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The estimation of the communication performance achievable with respect to environmental factors and configuration parameters play a key role in the optimization process performed by a cognitive radio according to the original definition by Mitola. In this paper, the authors propose the use of multilayered feedforward neural networks as an effective technique for real-time characterization of the communication performance, which is based on measurements carried out by the device and therefore offers some interesting learning capabilities.
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