Spectrum Coexistence Issues: Challenges and Research Directions

Spectrum congestion is a problem to both commercial and military systems. A recent Presidential Council of Advisors on Science and Technology (PCAST) study suggests that this problem is further amplified for military systems because not only demand for more bandwidth is increasing but portion of the spectrum primarily allocated to the military is recommended for commercial use. PCAST report also suggests that spectrum congestion is mainly due to inefficient use of spectrum rather than spectrum scarcity. This paper deals with challenges evolving due to coexistence of various commercial and military communications systems in wideband non-contiguous spectrum. A non-contiguous spectrum environment is characterized by spectrum holes fragmented across frequency range of interest. %This paper, in particular, focuses on different types of interference which will affect the quality of a spectrum hole and its utility for spectrum sharing application. In particular, we discuss the impact of spectrum sharing from the system perspective including hardware design, physical (PHY) layer intricacies, and medium access control (MAC) layer spectrum management. Since the number of spectrum holes required is application dependent, non-contiguous waveform design and pertinent design modifications and MAC layer improvements are also an intrinsic part of this paper.

[1]  Roger B. Myerson,et al.  Graphs and Cooperation in Games , 1977, Math. Oper. Res..

[2]  Sumit Roy,et al.  Modeling and Validation of Channel Idleness and Spectrum Availability for Cognitive Networks , 2012, IEEE Journal on Selected Areas in Communications.

[3]  Zhiqiang Wu,et al.  Interference Tolerant Agile Cognitive Radio: Maximize Channel Capacity of Cognitive Radio , 2007, 2007 4th IEEE Consumer Communications and Networking Conference.

[4]  Arshan Naji A Fast Locking Scheme for PLL Frequency Synthesizers , 2013 .

[5]  Andrea J. Goldsmith,et al.  Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective , 2009, Proceedings of the IEEE.

[6]  M.A. Temple,et al.  Communication Waveform Design Using an Adaptive Spectrally Modulated, Spectrally Encoded (SMSE) Framework , 2007, IEEE Journal of Selected Topics in Signal Processing.

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

[8]  C.R. Nassar,et al.  The road to 4G: two paradigm shifts, one enabling technology , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[9]  Behzad Razavi An Adaptive PLL Tuning System Architecture Combining High Spectral Purity and Fast Settling Time , 2003 .

[10]  Behrouz Farhang-Boroujeny,et al.  Multicarrier communication techniques for spectrum sensing and communication in cognitive radios , 2008, IEEE Communications Magazine.

[11]  J.D. Poston,et al.  Discontiguous OFDM considerations for dynamic spectrum access in idle TV channels , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[12]  Dharma P. Agrawal,et al.  A framework for statistical wireless spectrum occupancy modeling , 2010, IEEE Transactions on Wireless Communications.

[13]  P. Vaidyanathan,et al.  Periodically nonuniform sampling of bandpass signals , 1998 .

[14]  Ling Luo,et al.  Joint Optimization of Spectrum Sensing for Cognitive Radio Networks , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[15]  Ling Luo,et al.  A two-stage sensing technique for dynamic spectrum access , 2009, IEEE Transactions on Wireless Communications.

[16]  Zhu Han,et al.  Coalitional Games for Distributed Collaborative Spectrum Sensing in Cognitive Radio Networks , 2009, IEEE INFOCOM 2009.

[17]  Athanasios V. Vasilakos,et al.  Novel overlay/underlay cognitive radio waveforms using SD-SMSE framework to enhance spectrum efficiency- part i: theoretical framework and analysis in AWGN channel , 2009, IEEE Transactions on Communications.

[18]  Balasubramaniam Natarajan,et al.  Flexible spectrum use and better coexistence at the physical layer of future wireless systems via a multicarrier platform , 2004, IEEE Wireless Communications.

[19]  Stephen P. Boyd,et al.  Optimal power control in interference-limited fading wireless channels with outage-probability specifications , 2002, IEEE Trans. Wirel. Commun..

[20]  Roger B. Myerson,et al.  Game theory - Analysis of Conflict , 1991 .

[21]  Michael A. Temple,et al.  Novel overlay/underlay cognitive radio waveforms using SD-SMSE framework to enhance spectrum efficiency-part II: analysis in fading channels , 2010, IEEE Transactions on Communications.

[22]  Dave Cavalcanti,et al.  Coexistence challenges for heterogeneous cognitive wireless networks in TV white spaces , 2011, IEEE Wireless Communications.

[23]  Matthew O. Jackson,et al.  The Evolution of Social and Economic Networks , 2002, J. Econ. Theory.

[24]  F.K. Jondral,et al.  Mutual interference in OFDM-based spectrum pooling systems , 2004, 2004 IEEE 59th Vehicular Technology Conference. VTC 2004-Spring (IEEE Cat. No.04CH37514).

[25]  Hesham El Gamal,et al.  The Water-Filling Game in Fading Multiple-Access Channels , 2005, IEEE Transactions on Information Theory.

[26]  Behzad Razavi,et al.  A study of phase noise in CMOS oscillators , 1996, IEEE J. Solid State Circuits.

[27]  Zhiqiang Wu,et al.  Real-time cyclostationary analysis for cognitive radio via software defined radio , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[28]  Eitan Altman,et al.  Evolutionary Power Control Games in Wireless Networks , 2008, Networking.

[29]  Husheng Li,et al.  Spectrum utilization efficiency of cognitive radio systems with limited sampling capability: The impact of spectrum non-contiguity , 2012, 2012 IEEE International Symposium on Dynamic Spectrum Access Networks.

[30]  G. Demange,et al.  Group Formation in Economics , 2005 .