The ergodic capacity of fading channels modeled with aκ-μshadowed distribution is investigated to derive closed-form expressions. Theκ-μshadowed distribution is of particular interest because it contains, as special cases, other classical ones like one-side Gaussian, Rayleigh, Rician, Nakagami-m,κ-μ, and Rician shadowed distributions. The paper discusses the physical meaning of the distribution parameter variations and also their impact on the channel capacity. These results can be used to study the behavior of different channels like the ones in underwater acoustic communications, land mobile satellite systems, body centric communications, and other wireless communication applications. The analytical closed-form expression results are validated with numerical simulations.
Underwater acoustic sensor networks are a promising technology that allow real-time data collection in seas and oceans for a wide variety of applications. Smaller size and weight sensors can be achieved with working frequencies shifted from audio to the ultrasonic band. At these frequencies, the fading phenomena has a significant presence in the channel behavior, and the design of a reliable communication link between the network sensors will require a precise characterization of it. Fading in underwater channels has been previously measured and modeled in the audio band. However, there have been few attempts to study it at ultrasonic frequencies. In this paper, a campaign of measurements of ultrasonic underwater acoustic channels in Mediterranean shallow waters conducted by the authors is presented. These measurements are used to determine the parameters of the so-called κ-μ shadowed distribution, a fading model with a direct connection to the underlying physical mechanisms. The model is then used to evaluate the capacity of the measured channels with a closed-form expression.
The recently proposed Fluctuating Two-Ray (FTR) model is gaining momentum as a reference fading model in scenarios where two dominant specular waves are present. Despite the numerous research works devoted to the performance analysis under FTR fading, little attention has been paid to effectively understanding the interplay between the fading model parameters and the fading severity. According to a new scale defined in this work, which measures the hyper-Rayleigh character of a fading channel in terms of the Amount of Fading, the outage probability and the average capacity, we see that the FTR fading model exhibits a full hyper-Rayleigh behavior. However, the Two-Wave with Diffuse Power fading model from which the former is derived has only strong hyper-Rayleigh behavior, which constitutes an interesting new insight. We also identify that the random fluctuations in the dominant specular waves are ultimately responsible for the full hyper-Rayleigh behavior of this class of fading channels.
In this paper we investigate the impact of lineof-sight (LoS) condition in the ergodic spectral efficiency of cellular networks. To achieve this goal, we have considered the κ-µ shadowed model, which is a general model that provides an excellent fit to a wide set of propagation conditions. To overcome the mathematical complexity of the analysis, we have split the analysis between large and small-scale effects. Building on the proposed framework, we study a number of scenarios that range from heavily-fluctuating LoS to deterministic-LoS. Finally, we shed light on the interplay between fading severity and spectral efficiency by means of the amount of fading.Index Terms-Wireless communications, average spectral efficiency, κ-µ shadowed fading model, amount of fading
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.