Abstract:
Sensing is one of the most challenging issues in cognitive radio networks. Selection of sensing parameters raises
several tradeoffs between spectral efficiency, energy efficiency and interference caused to primary users (PUs). In this
paper we provide representative mathematical models that can be used to analyze sensing strategies under a wide range
of conditions. The activity of PUs in a licensed channel is modeled as a sequence of busy and idle periods, which are
represented as alternating Markov phase renewal processes. The representation of the secondary users (SUs) behavior
is also largely general: the duration of transmissions, sensing periods and the intervals between consecutive sensing
periods are modeled by phase type distributions, which constitute a very versatile class of distributions. Expressions
for several key performance measures in cognitive radio networks are obtained from the analysis of the model. Most
notably, we derive the distribution of the length of an effective white space; the distributions of the waiting times until
the SU transmits a given amount of data, through several transmission epochs or uninterruptedly; and the goodput
when an interrupted SU transmission has to be restarted from the beginning due to the presence of a PU.
