Capacity Bounds for Additive Symmetric $\alpha $ -Stable Noise Channels

Power Control in Parallel Symmetric $\alpha$ -Stable Noise Channels

“…We show that Gaussian inputs perform comparably or outperform truncated symmetric α-stable inputs, and also nearly achieve a numerical approximation of the capacity obtained via the Blahut-Arimoto algorithm [7,8]. This is despite the fact that the truncated symmetric αstable inputs approximately match the input with the noise distribution, and are known to be a good choice with fractional moment constraints [4].…”

“…In [3], the capacity of the Cauchy noise channel (α = 1) was derived subject to a logarithmic constraint. More generally, capacity bounds for α > 1 subject to absolute moments constraints were obtained in [4]. The extension to the complex isotropic α-stable channel was also studied in [5] and vector α-stable channels in [6].…”

CupCarbon: A new platform for the design, simulation and 2D/3D visualization of radio propagation and interferences in IoT networks

“…To avoid too heavy calculations, it is possible to randomly generate the interference. However, as shown in many papers (see [6], [7] for instance) a Gaussian model is not accurate. One solution is to accurately evaluate the main contributions of the interference and to randomly generate the global contribution of the less impacting interferers.…”

Wireless Communication in Dynamic Interference

“…The α-stable random variables are an important class of random variables with heavy-tailed probability density functions, which have been widely used to model impulsive signals [13]. The probability density function of an α-stable random variable is parameterized by four parameters: the exponent 0 < α ≤ 2; the scale parameter γ ∈ R + ; the skew parameter β ∈ [−1, 1]; and the shift parameter δ ∈ R. As such, a common notation for an α-stable distributed random variable is X ∼ S α (γ, β, δ).…”