Wireless spectrum is a limited resource that must be used efficiently. It is also
a broadcast medium, hence, additional procedures are required to maintain communication
over the wireless spectrum private. In this thesis, we investigate three key
issues related to efficient use and privacy of wireless spectrum use. First, we propose
GAVEL, a truthful short-term auction mechanism that enables efficient use of the wireless
spectrum through the licensed shared access model. Second, we propose CPRecycle,
an improved Orthogonal Frequency Division Multiplexing (OFDM) receiver that
retrieves useful information from the cyclic prefix for interference mitigation thus improving
spectral efficiency. Third and finally, we propose WiFi Glass, an attack vector
on home WiFi networks to infer private information about home occupants.
First we consider, spectrum auctions. Existing short-term spectrum auctions do
not satisfy all the features required for a heterogeneous spectrum market. We discover
that this is due to the underlying auction format, the sealed bid auction. We propose
GAVEL, a truthful auction mechanism, that is based on the ascending bid auction
format, that avoids the pitfalls of existing auction mechanisms that are based on the
sealed bid auction format. Using extensive simulations we observe that GAVEL can
achieve better performance than existing mechanisms.
Second, we study the use of cyclic prefix in Orthogonal Frequency Division Multiplexing.
The cyclic prefix does contain useful information in the presence of interference.
We discover that while the signal of interest is redundant in the cyclic prefix,
the interference component varies significantly. We use this insight to design CPRecycle,
an improved OFDM receiver that is capable of using the information in the
cyclic prefix to mitigate various types of interference. It improves spectral efficiency
by decoding packets in the presence of interference. CPRecycle require changes to the
OFDM receiver and can be deployed in most networks today.
Finally, home WiFi networks are considered private when encryption is enabled
using WPA2. However, experiments conducted in real homes, show that the wireless
activity on the home network can be used to infer occupancy and activity states such as
sleeping and watching television. With this insight, we propose WiFi Glass, an attack
vector that can be used to infer occupancy and activity states (limited to three activity
classes), using only the passively sniffed WiFi signal from the home environment.
Evaluation with real data shows that in most of the cases, only about 15 minutes of
sniffed WiFi signal is required to infer private information, highlighting the need for
countermeasures
