Wireless Communication in Dynamic Interference

Abstract

Abstract-Fast varying active transmitter sets are a key feature of wireless communication networks with very short transmissions arising in machine-to-machine communications. A consequence is that the interference is dynamic, leading to nonGaussian statistics. In this paper, we study the behavior of largescale communication networks in the presence of isotropic α-stable interference, which forms a model for dynamic interference. We first characterize the achievable rate of each link by considering a non-Gaussian input distribution, which is shown to outperform a Gaussian input. Moreover, we analyze the area spectral efficiency, which is the total rate per square meter. Our analysis suggests that analogously to the common model of slowly varying active transmitter sets, dense networks maximize the area spectral efficiency.

Copula-Based Interference Models for IoT Wireless Networks

“…One feature observed in IoT networks is the presence of impulsive interference, where high amplitude interference is significantly more likely than in Gaussian models. This behavior has been observed both in experimental studies [4] and also in theoretical analysis [5], [6]. As a consequence, Gaussian models are often not appropriate and the interference statistics lie in a more general class of models.…”

“…Nevertheless, obtaining the exact copula is a challenging problem, both in its derivation and also for efficient parameter estimation and simulation. As such, we seek a copula that captures the joint dependence in the interference random vector Z in (6). This copula is required to: 1) well-approximate the true joint distribution of the interference random vector Z; 2) and have a closed-form representation or be computationally feasible to numerically evaluate.…”

On Capacity Sensitivity in Additive Vector Symmetric α-Stable Noise Channels

“…In order to study approximations of one channel by another, it is natural to introduce the capacity sensitivity [13], [21], [22]. In particular, the capacity sensitivity is the capacity gap between two channels, and is defined formally as follows.…”

“…for ∆ > 0. The first studies of general constraint perturbations for the capacity optimization problem were performed in [13], [21]. However, the analysis was limited to the case of scalar noise channels.…”

“…Since the identities in (21), (23), (24) and (25) hold for all δ > 0, weak continuity of I(X; Y) follows by taking δ → 0 (and hence R(δ) → ∞). The existence part of Theorem 1 then holds by applying the extreme value theorem.…”