Near vs. Far Field: Interference Aggregation in TV Whitespaces

We investigate the behavior of aggregate interference generated by cognitive radios. We find that a phase change occurs in the behavior of aggregate interference as the density of the white-space devices is increased for a fixed protection radius. For a deterministic grid model, the mean of the interference behaves differently depending on whether the problem is one of ``near field'' or ``far field''. For a more realistic Poisson-placement model, we show that the shape of the distribution of interference changes from a heavy-tailed distribution to something that is approximately Gaussian. Investigating models with random fading of signal at each transmitter, we show that fading can alter the boundary of near and far fields. These phase-changes suggest that in designing rules for whitespace devices, the FCC rules may have to be sensitive to whether the situation is one of near or far field. For Poisson placement of nodes, our results also suggest that central limit theorem-style arguments might help in obtaining a conceptually and computationally improved understanding of interference aggregation.

[1]  C. C. Heyde,et al.  Central Limit Theorem , 2006 .

[2]  Candice King,et al.  Fundamentals of wireless communications , 2013, 2013 IEEE Rural Electric Power Conference (REPC).

[3]  Syed Ali Jafar,et al.  Interference Alignment and Degrees of Freedom of the $K$-User Interference Channel , 2008, IEEE Transactions on Information Theory.

[4]  Anant Sahai,et al.  Potential collapse of whitespaces and the prospect for a universal power rule , 2011, 2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[5]  François Baccelli,et al.  Interference Networks With Point-to-Point Codes , 2011, IEEE Transactions on Information Theory.

[6]  Te Sun Han,et al.  A new achievable rate region for the interference channel , 1981, IEEE Trans. Inf. Theory.

[7]  Jeffrey G. Andrews,et al.  A Tractable Approach to Coverage and Rate in Cellular Networks , 2010, IEEE Transactions on Communications.

[8]  Jeffrey G. Andrews,et al.  Series Expansion for Interference in Wireless Networks , 2011, IEEE Transactions on Information Theory.

[9]  Tien Viet Nguyen,et al.  A Probabilistic Model of Carrier Sensing Based Cognitive Radio , 2010, 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN).

[10]  M. Haenggi,et al.  Interference in Large Wireless Networks , 2009, Found. Trends Netw..

[11]  Hua Wang,et al.  Gaussian Interference Channel Capacity to Within One Bit , 2007, IEEE Transactions on Information Theory.

[12]  Vahid Tarokh,et al.  The Primary Exclusive Region in Cognitive Networks , 2008, 2008 5th IEEE Consumer Communications and Networking Conference.

[13]  N. Hoven,et al.  Power scaling for cognitive radio , 2005, 2005 International Conference on Wireless Networks, Communications and Mobile Computing.

[14]  François Baccelli,et al.  Stochastic geometry and wireless networks , 2009 .

[15]  Jeffrey G. Andrews,et al.  Stochastic geometry and random graphs for the analysis and design of wireless networks , 2009, IEEE Journal on Selected Areas in Communications.

[16]  Mohamed-Slim Alouini,et al.  Area spectral efficiency of cellular mobile radio systems , 1999 .

[17]  Amir K. Khandani,et al.  Communication Over MIMO X Channels: Interference Alignment, Decomposition, and Performance Analysis , 2008, IEEE Transactions on Information Theory.

[18]  A. Goldsmith,et al.  Area spectral efficiency of cellular mobile radio systems , 1997, 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion.

[19]  Jeffrey G. Andrews,et al.  A Stochastic-Geometry Approach to Coverage in Cellular Networks with Multi-Cell Cooperation , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.