Detecting Primary Signals for Efficient Utilization of Spectrum Using Q-Learning

The efficient utilization of underutilized spectrum is the main theme of current research. The cognitive radio with the help of Q-learning algorithm is used to detect the presence of primary signals and utilize the spectrum in the absence of primary signals. The proposed Q-learning algorithm model identifies previously known signals and learns to detect the signals which otherwise could not be detected, and helps for efficient utilization of spectrum. The simulations are further confirmed with results obtained through MATLAB gatool.

[1]  Niels Kang Hoven.  On the feasibility of cognitive radio , 2005 .

[2]  Victor C. M. Leung,et al.  Efficient QoS Provisioning for Adaptive Multimedia in Mobile Communication Networks by Reinforcement Learning , 2004, BROADNETS.

[3]  Anant Sahai,et al.  Cooperative Sensing among Cognitive Radios , 2006, 2006 IEEE International Conference on Communications.

[4]  Jörg Huschke,et al.  Spectrum management and radio resource management considering cognitive radio systems , 2005 .

[5]  Anant Sahai,et al.  Some Fundamental Limits on Cognitive Radio , 2004 .

[6]  N. Hoven,et al.  Power scaling for cognitive radio , 2005, 2005 International Conference on Wireless Networks, Communications and Mobile Computing.

[7]  Jon M. Peha.  Wireless communications and coexistence for smart environments , 2000, IEEE Wirel. Commun..

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

[9]  David Grandblaise,et al.  DYNAMIC SPECTRUM ALLOCATION (DSA) AND RECONFIGURABILITY , 2002 .

[10]  A. Hugine,et al.  Cognitive radio applications to dynamic spectrum allocation: a discussion and an illustrative example , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[11]  David Vengerov,et al.  A Reinforcement Learning Framework for Dynamic Resource Allocation: First Results. , 2005, Second International Conference on Autonomic Computing (ICAC'05).

[12]  R.W. Brodersen,et al.  Implementation issues in spectrum sensing for cognitive radios , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..

[13]  References , 1971 .

[14]  Charles W. Bostian,et al.  COGNITIVE RADIOS WITH GENETIC ALGORITHMS: INTELLIGENT CONTROL OF SOFTWARE DEFINED RADIOS , 2004 .

[15]  P. Cordier,et al.  Dynamic Spectrum Allocation Algorithm for Cognitive Networks , 2007, 2007 Third International Conference on Wireless and Mobile Communications (ICWMC'07).

[16]  Danijela Cabric,et al.  White paper: Corvus: A cognitive radio approach for usage of virtual unlicensed spectrum , 2004 .

[17]  A. Sahai,et al.  Opportunistic spectrum use for sensor networks : the need for local cooperation , .

[18]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[19]  K. Ramchandran,et al.  Detecting primary receivers for cognitive radio applications , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[20]  Victor C. M. Leung,et al.  Efficient QoS Provisioning for Adaptive Multimedia in Mobile Communication Networks by Reinforcement Learning , 2003, First International Conference on Broadband Networks.

[21]  Carlos Teixeira,et al.  A Summary of Dynamic Spectrum Allocation Results from DRiVE , 2002 .

[22]  William A. Arbaugh,et al.  Dynamic spectrum access in cognitive radio networks , 2006 .

[23]  David Grandblaise,et al.  Reconfigurability support for dynamic spectrum allocation: from the DSA concept to implementation , 2003, SympoTIC'03. Joint 1st Workshop on Mobile Future and Symposium on Trends in Communications.

[24]  R. Tafazolli,et al.  Dynamic spectrum allocation in a multi-radio environment: concept and algorithm , 2001 .