Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Radio Resource Allocation Over Fading Channels Under Statistical Delay ConstraintsIntroduction

Radio Resource Allocation Over Fading Channels Under Statistical Delay Constraints: Introduction [The past decade has seen the tremendous growth of wireless communications with the increasing demand for various emerging applications such as video transmissions, mobile entertainment, mobile healthcare etc., which require higher data rate and/or more stringent delay quality-of-service (QoS). Consequently, in the development of next-generation wireless systems, it is a crucial task to provide wireless connections with better QoS such as higher data rate, smaller delay etc. [1, 2]. However, such task is not easy due to many inherent challenges. One challenge is the fact that wireless signal strength randomly fluctuates over time due to varying fading [3]. There are large-scale fading effects, where the received signal strength changes over distance because of the path loss and shadowing, and small-scale fading effects, where the received signal strength changes because of the constructive and destructive interference of multiple reflecting and refracting signal paths. In addition, the available radio resources are limited. Hence, efficient (radio) resource allocation is crucial to combat the fading effects of wireless channels, and providing satisfactory QoS to the users [4].] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Radio Resource Allocation Over Fading Channels Under Statistical Delay ConstraintsIntroduction

Download PDF
8 pages

Loading next page...
 
/lp/springer-journals/radio-resource-allocation-over-fading-channels-under-statistical-delay-Wky8wVFDTx
Publisher
Springer International Publishing
Copyright
© Springer International Publishing AG 2017
ISBN
978-3-319-57692-3
Pages
1 –9
DOI
10.1007/978-3-319-57693-0_1
Publisher site
See Chapter on Publisher Site

Abstract

[The past decade has seen the tremendous growth of wireless communications with the increasing demand for various emerging applications such as video transmissions, mobile entertainment, mobile healthcare etc., which require higher data rate and/or more stringent delay quality-of-service (QoS). Consequently, in the development of next-generation wireless systems, it is a crucial task to provide wireless connections with better QoS such as higher data rate, smaller delay etc. [1, 2]. However, such task is not easy due to many inherent challenges. One challenge is the fact that wireless signal strength randomly fluctuates over time due to varying fading [3]. There are large-scale fading effects, where the received signal strength changes over distance because of the path loss and shadowing, and small-scale fading effects, where the received signal strength changes because of the constructive and destructive interference of multiple reflecting and refracting signal paths. In addition, the available radio resources are limited. Hence, efficient (radio) resource allocation is crucial to combat the fading effects of wireless channels, and providing satisfactory QoS to the users [4].]

Published: May 12, 2017

Keywords: Fading Channel; Power Allocation; Energy Harvesting; Markov Decision Process; Delay Constraint

There are no references for this article.