Abstract
Original language  Undefined 

Awarding Institution 

Supervisors/Advisors 

Thesis sponsors  
Award date  14 Nov 2013 
Place of Publication  Enschede 
Publisher  
Print ISBNs  9789036535724 
DOIs  
Publication status  Published  14 Nov 2013 
Keywords
 Wireless networks
 METIS302431
 IR89379
 statistical modeling
 EWI24458
Cite this
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Statistical modeling and analysis of interference in wireless networks. / Wildemeersch, Matthias; Wildemeersch, Matthias.
Enschede : Centre for Telematics and Information Technology (CTIT), 2013. 135 p.Research output: Thesis › PhD Thesis  Research external, graduation UT › Academic
TY  THES
T1  Statistical modeling and analysis of interference in wireless networks
AU  Wildemeersch, Matthias
AU  Wildemeersch, Matthias
PY  2013/11/14
Y1  2013/11/14
N2  In current wireless networks, interference is the main performancelimiting factor. The quality of a wireless link depends on the signal and interference power, which is strongly related to the spatial distribution of the concurrently transmitting network nodes, shortly denominated as the network geometry. Motivated by the ongoing revision of wireless network design, this dissertation aims to describe the relation between geometry and network performance. Given the exponential growth of wireless devices, it is meaningful to evaluate how network interference affects signal detection. We propose a unified statistical approach based on the characteristic function of the decision variable to describe the detection performance, accounting for single and multiple interference, as well as different detection schemes and architectures. The proposed framework is able to capture the deployment density of the interferers, transmission power, and fading distribution of the interferers and the signal of interest. In addition, we establish a fundamental limit of the interferer node density beyond which robust energy detection is impossible. This work highlights the crucial role of spatial statistics in the evaluation of signal detection. The capacity gain obtained through the densification of the network architecture comes at the expense of an increase in energy consumption. Although small cell access points consume little energy in comparison with the macrocell base stations, the massive deployment of these additional small cell base stations entails a significant increase in energy consumption. We extend the capacity analysis of small cell networks to include the energy consumption of the small cell tier. Considering a distributed sleep mode strategy for the small cell access points, we cast the tradeoff between energy consumption and capacity as a set of optimization problems. We develop an analytical framework, which can be used in practice to correctly set sensing time and sensing probability whilst guaranteeing user quality of service. Furthermore, the analytical tool accounts for the network load and predicts the achievable energy reduction of the small cell tier by means of distributed sleep mode strategies as a function of the user density. Finally, given that current networks are interferencelimited, we study i how signal processing can improve the signal quality. We present a probabilistic framework to describe the performance gain of successive interference cancellation and show that the benefit is modest when users connect to the base station that provides the highest average signaltointerference ratio. We extend the analysis to include novel ways to associate users to their access points and demonstrate that the benefits of successive interference cancellation are substantial for these operational scenarios. By systematically incorporating the spatial statistics in the performance analysis, this dissertation presents a methodology and analytical toolset useful to enhance the understanding of the design, operation, and evaluation of future wireless networks. ii
AB  In current wireless networks, interference is the main performancelimiting factor. The quality of a wireless link depends on the signal and interference power, which is strongly related to the spatial distribution of the concurrently transmitting network nodes, shortly denominated as the network geometry. Motivated by the ongoing revision of wireless network design, this dissertation aims to describe the relation between geometry and network performance. Given the exponential growth of wireless devices, it is meaningful to evaluate how network interference affects signal detection. We propose a unified statistical approach based on the characteristic function of the decision variable to describe the detection performance, accounting for single and multiple interference, as well as different detection schemes and architectures. The proposed framework is able to capture the deployment density of the interferers, transmission power, and fading distribution of the interferers and the signal of interest. In addition, we establish a fundamental limit of the interferer node density beyond which robust energy detection is impossible. This work highlights the crucial role of spatial statistics in the evaluation of signal detection. The capacity gain obtained through the densification of the network architecture comes at the expense of an increase in energy consumption. Although small cell access points consume little energy in comparison with the macrocell base stations, the massive deployment of these additional small cell base stations entails a significant increase in energy consumption. We extend the capacity analysis of small cell networks to include the energy consumption of the small cell tier. Considering a distributed sleep mode strategy for the small cell access points, we cast the tradeoff between energy consumption and capacity as a set of optimization problems. We develop an analytical framework, which can be used in practice to correctly set sensing time and sensing probability whilst guaranteeing user quality of service. Furthermore, the analytical tool accounts for the network load and predicts the achievable energy reduction of the small cell tier by means of distributed sleep mode strategies as a function of the user density. Finally, given that current networks are interferencelimited, we study i how signal processing can improve the signal quality. We present a probabilistic framework to describe the performance gain of successive interference cancellation and show that the benefit is modest when users connect to the base station that provides the highest average signaltointerference ratio. We extend the analysis to include novel ways to associate users to their access points and demonstrate that the benefits of successive interference cancellation are substantial for these operational scenarios. By systematically incorporating the spatial statistics in the performance analysis, this dissertation presents a methodology and analytical toolset useful to enhance the understanding of the design, operation, and evaluation of future wireless networks. ii
KW  Wireless networks
KW  METIS302431
KW  IR89379
KW  statistical modeling
KW  EWI24458
U2  10.3990/1.9789036535724
DO  10.3990/1.9789036535724
M3  PhD Thesis  Research external, graduation UT
SN  9789036535724
PB  Centre for Telematics and Information Technology (CTIT)
CY  Enschede
ER 