Signature Based Spectrum Sensing Algorithms for IEEE 802'22 WRAN

1
Signature Based Spectrum Sensing Algorithms for
IEEE 802.22 WRAN

• Hou-Shin Chen, Wen Gao, and David G. Daut

Dept. of Electrical and Computer Engineering,
Rutgers University Thomson Corporate Research,
Princeton
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Outline

• Why spectrum sensing
• Signatures of the ATSC DTV signal
• Field Sync correlation detector
• Segment Sync autocorrelation detector
• Simulation procedures
• Simulation results
• Conclusions

3
Why Spectrum Sensing ?

• Wireless spectrum is clouded today.
• When licensed bands are not used, we should be
  able to utilize these bands.
• This is the idea of negotiated, or opportunistic,
  spectrum sharing which is called Cognitive Radio
  or Smart Radio.
• We have to sense the spectrum before we operate
  CR and during operation we have to monitor the
  spectrum.
• Recently, IEEE 802.22 group was set to implement
  CR in the digital TV broadcasting bands.

4
Signatures of the ATSC DTV Signal

• A two-level (binary) four-symbol data Segment
  Sync is inserted at the beginning of each data
  segment of the ATSC DTV signal.

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Signatures of the ATSC DTV Signal

• Multiple data segments (313 segments) comprise a
  data field. The first data segment in a data
  field is called the data field sync segment
  (Field Sync).

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Field Sync Correlation Detector

• Use a matched filter that matches the field sync
  pattern
• 1 -1 -1 1 PN511PN63zeros(1,63)PN63
• Decision statistics the maximum magnitude value
  of the field sync correlator output within a
  field (24.2ms)
• ATSC DTV signal is a VSB modulated signal, and
  therefore instead of using binary sequence, we
  should use a VSB modulated sequence.

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Some Observations

• The effects of multi-path fading channel and
  frequency offset will severely destroy the
  detection ability of the correlation detecting
  method.
• The time difference between two consecutive data
  Segment Sync is only 0.077 ms (828 symbols) which
  is very short, we can assume that they encounter
  the same channel effects including frequency
  offset, timing offset, and multi-path fading
  effect.
• Thus, we use autocorrelation of the two
  consecutive data Segment Sync as our basic
  approach to eliminate channel effects.

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Segment Sync Autocorrelation Detector
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Maximum Combining Segment Sync Autocorrelation
Detector

• When we accumulate a large number of data Segment
  Sync elements, i.e., when the sensing time is
  long.
• Timing drift effects will restrict the
  improvement of the performance that comes from a
  longer sensing time.
• Slice the total sensing time into several time
  slots and then apply a SSAD detector to each time
  slot.
• Use the average of the maximum absolute value of
  autocorrelation of each time slot as our
  detection statistic.

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Simulation Procedures
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Labels and Their Corresponding Files

• A WAS_49_34_06142000_opt
• B WAS_49_39_06142000_opt
• C WAS_47_48_06132000_opt
• D WAS_32_48_06012000_OPT
• E WAS_3_27_06022000_REF
• F WAS_06_34_06092000_REF
• G WAS_51_35_05242000_REF
• H WAS_68_36_05232000_REF
• I WAS_86_48_07122000_REF
• J WAS_311_35_06052000_REF
• K WAS_311_36_06052000_REF
• L WAS_311_48_06052000_REF

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Simulation results on Field-Sync based DTV signal
detection
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Simulation results on Field-Sync based DTV signal
detection
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Simulation results on Segment-Sync based DTV
signal detection
SNR (dB)
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Simulation results on Segment-Sync based DTV
signal detection
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Conclusions

• For Field Sync correlation detector, employing
  complex pilot sequence has better performance
  than using binary pilot sequence.
• For Segment Sync autocorrelation detector, doing
  maximum peak combining can improve detection
  performance.
• Both the results of Segment Sync detector and
  Field Sync detector provide high confidence about
  the presence of ATSC DTV signals when SNR equals
  -10 dB.