Channel selection through a recommender system

Sun, Huanhuan; Zhong, Yi; Zhang, Wenyi

doi:10.1109/wcsp.2010.5632893

Cited by 3 publications

(1 citation statement)

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“…Paper [16] proposes to apply collaborative filtering to cognitive radio, for secondary users to choose suitable channels. A modified collaborative filtering algorithm which considers the location of each secondary user is proposed in [17]. In [18], the author presents a dynamic feature model for secondary users according to the theory of interacting particle systems.…”

Section: Related Workmentioning

confidence: 99%

Predictive and Recommendatory Spectrum Decision for Cognitive Radio

Chen,

Xie

et al. 2017

Preprint

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Cognitive radio technology enables improving the utilization efficiency of the precious and scarce radio spectrum. How to maximize the overall spectrum efficiency while minimizing the conflicts with primary users is vital to cognitive radio. The key is to make the right decisions of accessing the spectrum. Spectrum prediction can be employed to predict the future states of a spectrum band using previous states of the spectrum band, whereas spectrum recommendation recommends secondary users a subset of available spectrum bands based on secondary user's previous experiences of accessing the available spectrum bands. In this paper, a framework for spectrum decision based on spectrum prediction and spectrum recommendation is proposed. As a benchmark, a method based on extreme learning machine (ELM) for single-user spectrum prediction and a method based on Q-learning for multiple-user spectrum prediction are proposed. At the stage of spectrum decision, two methods based on Q-learning and Markov decision process (MDP), respectively, are also proposed to enhance the overall performance of spectrum decision. Experimental results show that the performance of the spectrum decision framework is much better.

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Section: Related Workmentioning

confidence: 99%