Equivalence and comparison of heterogeneous cellular networks

We consider a general heterogeneous network in which, besides general propagation effects (shadowing and/or fading), individual base stations can have different emitting powers and be subject to different parameters of Hata-like path-loss models (path-loss exponent and constant) due to, for example, varying antenna heights. We assume also that the stations may have varying parameters of, for example, the link layer performance (SINR threshold, etc). By studying the propagation processes of signals received by the typical user from all antennas marked by the corresponding antenna parameters, we show that seemingly different heterogeneous networks based on Poisson point processes can be equivalent from the point of view a typical user. These neworks can be replaced with a model where all the previously varying propagation parameters (including path-loss exponents) are set to constants while the only trade-off being the introduction of an isotropic base station density. This allows one to perform analytic comparisons of different network models via their isotropic representations. In the case of a constant path-loss exponent, the isotropic representation simplifies to a homogeneous modification of the constant intensity of the original network, thus generalizing a previous result showing that the propagation processes only depend on one moment of the emitted power and propagation effects. We give examples and applications to motivate these results and highlight an interesting observation regarding random path-loss exponents.

[1]  Timothy X. Brown,et al.  Multi-Tier Network Performance Analysis Using a Shotgun Cellular System , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[2]  Timothy X. Brown,et al.  Downlink coverage analysis in a heterogeneous cellular network , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[3]  Malvin Carl Teich,et al.  Power-law shot noise , 1990, IEEE Trans. Inf. Theory.

[4]  Gordon L. Stuber,et al.  Principles of mobile communication (2nd ed.) , 2001 .

[5]  Bartlomiej Blaszczyszyn,et al.  Impact of the geometry, path-loss exponent and random shadowing on the mean interference factor in wireless cellular networks , 2010, WMNC2010.

[6]  Jeffrey G. Andrews,et al.  Heterogeneous Cellular Networks with Flexible Cell Association: A Comprehensive Downlink SINR Analysis , 2011, IEEE Transactions on Wireless Communications.

[7]  Jeffrey G. Andrews,et al.  Modeling and Analysis of K-Tier Downlink Heterogeneous Cellular Networks , 2011, IEEE Journal on Selected Areas in Communications.

[8]  Sayandev Mukherjee,et al.  Distribution of Downlink SINR in Heterogeneous Cellular Networks , 2012, IEEE Journal on Selected Areas in Communications.

[9]  Holger Paul Keeler,et al.  SINR-based k-coverage probability in cellular networks with arbitrary shadowing , 2013, 2013 IEEE International Symposium on Information Theory.

[10]  Holger Paul Keeler,et al.  Using Poisson processes to model lattice cellular networks , 2013, 2013 Proceedings IEEE INFOCOM.

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

[12]  Moe Z. Win,et al.  Secure Communication in Stochastic Wireless Networks—Part I: Connectivity , 2012, IEEE Transactions on Information Forensics and Security.

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

[14]  H. Pollak,et al.  Amplitude distribution of shot noise , 1960 .

[15]  Martin Haenggi A Geometric Interpretation of Fading in Wireless Networks: Theory and Applications , 2008, IEEE Transactions on Information Theory.

[16]  François Baccelli,et al.  Stochastic Geometry and Wireless Networks, Volume 1: Theory , 2009, Found. Trends Netw..

[17]  Gordon L. Stüber Principles of mobile communication , 1996 .