Adaptive cooperative sensing in cognitive radio networks with ensemble model for primary user detection. (27th November 2019) - Prasad, Venkata Vara; Rao, Polipalli Trinatha
Summary: Opportunistic spectrum sharing ability enables higher spectrum utilization in cognitive radio networks. Detecting the presence of primary user in the network is the most important functionality in cognitive radio network as the cognitive users cannot use the spectrum with interference to primary users. Most solutions proposed for primary user detection suffer from hidden terminal problem resulting from multipath fading and shadow effects. The work focus on Rayleigh and Nakagami fading channel with comparable nonfading AWGN channel in cognitive radio. An ensemble model to detect the presence of primary user with high confidence is proposed in this work. The approach is based on training machine learning models with energy vectors in presence and absence of primary users. The trained model is then used to predict the primary user based on the energy vector. Abstract : In the proposed paper, an attempt is made to ensemble machine learning model for primary user (PU) detection and to provide an efficient spectrum sensing. A new fusion algorithm with different features are used for each of the classifiers to detect the accuracy for each classifier as a part of hybrid classifier. Each CRU makes multiple parameter measurements using different mechanism about the PU signal and these measurements are sent to the cluster head (CH).