This brief offers a valuable resource on principles of quality-of-service (QoS) provisioning and the related link-layer resource management techniques for high data-rate wireless networks. The primary emphasis is on protocol modeling and analysis. It introduces media access control (MAC) protocols, standards of wireless local area networks (WLANs), wireless personal area networks (WPANs), and wireless body area networks (WBANs), discussing their key technologies, applications, and deployment scenarios. The main analytical approaches and models for performance analysis of the fundamental resource scheduling mechanisms, including the contention-based, reservation-based, and hybrid MAC, are presented.
To help readers understand and evaluate system performance, the brief contains a range of simulation results. In addition, a thorough bibliography provides an additional tool. This brief is an essential resource for engineers, researchers, students, and users of wireless networks.
Описание: This book presents cutting-edge research contributions that address various aspects of network design, optimization, implementation, and application of cognitive radio technologies. It demonstrates how to make better utilization of the available spectrum, cognitive radios and spectrum access to achieve effective spectrum sharing between licensed and unlicensed users. The book provides academics and researchers essential information on current developments and future trends in cognitive radios for possible integration with the upcoming 5G networks. In addition, it includes a brief introduction to cognitive radio networks for newcomers to the field.
Автор: Wang Название: QoE Management in Wireless Networks ISBN: 3319424521 ISBN-13(EAN): 9783319424521 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This SpringerBrief presents research results on QoE management schemes for mobile services, including user services, and resource allocation. Along with a review of the research literature, it offers a data-driven architecture for personalized QoE management in wireless networks. The primary focus is on introducing efficient personalized character extraction mechanisms, e.g., context-aware Bayesian graph model, and cooperative QoE management mechanisms. Moreover, in order to demonstrate in the effectiveness of the QoE model, a QoE measurement platform is described and its collected data examined. The brief concludes with a discussion of future research directions.
The example mechanisms and the data-driven architecture provide useful insights into the designs of QoE management, and motivate a new line of thinking for users' satisfaction in future wireless networks.
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