Optimal Spectrum Handoff Control for CRN Based on Hybrid Priority Queuing and Multi-Teacher Apprentice Learning.

An optimal spectrum handoff scheme for cognitive radio networks (CRNs) is presented in this paper. This scheme has two novel features: 1) Hybrid rule-based priority queuing model: To overcome the limitations of preemptive resume priority and nonpreemptive resume priority (PRP/NPRP) queuing models, a hybrid queuing model with discretion rule is proposed to characterize the spectrum access priority among secondary users (SUs). This hybrid queuing model is then used to calculate the channel waiting time during spectrum handoff; and 2) Multiteacher apprentice learning: Unlike existing CRN cognition engine designs that focus on spectrum adaptation through SU self-learning (i.e., an SU learns how to adapt to the dynamic CRN environment by itself), we propose the concept of multiteacher knowledge transfer, wherein the multiple SUs that already have mature spectrum adaptation strategies share their knowledge with an inexperienced SU. Our simulation results show that the proposed new designs improve the spectrum handoff accuracy for the complex CRN environments. [ABSTRACT FROM PUBLISHER]