Machine Learning-Aided Radio Scenario Recognition for Cognitive Radio Networks in Millimeter-Wave Bands

Radio scenario recognition is critically important to acquire comprehensive situation awareness for cognitive radio networks in the millimeter-wave bands, especially for dense small cell environment. In this paper, a generic framework of machine learning-aided radio scenario recognition scheme is proposed to acquire the environmental awareness. Particularly, an advanced back propagation neural network-based AdaBoost classification algorithm is developed to recognize various radio scenarios, in which different channel conditions such as line-of-sight (LOS), non-line-of-sight (NLOS), and obstructed line-of-sight (OLOS) are encountered by the desired signal or co-channel interference. Moreover, the advanced AdaBoost algorithm takes the offline training performance into account during the decision fusion. Simulation results show that machine learning can be exploited to recognize the complicated radio scenarios reliably and promptly.

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