Abstract
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
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
Wildemeersch, M., & Wildemeersch, M. (2013). Statistical modeling and analysis of interference in wireless networks. Enschede: Centre for Telematics and Information Technology (CTIT). https://doi.org/10.3990/1.9789036535724