Interference Modeling for Cellular Networks Under Beamforming Transmission

Abstract

We propose analytical models for the interference power distribution in a cellular system employing MIMO beamforming in rich and limited scattering environments, which capture non line-of-sight signal propagation in the microwave and mmWave bands, respectively. Two candidate models are considered:the Inverse Gaussian and the Inverse Weibull, both are two-parameter heavy tail distributions. We further propose a mixture of these two distributions as a model with three parameters. To estimate the parameters of these distributions, three approaches are used: moment matching, individual distribution maximum likelihood estimation (MLE), and mixture distribution MLE with a designed expectation maximization algorithm. We then introduce simple fitted functions for the mixture model parameters as polynomials of the channel path loss exponent and shadowing variance. To measure the goodness of these models, the information-theoretic metric relative entropy is used to capture the distance from the model distribution

Interference Minimization in 5G Physical-Layer Network Slicing

“…We consider a statistical approach to model the inter-slice interference power generated by the various beams that are transmitted by each sector. Specifically, as proposed in other works [18] [19][20], we assume that the main lobe of each beam of beamwidth θ t ∈ [0, 2π/3] provides a constant transmission gain g t = Q and it is directed towards the intended user, whereas side lobes are approximated with an overall transmission gain of g t = q with q < Q and are pointed in other directions. Similarly, we model each user with steerable directional antennas that have constant receiving gain g r = Q for beamwidth θ r ∈ [0, 2π] and g r = q otherwise.…”

Entropy Estimation of Inverse Weibull Distribution under Adaptive Type-II Progressive Hybrid Censoring Schemes

“…In point estimation, following the method for computation using descriptions ofβ andλ as in Equations (13) and (14), we derive the estimates under maximum likelihood estimations method and Bayes estimation methods against squared error loss function, Linex loss function, and general entropy loss function. According to discussions in Section 3, balanced loss function-based estimations against three loss functions above are also calculated through Lindley's approximation.…”

“…Regardless of various pre-exponential factors and activation energies, inverse Weibull distributions perform better in reaction sequences description. Reference [13] preferred the hybrid model based on inverse Weibull distribution to analyze the capacity of a cellular network in a cellular system employing multi-input multi-output beam-forming in rich and limited scattering environments. Such wide applications of inverse Weibull distribution are attributed to its flexible scale and shape parameters.…”

Interference Power Characterization in Directional Networks and Full-Duplex Systems

“…The authors in [8] have also considered the effect of non-binary object blockage in the aggregate interference, by assuming that a single obstacle can cause a partial blockage. The modeling of interference in cellular MIMO beamforming mmWave communications was also tackled in [9], by considering two models (inverse Gaussian and the inverse Weibull) and a mixture of them. On the other hand, the statistical characterization of residual SI has received limited attention due to the difficulty of the mathematical modeling process.…”