Abstract
Machine learning is a powerful tool for cognitive radio users to learn its sensing and transmission strategy from the experience. This chapter provides a brief introduction to a variety of machine-learning techniques. The basic setup of machine learning, as well as the dichotomy, is explained. Then, the supervised, unsupervised, semi-supervised, and reinforcement learning techniques are briefly discussed. The single-agent learning is then extended to the case of multiagent learning. Then, the machine-learning techniques are applied in various cases of machine learning, such as channel selection and routing.
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Li, H. (2017). Adaptive Learning in Cognitive Radio. In: Zhang, W. (eds) Handbook of Cognitive Radio . Springer, Singapore. https://doi.org/10.1007/978-981-10-1389-8_41-1
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DOI: https://doi.org/10.1007/978-981-10-1389-8_41-1
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