Training is the task of guiding a cognitive radio engine through the
process of learning a desired system's behavior and capabilities. The
training speed and expected performance during this task are of
paramount importance to the system's operation, especially when the
system is facing new conditions.
In this paper, we provide a thorough examination of cognitive engine
training, and we analytically estimate the number of trials needed to
conclusively find the best-performing communication method in a list of
methods sorted by their possible throughput. We show that, even if only
a fraction of the methods meet the minimum packet success rate
requirement, near maximal performance can be reached quickly.
Furthermore, we propose the Robust Training Algorithm (RoTA) for
applications in which stable performance during training is of utmost
importance. We show that the RoTA can facilitate training while
maintaining a minimum performance level, albeit at the expense of
training speed. Finally, we test four key training techniques
(epsilon-greedy; Boltzmann exploration; the Gittins index strategy; and
the RoTA) and we identify and explain the three main factors that affect
performance during training: the domain knowledge of the problem, the
number of methods with acceptable performance, and the exploration rate.
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