Channel Assortment Strategy for Reliable Communication in Multi-Hop Cognitive Radio Networks Mubashir Husain Rehmani Advisors: Aline Carneiro Viana, Hicham Khalife, and Serge Fdida Cognitive Radio Networks (CRNs) and Channel Assortment in Multi-Hop CRNs ñ Motivation ñ Challenges of Channel Selection in CRNs Limited available spectrum in today's wireless networks Fixed spectrum assignment policy Geographical and temporal utilization of spectrum varies from 15%-85% (FCC)[1] Results in the: Inefficiency in spectrum usage Creation of spectrum holes ñ Cognitive Radio Nodes Traffic pattern and channels’ occupancy of PR nodes CR transmissions should not degrade the reception quality of PR nodes CR node should immediately interrupt its transmission whenever a neighboring PR activity is detected [2] ñ Best Channel? Best strategy for all CR nodes is to dynamically switch to less PR occupied channels In CRNs, Cognitive Radio (CR) nodes: Utilize free parts of unlicensed spectrum Opportunistically exploit licensed band IEEE WoWMoM 2010 PhD Forum Channel selection is more challenging in CRNs But Generates contention and collision problems, as all CR nodes compete for the same channel resource ñ Why Channel Selection? Channel selection plays a vital role in efficient and reliable data dissemination Wastes the valuable additional capacity on different channels that the cognitive radio concept offers Channel Assortment Strategy SURF CR Occupancy Number of active CR nodes competing for the channel ñ Goal How to find a good compromise between transmission opportunity in terms of PR occupancy and the number of CR neighbors ñ SURF Each CR node sense channels Classify sensed channels in the decreasing order of availability for transmission and/or overhearing Approaches are present in [3] that provides PR occupancy (PRO) Assign weights to channels based upon PR occupancy and CR occupancy (CRO) PR Occupancy Ratio of the PR nodes utilizing the channels over total number of PR nodes on that channel Based on PR nodes that are in activity and is based on PR nodes' state (i.e. if they are active – ON – or not – OFF) CRO= 1 – PRO Channels’ weight is calculated based on formula: Pw = e-PRO x CRO We introduce Tenancy Factor ‘β’ which enables SURF algorithm to avoid channels with high CR contentions To allow nodes having a good compromise between number of CR receivers and number of CR competing transmitters Simulation Results Comparison: SURF, RD, SB, and CA SURF allows the message dissemination to 55% nodes in the network SURF guarantees the delivery of approximately 60% of messages contrarily to less than 20% for RD and SB, and 80% for CA These results show the good level of network connectivity provided by SURF, suitable for reliable dissemination Future Work Investigate SURF performance under dynamic traffic by consideration of data rates and traffic volume generated by CR nodes Optimize data dissemination delay References Prediction and history can also be accounted to enhance the performance [1] I. F. Akyildiz, W. -Y. Lee, M. C. Vuran, and S. Mohanty, “Next Generation/ dynamic spectrum access/ cognitive radio wireless networks: a survey”, Computer Networks: The International Journal of Computer and Telecommunications Networking, Vol. 50, Issue 13, pp. 2127-2159, 2006 [2] H. Khalife, S. Ahuja, N. Malouch, and M. Krunz, Probabilistic path selection in opportunistic cognitive radio networks”, in Proceeding of the IEEE GlobeCom Conference, 30 Nov-4 Dec 2008, pp. 1-5 [3] T. Yucek and H. Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications”, IEEE Communications Surveys and Tutorials, Vol. 11, No. 1, First Quarter 2009 M. H. Rehmani : Mubashir.Rehmani@lip6.fr H. Khalife : Hicham.Khalife@labri.fr (LIP6/UPMC) (LaBRI/ENSEIRB) A. C. Viana : Aline.Viana@inria.fr S. Fdida: Serge.Fdida@lip6.fr (ASAP/INRIA and TU-Berlin) (LIP6/UPMC)