A cognitive management framework for spectrum selection

To increase cognitive radio (CR) operation efficiency, there has been an interest in enhancing the awareness level of spectrum utilization. In this context, this paper builds a new cognitive management functional architecture for spectrum selection (SS). It relies on a knowledge manager (KM) retaining a set of advanced statistics that track the suitability of spectral resources to support a set of heterogeneous applications under varying interference conditions. Based on this architecture, a novel proactive strategy is proposed for both SS and spectrum mobility (SM) functionalities. The required interactions between the proposed decision-making processes are described, and their capability to exhibit robustness to unexpected changes in the radio environment is highlighted. The results show that the proposed strategy efficiently exploits the KM support for low loads, while the SM functionality introduces significant gains for high loads with respect to other strategies. Finally, to assess the practicality of the proposed approach, the signaling requirements in the radio interface are evaluated.

[1]  Johanna Vartiainen,et al.  Combination of short term and long term database for cognitive radio resource management , 2010, 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010).

[2]  Marko Höyhtyä,et al.  Priority channel selection based on detection history database , 2010, 2010 Proceedings of the Fifth International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[3]  Qing Zhao,et al.  Detecting, tracking, and exploiting spectrum opportunities in unslotted primary systems , 2008, 2008 IEEE Radio and Wireless Symposium.

[4]  Reconfigurable Radio Systems (rrs); Feasibility Study on Control Channels for Cognitive Radio Systems Intellectual Property Rights , 2022 .

[5]  Honggang Zhang,et al.  Ultra-Wideband Cognitive Radio for Dynamic Spectrum Accessing Networks , 2008 .

[6]  Leonardo Badia,et al.  Demand and pricing effects on the radio resource allocation of multimedia communication systems , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[7]  Sijing Zhang,et al.  Reinforcement learning based radio resource scheduling in LTE-advanced , 2011, The 17th International Conference on Automation and Computing.

[8]  Victor C. M. Leung,et al.  Dynamic channel selection with reinforcement learning for cognitive WLAN over fiber , 2012, Int. J. Commun. Syst..

[9]  Petri Mähönen,et al.  Using cognitive radio principles for wireless resource management in home networking , 2011, 2011 IEEE Consumer Communications and Networking Conference (CCNC).

[10]  Oriol Sallent,et al.  An Application of Reinforcement Learning for Efficient Spectrum Usage in Next-Generation Mobile Cellular Networks , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[11]  Oriol Sallent,et al.  Exploiting knowledge management for supporting spectrum selection in Cognitive Radio networks , 2012, 2012 7th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM).

[12]  Fangwen Fu,et al.  Detection of Spectral Resources in Cognitive Radios Using Reinforcement Learning , 2008, 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[13]  Oriol Sallent,et al.  A framework based on a fittingness factor to enable efficient exploitation of spectrum opportunities in Cognitive Radio networks , 2011, 2011 The 14th International Symposium on Wireless Personal Multimedia Communications (WPMC).

[14]  Kok-Lim Alvin Yau,et al.  A context-aware and Intelligent Dynamic Channel Selection scheme for cognitive radio networks , 2009, 2009 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[15]  Oriol Sallent,et al.  Cognitive control channels: from concept to identification of implementation options , 2012, IEEE Communications Magazine.

[16]  Ananthram Swami,et al.  Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework , 2007, IEEE Journal on Selected Areas in Communications.

[17]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[18]  Aarne Mämmelä,et al.  Application of Fuzzy Logic to Cognitive Radio Systems , 2009, IEICE Trans. Commun..

[19]  Haitao Zheng,et al.  Reliable open spectrum communications through proactive spectrum access , 2006, TAPAS '06.

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

[21]  Jeffrey O. Kephart,et al.  The Vision of Autonomic Computing , 2003, Computer.

[22]  G. Dimitrakopoulos,et al.  Introducing reconfigurability and cognitive networks concepts in the wireless world , 2006, IEEE Vehicular Technology Magazine.

[23]  Allen B. MacKenzie,et al.  Cognitive networks: adaptation and learning to achieve end-to-end performance objectives , 2006, IEEE Communications Magazine.

[24]  Vera Stavroulaki,et al.  Cognitive management systems for supporting operators in the emerging Future Internet era , 2010, 2010 IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications Workshops.

[25]  Oriol Sallent,et al.  Strengthening Radio Environment Maps with primary-user statistical patterns for enhancing cognitive radio operation , 2011, 2011 6th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM).

[26]  Reconfigurable Radio Systems ( RRS ) ; Functional Architecture ( FA ) for the Management and Control of Reconfigurable Radio Systems , 2009 .

[27]  Honggang Zhang,et al.  Spectrum Self-Coexistence in Cognitive Wireless Access Networks , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.