Blind Spectrum Selection in a Decentralized Cognitive Radio Networks with Heterogeneous Applications
Yongqun Chen, Huaibei Zhou, Ruoshan Kong, Junyuan Huang, Hang Qin · International Journal of Signal Processing Image Processing and Pattern Recognition · 2016
In this paper, we consider a cognitive radio network in which the sensing ability of cognitive radio is limited and the channel statistics are not known as a priori information in the opportunistic spectrum access(OSA) framework. It is a special challenge to design a joint spectrum sensing and access strategy for secondary users with diverse service requirements of heterogeneous applications, i.e. the real-time applications and best-effort applications. We formulate the spectrum decision problem as a decentralized multi-armed bandit problem and propose slot structures for cognitive radio network to cope with collisions between heterogeneous applications. The proposed scheme is proved achieving logarithmic regrets in time asymptotically and simulation results show that each user orthogonalizes into their rank-optimal channels according to their pre-allocated priorities, which indicates efficient spectrum utilization while satisfying service requirements.