textThe deployment of broadband wireless data networks, e.g., wireless local area
networks (WLANs) [29], experienced tremendous growth in the last several
years, and this trend is continuously gaining momentum. In fact, WLAN is
becoming an indispensable component of the modern telecommunication infrastructure.
Despite this optimistic outlook, however, little is known about
the impact of the wireless channel on the characteristics of WLAN traffic.
This dissertation characterizes the correlation structures of WLAN channel
with traffic statistics from a cross-layer point of view, and provides new measurement
methodologies and statistical models for WLAN networks.
Currently WLAN standards are designed within the paradigm of the
layered network architecture. For example, the architecture of IEEE 802.11
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is almost identical to the Ethernet. However, wireless networks are fundamentally
different from their wired peers due to the shift of transmission media
from cables to over-the-air radio waves. This transition exposes wireless
systems to the influence of radio propagation, and more importantly, to the
temporal and spacial fluctuations of the radio channel that can actually be
propagated up to upper layers. However, the current WLAN architecture isolates
network layers, and largely ignores this impact. Therefore, we believe
that a cross-layer based approach is necessary to understand and reflect this
underlying impact of the channel to the upper layers of the network, especially
in relation to WLAN traffic behavior.
Measurement is one of the fundamental tools used to quantify radio
propagation. As part of this dissertation, a complete framework for a measurement
methodology, including hardware, software, and measurement procedures,
is established. Characteristics of the propagation channel are estimated
from measurement data, and the channel knowledge is applied to the upper
layers for more realistic and accurate modeling.
In WLAN environments, knowledge of the traffic characteristics is essential
for proper network provisioning, and for improving the performance
of the IEEE 802.11 standard and network devices, e.g., to design improved
MAC schemes, or to build better buffer scheduling algorithms with channel
knowledge, etc. Built upon extensive WLAN traffic traces, this dissertation
work presents cross-layer models for WLAN throughput predictions, traffic
statistics, and link layer characteristics.
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The main goal of this dissertation work is to experiment with and develop
new methods for identifying channel characteristics. Thereby utilizing
this knowledge, we show how to predict and improve WLAN performance.
Within the framework of the developed cross-layer measurement methodology,
we conducted extensive measurements in different physical environments
and different settings such as office buildings and stores, and (1) show that
the impact of the propagation channel can be quantified by using simple large
scale channel metric (throughput over longer period of time), and (2) also
present the existence of a Doppler effect within today’s WLAN packet traffic
at sub-second time scales. We also show the real-world WLAN usage pattern
from our measurement results. From this data, we conclude that the key issues
to study WLAN networks include accurate site-specific propagation channel
modeling and real-time autonomous traffic control.Electrical and Computer Engineerin
