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References

Published online by Cambridge University Press:  26 March 2018

Bartłomiej Błaszczyszyn
Affiliation:
Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt
Martin Haenggi
Affiliation:
University of Notre Dame, Indiana
Paul Keeler
Affiliation:
Weierstrass Institute for Applied Analysis and Statistics
Sayandev Mukherjee
Affiliation:
DOCOMO Innovations, Inc., Palo Alto
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Print publication year: 2018

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References

Abate, J. & Whitt, W. (1995), ‘Numerical inversion of Laplace transforms of probability distributions’, ORSA Journal on Computing 7(1), 36–43.CrossRefGoogle Scholar
Afshang, M., Saha, C., & Dhillon, H. S. (2017a), ‘Nearest-neighbor and contact distance distributions for Thomas cluster process’, IEEE Wireless Communications Letters 6(1), 130–133.Google Scholar
Afshang, M., Saha, C., & Dhillon, H.S. (2017b), ‘Nearest-neighbor and contact distance distributions for Matérn cluster process’, IEEE Communications Letters, to appear.
Ak, S., Inaltekin, H., & Poor, H. V. (2016), ‘Gaussian approximation for the downlink interference in heterogeneous cellular networks’. ArXiv, http://arxiv.org/abs/1601.06023.CrossRef
Andrews, J. G. (2010), ‘Understanding femtocell-overlaid cellular networks’. Keynote at the IEEE Femtocell Workshop. Available at http://users.ece.utexas.edu/∼jandrews/pubs/ AndrewsKeynote_Femnet2010.pdf.
Andrews, J. G. (2011), ‘Can cellular networks handle 1000× the data?’ Seminar at the University of Texas, Austin, Tx. Available at http://users.ece.utexas.edu/∼bevans/courses/realtime/ lectures/Andrews_Cellular1000x_Nov2011.pdf.
Andrews, J. G., Baccelli, F., & Ganti, R. K. (2011), ‘A tractable approach to coverage and rate in cellular networks’, IEEE Transactions on Communications 59(11), 3122–3134.CrossRefGoogle Scholar
Baccelli, F. & Błaszczyszyn, B. (2001), ‘On a coverage process ranging from the Boolean model to the Poisson Voronoi tessellation, with applications to wireless communications’, Advances in Applied Probability 33, 293–323.CrossRefGoogle Scholar
Baccelli, F. & Błaszczyszyn, B. (2009a), Stochastic Geometry and Wireless Networks, Volume I — Theory, Vol. 3, No. 3–4 of Foundations and Trends in Networking, Boston; Delft, The Netherlands: Now Publishers.Google Scholar
Baccelli, F. & Błaszczyszyn, B. (2009b), Stochastic Geometry and Wireless Networks, Volume II — Applications, Vol. 4, No. 1–2 of Foundations and Trends in Networking, Boston; Delft, The Netherlands: Now Publishers.Google Scholar
Baccelli, F., Błaszczyszyn, B., & Mühlethaler, P. (2003), A spatial reuse Aloha MAC protocol for multihop wireless mobile networks, in ‘Proceedings of the Annual Allerton Conference on Communication, Control, and Computing’. Monticello, IL.Google Scholar
Baccelli, F., Błaszczyszyn, B., & Mühlethaler, P. (2006), ‘An Aloha protocol for multihop mobile wireless networks’, IEEE Transactions on Information Theory 52(2), 421–436.CrossRefGoogle Scholar
Baccelli, F., Błaszczyszyn, B., & Mühlethaler, P. (2009), ‘Stochastic analysis of spatial and opportunistic Aloha’, IEEE Journal on Selected Areas in Communications, special issue on Stochastic Geometry and Random Graphs for Wireless Networks 27(7), 1105–1119.Google Scholar
Baccelli, F., Klein, M., Lebourges, M., & Zuyev, S. (1997), ‘Stochastic geometry and architecture of communication networks’, Telecommunication Systems 7(1), 209–227.CrossRefGoogle Scholar
Baccelli, F. & Zhang, X. (2015), A correlated shadowing model for urban wireless networks, in ‘Proceedings of 2015 IEEE Conference on Computer Communications (INFOCOM)’, IEEE, pp. 801–809.
Banani, S. A., Eckford, A. W., & Adve, R. S. (2014), The penalty for random deployment in hexagonal lattice networks with perturbed interferers, in ‘Heterogeneous and Small Cell Workshop, IEEE Globecom Workshops, 2014’, pp. 1272–1277.
Behnad, A., Wang, X., & Akhtar, A. M. (2015), ‘Communication neighbors comparison in a Poisson field of nodes’, IEEE Communications Letters 19(11), 2025–2028.CrossRefGoogle Scholar
Berman, A. & Plemmons, R. J. (1994), Nonnegative Matrices in the Mathematical Sciences, Philadelphia: Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
Biscio, C. A. N. & Lavancier, F. (2016), ‘Quantifying repulsiveness of determinantal point processes’, Bernoulli 22(4), 2001–2028.CrossRefGoogle Scholar
Błaszczyszyn, B. & Karray, M. K. (2012), Linear-regression estimation of the propagationloss parameters using mobiles' measurements in wireless cellular networks, in ‘2012 10th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt)’, Paderborn, Germany, May 14–18, 2012, pp. 54–59.Google Scholar
Błaszczyszyn, B. & Karray, M. K. (2016), ‘Spatial distribution of the SINR in Poisson cellular networks with sector antennas’, IEEE Transactions on Wireless Communications 15(1), 581–593.CrossRefGoogle Scholar
Błaszczyszyn, B. & Keeler, H. P. (2013), Equivalence and comparison of heterogeneous cellular networks, in 2013 IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC Workshops), London, September 08, 2013, pp. 153–157.Google Scholar
Błaszczyszyn, B. & Keeler, H. P. (2014), ‘SINR in wireless networks and the two-parameter Poisson-Dirichlet process’, IEEE Wireless Communications Letters 3(5), 525–528.Google Scholar
Błaszczyszyn, B. & Keeler, H. P. (2015), ‘Studying the SINR process of the typical user in Poisson networks by using its factorial moment measures’, IEEE Transactions on Information Theory 61(12), 6774–6794.CrossRefGoogle Scholar
Blaszczyszyn, B. & Mühlethaler, P. (2015), ‘Interference and SINR coverage in spatial nonslotted Aloha networks’, Annales des Telecommunications–Annals of Telecommunications 70(7), 345–358.Google Scholar
Błaszczyszyn, B., Jovanovic, M., & Karray, M. K. (2014), How user throughput depends on the traffic demand in large cellular networks, in 2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), Hammamet, Tunisia, May 12–16, 2014, pp. 611–619.Google Scholar
Błaszczyszyn, B., Karray, M. K., & Keeler, H. P. (2013), Using Poisson processes to model lattice cellular networks, in ‘Proceedings of IEEE INFOCOM 2013’, pp. 773–781.
Błaszczyszyn, B., Karray, M. K., & Keeler, H. P. (2015), ‘Wireless networks appear Poissonian due to strong shadowing’, IEEE Transactions on Wireless Communications 14(8), 4379–4390.CrossRefGoogle Scholar
Błaszczyszyn, B., Karray, M. K., & Klepper, F.-X. (2010), Impact of the geometry, path-loss exponent and random shadowing on the mean interference factor in wireless cellular networks, in Proceedings of the Third Joint IFIP Wireless and Mobile Networking Conference (WMNC) 2010, pp. 1–6.
Błaszczyszyn, B., Keeler, H. P., & Mühlethaler, P. (2017), Optimizing spatial throughput in device-to-device networks, in 2017 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), Paris, May 15–19, 2017, pp. 1–6.Google Scholar
Brown, T. X. (2000), ‘Cellular performance bounds via shotgun cellular systems’, IEEE Journal on Selected Areas in Communications, 18(11), 2443–2455.CrossRefGoogle Scholar
Chiu, S. N., Stoyan, D., Kendall, W. S., & Mecke, J. (2013), Stochastic Geometry and Its Applications, 3rd ed. Somerset, NJ: John Wiley & Sons.CrossRefGoogle Scholar
Cover, T. M. & Thomas, J. A. (1991), Elements of Information Theory, Somerset, NJ: JohnWiley & Sons.CrossRefGoogle Scholar
Daley, D. J. & Vere-Jones, D. (2008), An Introduction to the Theory of Point Processes, Vols I and II, 2nd ed. New York: Springer.CrossRefGoogle Scholar
Deng, N. & Haenggi, M. (2017), ‘A fine-grained analysis of millimeter-wave device-to-device networks’, IEEE Transactions on Communications 65(11), 4940–4954.CrossRefGoogle Scholar
Deng, N., Zhou, W., & Haenggi, M. (2015a), ‘The Ginibre point process as a model for wireless networks with repulsion’, IEEE Transactions on Wireless Communications 14(1), 107–121.CrossRefGoogle Scholar
Deng, N., Zhou, W., & Haenggi, M. (2015b), ‘Heterogeneous cellular network models with dependence’, IEEE Journal on Selected Areas in Communications 33(10), 2167–2181.CrossRefGoogle Scholar
Dhillon, H., Ganti, R., & Andrews, J. G. (2011), A tractable framework for coverage and outage in heterogeneous cellular networks, in ‘Information Theory and Applications Workshop (ITA), 2011’, IEEE, pp. 1–6.
Dhillon, H., Ganti, R., Baccelli, F., & Andrews, J. (2012), ‘Modeling and analysis of K-tier downlink heterogeneous cellular networks’, IEEE Journal on Selected Areas in Communications 30(3), 550–560.CrossRefGoogle Scholar
Dhillon, H., Kountouris, M., & Andrews, J. G. (2013), ‘Downlink MIMO HetNets: Modeling, ordering results and performance analysis’, IEEE Transactions on Wireless Communications 12(10), 5208–5222.CrossRefGoogle Scholar
Dick, J., Kuo, F. Y., & Sloan, I. H. (2013), ‘High-dimensional integration: the quasi-Monte Carlo way’, Acta Numerica 22, 133–288.CrossRefGoogle Scholar
ElSawy, H., Hossain, E., & Haenggi, M. (2013), ‘Stochastic geometry for modeling, analysis, and design of multi-tier and cognitive cellular wireless networks: A survey’, IEEE Communications Surveys & Tutorials 15(3), 996–1019.CrossRefGoogle Scholar
ElSawy, H., Sultan-Salem, A., Alouini, M. S., & Win, M. Z. (2017), ‘Modeling and analysis of cellular networks using stochastic geometry: A tutorial’, IEEE Communications Surveys & Tutorials 19(1), 167–203.CrossRefGoogle Scholar
Feller, W. (1968), An Introduction to Probability Theory and Its Applications, Vol. 1, 3rd ed. New York: J. Wiley & Sons.Google Scholar
Feller, W. (1970), An Introduction to Probability Theory and Its Applications, Vol 2, 2nd ed. Somerset, NJ: John Wiley & Sons.Google Scholar
Ganti, R. K. & Haenggi, M. (2016a), ‘Asymptotics and approximation of the SIR distribution in general cellular networks’, IEEE Transactions on Wireless Communications 15(3), 2130–2143.CrossRefGoogle Scholar
Ganti, R. K. & Haenggi,M. (2016b), SIR asymptotics in Poisson cellular networks without fading and with partial fading, in ‘IEEE International Conference on Communications (ICC16)’, Kuala Lumpur, Malaysia.Google Scholar
Gentner, D. & Last, G. (2011), ‘Palm pairs and the general mass-transport principle’, Mathematische Zeitschrift 267(3–4), 695–716.CrossRefGoogle Scholar
George, G., Mungara, R. K., Lozano, A., & Haenggi, M. (2017) ‘Ergodic Spectral Efficiency in MIMO Cellular Networks’, IEEE Transactions on Wireless Communications 16(5), 2835–2849.CrossRefGoogle Scholar
Gil-Pelaez, J. (1951), ‘Note on the Inversion Theorem’, Biometrika 38(3/4), 481–482.CrossRefGoogle Scholar
Gradshteyn, I. S. & Ryzhik, I. M. (2007), Table of Integrals, Series, and Products, 7th ed. Boston: Academic Press.Google Scholar
Grimmett, G. & Stirzaker, D. (2001), Probability and Random Processes, Oxford, UK: Oxford University Press.Google Scholar
Guo, A. & Haenggi, M. (2013), ‘Spatial stochastic models and metrics for the structure of base stations in cellular networks’, IEEE Transactions on Wireless Communications 12(11), 5800–5812.CrossRefGoogle Scholar
Guo, A. & Haenggi, M. (2015), ‘Asymptotic deployment gain: A simple approach to characterize the SINR distribution in general cellular networks’, IEEE Transactions on Communications 63(3), 962–976.CrossRefGoogle Scholar
Haenggi, M. (2008), ‘A geometric interpretation of fading in wireless networks: Theory and applications’, IEEE Transactions on Information Theory 54(12), 5500–5510.CrossRefGoogle Scholar
Haenggi, M. (2012), Stochastic Geometry for Wireless Networks, Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Haenggi, M. (2014a), ASAPPP: A simple approximative analysis framework for heterogeneous cellular networks. Keynote presentation at the 2014 Workshop on Heterogeneous and Small Cell Networks (HetSNets'14). Available at www.nd.edu/∼mhaenggi/talks/hetsnets14.pdf.
Haenggi, M. (2014b), ‘The mean interference-to-signal ratio and its key role in cellular and amorphous networks’, IEEE Wireless Communications Letters 3(6), 597–600.CrossRefGoogle Scholar
Haenggi, M. (2016), ‘The meta distribution of the SIR in Poisson Bipolar and cellular networks’, IEEE Transactions on Wireless Communications 15(4), 2577–2589.CrossRefGoogle Scholar
Haenggi, M. (2017), ‘User point processes in cellular networks’, IEEE Wireless Communications Letters 6(2), 258–261.CrossRefGoogle Scholar
Haenggi, M. & Ganti, R. K. (2008), ‘Interference in Large Wireless Networks’, Foundations and Trends in Networking 3(2), 127–248. Available at www.nd.edu/∼mhaenggi/pubs/now.pdf.CrossRefGoogle Scholar
Haenggi, M., Andrews, J. G., Baccelli, F., Dousse, O., & Franceschetti, M. (2009), ‘Stochastic geometry and random graphs for the analysis and design of wireless networks’, IEEE Journal on Selected Areas in Communications 27(7), 1029–1046.Google Scholar
Handa, K. (2009), ‘The two-parameter Poisson-Dirichlet point process’, Bernoulli 15(4), 1082–1116.CrossRefGoogle Scholar
Hirsch, C., Jahnel, B., Keeler, P., & Patterson, R. I. (2016), ‘Large deviation principles for connectable receivers in wireless networks’. Advances in Applied Probability, 48(4), 1061–1094.CrossRefGoogle Scholar
Hirsch, C., Jahnel, B., Keeler, P., & Patterson, R. I. (2017), ‘Large deviations in relay-augmented wireless networks’. Queueing Systems (October), 1–39. https://doi.org/10.1007/s11134-017-9555-9CrossRef
Hough, J. B., Krishnapur, M., Peres, Y., & Virág, B. (2009), Zeros of Gaussian Analytic Functions and Determinantal Point Processes, University Lecture Series 51, Providence, RI: American Mathematical Society.CrossRefGoogle Scholar
Huang, H., Papadias, C. B., & Venkatesan, S. (2012), MIMO Communication for Cellular Networks. New York: Springer.CrossRefGoogle Scholar
Jo, H.-S., Sang, Y. J., Xia, P., & Andrews, J. G. (2011), Outage probability for heterogeneous cellular networks with biased cell association, in ‘Proceedings of 2011 IEEE Global Telecommunications Conference (GLOBECOM 2011)’, IEEE, pp. 1–5.
Kalamkar, S. S. & Haenggi, M. (2017), Spatial outage capacity of Poisson Bipolar networks, in ‘IEEE International Conference on Communications (ICC'17)’, Paris, France.Google Scholar
Karray, M. K. (2010), Spectral and energy efficiencies of OFDMA wireless cellular networks, in ‘Wireless Days (WD), 2010 IFIP’, pp. 1–5.
Keeler, H. P. (2014), ‘Studying the SINR process in Poisson networks by using its factorial moment measures’, MATLAB Central File Exchange. Available at www.mathworks.com.au/matlabcentral/fileexchange/45299-studying-the-sinr-process-in-poisson-networks-byusing- its-factorial-moment-measures.
Keeler, H. P. & Błaszczyszyn, B. (2014), ‘SINR in wireless networks and the two-parameter Poisson-Dirichlet process’, IEEE Wireless Communications Letters 3(5), 525–528.CrossRefGoogle Scholar
Keeler, H. P., Błaszczyszyn, B., & Karray, M. K. (2013), SINR-based k-coverage probability in cellular networks with arbitrary shadowing, in ‘2013 IEEE International Symposium on Information Theory’, Istanbul, July 7–12, 2013, pp. 1167–1171.Google Scholar
Keeler, H. P., Ross, N., & Xia, A. (2016a), ‘When do wireless network signals appear Poisson?’
Keeler, H. P., Ross, N., Xia, A., & Błaszczyszyn, B. (2016b), ‘Stronger wireless signals appear more Poisson’, IEEE Wireless Communications Letters 5(6), 572–575.CrossRefGoogle Scholar
Kingman, J. F. C. (1993), Poisson Processes, Oxford, UK: Oxford University Press.Google Scholar
Kleinrock, L. (1996), ‘Nomadicity: Anytime, anywhere in a disconnected world’, Mobile Networks and Applications 1, 351–357.Google Scholar
Kuo, F. & Sloan, I. (2005), ‘Lifting the curse of dimensionality’, Notices of the AMS 52(11), 1320–1328.Google Scholar
Last, G. & Penrose, M. (2017), Lectures on the Poisson Process, Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Lavancier, F., Møller, J., & Rubak, E. (2015), ‘Determinantal point process models and statistical inference’, Journal of the Royal Statistical Society: Series B (Statistical Methodology) 77(4), 853–877.Google Scholar
Lee, C.-H. & Haenggi, M. (2012), ‘Interference and outage in Poisson cognitive networks’, IEEE Transactions on Wireless Communications 11(4), 1392–1401.CrossRefGoogle Scholar
Lee, C.-H., Shih, C.-Y. & Chen, Y.-S. (2013), ‘Stochastic geometry-based models for modeling cellular networks in urban areas’, Wireless Networks 19(6), 1063–1072.CrossRefGoogle Scholar
Lee, J., Zhang, X., & Baccelli, F. (2016), Shadowing and coverage in Poisson buildings, in ‘IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications’, pp. 1–9.
Li, Y., Baccelli, F., Dhillon, H. S., & Andrews, J. G. (2014), Fitting determinantal point processes to macro base station deployments, in ‘IEEE Global Communications Conference (GLOBECOM), 2014’, pp. 3641–3646.
Li, Y., Baccelli, F., Dhillon, H. S., & Andrews, J. G. (2015), ‘Statistical modeling and probabilistic analysis of cellular networks with determinantal point processes’, IEEE Transactions on Communications 63(9), 3405–3422.CrossRefGoogle Scholar
Li, C., Zhang, J., & Letaief, K. (2014), ‘Throughput and energy efficiency analysis of small cell networks with multi-antenna base stations’, IEEE Transactions on Wireless Communications 13(5), 2505–2517.Google Scholar
Lu, W. & Di Renzo, M. (2015), Stochastic geometry modeling of cellular networks: Analysis, simulation and experimental validation, in ‘Proceedings of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems’, pp. 179–188.CrossRef
MacDonald, V. H. (1979), ‘Advanced mobile phone system: The cellular concept’, Bell System Technical Journal 58(1), 15–41.Google Scholar
Madhusudhanan, P., Restrepo, J. G., Liu, Y., Brown, T. X., & Baker, K. R. (2011), Multi-tier network performance analysis using a shotgun cellular system, in ‘Global Telecommunications Conference (GLOBECOM 2011)’, pp. 1–6.
Madhusudhanan, P., Restrepo, J. G., Liu, Y., Brown, T. X., & Baker, K. R. (2014), ‘Downlink performance analysis for a generalized shotgun cellular system’, IEEE Transactions on Wireless Communications 13(12), 6684–6696.CrossRefGoogle Scholar
Matérn, B. (1986), Spatial Variation, Springer Lecture Notes in Statistics. 2nd ed. New York: Springer-Verlag.CrossRefGoogle Scholar
McCullagh, P. & Møller, J. (2006), ‘The permanental process’, Advances in Applied Probability 38(4), 873–888.CrossRefGoogle Scholar
Miyoshi, N. & Shirai, T. (2014a), ‘A cellular network model with Ginibre configured base stations’, Advances in Applied Probability 46(3), 832–845.CrossRefGoogle Scholar
Miyoshi, N. & Shirai, T. (2014b), Cellular networks with α-Ginibre configurated base stations, in M. Wakayama et al., eds. ‘The Impact of Applications on Mathematics’, New York: Springer, pp. 211–226.Google Scholar
Miyoshi, N. & Shirai, T. (2017), ‘Tail asymptotics of signal-to-interference ratio distribution in spatial cellular network models’, arXiv: https://arxiv.org/abs/1703.05024.
Møller, J. (2003), ‘Shot noise Cox processes’, Advances in Applied Probability 35(3), 614–640.CrossRefGoogle Scholar
Mukherjee, S. (2011), Downlink SINR distribution in a heterogeneous cellular wireless network with max-SINR connectivity, in ‘49th Annual Allerton Conference on Communication, Control, and Computing (Monticello, IL), 2011’, pp. 1649–1656.
Mukherjee, S. (2012), ‘Distribution of downlink SINR in heterogeneous cellular networks’, IEEE Journal on Selected Areas in Communications 30(3), 575–585.CrossRefGoogle Scholar
Mukherjee, S. (2014), Analytical Modeling of Heterogeneous Cellular Networks – Geometry, Coverage, and Capacity, Cambridge: Cambridge University Press.Google Scholar
Mungara, R. K., Morales-Jimenez, D., & Lozano, A. (2015), ‘System-level performance of interference alignment’, IEEE Transactions on Wireless Communications 14(2), 1060–1070.CrossRefGoogle Scholar
Nagamatsu, H., Miyoshi, N., & Shirai, T. (2014), Padé approximation for coverage probability in cellular networks, in ‘WiOpt 2014 : The 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks’, pp. 693–700.
Nakata, I. & Miyoshi, N. (2014), ‘Spatial stochastic models for analysis of heterogeneous cellular networks with repulsively deployed base stations’, Performance Evaluation 78, 7–17.CrossRefGoogle Scholar
National Institute of Standards and Technology. Digital Library of Mathematical Functions (2012). Release 1.0.5 of 2012-10-01. Available at http://dlmf.nist.gov/.
Nigam, G., Minero, P., & Haenggi, M. (2014), ‘Coordinated multipoint joint transmission in heterogeneous networks’, IEEE Transactions on Communications 62(11), 4134–4146.CrossRefGoogle Scholar
Nigam, G., Minero, P., & Haenggi, M. (2015), ‘Spatiotemporal cooperation in heterogeneous cellular networks’, IEEE Journal on Selected Areas in Communications 33(6), 1253–1265.CrossRefGoogle Scholar
Novlan, T., Ng, B. L., Zhang, J. C., Chen, H., & Liu, L. (2015), ‘Application of stochastic geometry in modeling future LTE-A and 5G wireless networks’. Talk at the Simons Conference on Networks and Stochastic Geometry, University of Texas, Austin, TX. Available at www.ma.utexas.edu/conferences/simons2015/Modeling3GPP_Samsung_Final.pdf.Google Scholar
Panchenko, D. (2013), The Sherrington-Kirkpatrick Model. New York: Springer.CrossRefGoogle Scholar
Papoulis, A. (1991), Probability, Random Variables, and Stochastic Processes, 3rd ed. New York: McGraw-Hill.Google Scholar
Pitman, J. & Yor, M. (1997), ‘The two-parameter Poisson–Dirichlet distribution derived from a stable subordinator’, The Annals of Probability 25(2), 855–900.Google Scholar
Rázc, S., Tari, A., & Telek, M. (2006), ‘A moments based distribution bounding method’, Mathematical and Computer Modelling 43(11), 1367–1382.Google Scholar
Rényi, A. (1967), ‘Remarks on the Poisson Process’, Studia Scientiarum Mathematicarum Hungarica 2, 119–123.Google Scholar
Renzo, M. D. & Guan, P. (2014), ‘A mathematical framework to the computation of the error probability of downlink MIMO cellular networks by using stochastic geometry’, IEEE Transactions on Communications 62(8), 2860–2879.CrossRefGoogle Scholar
Riihijärvi, J. & Mähönen, P. (2012), ‘A spatial statistics approach to characterizing and modeling the structure of cognitive wireless networks’, Ad Hoc Networks 10(5), 858–869.CrossRefGoogle Scholar
Ripley, B. D. (1976), ‘The second-order analysis of stationary point processes’, Journal of Applied Probability 13, 255–266.CrossRefGoogle Scholar
Ross, N. & Schuhmacher, D. (2017), ‘Wireless network signals with moderately correlated shadowing still appear Poisson’, IEEE Transactions on Information Theory 63(2), 1177–1198.CrossRefGoogle Scholar
Salehi, M., Mohammadi, A., & Haenggi, M. (2017), ‘Analysis of D2D underlaid cellular networks: SIR meta distribution and mean local delay’, IEEE Transactions on Communications 65(7), 2904–2916.CrossRefGoogle Scholar
Schilcher, U., Toumpis, S., Haenggi, M., Crismani, A., Brandner, G., & Bettstetter, C. (2016), ‘Interference functionals in Poisson networks’, IEEE Transactions on Information Theory 62(1), 370–383.CrossRefGoogle Scholar
Sesia, S., Toufik, I., & Baker, M., eds (2011), LTE, The UMTS Long Term Evolution: From Theory to Practice, 2nd ed. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
Shirai, T. & Takahashi, Y. (2003), ‘Random point fields associated with certain Fredholm determinants I: Fermion, Poisson and Boson point processes’, Journal of Functional Analysis 205(2), 414–463.CrossRefGoogle Scholar
Suryaprakash, V., Møller, J., & Fettweis, G. (2015), ‘On the modeling and analysis of heterogeneous radio access networks using a Poisson cluster process’, IEEE Transactions on Wireless Communications 14(2), 1035–1047.CrossRefGoogle Scholar
Tanbourgi, R., Dhillon, H. S., & Jondral, F. K. (2015), ‘Analysis of joint transmit–receive diversity in downlink MIMO heterogeneous cellular networks’, IEEE Transactions on Wireless Communications 14(12), 6695–6709.CrossRefGoogle Scholar
Temme, N. M. (2003), ‘Large parameter cases of the Gauss hypergeometric function’, Computational and Applied Mathematics 153, 441–462.Google Scholar
Torrisi, G. L. & Leonardi, E. (2014), ‘Large deviations of the interference in the Ginibre network model’, Stochastic Systems 4(1), 173–205.CrossRefGoogle Scholar
Vu, T.-T., Decreusefond, L., & Martins, P. (2014), ‘An analytical model for evaluating outage and handover probability of cellular wireless networks’, Wireless Personal Communications 74(4), 1117–1127.CrossRefGoogle Scholar
Wang, Y., Cui, Q., Haenggi, M., & Tan, Z. (2017), ‘On the SIR meta distribution for Poisson networks with interference cancellation’, IEEE Wireless Communications Letters. to appear.
Wang, Y., Haenggi, M., & Tan, Z. (2017), The meta distribution of the SIR for cellular networks with power control. ArXiv, http://arxiv.org/abs/1702.01864v1.
Wei, H., Deng, N., Zhou, W., & Haenggi, M. (2016), ‘Approximate SIR analysis in general heterogeneous cellular networks’, IEEE Transactions on Communications 64(3), 1259–1273.CrossRefGoogle Scholar
Yazdanshenasan, Z., Dhillon, H. S., Afshang, M., & Chong, P. H. J. (2016), ‘Poisson hole process: Theory and applications to wireless networks’, IEEE Transactions on Wireless Communications 15(11), 7531–754.CrossRefGoogle Scholar
Yu, B., Yang, L., Ishii, H., & Mukherjee, S. (2015), ‘Dynamic TDD support in macrocell-assisted small cell architecture’, IEEE Journal on Selected Areas in Communications 33(6), 1201–1213.CrossRefGoogle Scholar
Zhang, X. & Andrews, J. G. (2015), ‘Downlink cellular network analysis with multi-slope path loss models’, IEEE Transactions on Communications 63(5), 1881–1894.CrossRefGoogle Scholar
Zhang, X. & Haenggi, M. (2013), On decoding the kth strongest user in Poisson networks with arbitrary fading distribution, in ‘47th Asilomar Conference of Signals, Systems and Computers (Asilomar'13)’, Pacific Grove, CA.Google Scholar
Zhang, X. & Haenggi, M. (2014), ‘A stochastic geometry analysis of inter-cell interference coordination and intra-cell diversity’, IEEE Transactions on Wireless Communications 13(12), 6655–6669.CrossRefGoogle Scholar
Zhang, X., Baccelli, F., & Heath, R. W. (2015), An indoor correlated shadowing model, in ‘2015 IEEE Global Communications Conference (GLOBECOM)’, pp. 1–7.
Zorzi, M. & Pupolin, S. (1995), ‘Optimum transmission ranges in multihop packet radio networks in the presence of fading’, IEEE Transactions on Communications 43(7), 2201–2205.CrossRefGoogle Scholar

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