cognitive rf front-end
DESCRIPTION
Description of new approach for designing receiver RF front-ends. This approach is called Cognitive RF Front-end.TRANSCRIPT
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A Probabilistic Performance Metric
forRF Front-ends in Wireless Devices
Eyosias Yoseph Wireless@VT
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Presentation Thesis
Traditionally, deterministic metrics are used to quantify the performance of RF front-ends These metrics tell the performance of the RF front-end under
specific scenario They are not convenient to define the reliability of RF front-ends
In our research, we are developing probabilistic metrics These metrics can be used to define the reliability of RF front-
ends
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Content
Wireless history: from Marconi to Smartphones The Future of Wireless Probabilistic RF front-end metrics
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Content
Wireless history: from Marconi to Smartphones The Future of Wireless Probabilistic approach of modeling RF front-ends
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Wireless History: The Birth of Radio 1895: First long
distance radio communication (“Wireless telegraph”)
1902: First wireless trans-Atlantic telegraph
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Wireless History: Voice
1912: First radio based voice broadcast 1920: First mobile radios in Detroit police cars 1935: Frequency modulation was invented
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Wireless History: Mobile Telephony
1946: Public switching network based Mobile telephony was started 1972: Motorola made the first phone call from portable mobile
telephone
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Wireless History: Cellular Systems
Cellular systems were introduced in the 1980’s
Cellular systems divide the geographical areas into smaller cells
Each cell has its own tower Frequency is re-used between
cells Cellular technology presented a
significant increase in network capacity
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Wireless History: Frequency Reuse
f1 f2
f3
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Wireless History: Frequency Reuse
f1 f2
f3f3
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Wireless History: Smartphones Smartphones
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Trends in Wireless
Exponential growth Mobile video is the main source of traffic
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Trends in Wireless
FCC: The demand for spectrum is not matched by its availability
PCAST: This has huge opportunistic cost on the economy
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Content
Wireless history: from Marconi to Smartphones The Future of Wireless Probabilistic RF front-end metric
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The Future of Wireless: Spectrum Sharing
Fixed allocation based spectrum management has been used for decades PCAST recommended that the federal government shares its spectrum with
commercial users Example: 3.5 GHz radar bands
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The Future of Wireless: Spectrum Sharing
Fixed allocation based spectrum management has been followed for decades PCAST recommended a sharing federal spectrum for commercial use
Example: 3.5 GHz radar bands
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The Future of Wireless: Small Cells
Further reducing the size of the cells Example: WiFi router type base-stations
(femtocells) in each home
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Millimeter Wave 28 GHz and higher are being considered Easy to obtain 1 GHz of frequency Only for short range, line-of-sight communication Beam forming is crucial (multiple antenna use)
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Content
Wireless history: from Marconi to Smartphones The Future of Wireless Probabilistic RF front-end metric
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Poorly Selective Receivers
Upcoming wireless technologies likely contain poorly selective receivers
Spectrum Sharing
• Spectrum sharing uses tunable filter• Tunable filters have 10-20% bandwidth• At 1 GHz, this corresponds to 100 -200 MHz bandwidth
mmWave
• mmWave filters have Q 10• At 28 GHz, this corresponds to 2.8 GHz 3-
dB bandwidth
SAW filters can be as selective as 1 MHz at 1 GHz
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Receiver Selectivity 101fs/2
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPPre-selector
RF frequency
INPUT SEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DCMIXER OUTPUT
DSP frequency
DCADC OUTPUT
+fs/2-fs/2
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Receiver Selectivity 101fs/2
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPPre-selector
RF frequency
INPUT SEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DCMIXER OUTPUT
DSP frequency
DCADC OUTPUT
+fs/2-fs/2
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Receiver Selectivity 101fs/2
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPPre-selector
RF frequency
INPUT SEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DCMIXER OUTPUT
DSP frequency
DCADC OUTPUT
+fs/2-fs/2
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Receiver Selectivity 101fs/2
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPPre-selector
RF frequency
INPUT SEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DCMIXER OUTPUT
DSP frequency
DCADC OUTPUT
+fs/2-fs/2
What if it is impossible or hard to get sufficiently selective pre-selector filter for our receiver?
That is the problem in spectrum sharing based receiversThat is the problem in mmWave receivers
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Receiver Selectivity 101fs/2
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPPre-selector
RF frequency
INPUT SEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DCMIXER OUTPUT
DSP frequency
DCADC OUTPUT
+fs/2-fs/2
Wireless devices of the future will be poorly selective
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Selectivity and Reliability
Poor selectivity implies higher rate of “dropped-calls”
Hence, poor selectivity makes a wireless device less reliable
How can we improve the reliability of a poorly selective receiver (wireless device of the future)?
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Potential Solutions
Improving the filter technology MEMS SAW filters in mmWave range??
Filter technology did not show fast improvements in the past
Not very prospective
We propose re-defining selectivity using probabilistic performance metrics
We also propose the use of artificial intelligence to control the parameters of the receiver
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Receiver Selectivity
Signals outside the pass-band of the filter are rejected --- always
Input
Output Rejection
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Receiver Selectivity
Would this type of receiver work?
Input
What if a strong adjacent channel signal occurs only 0.01% of the time?
Level of rejection is not a reasonable reliablity and performance metric
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Receiver Selectivity
Would this type of receiver work?
Input
What if a strong adjacent channel signal occurs only 0.01% of the time?
We propose using probability of outage (“drop-call”) as reliablity and performance metric
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Practical Measurement
1 2 30
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Number of Active Signals
Prob
abilit
y of O
utage
("dr
op-ca
lls")
Filterless receiverModerately selective receiverHighly selective receiver
Without cognitive engine
Withcognitive engine
Number of Adjacent Channel Interferers
Prob
abilit
y of
Out
age
(“dr
op-c
alls”
)
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Strong Received Signals are Rare
Strong adjacent channel signals can be detrimental in a poorly selective receivers
But, probability of receiving a strong adjacent channel signal is very low Wireless@Virginia Tech showed that the probability of received signal
power is exponentially distributed in logarithmic domain
-100 -80 -60 -40 -20 0 200
0.2
0.4
0.6
0.8
1
Power, dBm
CDF
It is rare to receive a signal with power level more than -60 dBm
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Using Artificial Intelligencefs/2
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPPre-selector
RF frequency
INPUT SEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DCMIXER OUTPUT
DSP frequency
DCADC OUTPUT
+fs/2-fs/2
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Using Artificial Intelligencefs/2
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPPre-selector
RF frequency
INPUT SEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DCMIXER OUTPUT
DSP frequency
DCADC OUTPUT
+fs/2-fs/2
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Using Artificial Intelligencefs/2
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPPre-selector
RF frequency
INPUT SEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DCMIXER OUTPUT
DSP frequency
DCADC OUTPUT
+fs/2-fs/2
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Using Artificial Intelligencefs/2
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPPre-selector
RF frequency
INPUT SEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DCMIXER OUTPUT
DSP frequency
DCADC OUTPUT
+fs/2-fs/2
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Using Artificial Intelligencefs/2
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPPre-selector
RF frequency
INPUT SEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DCMIXER OUTPUT
DSP frequency
DCADC OUTPUT
+fs/2-fs/2
By intelligently Controlling the parameters of the receiver, the desired signal can be protected from interference of adjacent channel signals - without using RF filters.
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Proposed Receiver Architecture
Receiver Receiver
Towards filter-less receivers
Receiver
Cognitive Engine
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Practical Measurement
1 2 30
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Number of Active Signals
Prob
abilit
y of O
utage
("dr
op-ca
lls")
Filterless receiverModerately selective receiverHighly selective receiver
Without cognitive engine
Withcognitive engine
Number of Adjacent Channel Interferers
Prob
abilit
y of
Out
age
(“dr
op-c
alls”
)
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Conclusion
Reliability of a wireless device is not necessarily defined by the selectivity of its filter
Using probability of outage (“drop-call”) may be a better reliability metric
This is particularly true in dynamically changing spectrum scenarios in which strong received signals are rare
Adding a cognitive engine can improve the reliability of a poorly selective radio
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Thank you, Questions ? Comments?