A frequency rendezvous approach for decentralized dynamic spectrum access networks

In this paper, we propose a transmission frequency rendezvous approach for secondary users deployed in decentralized dynamic spectrum access networks. Frequency rendezvous is a critical step in bootstrapping a wireless network that does not possess centralized control. Current techniques for enabling frequency rendezvous in decentralized dynamic spectrum access networks either require pre-existing infrastructure or use one of several simplifying assumptions regarding the architecture, such as the use of regularly spaced frequency channels for communications. Our proposed approach is designed to be operated in a strictly decentralized wireless networking environment, where no centralized control is present and the spectrum does not possess pre-defined channels. This is accomplished via a combination of receiver pilot tones, a tone scanning protocol, and transmitter/receiver handshaking process. In our proposed rendezvous algorithm, the most important step is pilot tone detection and receiver query. In order to realize a shortest search time for the target receiver, an efficient scanning rule should be employed. In this paper, three scanning rules are proposed and evaluated, namely: frequency sequence scanning, pilot tone strength scanning, and cluster scanning. To validate our result, we test our scanning rules with actual paging band spectrum measurements.

[1]  Mohsen Guizani,et al.  Wireless Communications Systems and Networks , 2006 .

[2]  Chee-Yee Chong,et al.  Sensor networks: evolution, opportunities, and challenges , 2003, Proc. IEEE.

[3]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[4]  Feniosky Peña-Mora,et al.  Robust Mobile Ad Hoc Space for Collaboration to Support Disaster Relief Efforts Involving Critical Physical Infrastructure , 2006 .

[5]  Shuigeng Zhou,et al.  Distributed Localization Using a Moving Beacon in Wireless Sensor Networks , 2008, IEEE Transactions on Parallel and Distributed Systems.

[6]  Qilian Liang.  Ad hoc wireless network traffic-self-similarity and forecasting , 2002, IEEE Commun. Lett..

[7]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

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

[9]  Lili Qiu,et al.  Impact of Interference on Multi-Hop Wireless Network Performance , 2003, MobiCom '03.

[10]  Milind M. Buddhikot,et al.  DIMSUMnet: new directions in wireless networking using coordinated dynamic spectrum , 2005, Sixth IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks.

[11]  L.A. DaSilva,et al.  Sequence-Based Rendezvous for Dynamic Spectrum Access , 2008, 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[12]  Kui Wu,et al.  Reliable, distributed and energy-efficient broadcasting in multi-hop mobile ad hoc networks , 2002, 27th Annual IEEE Conference on Local Computer Networks, 2002. Proceedings. LCN 2002..

[13]  Matthew J. Zieniewicz,et al.  The Evolution of Army Wearable Computers , 2002, IEEE Pervasive Comput..

[14]  W. Press,et al.  Numerical Recipes: The Art of Scientific Computing , 1987 .

[15]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[16]  D. Turgut,et al.  Link Rendezvous Protocol for Cognitive Radio Networks , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[17]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[18]  Aura Ganz,et al.  IEEE 802.16 based last mile broadband wireless military networks with quality of service support , 2003, IEEE Military Communications Conference, 2003. MILCOM 2003..

[19]  Mihail L. Sichitiu,et al.  Localization of wireless sensor networks with a mobile beacon , 2004, 2004 IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE Cat. No.04EX975).