Digital Signal Processing Reference
In-Depth Information
Chapter 3
Cognitive Radio Design and Operation:
Mastering the Complexity in a Systematic Way
3.1 The Need for a Strategy
Wireless communication networks are complex and dynamic systems. So many
variables and uncertainties related to the environment and user requirements ex-
ist, which makes it extremely difficult to derive effective static control strategies. As
a result, wireless devices need to become increasingly smart by allowing dynamic
strategies that are able to track the changes in the network and adapt accordingly.
While the trend towards more flexibility both in terms of hardware and policy flex-
ibility was already introduced elaborately in Chap. 1, this chapter focuses on the
smart control this implies.
Several challenges need to be addressed when designing efficient control algo-
rithms for these flexible radios. Indeed, next-generation radios and networks need to
be
smart
to exploit the opportunity of their flexibility. However, while
dumb
strate-
gies can be readily and efficiently implemented, more complex control strategies
pose a burden on the run-time complexity for these radios. Hence, wireless devices
need to be smart at two layers: they need to be smart to exploit the dynamics of
their environment and they need to be designed in a smart way so that the run-time
complexity remains acceptable.
In this chapter, we demonstrate a general control strategy that is able to adapt
flexibly to the environment without heavily impacting the run-time complexity. In
the subsequent chapters, this strategy is instantiated for case studies to emphasize
different aspects of the general framework.
The method proposed in this topic can be roughly divided into a design time and
a run time process. The degree of intelligence and adaptation enabled in the run
time process typically determines how
smart
the radio is. As defined in Chap. 1,
a radio is only cognitive when it learns based on feedback from the environment.
It will be shown that the run time involves four main tasks: monitoring or observ-
ing, determining the current scenario, acting on the scenario following a procedure
and finally learning or calibrating the procedure. In the remainder of this topic, a
case study will then be presented that involves each of the four tasks, but however,
emphasis will be on one of the tasks:
