is a self-managing device that is capable of sensing radio spectrum and opportunistically utilizing unoccupied frequency bands. A CR can access licensed frequency bands without interfering with its primary users (PUs) by alternating between a spectrum sensing state and an available spectrum accessing state. This cognitive ability of a CR offers an excellent opportunity to secondary users (SUs) for accessing frequency bands not licensed to them. A network with cognitive capabilities is called a cognitive network. Vast majority of past and current research and developments for cognitive networks are in the lower two layers. While the research and development for the physical layer and the medium access layer have been advancing in tandem with the CR research, the research for development of the transport layer protocols for cognitive networks is still an unexplored area. This project is intended to identify requirements of protocols for the transport layer of cognitive networks, and design, implement, and evaluate transport protocols. The first-year project is intended to propose a spectrum-aware transport protocol to maximize the throughput in competitive cognitive networks, where multiple SUs compete with each other for resources (transmit power, rate, channel). The second-year project is intended to propose a transport protocol for solving spectrum allocation problem in cooperative cognitive networks, where SUs cooperate for achieving the same goal (i.e., fairness for spectrum allocation). The third-year project is intended to solve the power-saving problem in cooperative cognitive networks. We aim to design power-saving protocols for delay-sensitive applications and delay-constrained applications, respectively. Besides, we try to develop an experimental platform for cognitive networks. By the aid of the experimental platform, we are able to evaluate the performance of our proposed protocols.
