In this chapter, we present an optimization framework for link level and flow level scheduling in cognitive radio networks. In the centralized scheduling framework, a spectrum server coordinates the transmissions of a group of links sharing a common spectrum. With knowledge of the link gains in the network, the spectrum server schedules the on/off periods of the links so as to satisfy constraints on link fairness. We then compare the throughput regions of centralized scheduling and a probabilistic random access scheme, wherein in each slot, a link is active with a fixed probability chosen independent of other interfering links. We observe that for the case of two interfering links, the probabilistic scheme does not suffer any loss in the rate region relative to the centralized scheme if the interference between the links is sufficiently low. We then present a distributed algorithm where each link independently updates its transmission probability based on its measured throughput to achieve any desired feasible rate vector in the throughput region of the probabilistic scheme and prove its convergence. Finally, we present an optimization framework for end-to-end flow level scheduling of flows in network with mutually interfering links.
|Original language||English (US)|
|Title of host publication||Cognitive Wireless Networks|
|Subtitle of host publication||Concepts, Methodologies and Visions Inspiring the Age of Enlightenment of Wireless Communications|
|Number of pages||21|
|State||Published - 2007|
All Science Journal Classification (ASJC) codes