Spectrum Sensing Brief Overview of the Research at WINLAB P. Spasojevic IAB, December 2008
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?
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
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 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
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 • Vector Network Analyzer sweeps Start/Stop 15m Rx – 20 MHz wide channel 50 times per second – centered at 2.462 GHz and 5.2 GHz 18m 3.8m Tx Rx Tx VNA low loss RF cable A pylons mark the car route 4.4m console [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 Time varying channel caused by the moving vehicle : magnitude changes by ~10dB when the car is close to the antennas Time varying channel gain -45 -50 -55 dB Time invariant channel -60 when the car is not present Start/Stop -65 -70 0 5 10 15 20 25 30 time (s)
Spectrum Sensing in unlicensed band (( )) (( )) (( )) Sensor 4 Sensor 5 Sensor 1 (x 1 , y 1 ) (x 2 , y 2 ) (( )) (( )) f WiFi-1 f Sensor 2 f 1 f 2 WiFi-2 Sensor 3 f (x 3 , y 3 ) Bluetooth time freq • 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
Radio Scene Analysis in Unlicensed Bands: Goran Ivkovic • A network of sensors observes multiple packet based radio transmitters: Sink node •Each sensor computes spectrogram with some sensors time and frequency resolution Packet based radio transmitters characterized by their power spectra and on/off activity sequences in time • 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)
4 sensors/ 2 802.11b transmitters Four sensors, two 802.11b nodes: Recovered(full line) and true PSDs: DBPSK signal with Barker sequence spreading Average power vs. time at sensors non-overlapping transmissions in time (typical WLAN traffic ): Recovered on/off sequences: ACKs Packets = BW MHz 20 = μ T s 10
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 Join with CSMA-like MAC protocol Join with TDMA-like MAC protocol Dense Network
Adaptive MAC(CSMA/TDMA) • Switch between CSMA and Control link TDMA Data path CH1_CSMA • Based on Spectrum Awareness, choose lowest traffic CSMA channel as normal mode operation Sender CH3_CSMA • Switch to reserved TDMA CH4_CSMA channel if traffic QoS not A CH2_CSMA satisfied CH5_CSMA B CH1_CSMA Delay > 20% Receiver CH10_TDMA
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 overhead * By Liang Xiao, Larry Greenstein, Narayan Mandayam and Wade Trappe, supported in part by NSF grant CNS-0626439
Experiments with moving vehicle – H. Kremo Start/Stop Time invariant channel when the car is not present: -56 fixed multipath -58 -60 Time varying channel caused by the moving vehicle : -62 magnitude changes by ~10dB dB -64 when the car is close to the antennas -66 -68 Time varying channel gain: -70 VNA vs. RSSI -72 0 5 10 15 20 25 30 time (s)
Detecting a preamble of a 802.11b frame- D. Borota - 802.11b PHY Frame “Start of Frame” Locked clock, mod. select Scrambled 1’s Scrambled x’FRA0’ Data Rate SYNC SFD SIGNAL SERVICE LENGTH CRC (128 (or 56)) (16) (8) (8) (16) (16) Frame Details (data rate, size) Lock/Acquire Frame PLCP Preamble PLCP Header PSDU (144 (or 72)) (48) (2304 max) Preamble at 1Mbps (DBPSK) 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., P T ~ 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 WLAN BT packets BT WLAN packets Research done prior to the start of the project
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