Skip to Main Content
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 (ε-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.