Research Article
Modeling of Time and Frequency Random Access Network and Throughput Capacity Analysis
@ARTICLE{10.4108/eai.31-5-2017.152555, author={Vincent Savaux and Apostolos Kountouris and Yves Louet and Christophe Moy}, title={Modeling of Time and Frequency Random Access Network and Throughput Capacity Analysis}, journal={EAI Endorsed Transactions on Cognitive Communications}, volume={3}, number={11}, publisher={EAI}, journal_a={COGCOM}, year={2017}, month={5}, keywords={Throughput capacity, Network interference, Occupancy problem, Random access, Cognitive Radio, Sub- Nyquist sampling}, doi={10.4108/eai.31-5-2017.152555} }- Vincent Savaux
Apostolos Kountouris
Yves Louet
Christophe Moy
Year: 2017
Modeling of Time and Frequency Random Access Network and Throughput Capacity Analysis
COGCOM
EAI
DOI: 10.4108/eai.31-5-2017.152555
Abstract
In this paper, we model the random multi-user multi-channel access network by using the occupancy problem from probability theory, and we combine this with a network interference model in order to derive the achievable throughput capacity of such networks. Furthermore, we compare the randommulti-channel access with a cognitive radio systemin which the users are able to minimize the channels occupancy. Besides, we show that the sampling rate can be reduced under the Nyquist rate if the use of the spectrum resource is known at the gateway side. This scenario is referred as "cognitive radio" context. The mathematical developments and results are illustrated through various simulations results. The proposed model is particularly relevant in analyzing the performance of networks where the users are not synchronized neither in time nor in frequency as it is often the case in various Internet of Things (IoT) applications.
Copyright © 2017 Vincent Savaux et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
