Dynamic spectrum access in cognitive radio networks

Since the 1930's, the Federal Communications Commission (FCC) has con trolled the radio frequency energy spectrum. They license segments to particular users in particular geographic areas. A few, small, unlicensed bands were left open for anyone to use as long as they followed certain power regulations. With the recent boom in personal wireless technologies, these unlicensed bands have become crowded with everything from wireless networks to digital cordless phones. To combat the overcrowding, the FCC has been investigating new ways to manage RF resources. The basic idea is to let people use licensed frequencies, provided they can guarantee interference perceived by the primary license holders will be minimal. With advances in software and cognitive radio, practical ways of doing this are on the horizon. In 2003 the FCC released a memorandum seeking comment on the interference temperature model for controlling spectrum use. Analyzing the viability of this model and developing a medium access protocol around it are the main goals of this dissertation. A system implementing this model will measure the current interference temperature before each transmission. It can then determine what bandwidth and power it should use to achieve a desired capacity without violating an interference ceiling called the interference temperature limit. If a system consisting of interference sources, primaxy licensed users, and secondary unlicensed users is modeled stochastically, we can obtain some interesting results. In particular, if impact to licensed users is defined by a fractional decrease in coverage area, and this is held constant, the capacity achieved by secondary users is directly proportional to the number of unlicensed nodes, and is actually independent of the interference and primary users' transmissions. Using the basic ideas developed in the system analysis, Interference Temperature Multiple Access, a physical and data-link layer implementing the interference temperature model, was formulated, analyzed, and simulated. Overall, the interference temperature model is a viable approach for dynamic spectrum access, and ITMA is a concrete technique implementing it. With the help of cognitive radios, we can reform spectrum policy and have significantly more room to innovate, ushering in a new era of high-speed personal wireless communications.

[1]  Jeffrey H. Reed.  Software Radio , 2002 .

[2]  C. Cordeiro,et al.  Spectrum agile radios: utilization and sensing architectures , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[3]  Q. Zhao,et al.  Decentralized cognitive mac for dynamic spectrum access , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[4]  Panagiotis Papadimitratos,et al.  A bandwidth sharing approach to improve licensed spectrum utilization , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[5]  Joseph Mitola,et al.  The software radio architecture , 1995, IEEE Commun. Mag..

[6]  Cristina Comaniciu,et al.  Adaptive channel allocation spectrum etiquette for cognitive radio networks , 2005 .

[7]  Marvin K. Simon,et al.  Spread Spectrum Communications Handbook , 1994 .

[8]  Jun Zhao,et al.  Distributed coordination in dynamic spectrum allocation networks , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[9]  Stefan Mangold,et al.  Spectrum agile radio: radio resource measurements for opportunistic spectrum usage , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[10]  M.B. Pursley.  The role of spread spectrum in packet radio networks , 1987, Proceedings of the IEEE.

[11]  J. Douglas Faires,et al.  Numerical Analysis , 1981 .

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

[13]  Kang G. Shin,et al.  Efficient Discovery of Spectrum Opportunities with MAC-Layer Sensing in Cognitive Radio Networks , 2008, IEEE Transactions on Mobile Computing.

[14]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[15]  Catherine A. French,et al.  Spread-Spectrum Despreading Without the Code , 1986, IEEE Trans. Commun..

[16]  Jon M. Peha,et al.  Performance of unlicensed devices with a spectrum etiquette , 1999 .

[17]  Barry E. Mullins,et al.  Cater: an Opportunistic Medium Access Control Protocol for Wireless Local Area Networks , 1997 .

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

[19]  Bhaskar Krishnamachari,et al.  On myopic sensing for multi-channel opportunistic access: structure, optimality, and performance , 2007, IEEE Transactions on Wireless Communications.

[20]  S. Chuprun,et al.  Adaptive spectrum exploitation using emerging software defined radios , 1999, RAWCON 99. 1999 IEEE Radio and Wireless Conference (Cat. No.99EX292).

[21]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[22]  Ananthram Swami,et al.  Joint Design and Separation Principle for Opportunistic Spectrum Access in the Presence of Sensing Errors , 2007, IEEE Transactions on Information Theory.

[23]  Jon M. Peha,et al.  Etiquette modification for unlicensed spectrum: approach and impact , 1998, VTC '98. 48th IEEE Vehicular Technology Conference. Pathway to Global Wireless Revolution (Cat. No.98CH36151).

[24]  D. P. Satapathy,et al.  Spectrum Sharing Without Licenses: Opportunities and Dangers , 1996 .

[25]  N. Mandayam,et al.  Demand responsive pricing and competitive spectrum allocation via a spectrum server , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[26]  N.B. Mandayam,et al.  Scheduling variable rate links via a spectrum server , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[27]  Kiran Challapali,et al.  Spectrum Agile Radio: Detecting Spectrum Opportunities , 2004 .

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

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

[30]  A. Viterbi.  CDMA: Principles of Spread Spectrum Communication , 1995 .

[31]  William A. Arbaugh,et al.  Measuring Interference Temperature , 2006 .

[32]  Ananthram Swami,et al.  Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework , 2007, IEEE Journal on Selected Areas in Communications.

[33]  L. Kleinrock,et al.  Packet Switching in Radio Channels : Part Il-The Hidden Terminal Problem in Carrier Sense Multiple-Access and the Busy-Tone Solution , 2022 .

[34]  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..

[35]  J. Miller.  Numerical Analysis , 1966, Nature.

[36]  MitolaJ..  Software radio architecture , 1999 .

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

[38]  C. Cordeiro,et al.  C-MAC: A Cognitive MAC Protocol for Multi-Channel Wireless Networks , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[39]  Dimitri P. Bertsekas,et al.  Data Networks , 1986 .

[40]  Brian M. Sadler,et al.  A Survey of Dynamic Spectrum Access , 2007, IEEE Signal Processing Magazine.

[41]  L. Ma,et al.  Dynamic open spectrum sharing MAC protocol for wireless ad hoc networks , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..