spectrum sensing - winlaba ray-tracing channel emulation software tool (wise) – field test using...
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Spectrum Sensing Brief Overview of the Research at WINLAB
P. Spasojevic
IAB, December 2008
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What to Sense? Occupancy.
•
Measuring spectral, temporal, and spatial occupancy–
observation bandwidth and
–
observation time
intervals–
frequency and time sampling granularity
–
spatial coverage and resolution
•
What proportion of time/bandwidth was occupied?•
Which time/frequency slots were occupied?
•
Where?
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Spectrum Sensing: More Detail?
•
how many transmitters are there?•
the spectral/temporal occupancy for each transmitter•
transmit power•
signal power spectral density•
modulation type•
transmitter-to-sensor channel transfer functions•
transmitter location•
occupancy time-variation
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Why Sense?
•
Licensed spectrum: –
Detect
the presence of the primary user.
•
Unlicensed spectrum: –
Coordinate
an efficient use of spectrum between competing diverse networks.
•
Monitor spectrum:–
determine
selfish/malfunctioning transmitters.
•
Cognitive radio: –
Adapt signal modulation parameters/protocol
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Spectrum Sensing: Design
Considerations
•
Propagation characteristics:–
Channel temporal variation: coherence time–
Frequency variation: coherence bandwidth–
Spatial variation
•
Level
of transmitter signal description known in advance: –
signal known or partially known (802.22, 802.11b)–
signal unknown (cordless phones, future transmitters)
•
Level
of cognition detail needed
•
Collaborative
vs
non-collaborative approaches•
Processing/protocol complexity requirements
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Sensing Research at WINLAB: In Brief
•
channel characterization–
H. Kremo
•
unlicensed bands: experimental and theoretical–
G. Ivkovic, R. Miller, C. Raman, D. Borota•
licensed spectrum: detecting
the presence of the primary users–
Jing Lei
•
sensing
in vehicular channels–
H. Kremo, KC. Huang, D. Borota
•
coordination and scheduling for efficient use of spectrum–
C. Raman, KC. Huang•
sensing for security, monitoring–
L. Xiao, S. Liu
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Experimental characterization of the vehicular channel: H. Kremo
Tx
Rx
pylons mark the car route
3.8m
Start/Stop15m
18m
4.4m
•
Vector Network Analyzer sweeps–
20 MHz wide channel 50 times per second–
centered at 2.462 GHz and 5.2 GHzTx
VNA
console
low lossRF cable
A
Rx
[1] H. Kremo, I. Seskar, and P. Spasojevic, “Concurrent Measurements of the Vehicular Channel Transfer Function and the 802.11 Received Signal Strength Index”
in CCNC/IVCS ‘09
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Transfer function magnitude and power loss
Start/Stop
0 5 10 15 20 25 30-70
-65
-60
-55
-50
-45
time (s)
dB
Time invariant channel when the car is not
present
Time varying channel gain
Time varying channelcaused by the moving vehicle: magnitude changes by ~10dBwhen the car is close to the antennas
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Spectrum Sensing in unlicensed band
•
Experimental study demonstrating the limitations of RSSI based sensing [RamanSeskarMandayam]
•
Service discovery and device identification in CR networks [MillerXuKamatTrappe]–
PHY layer approaches to distinguish WiFi
& Bluetooth networks with limited bandwidth snapshots
(( ))
(( ))(( ))
(( )) (( ))
Sensor 2
Sensor 1
Sensor 3
Sensor 4 Sensor 5
(x1
, y1
)(x2
, y2
)
(x3
, y3
)
time
f
f
freq
f1 f2
f
Bluetooth
WiFi-1WiFi-2
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Radio Scene Analysis in Unlicensed Bands: Goran
Ivkovic
•
A network of sensors observes multiple packet based radio transmitters:
Packet based radio transmitters characterized by their power spectra and on/off activity sequences in time
sensors
Sink node
•Each sensor computesspectrogram with some time and frequency resolution
•
From
the collected spectrograms, we recover:
•
sources
to sensors channel gains(localization in space)•
PSD for each source(localization in frequency)•
on/off activity sequence for each source(localization in time)
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4 sensors/ 2 802.11b transmitters
Average power vs. time at sensorsnon-overlapping transmissions in time (typical WLAN traffic ):
sTMHzBW
μ1020
==
Four sensors, two 802.11b nodes:Recovered(full line) and true PSDs:
DBPSKsignal with Barker sequence spreading
Recovered on/off sequences:
Packets
ACKs
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Cooperative sensing in Cognitive Radio: Jing Lei
•
Cooperative sensing in a CR network based on message passing •
Tanner graph approach to identify white spaces in the CR network
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Adaptive MAC: KC HuangSparse Network
Dense Network
Join with CSMA-like MAC protocol
Join with TDMA-like MAC protocol
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Adaptive MAC(CSMA/TDMA)
•
Switch between CSMA and TDMA
•
Based on Spectrum Awareness, choose lowest traffic CSMA channel
as normal mode operation•
Switch to reserved TDMA channel if traffic QoS
not satisfied
CH10_TDMA
Control link
Data path
Sender
Receiver
CH1_CSMA
CH2_CSMACH4_CSMA
CH3_CSMA
CH5_CSMA
CH1_CSMA
Delay > 20%
A
B
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Anomalous Spectrum Usage Detection: Song Liu
•
submitted to Infocom
2009
•
Challenge: Conventional signal processing techniques are insufficient•
Heterogeneous communication modes –
hard to enumerate•
Primary User Emulation (PUE) attack•
Unknown attacking signal’s pattern
•
Goal: Effective detection mechanism relying on non-programmable features, e.g., propagation law
•
Approach•
Spectrum sensing –
RSS based detection at spatially distributed sensors, each at a
known distance from the authorized transmitter.
•
Significance testing –
detect unknown anomalous usages
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Capturing the Characteristics of the Received Power
•
Propagation Law–
The received power is roughly linear with the logarithmic distance between the transmitter and receiver
•
Normal Usage Condition–
A channel is dedicated to a single authorized user
•
Features of the Proposed Detection Methods–
Distinguishing between single and multiple transmissions in the same channel–
Utilizing a decision statistic that captures the above characteristics of the received power
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Fingerprints in the Ether*: Liang Xiao
•
Fingerprints in the Ether: Spectrum sensing in security domain–
Exploits multipath to distinguish users–
Detection of identity-based attacks, e.g., spoofing and Sybil attacks–
Challenges•
Channel time variation: terminal mobility & environmental changes•
Channel estimation error
•
Proposed a channel-based authentication scheme–
Perform the Generalized Likelihood Ratio Test derived from a generalized frequency-selective Rayleigh channel model, or a more practical version
–
Use the existing channel estimation mechanism: Low system overhead
* By Liang Xiao, Larry Greenstein, Narayan Mandayam and Wade Trappe, supported in part by NSF grant CNS-0626439
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0 5 10 15 20 25 30-72
-70
-68
-66
-64
-62
-60
-58
-56
time (s)
dB
Experiments with moving vehicle –
H. KremoStart/Stop
Time invariant channel when the car is not
present: fixed multipath
Time varying channelcaused by the moving vehicle: magnitude changes by ~10dBwhen the car is close to the antennas
Time varying channel gain:VNA vs. RSSI
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Detecting a preamble of a 802.11b frame-
D. Borota
-
802.11b PHY Frame
SYNC(128 (or 56))
SFD(16)
LENGTH(16)
SIGNAL(8)
CRC(16)
SERVICE(8)
PLCP Preamble(144 (or 72))
PLCP Header(48)
PSDU(2304 max)
Lock/Acquire FrameFrame Details(data rate, size)
Scrambled 1’s
Preamble at 1Mbps (DBPSK)
Data Rate Locked clock, mod. select“Start of Frame”Scrambled x’FRA0’
2Mbps (DQPSK)5.5 and 11 Mbps(CCK)
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Fingerprints in the Ether (cont.)•
Performance for indoor environments verified via:–
Numerical simulation based on a generic stochastic channel model
–
A ray-tracing channel emulation software tool (WiSE)–
Field test using network analyzer•
Works well, requiring reasonable values of the measurement bandwidth (e.g., W > 10 MHz), number of response samples (e.g., M ≤
10) and transmit power (e.g., PT ~ 100 mW)
–
Both the false alarm rate and miss rate in spoofing detection are below 4% (sample size M=8, SINR of the channel estimation ρ=20 dB, the normalized power of the channel variation due to environmental changes is 0.1, and the terminal displacement normalized by carrier wavelength is no more than 0.12)
•
Open issues: –
Target values for miss rate and false alarm rate–
Combining with existing higher-layer security protocols
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Spectral Density-Based Sensing: Signal Decomposition-
G. Ivkovic
BT packets
WLAN packets
WLAN
BT
Research done prior to the start of the project