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In Cognitive Radio(CR)networks,there is a common assumption that secondary devices always obey commands and are under full control.However,this assumption may become unrealistic for future CR networks with more intelligent,sophisticated and autonomous devices.Imperfect spectrum sensing and illegal behaviour of secondary users can result in harmful interference to primary users.In this paper,we propose a novel concept of Proactive-Optimization CR(POCR)networks,in which highly intelligent secondary users always try to proactively consider potentially harmful interference when making their behaviour decision.Furthermore,we propose an optimal transmission behaviour decision scheme for secondary users in POCR networks considering the possible harmful interference and penalties from primary users.Specifically,we formulate the system as a Partially-Observable Markov Decision Process(POMDP)problem.With this formulation,a low-complexity dynamic programming framework is presented to obtain the optimal behaviour policy.Extensive simulation results are presented to illustrate the significant performance improvement of the proposed scheme compared with the existing one that ignores the proactive-optimization of secondary users.
In Cognitive Radio (CR) networks, there is a common assumption that secondary devices always obey commands and are under full control. Hosted, future assumption may become unrealistic for future CR networks with more intelligent, sophisticated and autonomous devices. Imperfect spectrum sensing and illegal behavior of secondary users can result in harmful interference to primary users. In this paper, we propose a novel concept of Proactive-Optimization CR (POCR) networks, in highly highly reactive secondary users always try to proactively consider potentially harmful interference when making their behavior decision.Furthermore, we propose an optimal transmission behavior decision scheme for secondary users in POCR networks considering the possible harmful interference and penalties from primary users. Specifically, we formulate the system as a Partially-Observable Markov Decision Process (POMDP) problem .With this formulation, a low-complexity dynamic programming framework is presented to obtain the o Pific behavior policy is presented to illustrate the significant performance improvement of the proposed scheme compared with the existing one that ignores the proactive-optimization of secondary users.