Cognitive Radio Rides on the Cloud

Cognitive Radio (CR) is capable of adaptive learning and reconfiguration, promising consistent communications performance for C4ISR1 systems even in dynamic and hostile battlefield environments. As such, the vision of Network-Centric Operations becomes feasible. However, enabling adaptation and learning in CRs may require both storing a vast volume of data and processing it fast. Because a CR usually has limited computing and storage capacity determined by its size and battery, it may not be able to achieve its full capability. The cloud2 can provide its computing and storage utility for CRs to overcome such challenges. On the other hand, the cloud can also store and process enormous amounts of data needed by C4ISR systems. However, today's wireless technologies have difficulty moving various types of data reliably and promptly in the battlefields. CR networks promise reliable and timely data communications for accessing the cloud. Overall, connecting CRs and the cloud overcomes the performance bottlenecks of each. This paper explores opportunities of this confluence and describes our prototype system.

[1]  Christoffer Andersson,et al.  Mobile Media and Applications, From Concept to Cash: Successful Service Creation and Launch , 2006 .

[2]  Clay Wilson Network Centric Warfare: Background and Oversight Issues for Congress. CRS Report for Congress , 2005 .

[3]  Shohaib Aboobacker RAZOR: circuit-level correction of timing errors for low-power operation , 2011 .

[4]  Allen B. MacKenzie,et al.  Software Radio-Based Decentralized Dynamic Spectrum Access Networks: A Prototype Design and Experimental Results , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[5]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[6]  Ahmed M. Eltawil,et al.  Demonstration of highly programmable downlink OFDMA (WiMax) transceivers for SDR systems , 2009, MobiHoc '09.

[7]  Nikil D. Dutt,et al.  Cross-layer co-exploration of exploiting error resilience for video over wireless applications , 2008, 2008 IEEE/ACM/IFIP Workshop on Embedded Systems for Real-Time Multimedia.

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

[9]  John J. Garstka Network Centric Operations Conceptual Framework Version 1.0 , 2003 .

[10]  James F. Doyle,et al.  Peer-to-Peer: harnessing the power of disruptive technologies , 2001, UBIQ.

[11]  Clay Wilson,et al.  Network Centric Operations: Background and Oversight Issues for Congress , 2007 .

[12]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[13]  Ahmed M. Eltawil,et al.  Power Management for Cognitive Radio Platforms , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[14]  Trevor Mudge,et al.  A self-tuning DVS processor using delay-error detection and correction , 2005, VLSIC 2005.

[15]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[16]  Yitzchak M. Gottlieb,et al.  Policy-controlled dynamic spectrum access in multitiered mobile networks , 2010, 2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE.

[17]  Ying Wang,et al.  Universal Classifier Synchronizer Demodulator , 2008, 2008 IEEE International Performance, Computing and Communications Conference.

[18]  Doug Johnson,et al.  Computing in the Clouds. , 2010 .

[19]  Raja Jurdak Wireless Ad Hoc and Sensor Networks: A Cross-Layer Design Perspective , 2007 .

[20]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[21]  Bruce A. Fette,et al.  Cognitive Radio Technology , 2006 .

[22]  Charles W. Bostian,et al.  Artificial Intelligence in Wireless Communications , 2009 .

[23]  Wu-chun Feng,et al.  MOON: MapReduce On Opportunistic eNvironments , 2010, HPDC '10.

[24]  Frank H. P. Fitzek,et al.  Mobile Phone Programming: and its Application to Wireless Networking , 2007 .

[25]  Dipankar Raychaudhuri,et al.  Future Directions in Cognitive Radio Network , 2009 .

[26]  Feng Ge,et al.  A Parallel Computing Based Spectrum Sensing Approach for Signal Detection under Conditions of Low SNR and Rayleigh Multipath Fading , 2008, 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.