Optimal transmission behavior policy of secondary users in proactive-optimization cognitive radio networks

In cognitive radio (CR) networks, there is a common assumption that the secondary devices always obey the spectrum access rules and are under full control. However, this may become unrealistic for future CR networks composed of intelligent, complicated and autonomous devices. To solve this problem, the concept of “proactive-optimization” cognitive radio (POCR) is proposed in this paper, in which the highly-intelligent secondary users proactively optimize their own behavior decisions according to the available information including device state and network condition to maximize their long-term reward. Furthermore, we propose an optimal transmission behavior decision scheme for secondary users in POCR networks considering imperfect spectrum channel sensing results. Specifically, we formulate the system as a partially-observable Markov decision process (POMDP) problem. With this formulation, a low complexity dynamic programming framework is introduced to obtain the optimal behavior policy. Extensive simulation results are presented to illustrate the significant performance improvement of the proposed scheme compared with the existing one that ignores the secondary user behavior optimization.

[1]  Eitan Altman,et al.  A Hybrid Approach for Radio Resource Management in Heterogeneous Cognitive Networks , 2011, IEEE Journal on Selected Areas in Communications.

[2]  Won-Yeol Lee,et al.  A Spectrum Decision Framework for Cognitive Radio Networks , 2011, IEEE Transactions on Mobile Computing.

[3]  Brian M. Sadler,et al.  A Survey of Dynamic Spectrum Access , 2007, IEEE Signal Processing Magazine.

[4]  Edward J. Sondik,et al.  The Optimal Control of Partially Observable Markov Processes over a Finite Horizon , 1973, Oper. Res..

[5]  Srdjan Capkun,et al.  Implications of radio fingerprinting on the security of sensor networks , 2007, 2007 Third International Conference on Security and Privacy in Communications Networks and the Workshops - SecureComm 2007.

[6]  Hyundong Shin,et al.  Cognitive Network Interference , 2011, IEEE Journal on Selected Areas in Communications.

[7]  Husheng Li,et al.  A Graphical Framework for Spectrum Modeling and Decision Making in Cognitive Radio Networks , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[8]  F. Richard Yu,et al.  Dynamic Resource Allocation for Heterogeneous Services in Cognitive Radio Networks With Imperfect Channel Sensing , 2012, IEEE Trans. Veh. Technol..

[9]  George Atia,et al.  A technical framework for light- handed regulation of cognitive radios , 2009, IEEE Communications Magazine.

[10]  Vahid Asghari,et al.  Resource Management in Spectrum-Sharing Cognitive Radio Broadcast Channels: Adaptive Time and Power Allocation , 2011, IEEE Transactions on Communications.

[11]  Linda Doyle,et al.  A Model-Based Approach to Cognitive Radio Design , 2011, IEEE Journal on Selected Areas in Communications.

[12]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[13]  Haitao Zheng,et al.  Distributed Rule-Regulated Spectrum Sharing , 2008, IEEE Journal on Selected Areas in Communications.

[14]  Fumiyuki Adachi,et al.  Load-Balancing Spectrum Decision for Cognitive Radio Networks , 2011, IEEE Journal on Selected Areas in Communications.

[15]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[16]  Victor C. M. Leung,et al.  Optimal Cooperative Internetwork Spectrum Sharing for Cognitive Radio Systems With Spectrum Pooling , 2010, IEEE Transactions on Vehicular Technology.

[17]  M. K. Ghosh,et al.  Discrete-time controlled Markov processes with average cost criterion: a survey , 1993 .