19-05-0034-00-0000-DAA_for_MB-OFDM.ppt
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Transcript 19-05-0034-00-0000-DAA_for_MB-OFDM.ppt
September 2005
doc.: IEEE 802.19-05/0034r0
Detect and Avoid for MB-OFDM
Date: 2005-09-20
Authors:
Name
Company
Address
Jim Lansford
Alereon
7600C N. Capital of +1 512 345
Texas Hwy
4200 x2166
Suite 200
Austin, TX 78731
Phone
email
Jim.lansford@alereon.
com
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Submission
Slide 1
Jim Lansford, Alereon
September 2005
doc.: IEEE 802.19-05/0034r0
Abstract
Detect and Avoid (DAA) is under discussion by both
Japanese and European regulatory agencies as a noncollaborative coexistence technique. DAA requires the
receiver to detect the presence of interference and
suppress energy in that portion of the band. MBOFDM is particularly well suited to implement DAA,
because the FFT can be used as a channelized
radiometer, and the IFFT can be used to sculpt the
transmit spectrum.
Submission
Slide 2
Jim Lansford, Alereon
September 2005
doc.: IEEE 802.19-05/0034r0
Background
• UWB was developed as an “underlay”
– FCC bought into the concept
– Most of the rest of the world has been sceptical
– It’s a coexistence problem
• What are the issues?
– Detecting the presence of an “incumbent” signal (FWA)
– Dropping emissions in the shared band so that interference on the
“incumbent” is minimized
• This is a form of cognitive radio
– Similar to “waterfilling”
• TBD: Detection level/confidence and suppression level
Submission
Slide 3
Jim Lansford, Alereon
September 2005
doc.: IEEE 802.19-05/0034r0
How does MB-OFDM implement DAA?
• Detect: Channelized radiometer
– 128 channel FFT inherent in the design
– Integrate spectra to achieve desired Pd
• Avoid: Bandstop filter (frequency domain)
– 128 point IFFT
– Additional signal processing techniques can increase notch depth,
subject to RF linearity
Detect
90°
Mixer &
Filter
Submission
ADC
FFT/
IFFT
AGC
DAC
Despread
Demap
Deinterleave
Depuncture
Filter/
Decimate
Interp.
/Filter
Slide 4
Viterbi
Chan Est
CFO
Equalize
Spreading
Mapping
Interleave
Puncture
Convolutional
Coder
MAC/PHY Interface
Mixer &
Filter
Jim Lansford, Alereon
September 2005
doc.: IEEE 802.19-05/0034r0
Detect (1)
• Channelized radiometers have been used for decades
– Narrowband detection in a wideband channel
– Used in Radar and communication intercept receivers
– Theory well developed
• If FFT bins contain noise alone, distribution is Rayleigh
• If narrowband signal + noise, distribution is Rician
(●)2
∫
<
>
FFT
Submission
P(N)
Slide 5
P(S+N)
Jim Lansford, Alereon
September 2005
doc.: IEEE 802.19-05/0034r0
Detect (2)
• Averaging spectra reduces the
variance of the distribution
• For ensemble average of N
spectra, variance within a bin
decreases by 1/N, standard
deviation by 1/sqrt(N)
• Thus, detection probability
can be made arbitrarily close
to 1 but integration time can
become large
• There is a considerable body
of research for fluctuating
signals (such as pulsed
beacons)
Submission
Slide 6
Jim Lansford, Alereon
September 2005
doc.: IEEE 802.19-05/0034r0
Detect (3)
• For MB-OFDM system, FFT resolution is
528/128=4.125MHz
• kTB for a single tone is -174+66= -108dBm
• Assume 6.6dB NF in RF => N0= -101dBm
• FWA characteristics
– 5 or 10MHz BW
– 4W uplink, Downlink power <-90dBm at UWB system
• For FWA downlink, detection probability at -90dBm
should be more than adequate – unless DL is in a fade
– Uplink can always be easily detected
– Uplink confirms local activity after downlink detected
Submission
Slide 7
Jim Lansford, Alereon
September 2005
doc.: IEEE 802.19-05/0034r0
Detect (4)
• Detection test setup
– This is a demo
– Fading channel tests will be done later
UWB
transmitter
FWA signal
Generator
Submission
UWB
Receiver
+
Rhode & Schwartz
Spectrum Analyzer
Slide 8
Jim Lansford, Alereon
September 2005
doc.: IEEE 802.19-05/0034r0
Detect (5)
Ref
-80 dBm
* Att
0 dB
* RBW
1 MHz
* VBW
3 MHz
* SWT
10 ms
Ref
-80
-80 dBm
* Att
0 dB
* RBW
1 MHz
* VBW
3 MHz
* SWT
10 ms
-80
A
1 RM *
AVG
A
1 RM *
-85
AVG
-90
-85
-90
PA
PA
-95
-95
-100
-100
-105
-105
-110
-110
Center
3.31 GHz
Date: 12.SEP.2005
20 MHz/
01:04:29
Span
Center
200 MHz
Date: 12.SEP.2005
Spectrum analyzer
Noise floor
Submission
3.31 GHz
20 MHz/
Span
200 MHz
01:04:46
MB-OFDM signal
(zoomed: 200MHz)
Slide 9
Jim Lansford, Alereon
September 2005
doc.: IEEE 802.19-05/0034r0
Detect (6)
Ref
-80 dBm
* Att
0 dB
* RBW
1 MHz
* VBW
3 MHz
* SWT
10 ms
Ref
-80 dBm
* Att
0 dB
* RBW
1 MHz
* VBW
3 MHz
* SWT
10 ms
-80
-80
A
A
1 RM *
AVG
1 RM *
-85
AVG
-85
-90
-90
PA
PA
-95
-95
-100
-100
-105
-105
-110
-110
Center
3.31 GHz
Date: 12.SEP.2005
20 MHz/
01:05:25
“FWA” Signal
@ -90dBm
Submission
Span
Center
200 MHz
3.31 GHz
Date: 12.SEP.2005
20 MHz/
Span
200 MHz
01:05:05
“FWA” Signal + OFDM Signal
(10ms averaging, MB-OFDM @ -81dBm)
Slide 10
Jim Lansford, Alereon
September 2005
doc.: IEEE 802.19-05/0034r0
Avoid (1)
• Spectral “notches” have been discussed widely in
802.15.3a in reference to MB-OFDM
• Generally, can be considered frequency domain
bandstop filters (FIR)
– Like any FIR filter, intentional ISI can be introduced to control
notch depth and width
– EVM of RF chain must be consistent with desired depth
• Typically, these notches can be 15-20dB in depth
• More advanced techniques are under evaluation to
achieve deeper notches
Submission
Slide 11
Jim Lansford, Alereon
September 2005
doc.: IEEE 802.19-05/0034r0
Avoid (2)
Ref
-80 dBm
* Att
0 dB
* RBW
1 MHz
* RBW
1 MHz
* VBW
3 MHz
* VBW
3 MHz
* SWT
10 ms
* SWT
10 ms
Ref
-80
-80 dBm
* Att
0 dB
-80
A
1 RM *
AVG
A
1 RM *
-85
AVG
-90
-85
-90
20MHz
PA
PA
-95
-95
-100
-100
-105
-105
-110
Center
-110
3.31 GHz
Date: 12.SEP.2005
20 MHz/
01:06:05
Span
200 MHz
Center
Date: 12.SEP.2005
20 MHz/
Span
200 MHz
01:05:44
MB-OFDM signal plus “FWA”
inside 20MHz notch
MB-OFDM signal with
20MHz notch (5 tones)
Submission
3.31 GHz
Slide 12
Jim Lansford, Alereon
September 2005
doc.: IEEE 802.19-05/0034r0
Summary
• These measurements were made on an actual MBOFDM system
• BER/PER increase from removal of five tones is
insignificant
– Erasure performance has been well documented
• Detection algorithm works, but needs further study
– Fluctuating FWA signal
– Strategies for downlink+uplink detection
• Collaborative coexistence (in a laptop, for example)
would work even better
• Spectral sculpting really works…
Submission
Slide 13
Jim Lansford, Alereon
September 2005
doc.: IEEE 802.19-05/0034r0
References
• M.Skolnik, Introduction to Radar Systems, McGraw Hill,
1980
• Johanna Vartiainen, “Concentrated Signal Extraction using
Consecutive Mean Excision Algorithms,” Proceedings of
the 2005 Finnish Signal Processing Symposium FINSIG'05, August, 2005, Kuopio, Finland, pp. 87-90.
• S. M. Kay, Fundamentals of Statistical Signal Processing:
Detection Theory., Prentice Hall, 1998.
Submission
Slide 14
Jim Lansford, Alereon