EVALUACIÓN DEL DESEMPEÑO DE SISTEMAS DE RADIO COGNITIVO CON DIFERENTES DISTRIBUCIONES DEL TIEMPO DE SERVICIO DE LOS USUARIOS SECUNDARIOS (EVALUATION OF THE PERFORMANCE OF COGNITIVE RADIO SYSTEMS WITH DIFFERENT DISTRIBUTIONS OF THE SERVICE TIME OF SECONDARY USERS)

Este trabajo estudia el desempeño de sistemas de radio cognitivo con diferentes distribuciones de probabilidad del tiempo de servicio de los usuarios secundarios. Para ello, se desarrolló un simulador de eventos discretos del sistema de radio cognitivo. En particular, se considera que el tiempo de servicio de los usuarios secundarios sigue una distribución de probabilidad log-normal y ésta es aproximada mediante distribuciones de probabilidad hiper-exponenciales de diferente orden. Para el cálculo de los parámetros de las distribuciones hiper-exponenciales se utiliza el algoritmo de Maximización de la Esperanza (EM). Los resultados obtenidos muestran que, mediante la distribución hiper-exponencial se pueden aproximar diferentes distribuciones de probabilidad como la log-normal sin pérdida significativa en la precisión de los resultados numéricos de las diferentes métricas de desempeño. Este resultado es relevante porque facilita el tratamiento y análisis matemático de sistemas de radio cognitivo.In this paper, performance evaluation of cognitive radio networks (CRNs) with different probability density functions for the service time of secondary users is studied. To this end, a discrete event simulation program that captures the fundamental aspects of CRNs is developed. In particular, it is assumed that the secondary service time of many real life applications is well characterized by the log-normal distribution. In this work, the log-normal model is systematically approximated by n-th order hyper-exponential distributions. The parameters of the n-th order hyper-exponential distribution are computed by the well-known Expectation Maximization (EM) algorithm. Numerical results show that, the hyper-exponential distribution can be used for approximating the log-normal behaviour of the secondary service time without significative loss of precision on the obtained results for the different performance metrics. This result is relevant because the mathematical (queueing) analysis of CRN with log-normal service time is possible by means of approximating the log-normal behavior of the service time by the hyper-exponential model

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Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

Deep Learning in Mobile and Wireless Networking: A Survey

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts