  In this paper, performance of cognitive transmission over time-selective flat
fading channels is studied under quality of service (QoS) constraints and
channel uncertainty. Cognitive secondary users (SUs) are assumed to initially
perform channel sensing to detect the activities of the primary users, and then
attempt to estimate the channel fading coefficients through training. Energy
detection is employed for channel sensing, and different minimum
mean-square-error (MMSE) estimation methods are considered for channel
estimation. In both channel sensing and estimation, erroneous decisions can be
made, and hence, channel uncertainty is not completely eliminated. In this
setting, performance is studied and interactions between channel sensing and
estimation are investigated.
  Following the channel sensing and estimation tasks, SUs engage in data
transmission. Transmitter, being unaware of the channel fading coefficients, is
assumed to send the data at fixed power and rate levels that depend on the
channel sensing results. Under these assumptions, a state-transition model is
constructed by considering the reliability of the transmissions, channel
sensing decisions and their correctness, and the evolution of primary user
activity which is modeled as a two-state Markov process. In the data
transmission phase, an average power constraint on the secondary users is
considered to limit the interference to the primary users, and statistical
limitations on the buffer lengths are imposed to take into account the QoS
constraints of the secondary traffic. The maximum throughput under these
statistical QoS constraints is identified by finding the effective capacity of
the cognitive radio channel. Numerical results are provided for the power and
rate policies.
