The proliferation of wireless services and
applications over the past decade has led to the rapidly increasing demand in
wireless spectrum. Hence, we have been facing a critical spectrum shortage
problem. Several recent measurements have, however, reported that a large
portion of licensed radio spectrum is very underutilized in spatial and
temporal domains 1, 2. These facts have motivated the development of
dynamic spectrum access (DSA) techniques to enhance the efficiency and
flexibility of spectrum utilization. DSA can be categorized into three major
models, namely dynamic exclusive use, open sharing, and hierarchical access
models 2. The third model which is also referred to as opportunistic spectrum
access (OSA), which provides fundamental ground for an extremely active
research theme, i.e., the cognitive radio research. OSA in cognitive radio
networks can be further divided into three access paradigms, namely underlay,
overlay, and interweave 2–4.
research in this dissertation considers the interweave paradigm where the
licensed spectrum is shared between the primary and secondary networks whose
users are referred to as primary and secondary users (PUs and SUs),
respectively. In particular, SUs can opportunistically exploit spectrum holes (i.e.,
idle spectrum in time, frequency, and space) for their data transmission as
long as they do not severely interfere the transmissions of PUs. This access
principle implies that PUs have strictly higher priority than SUs in accessing
the underlying spectrum; hence, SUs can only access the licensed spectrum if
PUs do not occupy them. Toward this end, SUs can perform spectrum sensing to
explore spectrum holes and adopt suitable spectrum access mechanisms to share
the discovered available spectrum with one another 5.
the spectrum sensing and access functions are tightly coupled, they are usually
not treated jointly in the existing multi-user cognitive radio literature.
Moreover, it is desirable to employ a distributed cognitive MAC protocol for
spectrum sharing in many wireless applications, which is usually more
cost-efficient compared to the centralized cognitive MAC counterpart.