Resource Allocation on Cloud Computing Based Cognitive Radio Network


The main objective of cognitive radio networking is to develop radios that can sense the existing spectrum and identify and use free frequency bands. The motivation for these radios comes from the apparent scarcity of spectrum as indicated by the multiple allocation of most bands by the FCC. On the other hand, studies by the FCC show that 70% of the allocated spectrum is not utilized. This discrepancy between allocation and use provides the motivation for opportunistic use of the spectrum.

Our specific research focus is to develop mechanisms for opportunistic spectrum access which have the potential to mitigate spectrum scarcity and meet the increasing demand for spectrum.  Specifically, we are addressing the problem of satisfying Quality of Service (QoS) requirements for multiple Secondary Users (SU) in an Cognitive Radio Network. As the number of SUs vary with time, the computational demands to satisfy the QoS may change significantly over time.  To control the computational complexity and in the meantime to maintain QoS of SU, not only should the number of sensing measurements be limited inside an area, but the area from which measurements are collected should also be adjusted with time. Consequently, the overall computational requirements to meet the QoS in a region will scale significantly over time.

The wide-spread adoption of services implemented on cloud computing infrastructures has the potential to mitigate the aforementioned problem. We are currently developing game theoretic and Markov Decision Process models in the context of cloud computing to meet the QoS requirements of SUs. We have developed an approach based on game theory [1] to satisfy throughput requirements for multiple secondary users. The technique was implemented on secondary users and placed a strain on computational and storage resources on secondary users. We would like to implement the POMDP and game theoretic models in a MapReduce  programming model to alleviate the computational and storage requirements and extend the benefits from mere throughput requirements to overall QoS. 

Fig. 1: System Model

Fig. 2: Storm Topology for our System Model



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  • Danda B. Rawat, Bhed Bahadur Bista and Gongjun Yan, "CoR-VANETs: Game Theoretic Approach for Channel and Rate Selection in Cognitive Radio VANETs," In Proceedings of the Seventh International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA-2012). Victoria, Canada, November 12-14 2012.
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  • Danda B. Rawat, Bhed B. Bista and Gongjun Yan. Combined Admission, Power and Rate Control for Cognitive Radios in Dynamic Spectrum Access Ad-hoc Networks. In Proceedings of The 13-th International Conference on Network-Based Information Systems (NBiS-2010), Sept. 2010, Takayama, Gifu, Japan.
  • Danda B. Rawat and D. C. Popescu. Joint Precoder and Power Adaptation for Cognitive Radios in Interference Systems. In Proceedings of the 28th IEEE International Performance Computing and Communications Conference Workshop--IPCCC 2009, Dec. 14 – 16, 2009, Phoenix, AZ.
  • Danda B. Rawat and Gongjun Yan. Signal Processing for Spectrum Sensing in Cognitive Radio Systems: Challenges and Perspectives.  In Proceedings of the EEE/IFIP International Conference on INTERNET AH– ICI-2009, 3-5 Nov. 2009, Kathmandu, Nepal.

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