Digital Signal Processing Reference
In-Depth Information
•
Chapter 3
introduces a general control strategy that is able to adapt flexibly to
the environment without heavily impacting the run-time complexity. The method
proposed consists of a preparation ('design time') and an operation phase ('run
time process'). The degree of intelligence and adaptation enabled in the run time
process typically determines how
smart
the radio is. It will be shown that the run
time involves four main tasks: monitoring or observing, determining the current
scenario, acting on the scenario following a procedure and finally learning or cal-
ibrating the procedure. In the subsequent chapters, case studies will be presented
that involve each of the four tasks, but however, emphasis will be on one of the
tasks.
•
Chapter 4
discusses a scenario where licensed users share their resources with
opportunistic radios. In this case, the OR needs to sufficiently monitor the actual
situation to avoid interference to the licensed technology. In Chap. 4 the flow will
hence be applied to a OR scenario with special focus on the monitoring.
•
Chapter 5
discusses a coexistence situation. Specifically, it considers the scenario
where an IEEE 802.15.4 networks coexists with an IEEE 802.11 network in the
ISM band by adapting its channel. In this case, monitoring is shown not to be
sufficient and determining the actual situation is based on both monitoring and
learning based on feedback from the environment. It is shown how nevertheless,
is possible to instantiate the flow to design a radio that is capable to adapt based
on an identification of the actual scenario.
•
Chapter 6
gives an example of how the flow can be instantiated on a well-
defined procedure that does not necessary involve a CR or OR. The context will
be an IEEE 802.11 network in which multiple users coexist and have to share
the medium, while meeting performance constraints and minimizing energy cost.
Even for such an IEEE 802.11 network, a flexible radio within a network of flex-
ible radios can be managed smartly to improve the overall QoS or energy effi-
ciency of the network.
•
Chapter 7
then gives an example of the most important task of a cognitive radio:
learning and calibrating how it behaves in the environment. How to achieve this
efficiently is illustrated in the context of IEEE 802.11 networks that aim at min-
imizing the co-channel interference in a distributed way by adapting the output
power, sensing threshold and transmission parameters.
•
Chapter 8
finally closes the topic with major conclusions and a glance at the
future.
