Paper Info
Title
On the ergodic capacity of legacy systems in the presence of next generation interference
Abstract
Next generation wireless systems facilitating better utilization of the scarce radio spectrum have emerged as a response to inefficient rigid spectrum assignment policies. These are comprised of intelligent radio nodes that opportunistically operate in the radio spectrum of existing legacy systems; yet unwanted interference at the legacy receivers is unavoidable. In order to design efficient next generation systems and to minimize their harmful consequences, it is necessary to realize their impact on the performance of legacy systems. In this work, a generalized framework for the ergodic capacity analysis of such legacy systems in the presence of interference from next generation systems is presented. The analysis is built around a model developed for the statistical representation of the interference at the legacy receivers, which is then used to evaluate the ergodic capacity of the legacy system. Moreover, this analysis is not limited to the context of legacy systems, and is in fact applicaple to any interference limited system. Findings of analytical performance analyses are confirmed through selected computer-based Monte-Carlo simulations.
Year
DOI
Venue
2011
10.1109/ISWCS.2011.6125402
Wireless Communication Systems
Keywords
Field
DocType
Monte Carlo methods,next generation networks,radio networks,radio receivers,radiofrequency interference,statistical analysis,computer-based Monte-Carlo simulations,ergodic capacity analysis,intelligent radio nodes,interference limited system,interference statistical representation,legacy receivers,next generation interference,next generation wireless systems,scarce radio spectrum,spectrum assignment policies,Next generation networks,cognitive radio network,ergodic capacity,interference limited system
Next-generation network,Wireless systems,Computer science,Ergodic theory,Computer network,Interference (wave propagation),Legacy system,Radio spectrum,Cognitive radio,Statistical analysis
Conference
ISSN
ISBN
Citations 
2154-0217 E-ISBN : 978-1-61284-401-5
978-1-61284-401-5
2
PageRank 
References 
Authors
0.38
9
4
Name
Order
Citations
PageRank
Nurul H. Mahmood16413.25
Ferkan Yilmaz256236.54
Alouini Mohamed-Slim3122611194.14
Geir E. Øien438143.38