Analytical Modeling of

Transcription

1 Analytical Modeling of Heterogeneous Cellular Networks Geometry, Coverage, and Capacity SAYANDEV MUKHERJEE DOCOMO Innovations Inc., Palo Alto, CA Cambridge UNIVERSITY PRESS

2 Preface Acknowledgements List of notation List of acronyms and abbreviations xvi page ix xiii xv 1 Introduction Wireless-channel model Path-loss model Fading model Distribution of the SINR at an arbitrary user Why SINR distributions are usually found via simulation The role of analytic modeling 7 2 Structure of the SINR calculation problem Statement of the SINR calculation problem Candidate serving BSs and the serving BS Basic definitions SINR distributions Joint CCDF of SINRs from candidate serving BSs Joint CCDF of SINRs from BSs ordered by serving BS selection criterion Conventions and notation The canonical SINR probability Form of joint CCDF of SINRs from candidate serving BSs Form of joint CCDF of SINRs from BSs ordered by serving BS selection criterion Joint CCDF of SINR in canonical probability form Calculation of the canonical probability Z-matrices and M-matrices Expressions for P{AX> b) Expressions for the canonical probability PjAX> Wb) Approximating arbitrary PDFs by mixtures of Erlang PDFs 19

3 vi 2.5 Full solution to the canonical probability problem Determining when a Z-matrix is an M-matrix Analytic form of Laplace transform of W 21 3 Poisson point processes Stochastic models for BS locations Complete spatial randomness The Poisson point process Theorems about PPPs Mapping theorem Superposition theorem Coloring theorem Marking theorem Applicability ofppp to real-world deployments Other models for BS locations 34 4 SINR analysis for a single tier with fixed power Introduction Distribution of total interference power in a single-tier BS deployment PPP of received powers at user from BSs in a tier Distribution of total received power from all BSs in a tier Distribution of SINR in a single-tier BS deployment Serving BS known and fixed A note on serving BS selection criteria Serving BS is the one nearest to the user Serving BS is the one received most strongly at the user 55 5 SINR analysis for multiple tiers with fixed powers Introduction Joint CCDF of SINR from candidate serving BSs Candidate serving BS in each tier is the one nearest to the user Application: camping probability in a macro-femto network Candidate serving BS in each tier is the one received most strongly at the user Application: coverage probability in an HCN Distributions of serving tier and SINR from serving BS Serving BS is the "nearest" (after selection bias) candidate serving BS Serving BS is the "strongest" (after selection bias) candidate serving BS Serving BS is the max-sinr (after selection bias) candidate serving BS Selection bias and the need for interference control 119

4 vii 6 SINR analysis with power control Introduction Power control from the transmitter perspective Types of power control Distribution of SINR under power control Distribution of received power with i.i.d. BS transmit powers SINR distribution with non-adaptive power control Application: elcic and felcic in LTE Interference power at the receiver of a given link under OLPC Distribution of distance from BS to served user CCDF of SINR when all BSs use OLPC SINR distribution under CLPC Spectral and energy efficiency analysis Introduction Spectral efficiency Spectral efficiency on the link to an arbitrarily located user Spectral efficiency of an HCN Application: spectral efficiency of a macro-pico LTE HCN with elcic Energy efficiency Closing thoughts: future heterogeneous networks Introduction Analysis of a network with D2D links The role of WiFi in future HCNs Evolution of the network infrastructure New directions in analysis 157 Appendix A Some common probability distributions 159 A.l Discrete distributions 159 A. 1.1 Uniform distribution 159 A. 1.2 Bernoulli distribution 159 A. 1.3 Binomial distribution 159 A. 1.4 Poisson distribution 160 A. 1.5 Negative binomial distribution 160 A. 1.6 Generalized negative binomial distribution 160 A.2 Continuous distributions 160 A.2.1 Uniform distribution 160 A.2.2 Normal or Gaussian distribution 161 A.2.3 Circularly symmetric complex Gaussian distribution 161 A.2.4 Rayleigh distribution 161 A.2.5 Exponential distribution 161

5 viii A.2.6 Erlang distribution 162 A.2.7 Gamma distribution 162 A.2.8 Nakagami distribution 162 A.2.9 Lognormal distribution 162 Appendix B HCNsinLTE 163 B.l 3GPP and LTE 163 B.2 Support for HCNs in LTE 163 References 165 Author index 170 Subject index 171