Spectrum Sensing Brief Overview of the Research at WINLAB P. - - PowerPoint PPT Presentation

Spectrum Sensing

Brief Overview of the Research at WINLAB

• P. Spasojevic

IAB, December 2008

What to Sense? Occupancy.

• Measuring spectral, temporal, and spatial occupancy

–

• bservation bandwidth and

–

• bservation 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?

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
• ccupancy time-variation

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

Spectrum Sensing: Design Considerations

• Propagation characteristics:

– Channel temporal variation: coherence time – Frequency variation: coherence bandwidth – Spatial variation

• Level
• f transmitter signal description known in advance:

– signal known or partially known (802.22, 802.11b) – signal unknown (cordless phones, future transmitters)

• Level
• f cognition detail needed
• Collaborative

vs non-collaborative approaches

• Processing/protocol complexity requirements

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

Experimental characterization of the vehicular channel: H. Kremo

Tx Rx pylons mark the car route 3.8m Start/Stop 15m 18m 4.4m

• Vector Network Analyzer sweeps

– 20 MHz wide channel 50 times per second – centered at 2.462 GHz and 5.2 GHz

Tx VNA console low loss RF 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

Transfer function magnitude and power loss

Start/Stop 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 channel caused by the moving vehicle: magnitude changes by ~10dB when the car is close to the antennas

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-1 WiFi-2

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 computes

spectrogram 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)
• n/off activity sequence for each source(localization in time)

4 sensors/ 2 802.11b transmitters

Average power vs. time at sensors non-overlapping transmissions in time (typical WLAN traffic ):

s T MHz BW μ 10 20 = =

Four sensors, two 802.11b nodes: Recovered(full line) and true PSDs: DBPSK signal with Barker sequence spreading Recovered on/off sequences: Packets ACKs

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

Adaptive MAC: KC Huang

Sparse Network Dense Network Join with CSMA-like MAC protocol Join with TDMA-like MAC protocol

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_CSMA CH4_CSMA CH3_CSMA CH5_CSMA CH1_CSMA

Delay > 20% A B

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

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

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

• verhead

* By Liang Xiao, Larry Greenstein, Narayan Mandayam and Wade Trappe, supported in part by NSF grant CNS-0626439

5 10 15 20 25 30

• 72
• 70
• 68
• 66
• 64
• 62
• 60
• 58
• 56

time (s) dB

Experiments with moving vehicle –

• H. Kremo

Start/Stop

Time invariant channel when the car is not present: fixed multipath Time varying channel caused by the moving vehicle: magnitude changes by ~10dB when the car is close to the antennas Time varying channel gain: VNA vs. RSSI

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 Frame Frame 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)

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

Spectral Density-Based Sensing: Signal Decomposition-

• G. Ivkovic

BT packets WLAN packets WLAN BT

Research done prior to the start of the project