The document discusses deep learning solutions for large-scale spectrum monitoring, focusing on the evolution of wirelessly connected devices and the complexities of the radio spectrum. It emphasizes various communication technologies and spectrum allocations, illustrating the dynamic nature of spectrum usage. The presentation includes detailed charts and examples of radio services and their respective frequency allocations.

1. Deep learning solutions for large scale spectrum monitoring
Science Coffee, ESA
Sreeraj Rajendran and Sofie Pollin
June 15, 2019
KU Leuven, ESAT, Networked Systems

2. Connected society
• Decade of connecting everything
Connected Humans
1/35

3. Connected society
• Decade of connecting everything
Connected Humans
"Do not water me.
Random Belgian
weather expected"
Connected plants
1/35

4. Connected society
• Decade of connecting everything
Connected Humans
"Do not water me.
Random Belgian
weather expected"
Connected plants
"Hey Tesla, I am
out of milk, can
you get some?"
Connected devices
1/35

5. Key enabler: Wireless communication
1860s
Existence of EM waves
(James Maxwell)
1870s and 1880s
Huge’s and Hertz’s
experiments
1890s
Branly, Tesla, Bose,
Marconi
1930s
Administrative spec-
trum licensing
2010s
Crowded, dynamic
spectrum usage
2/35

6. The complex radio spectrum
• WiFi, Bluetooth, GSM, 4G, GPS, FM...
0 Hz 6 GHz
WiFi WiFiGSM 4GFM 4G GPS
3/35

7. The complex radio spectrum
• WiFi, Bluetooth, GSM, 4G, GPS, FM...
0 Hz 6 GHz
WiFi WiFiGSM 4GFM 4G GPS
THIS CHART WAS CREATED
BY
DELMON C. MORRISON
JUNE 1, 2011
UNITED
STATES
THE RADIO SPECTRUM
NON-FEDERALEXCLUSIVE
FEDERAL/NON-FEDERALSHAREDFEDERALEXCLUSIVE
RADIO SERVICES COLOR LEGEND
ACTIVITY CODE
ALLOCATION USAGE DESIGNATION
SERVICE EXAMPLE DESCRIPTION
Primary FIXED Capital Letters
Secondary Mobile 1st Capital with lower case letters
U.S. DEPARTMENT OF COMMERCE
National Telecommunications and Information Administration
Office of Spectrum Management
* EXCEPTAERONAUTICAL MOBILE (R)
** EXCEPTAERONAUTICAL MOBILE
ALLOCATIONS
FREQUENCY
STANDARDFREQUENCYANDTIMESIGNAL(20kHz)
FIXED
MARITIME MOBILE
Radiolocation
FIXED
MARITIME
MOBILE
FIXED
MARITIME
MOBILE
MARITIME
MOBILE
FIXED
AERONAUTICAL
RADIONAVIGATION
Aeronautical
Mobile
AERONAUTICAL
RADIONAVIGATION
Maritime
Radionavigation
(radiobeacons)
Aeronautical
Mobile
AERONAUTICAL
RADIONAVIGATION
AeronauticalRadionavigation
(radiobeacons)
NOT ALLOCATED RADIONAVIGATION
MARITIME MOBILE
FIXED
Fixed
FIXED
MARITIME MOBILE
0 kHz
MARITIMERADIONAVIGATION
(radiobeacons)
0
9
14
19.95
20.05
59
61
70
90
110
130
160
190
200
275
285
300
Radiolocation
300 kHz
FIXED
MARITIME
MOBILE
STANDARDFREQUENCYANDTIMESIGNAL(60kHz)
Aeronautical
Radionavigation
(radiobeacons)
MARITIME
RADIONAVIGATION
(radiobeacons)
Aeronautical
Mobile
Maritime
Radionavigation
(radiobeacons)
Aeronautical
Mobile
AeronauticalMobileRADIONAVIGATION
AERONAUTICAL
RADIONAVIGATION
MARITIME
MOBILE
Aeronautical
Radionavigation
MARITIMEMOBILE
MOBILE
BROADCASTING
(AM RADIO)
MARITIMEMOBILE
(telephony)
MOBILE
FIXED
STANDARDFREQ.ANDTIMESIGNAL(2500kHz)
FIXED
AERONAUTICAL
MOBILE(R)
RADIO-
LOCATION
FIXED
MOBILE
AMATEUR
RADIOLOCATION
MOBILEFIXED
MARITIME
MOBILE
MARITIMEMOBILE
FIXED
MOBILEBROADCASTING
AERONAUTICAL
RADIONAVIGATION
(radiobeacons)
MOBILE(distressandcalling)
MARITIMEMOBILE
(shipsonly)
AERONAUTICAL
RADIONAVIGATION
(radiobeacons)
AERONAUTICAL
RADIONAVIGATION
(radiobeacons)
MARITIMEMOBILE
(telephony)
MOBILE
except aeronautical mobile
MOBILE
exceptaeronauticalmobile
MOBILE
MOBILE
MARITIMEMOBILE
MOBILE(distressandcalling)
MARITIMEMOBILE
MOBILE
except aeronautical mobile
BROADCASTING
AERONAUTICAL
RADIONAVIGATION
(radiobeacons)
Non-Federal Travelers Information Stations (TIS), a mobile service, are authorized in the 535-1705 kHz band. Federal TIS operates at 1610 kHz.
300 kHz 3 MHz
Maritime
Mobile
3MHz 30 MHz
AERONAUTICAL
MOBILE(OR)
FIXEDMOBILE
exceptaeronauticalmobile(R)
FIXED
MOBILE
except aeronautical
mobile
AERONAUTICAL
MOBILE(R)
AMATEUR
MARITIMEMOBILEFIXED
MARITIME
MOBILE
FIXEDMOBILE
exceptaeronauticalmobile(R)
AERONAUTICALMOBILE(R)
AERONAUTICALMOBILE(OR)
MOBILE
exceptaeronauticalmobile(R)
FIXED
STANDARDFREQUENCYANDTIMESIGNAL(5MHz)
FIXEDMOBILE
FIXED
FIXED
AERONAUTICALMOBILE(R)
AERONAUTICALMOBILE(OR)
FIXEDMOBILE
exceptaeronauticalmobile(R)
MARITIMEMOBILE
AERONAUTICALMOBILE(R)
AERONAUTICALMOBILE(OR)
FIXED
AMATEURSATELLITEAMATEUR
AMATEUR
BROADCASTING
FIXED
MOBILE
except aeronautical
mobile (R)
MARITIMEMOBILEFIXED
AERONAUTICALMOBILE(R)
AERONAUTICALMOBILE(OR)
FIXED
BROADCASTING
FIXED
STANDARDFREQUENCYANDTIMESIGNAL(10MHz)
AERONAUTICALMOBILE(R)
AMATEUR
FIXED
Mobile
except
aeronautical mobile
(R)
AERONAUTICALMOBILE(OR)
AERONAUTICALMOBILE(R)
FIXED
BROADCASTING
FIXED
MARITIME
MOBILE
AERONAUTICALMOBILE(OR)
AERONAUTICALMOBILE(R)
RADIOASTRONOMY
FIXEDMobile
exceptaeronauticalmobile(R)
BROADCASTING
FIXEDMobile
exceptaeronauticalmobile(R)
AMATEUR
Mobile
exceptaeronauticalmobile(R)
FIXED
STANDARDFREQUENCYANDTIMESIGNAL(15MHz)
AERONAUTICALMOBILE(OR)
BROADCASTING
MARITIME
MOBILE
AERONAUTICALMOBILE(R)
AERONAUTICALMOBILE(OR)
FIXED
AMATEURSATELLITE
AMATEURSATELLITE
FIXED
3.0 3.025
3.155 3.23
3.4 3.5 4.0 4.063 4.438 4.65 4.7 4.75 4.85 4.995 5.005 5.06 5.45 5.68 5.73 5.59 6.2 6.525 6.685 6.765 7.0 7.1 7.3 7.4 8.1 8.195 8.815 8.965 9.04 9.4 9.9 9.995 10.005 10.1 10.15 11.175 11.275 11.4 11.6 12.1 12.23 13.2 13.26 13.36 13.41 13.57 13.87 14.0 14.25 14.35 14.99 15.01 15.1 15.8 16.36 17.41 17.48 17.9 17.97 18.03 18.068 18.168 18.78 18.9 19.02 19.68 19.8 19.99 20.01 21.0 21.45 21.85 21.924 22.0 22.855 23.0 23.2 23.35 24.89 24.99 25.01 25.07 25.21 25.33 25.55 25.67 26.1 26.175 26.48 26.95 26.96 27.23 27.41 27.54 28.0 29.7 29.8 29.89 29.91 30.0
BROADCASTING
MARITIMEMOBILE
BROADCASTING
FIXED
FIXED
MARITIMEMOBILE
FIXED
STANDARDFREQUENCYANDTIMESIGNAL(20MHz)
Mobile
Mobile
FIXED
BROADCASTING
FIXED
AERONAUTICALMOBILE(R)
MARITIMEMOBILE
AMATEURSATELLITEAMATEUR
FIXED
Mobile
exceptaeronauticalmobile(R)
FIXED
AERONAUTICALMOBILE(OR)
MOBILE
exceptaeronauticalmobile
FIXED
AMATEURSATELLITEAMATEUR
STANDARDFREQ.ANDTIMESIGNAL(25MHz)
LANDMOBILE
MARITIMEMOBILE
LANDMOBILE
FIXEDMOBILEexceptaeronauticalmobile
RADIOASTRONOMY
BROADCASTING
MARITIMEMOBILE
LANDMOBILE
MOBILE
exceptaeronauticalmobile
MOBILEexceptaeronauticalmobileFIXED
LANDMOBILE
FIXED
MOBILE
exceptaeronauticalmobile
FIXED
FIXED
MOBILEFIXED
AMATEURSATELLITEAMATEUR
LANDMOBILE
FIXED
FIXEDMOBILE
FIXED
AMATEUR
MOBILE
exceptaeronauticalmobile(R)
AMATEUR
FIXED
BROADCASTING
MARITIMEMOBILE
MOBILE
except aeronautical
mobile
300 325 335 405 415 435 495 505 510 525
535
1605 1615 1705 1800 1900 2000 2065 2107 2170 2173.5 2190.5 2194 2495 2505 2850 3000
30 MHz 300 MHz
FIXEDMOBILE
LANDMOBILE
MOBILE
MOBILE
MOBILE
LANDMOBILE
LANDMOBILE
FIXED
FIXED
FIXED
FIXED
FIXED
FIXED
LANDMOBILE
LANDMOBILERadioastronomy
FIXEDMOBILE
FIXEDMOBILE
LANDMOBILE
MOBILEFIXED
FIXEDLANDMOBILE
LANDMOBILE
FIXEDMOBILE
LANDMOBILE
FIXEDMOBILE
AMATEUR BROADCASTING
(TELEVISION )
FIXEDMOBILE
RADIOASTRONOMY
MOBILEFIXED
AERONAUTICALRADIONAVIGATION
MOBILE
MOBILE
FIXED
FIXED
BROADCASTING
(TELEVISION)
BROADCASTING
(FM RADIO)
AERONAUTICAL
RADIONAVIGATION
AERONAUTICAL
MOBILE(R)
AERONAUTICAL
MOBILE(R)
AERONAUTICALMOBILE
AERONAUTICALMOBILE
AERONAUTICALMOBILE(R)
AERONAUTICALMOBILE(R)
MOBILE-SATELLITE
(space-to-Earth)
MOBILE-SATELLITE
(space-to-Earth)
Mobile-satellite
(space-to-Earth)
Mobile-satellite
(space-to-Earth)
SPACERESEARCH
(space-to-Earth)
SPACERESEARCH
(space-to-Earth)
SPACERESEARCH
(space-to-Earth)
SPACERESEARCH
(space-to-Earth)
SPACEOPERATION
(space-to-Earth)
SPACEOPERATION
(space-to-Earth)
SPACEOPERATION
(space-to-Earth)
SPACEOPERATION
(space-to-Earth)
MET.SATELLITE
(space-to-Earth)
MET.SATELLITE
(space-to-Earth)
MET.SATELLITE
(space-to-Earth)
MET.SATELLITE
(space-to-Earth)
FIXEDMOBILE
AMATEUR-SATELLITEAMATEUR
AMATEUR
FIXEDMOBILE
MOBILE-SATELLITE
(Earth-to-space)
FIXEDMOBILE
FIXEDLANDMOBILE
FIXEDLANDMOBILE
RADIONAVIGATION-
SATELLITE
MARITIMEMOBILE
MARITIMEMOBILE
MARITIMEMOBILE
MOBILEexceptaeronauticalmobile
FIXEDLANDMOBILE
MARITIMEMOBILE
MOBILEexceptaeronauticalmobile
MARITIMEMOBILE(AIS)
MOBILEexceptaeronauticalmobile
FIXED
FIXEDLandmobile
FIXEDMOBILE
FIXEDMOBILEexcept
aeronauticalmobile
MobileFIXED
MOBILEexcept
aeronauticalmobile
FIXED
MOBILE
LANDMOBILE
MARITIMEMOBILE(distress,urgency,safetyandcalling)
MARITIMEMOBILE(AIS)
MOBILE
exceptaeronautical
mobile
FIXED
Amateur
AERONAUTICAL
MOBILE(R)
MOBILE-SATELLITE
(Earth-to-space)
BROADCASTING
(TELEVISION)
FIXED
AMATEUR
Landmobile
Fixed
30.0 30.56 32.0 33.0 34.0 35.0 36.0 37.0 37.5 38.0 38.25 39.0 40.0 42.0 43.69 46.6 47.0 49.6 50.0 54.0
72.0 73.0 74.6 74.8 75.2 75.4 76.0
88.0
108.0 117.975 121.9375 123.0875 123.5875 128.8125 132.0125 136.0 137.0 137.025 137.175 137.825 138.0 144.0 146.0 148.0 149.9 150.05 150.8 152.855 154.0 156.2475 156.7625 156.8375 157.0375 157.1875 157.45 161.575 161.625 161.775 161.9625 161.9875 162.0125 163.0375 173.2 173.4 174.0
216.0 217.0 219.0 220.0 222.0 225.0
300.0
FIXED
FixedLandmobile
LANDMOBILE
LANDMOBILE
300.0 328.6 335.4 399.9 400.05 400.15 401.0 402.0 403.0 406.0 406.1 410.0 420.0 450.0 454.0 455.0 456.0 460.0 462.5375 462.7375 467.5375 467.7375 470.0 512.0 608.0 614.0 698.0 763.0 775.0 793.0 805.0 806.0 809.0 849.0 851.0 854.0 894.0 896.0 901.0 902.0 928.0 929.0 930.0 931.0 932.0 935.0 940.0 941.0 944.0 960.0 1164.0 1215.0 1240.0 1300.0 1350.0 1390.0 1392.0 1395.0 1400.0 1427.0 1429.5 1430.0 1432.0 1435.0 1525.0 1559.0 1610.0 1610.6 1613.8 1626.5 1660.0 1660.5 1668.4 1670.0 1675.0 1695.0 1710.0 1761.0 1780.0 1850.0
2000.0 2020.0 2025.0 2110.0 2180.0 2200.0 2290.0 2300.0 2305.0 2310.0 2320.0 2345.0 2360.0 2390.0 2395.0 2417.0 2450.0 2483.5 2495.0 2500.0 2655.0 2690.0 2700.0 2900.0 3000.0
300 MHz
AERONAUTICALRADIONAVIGATION
FIXEDMOBILE
RADIONAVIGATIONSATELLITE
MOBILESATELLITE
(Earth-to-space)
STANDARDFREQUECYANDTIMESIGNAL-SATELLITE
(400.1MHz)
MET.AIDS
(Radiosonde)
MOBILE
SAT(S-E)
SPACERES.
(S-E)
SpaceOpn.
(S-E)
MET.SAT.
(S-E)
MET.AIDS
(Radiosonde)
SPACEOPN.
(S-E)
MET-SAT.
(E-S)
EARTH
EXPL
SAT.(E-S)
EarthExplSat
(E-S)
EarthExplSat
(E-S)
EARTHEXPL
SAT.(E-S)
MET-SAT.
(E-S)
MET.AIDS
(Radiosonde)
Met-Satellite
(E-S)
Met-Satellite
(E-S)
METEOROLOGICALAIDS(RADIOSONDE)
MOBILESATELLITE(Earth-to-space)
RADIO
ASTRONOMYFIXEDMOBILE
FIXEDMOBILESPACERESEARCH
(space-to-space)
RADIOLOCATIONAmateur
LANDMOBILE
FIXEDLANDMOBILE
LANDMOBILE
FIXEDLANDMOBILE
Meteorological
Satellite
(space-to-Earth)
LANDMOBILEFIXED
LANDMOBILE
FIXEDLANDMOBILE
LANDMOBILE
LANDMOBILEFIXED
BROADCASTING
(TELEVISION)
FIXED
BROADCASTING
(TELEVISION)
LANDMOBILE
(medicaltelemetryand
medicaltelecommand)
RADIOASTRONOMY
BROADCASTING
(TELEVISION)
BROADCASTING
(TELEVISION)
MOBILE
FIXEDMOBILE
FIXEDMOBILE
FIXEDMOBILE
FIXEDMOBILE
LANDMOBILE
FIXEDLANDMOBILE
AERONAUTICALMOBILE
LANDMOBILE
AERONAUTICALMOBILE
FIXEDLANDMOBILE
FIXEDLANDMOBILE
FIXEDMOBILE
RADIOLOCATION
FIXED
FIXEDLANDMOBILE
FIXEDMOBILE
FIXEDLANDMOBILE
FIXED
FIXEDLANDMOBILE
FIXEDMOBILE
FIXED
FIXED
AERONAUTICAL
RADIONAVIGATION
RADIONAVIGATION-SATELLITE
(space-to-Earth)(space-to-space)
EARTH
EXPLORATION-
SATELLITE
(active)
RADIO-
LOCATION
RADIONAVIGATION-
SATELLITE
(space-to-Earth)
(space-to-space)
SPACE
RESEARCH
(active)
Space research
(active)
Earth
exploration-
satellite
(active)
RADIO-
LOCATION
SPACE
RESEARCH
(active)
AERONAUTICAL
RADIO -
NAVIGATION
Amateur
AERONAUTICALRADIONAVIGATION
FIXEDMOBILERADIOLOCATION
FIXEDMOBILE**Fixed-satellite(Earth-to-space)
FIXEDMOBILE**
LANDMOBILE(medicaltelemetryandmedicaltelecommand)
SPACERESEARCH
(passive)RADIOASTRONOMY
EARTHEXPLORATION-SATELLITE
(passive)
LANDMOBILE
(telemetryandtelecommand)
LANDMOBILE
(medicaltelemetryand
medicaltelecommand
Fixed-satellite
(space-to-Earth)
FIXED(telemetryand
telecommand)
LANDMOBILE
(telemetry&telecommand)
FIXEDMOBILE**
MOBILE(aeronauticaltelemetry)
MOBILESATELLITE(space-to-Earth)
AERONAUTICAL
RADIONAVIGATION-SATELLITE
(space-to-Earth)(space-to-space)
MOBILESATELLITE
(Earth-to-space)
RADIODETERMINATION-
SATELLITE(Earth-to-space)
MOBILESATELLITE
(Earth-to-space)
RADIODETERMINATION-
SATELLITE(Earth-to-space)
RADIO
ASTRONOMY
MOBILESATELLITE
(Earth-to-space)
RADIODETERMINATION-
SATELLITE(Earth-to-space)
Mobile-satellite
(space-to-Earth)
MOBILESATELLITE(Earth-to-space)
MOBILESATELLITE
(Earth-to-space)RADIOASTRONOMY
RADIOASTRONOMY
FIXEDMOBILE**
METEOROLOGICALAIDS
(radiosonde)
METEOROLOGICAL
SATELLITE(space-to-Earth)
METEOROLOGICAL
SATELLITE(space-to-Earth)
FIXED
MOBILE
FIXEDMOBILESPACEOPERATION(Earth-to-space)
FIXEDMOBILE
MOBILESATELLITE(Earth-to-space)
FIXED
MOBILE
SPACERESEARCH(passive)
RADIOASTRONOMY
METEOROLOGICALAIDS
(radiosonde)
SPACE
RESEARCH
(Earth-to-space)
(space-to-space)
EARTH
EXPLORATION-
SATELLITE
(Earth-to-space)
(space-to-space)
FIXED
MOBILE
SPACEOPERATION
(Earth-to-space)
(space-to-space)
MOBILEFIXED
SPACE
RESEARCH
(space-to-Earth)
(space-to-space)
EARTH
EXPLORATION-
SATELLITE
(space-to-Earth)
(space-to-space)
SPACE
OPERATION
(space-to-Earth)
(space-to-space)
MOBILE
(ling of sight only
including aeronautical
telemetry, but excluding
flight testing of manned
aircraft)
FIXED
(line of sight only)
FIXED
SPACERESEARCH
(space-to-Earth)
(deepspace)
MOBILE**
Amateur
FIXEDMOBILE**AmateurRADIOLOCATION
RADIOLOCATIONMOBILEFIXED
Radio-
locationMobileFixed
BROADCASTING-SATELLITEFixedRadiolocation
FixedMobile
Radio-
location
BROADCASTING
SATELLITE
FIXEDMOBILERADIOLOCATION
RADIOLOCATION
MOBILE
MOBILE
AMATEUR
AMATEUR
Radiolocation
MOBILEFIXED
Fixed
Amateur
Radiolocation
MOBILESATELLITE
(space-to-Earth)
RADIODETERMINATION-
SATELLITE(space-to-Earth)
MOBILESATELLITE
(space-to-Earth)
RADIODETERMINATION-
SATELLITE(space-to-Earth)FIXEDMOBILE**
MOBILE**FIXED
Earth exploration-
satellite
(passive)
Space research
(passive)
Radio
astronomy
MOBILE**
FIXED
EARTH
EXPLORATION-
SATELLITE
(passive)
RADIO
ASTRONOMY
SPACERESEARCH
(passive)
AERONAUTICAL
RADIONAVIGATION
METEOROLOGICAL
AIDSRadiolocation
Radiolocation
RADIOLOCATION
MARITIME
RADIO-
NAVIGATION
MOBILEFIXED
BROADCASTING
BROADCASTING
Radiolocation
Fixed
(telemetry)
FIXED(telemetryand
telecommand)
LANDMOBILE(telemetry&telecommand)
AERONAUTICAL
RADIONAVIGATION
AERONAUTICAL
RADIONAVIGATION
AERONAUTICAL
RADIONAVIGATION
AERONAUTICAL
RADIONAVIGATION
AERONAUTICAL
RADIONAVIGATION
Space research
(active)
Earth
exploration-
satellite
(active)
EARTH
EXPLORATION-
SATELLITE
(active)
Fixed
FIXED
FIXEDMOBILE
ISM – 24.125 ± 0.125ISM – 5.8 ± .075 GHz
3GHz
RadiolocationAmateur
AERONAUTICAL
RADIONAVIGATION
(groundbased)
RADIOLOCATIONRadiolocation
FIXED-SATELLITE
(space-to-Earth)Radiolocation
FIXED
AERONAUTICAL
RADIONAVIGATION
MOBILE
FIXEDMOBILE
RADIOASTRONOMYSpaceResearch(passive)
RADIOLOCATION
RADIOLOCATION
RADIOLOCATION
METEOROLOGICAL
AIDS
Amateur
FIXED
SPACERESEARCH(deepspace)(Earth-to-space)
FixedFIXED-SATELLITE(space-to-Earth)
AERONAUTICALRADIONAVIGATION
RADIOLOCATIONRadiolocation
MARITIMERADIONAVIGATION
RADIONAVIGATION
Amateur
FIXED
RADIOASTRONOMY
BROADCASTING-SATELLITE
FixedMobile
Fixed
MobileFIXED
MOBILE
SPACERESEARCH
(passive)RADIOASTRONOMY
EARTHEXPLORATION-
SATELLITE(passive)
FIXED
FIXEDMOBILE
FIXED-SATELLITE
(space-to-Earth)
FIXED
MOBILE
MOBILE
AERONAUTICALRADIONAVIGATION
Standard frequency
and time signal
satellite
(Earth-to-space)
FIXED
FIXEDMOBILE**
FIXEDMOBILE**
FIXEDSATELLITE
(Earth-to-space)
Amateur
MOBILE
BROADCASTING-SATELLITE
FIXED-SATELLITE(space-to-Earth)
MOBILE
FIXEDMOBILE
INTER-SATELLITE
AMATEURAMATEUR-SATELLITE
Radio-
locationAmateurRADIO-
LOCATION
FIXED
INTER-SATELLITERADIONAVIGATION
RADIOLOCATION-SATELLITE(Earth-to-space)
FIXED-SATELLITE
(Earth-to-space)
MOBILE-SATELLITE
(Earth-to-space)
MOBILEINTER-SATELLITE
30 GHz
Earth
exploration-
satellite
(active)
Spaceresearch
(active)
RADIOLOCATION
RADIOLOCATION
AERONAUTICAL
RADIONAVIGATION
(groundbased)
FIXED-SATELLITE
(space-to-Earth)
FIXED
RADIONAVIGATION-SATELLITE
(Earth-to-space)
AERONAUTICALRADIONAVIGATION
AERONAUTICALRADIONAVIGATION
RADIONAVIGATION-SATELLITE
(space-to-Earth)(space-to-space)
AERONAUTICAL
RADIONAVIGATION
FIXED-SATELLITE
(Earth-to-space)
Earth
exploration-
satellite (active)
Space research
Radiolocation
EARTH
EXPLORATION-
SATELLITE
(active)
SPACE RESEARCH
(active)
RADIOLOCATION
Earth
exploration-
satellite (active)
Radiolocation
Space research
(active)
EARTH
EXPLORATION-
SATELLITE
(active)
SPACE RESEARCH
(active)
RADIOLOCATION
Radiolocation
Space research
(active)
EARTH
EXPLORATION-
SATELLITE
(active)
SPACE RESEARCH
(active)
RADIOLOCATION
AERONAUTICAL
RADIONAVIGATION
Earth
exploration-
satellite (active)
Radiolocation
Space research
(active)
EARTH
EXPLORATION-
SATELLITE
(active)
SPACE RESEARCH
(active)
RADIOLOCATION
RADIONAVIGATION
Earth
exploration-
satellite (active)
Space research
(active)
EARTH
EXPLORATION-
SATELLITE
(active)
SPACE RESEARCH
(active)
MARITIME
RADIONAVIGATION
RADIOLOCATION
MARITIMERADIONAVIGATIONRADIOLOCATION
MARITIME
RADIONAVIGATION
AmateurRADIOLOCATION
MOBILE
FIXED-SATELLITE
(Earth-to-space)
FIXED
FIXED-SATELLITE
(Earth-to-space)
FIXEDFIXED-SATELLITE(Earth-to-space)(space-to-Earth)
FIXEDFIXED-SATELLITE(Earth-to-space)(space-to-Earth)MOBILE
FIXED-SATELLITE(Earth-to-space)MOBILE
FIXEDMOBILE
FIXED
FIXED
FIXEDSPACERESEARCH(Earth-to-space)
FIXED
MOBILE-SATELLITE(space-to-Earth)
FIXEDMobile-satellite(space-to-Earth)FIXED-SATELLITE(space-to-Earth)
FIXED
Mobile-satellite
(space-to-Earth)
METEOROLOGICAL
SATELLITE(space-to-Earth)
FIXED-SATELLITE(space-to-Earth)
FIXEDMobile-satellite(space-to-Earth)FIXED-SATELLITE(space-to-Earth)
FIXED-SATELLITE(Earth-to-space)MOBILE-SATELLITE(Earth-to-space)Fixed
FIXED
Mobile-satellite
(Earth-to-space)
(noairborne)
FIXEDSATELLITE
(Earth-to-space)
EARTHEXPLORATION-
SATELLITE(space-to-Earth)
Mobile-satellite
(Earth-to-space)
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FIXED
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Mobile-satellite
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Earth
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Radio-
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Space
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EARTH
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RADIO-
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RadiolocationRADIOLOCATION
Radiolocation
Radiolocation
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Space
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Radiolocation
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Amateur-satelliteAmateurRadiolocationRADIOLOCATION
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FIXEDEARTHEXPLORATION-SATELLITE(passive)SPACERESEARCH(passive)
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FIXED-SATELLITE(space-to-Earth)FIXED
FIXED-SATELLITE(space-to-Earth)
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Space
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EARTH
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SPACE
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RADIO -
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Radio-
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Space
research
RADIO -
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Space
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FIXED-
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Radio -
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FIXED-SATELLITE(Earth-to-space)Mobile-satellite(Earth-to-space)
Space
research
Mobile-satellite(space-to-Earth)FIXED-SATELLITE(Earth-to-space)
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(Earth-to-space)
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MOBILESPACERESEARCHFixed
FIXEDSPACERESEARCHMobile
FIXED-SATELLITE
(Earth-to-space)
AERONAUTICAL
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Radiolocation
FIXED-SATELLITE(Earth-to-space)
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and
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SPACERESEARCH
(passive)
FIXEDMOBILE**
EARTH
EXPLORATION-
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(passive)
MOBILE**
FIXED
SPACE
RESEARCH
(passive)
RADIO
ASTRONOMY
MOBILEFIXED
FIXEDMOBILE
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EARTHEXPLORATION-
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RADIONAVIGATION
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Standard frequency and
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MOBILE
INTER-SATELLITE
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Inter-satellite
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Spaceresearch
Radiolocation
Radiolocation
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RADIOLOCATION
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Earth
exploration-
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3.0 3.1 3.3 3.5 3.6 3.65 3.7 4.2 4.4 4.5 4.8 4.94 4.99 5.0 5.01 5.03 5.15 5.25 5.255 5.35 5.46 5.47 5.57 5.6 5.65 5.83 5.85 5.925 6.425 6.525 6.7 6.875 7.025 7.075 7.125 7.145 7.19 7.235 7.25 7.3 7.45 7.55 7.75 7.85 7.9 8.025 8.175 8.215 8.4 8.45 8.5 8.55 8.65 9.0 9.2 9.3 9.5 9.8 10.0 10.45 10.5 10.55 10.6 10.68 10.7 11.7 12.2 12.7 13.25 13.4 13.75 14.0 14.2 14.4 14.5 14.7145 14.8 15.1365 15.35 15.4 15.43 15.63 15.7 16.6 17.1 17.2 17.3 17.7 17.8 18.3 18.6 18.8 19.3 19.7 20.2 21.2 21.4 22.0 22.21 22.5 22.55 23.55 23.6 24.0 24.05 24.25 24.45 24.65 24.75 25.05 25.25 25.5 27.0 27.5 29.5 30.0
MOBILE
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AMATEURAMATEUR-SATELLITE
AmateurAmateur-satellite
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(passive)
FIXEDMOBILE**
SPACE
RESEARCH
(passive)
EARTH
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RADIONAVIGATION
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(deepspace)(Earth-to-space)
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(deepspace)
(Earth-to-space)
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EARTHEXPLORATION-
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FIXEDMOBILE
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FIXEDMOBILEBROADCASTINGBROADCASTINGSATELLITE
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FIXED-SATELLITE
(Earth-to-space)
RADIOASTRONOMY
FIXED-SATELLITE(Earth-to-space)MOBILE-SATELLITE(Earth-to-space)
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FIXEDMOBILEFIXED-SATELLITE(Earth-to-space)
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FIXED
FIXED
30.0 31.0 31.3 31.8 32.3 33.0 33.4 34.2 34.7 35.5 36.0 37.0 37.5 38.0 38.6 39.5 40.0 40.5 41.0 42.0 42.5 43.5 45.5 46.9 47.0 47.2 48.2 50.2 50.4 51.4 52.6 54.25 55.78 56.9 57.0 58.2 59.0 59.3 64.0 65.0 66.0 71.0 74.0 76.0 77.0 77.5 78.0 81.0 84.0 86.0 92.0 94.0 94.1 95.0 100.0 102.0 105.0 109.5 111.8 114.25 116.0 122.25 123.0 130.0 134.0 136.0 141.0 148.5 151.5 155.5 158.5 164.0 167.0 174.5 174.8 182.0 185.0 190.0 191.8 200.0 209.0 217.0 226.0 231.5 232.0 235.0 238.0 240.0 241.0 248.0 250.0 252.0 265.0 275.0 300.0
30GHz 300 GHz
Amateur-
satellite
Amateur-satellite
Amateur-satellite
RADIO
ASTRONOMY
RADIO
ASTRONOMY
RADIO
ASTRONOMY
RADIO
ASTRONOMY
BROADCASTING
SATELLITE
SPACERESEARCH
(space-to-Earth)
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SATELLITE
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NAVIGATION-
SATELLITE
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(space-to-Earth)
Spaceresearch
(space-to-Earth)
RADIO
ASTRONOMY
RADIO
ASTRONOMY
ISM - 6.78 ± .015 MHz ISM - 13.560 ± .007 MHz ISM - 27.12 ± .163 MHz
ISM - 40.68 ± .02 MHz
3 GHz
ISM - 915.0± .13 MHz
ISM - 2450.0± .50 MHz
3 GHz
ISM - 122.5± 0.500 GHz
This chart is a graphic single-point-in-time portrayal of the Table of FrequencyAllocations used by the FCC and
NTIA. As such, it may not completely reflect all aspects, i.e. footnotes and recent changes made to the Table
of FrequencyAllocations. Therefore, for complete information, users should consult the Table to determine the
current status of U.S. allocations. ISM - 61.25± 0.25 GHz
ISM - 245.0± 1 GHz
AERONAUTICAL
MOBILE
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MOBILE SATELLITE
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RADIONAVIGATION
AMATEUR
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LAND MOBILE
LAND MOBILE
SATELLITE
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MARITIME
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AND TIME SIGNAL
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ASTRONOMY
MOBILE
MOBILEFIXED
FIXED
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ASTRONOMY
RADIO
ASTRONOMY
RADIO
ASTRONOMY
RADIO
ASTRONOMY
Radiolocation
Radiolocation
Radiolocation
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RADIOASTRONOMY
FIXED-SATELLITE
(space-to-Earth)
SPACERESEARCH
(space-to-Earth)
AERONAUTICAL
MOBILE(R)
MOBILE**
SPACEOPERATION(Earth-to-space)
3/35

8. Why spectrum knowledge is important?
• Airport scenario
• Location and status updates
• Surveillance radars
• ATC communications
• Jamming and spoofing attacks
• Belgium Air Traffic Control
• 20 reported radio issues in 2017
"Landing on track
number 2"
"Are they deaf?"
4/35

9. Telecom operators
• Densified small cells
• Network outages
• Fake base stations
• Illegal signal boosters
"Extremely authentic
base station
requesting user
details..."
5/35

10. Defense, ISM, End users
ISM bands: Interference and
usage analysis
6/35

11. Defense, ISM, End users
ISM bands: Interference and
usage analysis
Defense: Enemy signal
classification and
localization
6/35

12. Defense, ISM, End users
ISM bands: Interference and
usage analysis
Defense: Enemy signal
classification and
localization
End users and amateurs:
Radiation and channel
activity monitoring
6/35

13. How is this spectrum monitored now?
• Fixed monitoring stations
• Mobile monitoring units
• Sub-optimal manual monitoring
• BIPT
• 440 interference cases
• 15,007 devices seized
• 497 monitoring visits
"Sir, we are
experiencing extreme
jamming here.."
"Thank you for reaching
us. Kindly fill the Word
document for further
assistance"
7/35

14. Research problems
Data collection
• Spectrum monitoring
• Large scale
• Cost-effective
8/35

15. Research problems
Data collection
• Spectrum monitoring
• Large scale
• Cost-effective
Knowledge extraction
• Anomaly detection
• Signal
classification
• Importance of
supervision
8/35

16. Research problems
Data collection
• Spectrum monitoring
• Large scale
• Cost-effective
Knowledge extraction
• Anomaly detection
• Signal
classification
• Importance of
supervision
Knowledge exploitation
• Spectrum awareness
utilization
• Improving wireless
performance
8/35

17. Spectrum sensing network
"Full spectrum awareness"
Scalability
Multipleusecases
Sensorreliability
Sensorvariations
Largespectrumdata
Low-costsensors
SecurityandPrivacy
Dataaggregationtools

18. But why is it so difficult?
Hmmm!!
How difficult
can this be? It
is just putting
some sensors and
collecting data!!
9/35

19. But why is it so difficult?
Hmmm!!
How difficult
can this be? It
is just putting
some sensors and
collecting data!! But who will put all
these sensors?
That’s true!!
Electromagnetic
propagation is a
mess.
9/35

20. But why is it so difficult?
Hmmm!!
How difficult
can this be? It
is just putting
some sensors and
collecting data!! But who will put all
these sensors?
That’s true!!
Electromagnetic
propagation is a
mess.
Also how will you
handle all these
spectrum data?
Hmmm!!
9/35

21. But why is it so difficult?
Hmmm!!
How difficult
can this be? It
is just putting
some sensors and
collecting data!! But who will put all
these sensors?
That’s true!!
Electromagnetic
propagation is a
mess.
Also how will you
handle all these
spectrum data?
Hmmm!!
What about
sensor variations,
security and privacy
issues?
???
9/35

22. But why is it so difficult?
Hmmm!!
How difficult
can this be? It
is just putting
some sensors and
collecting data!! But who will put all
these sensors?
That’s true!!
Electromagnetic
propagation is a
mess.
Also how will you
handle all these
spectrum data?
Hmmm!!
What about
sensor variations,
security and privacy
issues?
???
Tripel Karmeliet!!
Ok...Let us find
some short term
solutions. Duvel or
Achel?
9/35

23. Previous attempts
• Microsoft spectrum observatory
• High cost sensor setup
• Google Spectrum
• TV white-space monitoring
• IBM Horizon
• Centralized IoT framework
10/35

24. Crowdsourced sensors: Electrosense
• http://electrosense.org/
• https://github.com/electrosense
Rajendran, S., Calvo-Palomino, R., Fuchs, M., Bergh, B.V.D.,
Cordobés, H., Giustiniano, D., Pollin, S. and Lenders, V., 2017.
Electrosense: Open and Big Spectrum Data. IEEE
Communications Magazine.
11/35

25. Spectrum monitoring: Challenges and solutions
• Enabling large scale deployment
• Sensor variations
• Scalability
• Large spectrum data
• Security and Privacy
• Crowdsourcing
• Low-cost sensors
• Raspberry Pi + RTLSDR
• Frequency: 0 - 6 GHz @ 3.2 MS/s
12/35

26. Spectrum monitoring: Challenges and solutions
• Enabling large scale deployment
• Sensor variations
• Scalability
• Large spectrum data
• Security and Privacy
• Support for high end SDRs through
GNURadio
• gr-electosense
• Native multi-SDR support in progress
12/35

27. Spectrum monitoring: Challenges and solutions
• Enabling large scale deployment
• Sensor variations
• Scalability
• Large spectrum data
• Security and Privacy
• Scalable data storage solutions
• Distributed
• Fault-tolerant
• Low-latency
• Sensor control
• Centralized controller
• Measurement campaigns
12/35

28. Spectrum monitoring: Challenges and solutions
• Enabling large scale deployment
• Sensor variations
• Scalability
• Large spectrum data
• Security and Privacy
• Sensing datarate: 50 Kb/s to 50 Mb/s
• Backend: Batch and stream processing,
• Multiple pipelines
12/35

29. Spectrum monitoring: Challenges and solutions
• Enabling large scale deployment
• Sensor variations
• Scalability
• Large spectrum data
• Security and Privacy
• Service model: Open Spectrum Data as a Service
(OSDaaS)
• Obfuscated sensor location for locational privacy
• Sensor registration and secure TLS channels
• IQ data only for user owned sensors
• Restricted frequency resolution (60 seconds and
100 kHz)
• Public decoders (new feature): managed by
Electrosense
12/35

30. We have the data, now what?
• Knowledge extraction
Expert feature based models
"Let us build
models based
on our expert
knowledge!!"
13/35

31. We have the data, now what?
• Knowledge extraction
Expert feature based models
"Let us build
models based
on our expert
knowledge!!"
Data-aided learning
"You listen carefully...I
have a lot of data..Do
whatever you want..I
need those models
ASAP"
13/35

32. Unsupervised anomaly detection
"SPECTRUM PARTY"
How did
you even
get-in?
I BEHAVED
NORMAL!!
Sorry sir, I can’t
let you in. Your eye
blinking pattern
looks abnormal
MY WHATT !!

33. Anomaly detection: Challenges
• What is an anomaly?
• Why is it challenging?
• Our interest
• Jammers
• New transmitters
• Transmitter power variations
• Transmitter frequency offset
• Unusual transmission patterns
• Out of band transmissions
• Duty cycle variations
14/35

34. Anomaly detection: Challenges
• What is an anomaly?
• Why is it challenging?
• Our interest
• Supervised setup
• Manual labeling is impractical and suboptimal
• Wireless spectrum usage is very complex
• Sensor variations
• Unsupervised setup
• Less frequent events (outliers) as anomalous
• Not fully true in wireless scenario: ISM bands (less
frequent), pirate stations (frequent)
14/35

35. Anomaly detection: Challenges
• What is an anomaly?
• Why is it challenging?
• Our interest
• Single anomaly detector for different frequency bands
• Insights to anomaly cause
• Fully unsupervised anomaly detection
• Assumptions
• Low anomalous behavior probability in the source
dataset.
• No explicit anomaly labeling is done.
• No expert feature extraction is performed.
14/35

36. Basic model idea (Learn like kids)
"Woof woof"
"Woof woof!!"
Features for
reconstruction
15/35

37. Basic model idea (Learn like kids)
"Woof woof"
"Woof woof!!"
Features for
reconstruction
"Woof
woof"
Inference
15/35

38. Basic model idea (Learn like kids)
"Woof woof"
"Woof woof!!"
Features for
reconstruction
"Woof
woof"
Inference
"Encoder"
"Features"
"Decoder"
Autoencoder
15/35

39. Basic model idea (Learn like kids)
"Woof woof"
"Woof woof!!"
Features for
reconstruction
"Woof
woof"
Inference
"Encoder"
"Features"
"Decoder"
Autoencoder
"No...Meow!!"
"Oh...Meow?"
Semi-supervision
15/35

40. Spectrum Anomaly detector with Interpretable FEatures (SAIFE)
Input
PSD vector ( )x
Reconstructed PSD vector ( )xˆ
LSTM Encoder
CNN Decoder
ClassFeatures
linear softmax
Draw samples from
 (z|0, I)
Discriminator
Input
Draw samples from
Cat(y)
Discriminator
Input
Sigmoid out Sigmoid out
Latent Variables
• Adversarial Autoencoder based model
• Unsupervised reconstruction based model
• Single model that can be trained over
multiple frequency bands
Rajendran, Sreeraj; Meert, Wannes; Lenders, Vincent; Pollin,
Sofie; 2018. SAIFE: Unsupervised Wireless Spectrum Anomaly
Detection with Interpretable Features. IEEE International
Symposium on Dynamic Spectrum Access Networks;
Rajendran, Sreeraj; Meert, Wannes; Lenders, Vincent; Pollin,
Sofie; 2019. Unsupervised Wireless Spectrum Anomaly Detection
with Interpretable Features. IEEE Transactions on Cognitive
Communications and Networking; 2019
16/35

41. SAIFE pros
• Anomaly detection accuracy for
different anomalies (cfar=10%)
• Interpretable features
• Localize anomalies in the spectrogram
• Wireless data compression
−20 −10 0 10 20
0
0.2
0.4
0.6
0.8
1
scont
−20 −10 0 10 20
0
0.2
0.4
0.6
0.8
1
randpulses
−20 −10 0 10 20
0
0.2
0.4
0.6
0.8
1
wpulse
−20 −10 0 10 20
0
0.2
0.4
0.6
0.8
1
oclass
osvm ifo rcov
loda
17/35

42. SAIFE pros
• Anomaly detection accuracy for
different anomalies (cfar=10%)
• Interpretable features
• Localize anomalies in the spectrogram
• Wireless data compression
−20 −10 0 10 20
0
0.2
0.4
0.6
0.8
1
scont
−20 −10 0 10 20
0
0.2
0.4
0.6
0.8
1
randpulses
−20 −10 0 10 20
0
0.2
0.4
0.6
0.8
1
wpulse
−20 −10 0 10 20
0
0.2
0.4
0.6
0.8
1
oclass
osvm ifo rcov
loda saife20 saife100
17/35

43. SAIFE pros
• Anomaly detection accuracy for
different anomalies (cfar=10%)
• Interpretable features
• Localize anomalies in the spectrogram
• Wireless data compression
• Learned bandwidth and position features in
a semi-supervised fashion
17/35

44. SAIFE pros
• Anomaly detection accuracy for
different anomalies (cfar=10%)
• Interpretable features
• Localize anomalies in the spectrogram
• Wireless data compression
• Anomaly localization
(Input, Reconstructed, Anomalous regions)
17/35

45. SAIFE pros
• Anomaly detection accuracy for
different anomalies (cfar=10%)
• Interpretable features
• Localize anomalies in the spectrogram
• Wireless data compression
• Variable lossy compression ratios
• 19X on the Synthetic dataset
• 60X on Electrosense dataset
• 120X on the SDR dataset
20 40 60 80 100
0
20
40
60
80
100
Number of features
Accuracy/Reconstructionerror
meanerr maxerr
cacc aanom
17/35

46. Anomaly detection demo
• SAIFE in GSM-900 band
• Sweeping with 20MHz SDR
bandwidth
• Anomalous regions on the
bottom of the plot
18/35

47. Anomaly detection on Electrosense data
19/35

48. Crowdsourced anomaly detection
That signal looks
blurry..."ANOMALY"
ANOMALY!!
Pretty normal
for me!

49. Anomaly detection
• Key challenges
• Open questions
• Data challenges
• Wireless spectrum usage is very complex
• Manual labeling is impractical and suboptimal
• Sensor variations
• Less frequent events (outliers) are not always
anomalous
20/35

50. Anomaly detection
• Key challenges
• Open questions
• Data challenges
• How to incorporate user feedback?
• How to share expert feedback across users?
• How to generalize feedback across sensors?
20/35

51. Anomaly detection
• Key challenges
• Open questions
• Data challenges
• Higher dimensional data → Interpretable common
feature space
• Detecting meaningful anomalies: less frequent or
frequent events
• Knowledge sharing across sensors
• User preferences and incorrect labeling
20/35

52. Basic model idea: Ask experts when needed
Dog trained model
Features
Unsupervised anomaly
detection
21/35

53. Basic model idea: Ask experts when needed
Dog trained model
Features
Unsupervised anomaly
detection
Features
Cat trained model
Anomaly feature extraction
21/35

54. Basic model idea: Ask experts when needed
Dog trained model
Features
Unsupervised anomaly
detection
Features
Cat trained model
Anomaly feature extraction
Semi-supervised
clustering
G1
G2
G1 -
Anomaly
G2G1
G2 -
Anomaly
Semi-supervised clustering and
anomaly labeling
21/35

55. Anomaly detection framework: Contributions
Global Encoder
Local DecoderS1
Features
S1
X1
X
ˆ
1
Global Encoder
Local DecoderSN
Features
SN
XN
X
ˆ
N
Fd
Data feature module
Unsupervised
anomaly
detection based
filtering based on
Global Anomaly
Encoder
Global Anomaly
Decoder
Anomaly
Features
Error input
−Xi X
ˆ
i
Reconstructed
Error input
Anomaly feature module
Fa
Active feature
clustering
Active anomaly
detector
( , )Fa Fd
Users
Final Anomalies
Expert Feedback
Ascore
1. Data driven common interpretable feature space
Rajendran, Sreeraj; Lenders, Vincent; Meert, Wannes; Pollin, Sofie; 2019. Crowdsourced wireless spectrum anomaly detection.
IEEE Transactions on Cognitive Communications and Networking; 2019 –Submitted. 22/35

56. Anomaly detection framework: Contributions
Global Encoder
Local DecoderS1
Features
S1
X1
X
ˆ
1
Global Encoder
Local DecoderSN
Features
SN
XN
X
ˆ
N
Fd
Data feature module
Unsupervised
anomaly
detection based
filtering based on
Global Anomaly
Encoder
Global Anomaly
Decoder
Anomaly
Features
Error input
−Xi X
ˆ
i
Reconstructed
Error input
Anomaly feature module
Fa
Active feature
clustering
Active anomaly
detector
( , )Fa Fd
Users
Final Anomalies
Expert Feedback
Ascore
1. Data driven common interpretable feature space
2. Unsupervised detection followed by semi-supervised detection for detecting meaningful anomalies
Rajendran, Sreeraj; Lenders, Vincent; Meert, Wannes; Pollin, Sofie; 2019. Crowdsourced wireless spectrum anomaly detection.
IEEE Transactions on Cognitive Communications and Networking; 2019 –Submitted. 22/35

57. Anomaly detection framework: Contributions
Global Encoder
Local DecoderS1
Features
S1
X1
X
ˆ
1
Global Encoder
Local DecoderSN
Features
SN
XN
X
ˆ
N
Fd
Data feature module
Unsupervised
anomaly
detection based
filtering based on
Global Anomaly
Encoder
Global Anomaly
Decoder
Anomaly
Features
Error input
−Xi X
ˆ
i
Reconstructed
Error input
Anomaly feature module
Fa
Active feature
clustering
Active anomaly
detector
( , )Fa Fd
Users
Final Anomalies
Expert Feedback
Ascore
1. Data driven common interpretable feature space
2. Unsupervised detection followed by semi-supervised detection for detecting meaningful anomalies
3. Active anomaly clustering for grouping similar anomalies across frequency bands and sensors
Rajendran, Sreeraj; Lenders, Vincent; Meert, Wannes; Pollin, Sofie; 2019. Crowdsourced wireless spectrum anomaly detection.
IEEE Transactions on Cognitive Communications and Networking; 2019 –Submitted. 22/35

58. Anomaly detection framework: Contributions
Global Encoder
Local DecoderS1
Features
S1
X1
X
ˆ
1
Global Encoder
Local DecoderSN
Features
SN
XN
X
ˆ
N
Fd
Data feature module
Unsupervised
anomaly
detection based
filtering based on
Global Anomaly
Encoder
Global Anomaly
Decoder
Anomaly
Features
Error input
−Xi X
ˆ
i
Reconstructed
Error input
Anomaly feature module
Fa
Active feature
clustering
Active anomaly
detector
( , )Fa Fd
Users
Final Anomalies
Expert Feedback
Ascore
1. Data driven common interpretable feature space
2. Unsupervised detection followed by semi-supervised detection for detecting meaningful anomalies
3. Active anomaly clustering for grouping similar anomalies across frequency bands and sensors
4. Anomaly label propagation for incorporating user specific anomalies
Rajendran, Sreeraj; Lenders, Vincent; Meert, Wannes; Pollin, Sofie; 2019. Crowdsourced wireless spectrum anomaly detection.
IEEE Transactions on Cognitive Communications and Networking; 2019 –Submitted. 22/35

59. Anomaly detection framework: Contributions
Global Encoder
Local DecoderS1
Features
S1
X1
X
ˆ
1
Global Encoder
Local DecoderSN
Features
SN
XN
X
ˆ
N
Fd
Data feature module
Unsupervised
anomaly
detection based
filtering based on
Global Anomaly
Encoder
Global Anomaly
Decoder
Anomaly
Features
Error input
−Xi X
ˆ
i
Reconstructed
Error input
Anomaly feature module
Fa
Active feature
clustering
Active anomaly
detector
( , )Fa Fd
Users
Final Anomalies
Expert Feedback
Ascore
1. Data driven common interpretable feature space
2. Unsupervised detection followed by semi-supervised detection for detecting meaningful anomalies
3. Active anomaly clustering for grouping similar anomalies across frequency bands and sensors
4. Anomaly label propagation for incorporating user specific anomalies
Rajendran, Sreeraj; Lenders, Vincent; Meert, Wannes; Pollin, Sofie; 2019. Crowdsourced wireless spectrum anomaly detection.
IEEE Transactions on Cognitive Communications and Networking; 2019 –Submitted. 22/35

60. Anomaly framework evaluation
• Clustering performance
• Anomaly detection
performance
• Queries answered from all sensors
50 100 150 200 250 300 350 400
0
20
40
60
80
100
Number of queries
ARI(%)
0 dB 10 dB 20 dB Avg
23/35

61. Anomaly framework evaluation
• Clustering performance
• Anomaly detection
performance
• Queries answered from a single sensor
0 50 100 150 200 250 300 350 400
0
20
40
60
80
100
Number of queries
ARI(%)
0 dB 10 dB 20 dB Avg
23/35

62. Anomaly framework evaluation
• Clustering performance
• Anomaly detection
performance
• Average anomaly detection accuracy with correct labeling.
• α: impact of user provided labels vs labels from clustering
• k: label propagation depth for a single labeled instance
10 50 100 200 300 400 500
40
60
80
100
Number of queries
Accuracy(%)
α, k = 0.5, 10 α, k = 1, 10 α, k = 2, 10
α, k = 0.5, 50 α, k = 1, 50 α, k = 2, 50
α, k = 0.5, 100 α, k = 1, 100 α, k = 2, 100
23/35

63. Anomaly framework evaluation
• Clustering performance
• Anomaly detection
performance
• Average anomaly detection accuracy with incorrect cluster
and anomaly labeling probability Pi = 0.05.
10 50 100 200 300 400 500
40
60
80
100
Number of queries
Accuracy(%)
α, k = 0.5, 10 α, k = 2, 10 α, k = 0.5, 50
α, k = 1, 50 α, k = 2, 50 α, k = 0.5, 100
α, k = 1, 100 α, k = 2, 100
23/35

64. Signal classification
Anonymity is a
myth
How
are you
FM?
Hey
BPSK
and
QPSK
Come on man,
you didn’t even
see our face

65. Signal classification: Challenges
• Automatic Modulation
Classification (AMC)
• Why is it challenging?
• Time domain samples → Modulation classes
• Multiple usecases
• Hostile signal classification
• Situational awareness in DSA
• Interference analysis
24/35

66. Signal classification: Challenges
• Automatic Modulation
Classification (AMC)
• Why is it challenging?
• Robustness
• Time shift variance x[n - N]
• Channel variations x[n] * h[n]
• Symbol rate variations x[n/D]
• SNR variations
• Conventional feature based models
• Higher dimensional features are processor hungry
• Moments should be selected based on the
modulation schemes
• Averaging requires more samples
24/35

67. Basic model idea
Input valve
Forget valve
Output valve
LSTM cell
25/35

68. Basic model idea
Input valve
Forget valve
Output valve
LSTM cell
BPSK QPSK FM GFSK QAM16
Multiple layers
25/35

69. LSTM model (Supervised)
af af−1 af−n+2 af−n+1 af−n
Input dimension: [n frequency bins,1],
af - averaged magnitude FFT bin at time t.
OR
pt
at
pt−1
at−1
pt−n+2
at−n+2
pt−n+1
at−n+1
pt−n
at−n
Input dimension: [n timesteps,2],
at, pt - amplitude and phase at time t.
LSTM-1, 128 cells
LSTM-2, 128 cells
Fully connected linear layer
Softmax layer
P(Classes)
. . .
. . .
. . .
. . .
• Time domain amplitude-phase model
• Learns temporal amplitude-phase variations
• Variable length model
• Symbol rate independent representations
Rajendran S, Meert W, Giustiniano D, Lenders V, Pollin S. Deep Learning
Models for Wireless Signal Classification with Distributed Low-Cost Spectrum
Sensors. IEEE Transactions on Cognitive Communications and Networking.
2018 Jun 10
26/35

70. Model pros
• Classification accuracy
• Quantized models
• Semi-supervision
−20 −10 0 10 20
0
20
40
60
80
100
SNR(dB)
ClassificationAccuracy(%)
CNN Nonlinear-SVM Linear-SVM
K-nearest neighbors Random Forest:150 Gaussian Naive Bayes
27/35

71. Model pros
• Classification accuracy
• Quantized models
• Semi-supervision
−20 −10 0 10 20
0
20
40
60
80
100
SNR(dB)
ClassificationAccuracy(%)
CNN LSTM:2-layer Nonlinear-SVM
Linear-SVM K-nearest neighbors Random Forest:150
Gaussian Naive Bayes
27/35

72. Model pros
• Classification accuracy
• Quantized models
• Semi-supervision
• Sensor has processing power and memory limitations
• Sending full IQ data to the backend is costly
• Run models on the sensor itself
−20 −10 0 10 20
20
40
60
80
100
SNR(dB)
ClassificationAccuracy(%)
CNN: full precision
CNN: binary
LSTM: full precision
LSTM: TW_4BA
LSTM: TW_FA
27/35

73. Model pros
• Classification accuracy
• Quantized models
• Semi-supervision
0 5 10 15 20
40
60
80
SNR(dB)
ClassificationAccuracy(%)
Fully supervised LSTM,
100% labeled samples
Fully supervised LSTM,
10% labeled samples
Fully supervised LSTM,
20% labeled samples
Semi-supervised Info-GAN,
10% labeled, 90% unlabeled
Semi-supervised Info-GAN,
20% labeled, 80% unlabeled
27/35

74. Signal classification demo
• WiFi, Bluetooth
classification
• 2.4GHz band
• LSTM Classifier
28/35

75. Situational awareness for spectrum sharing
Do not try to transmit in all
time frequency slots. That’s
impossible. Instead...only try to
realize the truth...
What truth?
There are no slots
There are no slots?
Then you will see that it is
not the slot that bends; It
is only yourself
Adapated from "The Matrix"

76. Exploiting spectrum knowledge
• How much learning/situational awareness is needed?
• DARPA spectrum challenge
• Colosseum: a complex SDR testbed for electromagnetic space
• 128 two-antenna SDR units
• Closed framework and solutions
• DySPAN spectrum challenges
• Two challenges were organized
• Prioritized learning and feedback
• All solutions and framework made public
29/35

77. Spectrum challenge-2
• Challenge setup
• Challenge phases
• Results
• Challenge conclusion
Teams
Given
PU Tx PU Rx
Visual-
ization
Real-time
Database
SU Tx SU Rx
Layer-2 packets Layer-2 feedback
Rajendran Sreeraj, Van den Bergh Bertold, Vermeulen Tom, Pollin Sofie ”,IEEE 5G spectrum sharing challenge:
A practical evaluation of learning and feedback,IEEE Communications Magazine,54,11,210-216,2016,IEEE.
30/35

78. Spectrum challenge-2
• Challenge setup
• Challenge phases
• Results
• Challenge conclusion
• Situation awareness
• SU should detect 10 scenarios
• Agile spectrum usage can be tested in this phase
• Agile spectrum usage
• SU should share the spectrum for data
transmission
• SU is penalized for interference
• Spectrum usage metric calculated in this phase
Wunsch Felix, Paisana Francisco, Rajendran Sreeraj, Selim Ahmed, Alvarez Pedro, Müller Sebastian,
Koslowski Sebastian, Van den Bergh Bertold, Pollin Sofie,DySPAN Spectrum Challenge: Situational
Awareness and Opportunistic Spectrum Access Benchmarked,IEEE Transactions on Cognitive
Communications and Networking,3,3,550-562,2017,IEEE.
30/35

79. Spectrum challenge-2
• Challenge setup
• Challenge phases
• Results
• Challenge conclusion
KIT Trinity Drexel FORTH
101
102
103
104
105
106
Score
Run-1 Run-2
(a) Agile spectrum usage score
KIT Trinity Drexel FORTH
0
25
50
75
90
100
Classificationaccuracy(%)
Run-1 Run-2
(b) Situation awareness score
30/35

80. Spectrum challenge-2
• Challenge setup
• Challenge phases
• Results
• Challenge conclusion
• Machine learning especially deep learning can be
leveraged for improving situational awareness
• Situational awareness and transmission prediction are
key to the success
• Challenge results back the need of frameworks like
Electrosense
30/35

81. Conclusion
Data collection
• Crowdsourcing
• Low-cost sensors and
Bigdata
31/35

82. Conclusion
Data collection
• Crowdsourcing
• Low-cost sensors and
Bigdata
Anomaly detection
• Unsupervised deep
learning models
provides good
performance
31/35

83. Conclusion
Data collection
• Crowdsourcing
• Low-cost sensors and
Bigdata
Anomaly detection
• Unsupervised deep
learning models
provides good
performance
Semi-supervision
• Semi-supervision can
improve anomaly
detection
31/35

84. Conclusion
Signal classification
• Fast signal classification
• Machine learning accelerators
and edge computing
32/35

85. Conclusion
Signal classification
• Fast signal classification
• Machine learning accelerators
and edge computing
Knowledge exploitation
• Spectrum awareness and
transmission prediction
• Spectrum monitoring
frameworks + machine learning
32/35

86. Future work
• Machine learning
• Interpretable feature extraction
• Expert feature extraction
• Models for subsampled signals
• Anomaly geo-localization
• Crowd engagement and sustainability
• Sensor data quality
33/35

87. Selected publications
Articles in International Journals
• Rajendran, Sreeraj; Meert, Wannes; Lenders, Vincent; Pollin, Sofie; 2019. Unsupervised Wireless Spectrum Anomaly
Detection with Interpretable Features. IEEE Transactions on Cognitive Communications and Networking; 2019.
• Rajendran, Sreeraj; Meert, Wannes; Giustiniano, Domenico; Lenders, Vincent; Pollin, Sofie; Deep Learning Models for
Wireless Signal Classification with Distributed Low-Cost Spectrum Sensors. IEEE Transactions on Cognitive
Communications and Networking; 2018; Vol. 4; iss. 3; pp. 433 - 445.
• Rajendran, Sreeraj; Calvo-Palomino, Roberto; Fuchs, Markus; Van den Bergh, Bertold; Cordobés, Héctor; Giustiniano,
Domenico; Pollin, Sofie; Lenders, Vincent; Electrosense: Open and Big Spectrum Data. IEEE Communications Magazine;
2018; Vol. 56; iss. 1; pp. 210 - 217.
• Rajendran, Sreeraj; Van den Bergh, Bertold; Vermeulen, Tom; Pollin, Sofie; IEEE 5G Spectrum Sharing Challenge: A Practical
Evaluation of Learning and Feedback. IEEE Communications Magazine; 2016; Vol. 54; iss. 11; pp. 210 - 216.
Journals submitted and under review
• Rajendran, Sreeraj; Lenders, Vincent; Meert, Wannes; Pollin, Sofie; 2019. Crowdsourced wireless spectrum anomaly
detection. IEEE Transactions on Cognitive Communications and Networking; 2019 –Submitted.
Book chapter under review
• Rajendran, Sreeraj and Pollin, Sofie; Large scale wireless spectrum monitoring: Challenges and Solutions, Wiley book on
Spectrum Sharing, 2019.
Articles in Conferences
• Rajendran, Sreeraj; Meert, Wannes; Lenders, Vincent; Pollin, Sofie; 2018. SAIFE: Unsupervised Wireless Spectrum Anomaly
Detection with Interpretable Features. IEEE International Symposium on Dynamic Spectrum Access Networks; 2018
Publisher: IEEE
34/35

88. Thank You
35/35