Cognitive RF Front-end

A Probabilistic Performance Metric

forRF Front-ends in Wireless Devices

Eyosias Yoseph Wireless@VT

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

Content

Wireless history: from Marconi to Smartphones The Future of Wireless Probabilistic RF front-end metrics

Content

Wireless history: from Marconi to Smartphones The Future of Wireless Probabilistic approach of modeling RF front-ends

Wireless History: The Birth of Radio 1895: First long

distance radio communication (“Wireless telegraph”)

1902: First wireless trans-Atlantic telegraph

http://en.wikipedia.org/wiki/File:Guglielmo_Marconi.jpg

Wireless History: Voice

1912: First radio based voice broadcast 1920: First mobile radios in Detroit police cars 1935: Frequency modulation was invented

Wireless History: Mobile Telephony

1946: Public switching network based Mobile telephony was started 1972: Motorola made the first phone call from portable mobile

telephone

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

Wireless History: Frequency Reuse

f1 f2

f3

Wireless History: Frequency Reuse

f1 f2

f3f3

Wireless History: Smartphones Smartphones

https://www.google.com/url?q=http://www.herworldplus.com/lifestyle/gadgets/samsung-tops-apples-smartphone-sales&sa=U&ei=bbNtU5PuIoK2yATm6oH4BQ&ved=0CD4Q9QEwCA&sig2=0ic7rTv31lXPRgneXqUMaQ&usg=AFQjCNHygVx-rn5tTeX0Uj1DjQ361LMqzQ

Trends in Wireless

Exponential growth Mobile video is the main source of traffic

Trends in Wireless

FCC: The demand for spectrum is not matched by its availability

PCAST: This has huge opportunistic cost on the economy

Content

Wireless history: from Marconi to Smartphones The Future of Wireless Probabilistic RF front-end metric

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

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

The Future of Wireless: Small Cells

Further reducing the size of the cells Example: WiFi router type base-stations

(femtocells) in each home

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)

Content

Wireless history: from Marconi to Smartphones The Future of Wireless Probabilistic RF front-end metric

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

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


fLO fs

Antenna


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


fLO fs

Antenna


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

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)?

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

Receiver Selectivity

Signals outside the pass-band of the filter are rejected --- always

Input

Output Rejection


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


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

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”

)

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

Using Artificial Intelligencefs/2

fLO fs

Antenna


RF frequency

INPUT SEPECTRUM

RF frequency

fLO

LNAOUTPUT

Baseband frequency

DCMIXER OUTPUT

DSP frequency

DCADC OUTPUT

+fs/2-fs/2

Using Artificial Intelligencefs/2

fLO fs

Antenna


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.

Proposed Receiver Architecture

Receiver Receiver

Towards filter-less receivers

Receiver

Cognitive Engine

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”

)

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

Thank you, Questions ? Comments?

Cognitive RF Front-end

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