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A Machine Learning Approach to Predicting Coverage in Random Wireless Networks

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Fig. 1: (a) A PPP realization of base stations with λBS/A HCPP realization of base stations extracted from a PPP with = 4.4∗10−6 BS/m2 , (b) λ = 4.4∗10−6m2 when Rc = 200 m, (c) 256 BS positions following a hexagonalpattern.
Fig. 2: (a) Coverage probability for correlated and uncorrelated shadowing and PPP base stations locations, with NBAS, σcoverage probability for2 = −100 dB, P = 1 mw,λ = 1.2 ∗ 10−6 BS per m2, m = 2, dcorr = 150 m, σs = 50 (b) Coverage probability for correlated and uncorrelated shadowing and PPP base stationslocations, with NBAS, σ2 = 0 mW, P = 1 mw, λ = 1.2 ∗ 10−6 BS per m2, m = 2, dcorr = 150 m, σs = 50 (c) Coverage probability for correlatedshadowing and HCPP base stations locations, with NBAS, σ2 = −100 dB, P = 1 mw, λ = 1.2 ∗ 10−6 BS per m2, Rc = 200 m, m = 2, dcorr = 150m, σs = 50(d) Coverage probability for correlated and uncorrelated shadowing and HCPP base stations locations, with NBAS, σ2 = 0 mW, P = 1 mW,λ = 1.2 ∗ 10−6 BS per m2, Rc = 200 m, m = 2, dcorr = 150 m, σs = 50 (e) Coverage probability for correlated shadowing and PPP base stationslocations, with different BS-user associations scheme, σ2 = −100 dB, P = 1 mw, λ = 1.2 ∗ 10−6 BS per m2, m = 2, dcorr = 150 m, σs = 50 ,(f) 256 BS and correlated shadowing, with NBAS, σ2 = −100 dB, P = 1 mW, m = 2, dcorr = 150 m, σs = 50.
Fig. 3: Neural networks plots for β2 estimation.
TABLE II: Simulation settings

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