Koilpillai CR Keynote

8/2/2019 Koilpillai CR Keynote

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Koilpillai / Dec 2010 / Cognitive Radio 2

IWCR 2010 Cognitive Radio Keynote IITM Proprietary Information

Electrical EngineeringIIT MadrasOutline of Presentation

Wireless today SDR, Wireless Broadband,

Cognitive Radio a paradigm shift

Different approaches Spectral sensing

Multi-Carrier Techniques

802.22 A CR-based standard

Major CR initiatives

Cognitive Radio Test-beds

Cognitive Applications in wirelessFocus on the signal processing aspects in all topics


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Electrical EngineeringIIT MadrasWireless in the Media

Cellular

India crosses 635M subs

Growth of broadband wireless

HSPA, EV-DO, WiMAX

GSM subscribers crossed 4.7 Billion ...

Convergence, Quad play

Voice, Voice, Data, Video, Mobility

Telephony, Internet, TV, Cellular

Spectrum auctions April - June 2010

Introduction of 3G - 4G Technology

Broadband Wireless Access ITU Activity IMT Advanced (4G)

UMTS + LTE > 535M

WiMAX deployments: 593


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Electrical EngineeringIIT Madras

Cellular Evolution Timeline

1G (AMPS, NMT, TACS, ) 1981 Analogue voice transmission

2G (GSM, IS-54, PDC, IS-95) 1991 - 95 Digital cellular Digital voice, low-speed circuit data (9.6 Kbps), SMS

2.5G (GPRS, cdmaOne) 1999 - 00 Introduction of packet data Improved voice, medium speed CS and PS data (~100 Kbps), enhanced SMS

3G (WCDMA, EDGE, cdma2000) 2002 - 03 IMT-2000 requirements, Improved voice, high speed PS data (384Kbps - 2 Mbps) Improved spectral efficiency and capacity, Multimedia applications

3.5G (HSPA, 1xEV, IEEE 802.16e) 2003 - present High speed packet data (2-14 Mbps)

4G (LTE, IEEE 802.16m, ) 2010+


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Electrical EngineeringIIT MadrasEvolution beyond 3G

GSM

GPRS WCDMA

Rel. 7

Rel. 6Rel. 5

(HSDPA)

1xEV-DV

1xEV-DOcdmaOne cdma2000

IEEE802.16 d/e

IEEE802.16 m

3.5G/4G - Focus on high data rates, spectral efficiency

LTELTE-Adv

Super 3G = 3.9G


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Electrical EngineeringIIT MadrasFamily of Networks

Hierarchy of terrestrial networks

India has all scenarios

Different types of wirelessnetworks

Wide range of data rates, range

Significant developments in

WAN / MAN

Range of environments

Dense urban

Inter-BS distance < 500m

Sparse rural

Unlicensed devices

Ref: Cordeiro et al., IEEE 802.22: The First Worldwide

Wireless Standard based on Cognitive Radio, IEEE, 2005


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Broadband Usage

Compelling case for

broadband wireless

access (BWA) in India Broadband, wireless

Quad play

Voice, Data, Video,

Mobility

Ref: Kori (Alcatel Lucent)

WiMAX Overview, Bangalore,

Jan 2009


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IMT Advanced ITU initiative (started 06/2003)

Development of systems beyond IMT-2000 (open and global standards)

Enhanced IMT-2000 systems should support Evolution of new applications, products, and services

Aspects: Enhanced mobile access, Seamless networking.

Data rate requirements:

100 Mbits/s for high mobility 1 Gbits/s for low mobility (nomadic/local wireless) access

Timeline: Initial proposals for IMT-Advanced submitted - 2009

Standardization 2009 - 2011 Current global standards

IEEE 802.16m, 3GPP LTE Advanced

All based on Orthog Freq. Division Multiple Access (OFDMA) & MIMO


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Electrical EngineeringIIT MadrasIndia Spectrum Scenario

Spectrum auctions (3G+BWA) held during April-June 2010 2x20 MHz TDD (in 2.3 GHz and 2.5 GHz), 22 Service Areas

In 3G core band, two-four(5+5) MHz FDD slots, 22 Service Areas

Some differences based on geographic location

Auction method

Start from reserve price

Bidders will be asked to increase in 10% steps

Bidding continues until number of bidders left = no. of available frequency slots

Total revenue from auctions

3G Spectrum = Rs. 67,719 crores

BWA Spectrum = Rs 38,543 crores A deviation from the earlier model (for 2G spectrum)

Low license fee + revenue sharing model

Very successful model


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Electrical EngineeringIIT MadrasMarket Case study

Dense Urban (Case: Mumbai) 70% of 16M people

In area of 600 sq Km

~3733 households per sq kmAssuming 5 per household

~ 50% wireless internet subscribers

~ 1866 wireless internet/sq km

cell radius = 0.75 km

~ 3300 subscribers/cell

Assuming 5 competitive operators in each area =>

660 subscribers/operator/cell

Typical scenarios evaluated by Indian operators Participating Operators

Tata Teleservices, BSNL, Airtel, Reliance, Hutch, IDEA Cellular, Aircel, VSNL, MTNL


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Software Defined Radio (SDR)


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Radio Functionality Evolution

Source: Prasad et al. IEEE Comm Magazine, April 2008


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Electrical EngineeringIIT MadrasSoftware Defined Radio (SDR)

J. Mitola, The software radio architecture

IEEE Communications Magazine, May 1995


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Electrical EngineeringIIT MadrasDSP view of Software Radio

Software radio approach (cost effective and computationally efficient) Extensive use of DSP techniques (including A/D)

Multirate DSP and filtering

Current situation for wireless systems Multiple standards and MIMO (cellular 3/3.5/4G, WiMAX, WLAN, Systems with different bandwidth, channel spacing, symbol rates

Narrowband systems requiring large number of transceivers

Conventional approach (multi-channel receiver)

SDR approach (multi-channel receiver)

RF stage

Filter, mixerBandpass filter

IF stage

Filter, (I,Q) mixerA/D

Baseband

processingr

RF stage

Filter, mixerBandpass filter

IF stage

(I,Q) mixer

High speed

A/D

DSP-based

Channeliser

Baseband

processing


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A/D Challenges in Software Radio

Very stringent A/D requirements Speed, dynamic range

Dynamic range determined by max. variation bet. strongest and weakest signal

No clipping of strongest signal Enough SNR to detect weakest signal

SDR Flexible and efficient receiver and transmitter architectures

SDR poses system design challenges clocks, sampling rate, filtering,

Very stringent real-time requirements

Frequency

Quantization noise

Weak signal

Strong signal

Mag

nitudeResponse


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Electrical EngineeringIIT MadrasCOTS SDR Architecture

Ref: www.vanu.com

Commercial product Vanu Inc

Multistandard

GSM / GPRS / EDGE Cdma / EV-DO

Flexibility

Scaleability

Cost-effectiveness


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Vanu SDR Architecture

Ref: www.vanu.com


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Electrical EngineeringIIT MadrasSDR Summary

Many technical challenges have been solved

SDR now commercially viable and attractive

Drivers for SDR Advances in processors, DSPs, FPGAs, High speed, high-resolution A/D,

Multi-standard support, MIMO capability,

Efficient software tools and structures

SDR: A flexible platform New technology development such as 4G systems

Technology migration

Focus on basestations and not user equipment (UE)

Numerous national and international initiatives

Multiple SDR test beds

Open-source material available

SDR Forum an active group

The next step in SDR Migration towards Cognitive Radio


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SDR Cognitive Radio


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Electrical EngineeringIIT MadrasCognitive Radio Motivation

Increasing demand for radio spectrum Broadband wireless demand is rapidly growing

Current approach to spectrum allocation Fixed allocation to licensed users

Existing scenario Under-utilization of spectrum

Spatial and temporal spectral holes exist

Innovative approach to improve spectrum utilization Cognitive Radio

Initiated by FCC regarding secondary usage of spectrum

Cognitive Radio techniques much broader than DSA A radio that is aware of its surroundings and adapts intelligently Reed et al.


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Koilpillai / Dec 2010 / Cognitive Radio 21IWCR 2010 Cognitive Radio Keynote IITM Proprietary Information

Electrical EngineeringIIT MadrasCognitive Radio

Increasing demand for spectrumIncreasing demand for spectrum

Existing scenarioExisting scenario

UnderUnder--utilization of spectrumutilization of spectrum

Innovative approach to improve spectrumInnovative approach to improve spectrumutilizationutilization

Cognitive RadioCognitive Radio

Ref: M.A.McHenry, NSF Spectrum Occupancy Measurements Project Summary, August 2005

Ghasemi and Sousa, IEEE Communications Magazine, April 2008


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Electrical EngineeringIIT MadrasCR Scenario

CR: Opportunistic Unlicensed Access

To temporarily unused frequency bands (across the entire licensed radio spectrum)

A means to increase efficiency of spectrum usage

Stringent safeguards required On-going licensed operations should not be compromised

Spectrum sensing based access

White spaces primary user absent, and free of RF interferers

Gray spaces primary user absent but partially occupied by interferers

Black spaces primary user present

Main functionality of Cognitive Radios

Ability to reliably identify unused frequency bands

Sensing must be reliable and autonomous

Radically different paradigm

Secondary (unlicensed) users - Opportunistic use of unused licensed bands

Inceased utilization of radio spectrum


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Electrical EngineeringIIT MadrasTV Bands

White spaces exist in TV bands

Channels 14-69 in 470-806 MHz

6 MHz per channel (7 MHz or 8 MHz based on country)

Excellent propagation characteristics in this frequency band

Predictable spatio-temporal usage of TV channels

IEEE 802.22

An air-interface (PHY & MAC)

Opportunistic secondary access to TV spectrum

Safeguards to protect primary user

From secondary user interference

First commercial application of Cognitive Radio

Dynamic Spectrum Access (DSA)

Consider different options (to facilitate secondary users) Focus on reliable and robust spectrum sensing

@ -116 dBm


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SDR Cognitive Radio

Cognitive Radio =SDR + Sense + Learn + Adapt + Use


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Spectrum Sensing


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Electrical EngineeringIIT MadrasMethods of Spectrum Sensing

Energy Detector

Correlation-based detector Cyclostationarity-based detector

Hybrid Detector

Filter bank Method Multi-taper Method (MTM)

Performance of spectrum sensing

Sensing Criteria (Regulatory aspects)

Sensing Period

Detection Sensitivity


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Electrical EngineeringIIT MadrasAspects of Spectrum Sensing

Time-varying channel

Lack of apriori information

SNR level, interference,

Signal blockage (shadowing, hidden-node)

Primary signal transition ON OFF

Single shot detection vs. sequential detection

Interference due to other CR users

Decentralized vs centralized approach Cooperative sensing


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Electrical EngineeringIIT MadrasSpectrum Sensing

Optimum receiver

If structure of primary signal known

Optimum (in AWGN): Matched Filter (MF) followed by Threshold

Not practical for large # of primary users

Need for coherent detector for each transmitted signal

Alternative Energy Detector

Measures energy of signal in primary band

Compare with properly set threshold

Requires longer sensing time to achieve desired level of performance

Low computational complexity

ED - An attractive candidate for Cognitive Radio

Drawbacks of ED Cannot discriminate between sources of input energy (signal vs. noise)

Uncertainty of noise floor will degrade performance - Especially at low SNR

ED can be effectively combined with more robust detectors Hybrid Detectors


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Electrical EngineeringIIT MadrasSpectral Sensing

Binary hypothesis testing problem

Decision statistic (Energy detector)

Signal absent, is Central Chi-Square Variable with Ndegrees of freedom

When signal present, non-Central Chi-Square Variable

[ ]

signaldtransmitte

variancewithAWGNmean(zeronoise

signaldtransmitte

signal)receivedofwindownobservatiosamplepresentUserPrimary

absentUserPrimary

=

=

=

=

+=

=

][

][

(110][][][:

][][:

2

1

0

ny

nw

nx

N)(N-,,nnwnxnyH

nwnyH

w

L

0

1

1

0

2][

1

H

HandnyN

N

n


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Electrical EngineeringIIT MadrasEnergy Detector

Decision statistic

If N large, invoke CLT

0

1

1

0

2][

1

H

HandnyN

N

n


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Electrical EngineeringIIT MadrasEnergy Detector Performance

Primary user signal GSM signal

Signal bandwidth 200 KHz

One time slot 142 data symbols (incl. 26-bit midamble)

+ 14.25 symbols (Tail + Guard symbols)

Assume one sample / symbol

8 slots 1136 samples

Noise power = -116 dBm Using noise figure = 7 dB for receiver

Performance plots for Energy Detector

Good performance at very low SNRs ~ -8 dB

ED is a strong candidate for first stage of Hybrid detector


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Electrical EngineeringIIT MadrasSpectral Sensing Performance (2)

Robustness of energy detector enhanced if longer sensing period is used

Performance in fading is poorer than in AWGN (as expected)

Noise uncertainty causes major degradation in performance

Energy detector not suited as a stand-alone detector

Performance in fadingAWGN, Effect of sensing Period


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Correlation Detector

Well-suited for signals like GSM

Training sequence in every burst

Normal burst 26 symbols

Sync burst 64 symbols

Correlation provides processing gain

Against noise and interference

Better performance than ED

Computational complexity is higher

Needs to be done at oversampled rate

Used if primary user signal structure known

( ) ( )

=

=1

0

1)(

N

n

mnxnxN

mRxx


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Electrical EngineeringIIT MadrasCyclostationary Detector

Cyclostationary Property Digital modulated signals

Let be a zero-mean complex signal

The autocorrelation function is given by

If is periodic in CYCLOSTATIONARY Many of the communications signals have cyclostationarity property

Fourier Series representation

- cyclic autocorrelation function Discrete in , Continuous in

- conjg cyclic autocorrelation and FT conjg spectral corr density funcn

The values of for which and are non-zero depends on

Symbol rate

Cyclic signature of signal can be exploited to detect signal

Even in presence of noise and interference

( ) )()(, += txtxEtR xx)(tx

( ),tR xx t

( ) ( ) tjofmult

xx eRtR xx

2, =

( )

xxR

( )xx

R ( )fSxx

( )xx

R ( )fSxx


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Electrical EngineeringIIT MadrasCyclostationary Property of GMSK

Focus on GMSK signal (BT=0.3)

Discrete cyclic autocorrelation with peak at

Pioneering work by Gardner

Cyclic Autocorrelation Spectral correlation

2

sf=


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Electrical EngineeringIIT MadrasCyclostationary Detector

Exploiting multiple cyclic frequencies improves performance

Cyclostationary feature detector is sensitive to frequency-selective fading

Detector is also sensitive to frequency offset

Cyclostationary feature detector is an attractive method for overall performance

AWGN with 1 & 2 alpha Performance in fading


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Electrical EngineeringIIT MadrasLow-Complexity Hybrid Detector for GMSK

A robust detector for GSM signals (GMSK)

Energy detector has low computational complexity

Practical scenario of 1 dB noise uncertainty is used

If ED does not detect GSM signal, the correlation detector is used

Can achieve performance of the correlation based detector

Which has much higher computational complexity

Performance in fading


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Electrical EngineeringIIT MadrasSpectrum Sensing Summary

Many methods available

Properties utilised: Signal energy, Correlation, Cyclostationarity

Computational complexity and estimation time are important factors

Searching over a vast frequency range

Focus on robustness (at low SNR) and reliability

Minimize probability of missed detection

To avoid interference to primary user

Uncertainties regarding measurement

Noise and interference environment

Strong motivation for Hybrid Detectors

Sensing Criteria (Regulatory aspects) Sensing Period



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Electrical EngineeringIIT MadrasRegulatory Constraints

Satisfactory protection of primary user from harmful interference

Essential for realization of opportunistic spectrum access

Regulatory constraints

Sensing Periodicity (Tp) Period with which UL user must check for presence of primary user


Signal level at which the UL user must detect primary user reliably

Sensing Period (Tp) Max. time (delay) UL user unaware of reappearance of primary user

Max. duration of harmful interference

Determines QoS degradation of primary user

Delay of primary user in accessing channel

Depends on type of primary user service delay sensitivity

Must be set by regulator for each licensed band


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Electrical EngineeringIIT MadrasDetection Sensitivity

CR network must avoid harmful interference to PU

SIR of PU must not fall below threshold

Threshold depends on

PU receivers robustness to interference

Example: SIR = 34 dB for analog TV, and SIR = 23 dB for digital TV

Characteristics of interfering signal

Power, waveform, continuous vs intermittent, ) May influence choice of transmission option chosen by CR

Interference Range of secondary user

Max distance from Primary user at which harmful interference occurs

( )

( )

( )

( ) bs

p

PDLP

RLP

+=

ddL

R

PPP bsp

distanceatFading)and(ShadowinglossPath)(

receiverandertransmittPUbetweendistanceMax

ceinterferennoisebackgroundandSU,PU,ofPower,,

=

=

+=


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Electrical EngineeringIIT MadrasDetection Sensitivity

Threshold to be satisfied even if PU Rx is at edge of coverage

Provided SU maintains distance D

SU (CR) must be able to detect PU at distance (R+D)


( )

( ) bs

p

PDLP

RLP

+=

( )

n

p

P

RDLP +=min

Ref: Ghasemi et al., IEEE Communications Mag,

April 2008

ddL

R

PPP bsp

distanceatFading)and(ShadowinglossPath

receiverandrtransmittePUbetweendistanceMax

ceinterferennoisebackgroundandSU,PU,ofPower

=

=

+=

)(

,,


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Electrical EngineeringIIT MadrasDetection Sensitivity (3)

Detection Sensitivity ( )

The minimum SNR at which PU signal must be reliably detected

Example: Probability of detection = 0.99

Regulator must specify the following

can be obtained

Strong dependence between and

As

Also, if the number of secondary users increases,

( )

( )bs

p

PDLP

RLP

+=

( )

n

p

P

RDLP +=min

min

min,, pPR

min sP

sPmin

DPs or


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Electrical EngineeringIIT MadrasUncertainties in Sensing

Channel Uncertainty

Due to fading / shadowing of PU signal

Higher detection sensitivity requirement

Benefit from cooperative sensing (by multiple CR devices) Increasing sensing period Tp will help

Antenna diversity is a benefit

Noise Uncertainty

Rx noise power level not known a priori has to be estimated

Can have variations (due to temperature, calibration, )

Significant impact on performance if energy detector is used

Weak PU signal indistinguishable from noise If SNR falls below threshold

Feature detectors (e.g., cyclostationarity-based) are more robust


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Electrical EngineeringIIT MadrasUncertainties in Sensing

Aggregate Interference Uncertainty

PU may experience harmful

interference

If multiple CR networks active

Requires more sensitive detectors

Detect PU at distance

Alternative system level coordination among CR devices

Cooperative sensing

( )RDD +>

Channel Uncertainty

Due to fading / shadowing of PU signal

Noise Uncertainty

Ref: Ghasemi et al., IEEE Communications Mag, April 2008

C ti S i


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Cooperative Sensing

To alleviate the problem Cooperative Sensing

Independent measurements at different locations / CRs

Exchange of sensing information among CR nodes

Diversity gain achieved (handles fading and shadowing)

Improved probability of detecting PU

Without increasing sensitivity of each individual SU Rx

Introduces additional communications overhead

Requires functionality of Band Manager (Fusion Centre)

Collects information, makes decisions and shares information with all CR nodes

Shadowing is correlated over short distances

Cooperation to be done over larger distances (few nodes)

Different from conventional view of Mesh / Ad Hoc networks (many nodes in close

proximity)


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Multicarrier Techniques in CR

El t i l E i i


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Electrical EngineeringIIT MadrasMultiple Access

Frequency

Code

Time

Time

CDMA

WCDMA,

cdma2000

TD-SCDMA

802.11b

Frequency

Code

TDMA

GSM,

EDGEFrequency

Code

Time

FDMA

Frequency

Code

Time

OFDM

802.11 a/g

802.16

802.20?

El t i l E i iOFDM


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OFDM

Effect of Multipath CDMA loss of orthogonality

More severe if spreading factor is low

TDMA need for complex equalization

More severe for higher baud rates OFDM attractive for high speed data in multipath fading

OFDM Orthogonal Freq Division Multiplexing (Multicarrier)

Narrow carriers low baud rate long symbol duration

An attractive candidate forbroadband wireless Efficient digital multicarrier implementation using DFT/IDFT

Opportunity to do optimized coding and modulation in each carrier

Maximize capacity utilization based on channel condition

A active area of research Issues: High peak-to-average ratio, sensitivity to frequency & timing errors

OFDM used for WLAN, WWAN, Digital Audio Broadcasting, 4G,

OFDM Multi-carrier Modulation Multi-tone Modulation

Electrical Engineering


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Electrical EngineeringIIT MadrasMulticarrier Techniques

Multicarrier techniques widely used in Cognitive Radio (PHY)

OFDM, Filterbank-based multicarrier, Multi-resolution filter banks

Spectrum sensing determine spectral holes

Spectrum usage communication Transmit data w/o interfering with Primary user

In non-overlapping parts of spectrum

Multicarrier techniques efficient and effective

CR transmission can be TDD or FDD

TDD has inherent advantages for CR

Tx and Rx in in same band knowledge of channel

Implicit sensing of channel during Rx period (Tx OFF)

802.22 WRAN standard focus on TDD

OFDM based



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Frequency

T

IM

E

Spectral Adaptation Waveforms

OFDM Carriers in Available Spectrum

Ref: B. Fette, SDR Technology Implementation for the Cognitive Radio, General Dynamics



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OFDM

Widely studied and well-understood (based on IFFT / FFT)

Used for spectral sensing

Underlying filter is the Rectangular window Poor side-lobe suppression

Significant interference between sub-carriers

Not suitable for spectral sensing / transmission (non-contiguous bands)

Acceptable for contiguous bands Approaches to consider

Muti-Taper Method (MTM) for spectral estimation

Filterbank Multi-Carrier

Filterbank-based approaches can overcome spectral leakage problems Less used than OFDM

Electrical EngineeringFilt b k i


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Electrical EngineeringIIT MadrasFilterbank view

Raised cosine filtering before FFT

Reduces side-lobes

Improved freq selectivity

At expense of lower time selectivity

Filterbank Multicarrier Length 6x256=1536,

256-channel filterbank

Ref: Boroujeny et al., IEEE Communications Mag, April 2008

( )( )

CP)(incl.periodsymbolOFDM

channel-suboffrequencyCentre

sinc

=

=

=

s

th

i

sii

T

if

TffKf 2)(

Frequency response of FFT filter

Electrical EngineeringM lti i T h i


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Multitaper Method (MTM)

Advanced, non-parametric spectral estimation method

A set of filters (Slepian 1978, Bell Labs)

Discrete Prolate Spheroidal Sequences (DPSS)

Optimal trade-off between

time selectivity and frequency selectivity

Combine the output of a family of filters

Near-optimal performance in spectral sensing (Haykin, 2005)

Example: A set of 5 DPSS based filters and their responses

Filterbank Method

Similar performance to MTM

Can be used for sensing and for transmission Lower computational complexity than MTM

MTM five filters of length 2048

Electrical EngineeringM lti i A h


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54

Multicarrier Approach

Orthogonal Frequency Division Multiplexing

Orthogonal Frequency Division Multiplexing with Filterbank

FB-MC subcarrier spectrum employing a square-rootraised cosine prototype lowpass filter with a rolloff of 0.25

Ref: Wyglinski et al, Cognitive

Radio Communications

Academic Press, 2010

Electrical EngineeringOFDM based CR


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g gIIT Madras

OFDM based CR

OFDM ideally suited for CR

Electrical EngineeringOFDM based CR


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g gIIT Madras

OFDM based CR

OFDM well-suited for CR applications

Ref: Arslan., IEEE Wireless Communications April 2009



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IIT Madras

IEEE 802.22

CR-Based Wireless Regional Area

Network (WRAN)

Electrical EngineeringIEEE 802 22


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IIT MadrasIEEE 802.22

Project started by IEEE in Nov 2004 Charter: To develop a CR-based WRAN

PHY and MAC specifications

Transmission in unused TV and guard bands (54 MHz 862 MHz) Very favourable propagation characteristics

Channel BW 6 MHz (may be 7 MHz / 8 MHz in some countries)

Spectrum sensing for identifying white spaces Distributed sensing

FCC maintained server info about unused channels (by geographical location

Localised sensing

CPEs perform periodic measurements and send measurements to BTS

BTS makes decision to use the current channel or any other alternatives

Application scenarios Wireless broadband in rural / remote areas

Performance comparable to todays DSL technology Unlicensed devices lower cost and increased affordability

TV migration : moving from broadcast to cable and satellite

Broadcast TV channels available

Electrical EngineeringComparison of Networks


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IIT MadrasComparison of Networks

WRAN Aspects

Large coverage footprint

Up to 100 Km

Larger cells than cellular

Leverage two factors

Higher EIRP

Attractive propgn characteristics Ideal for rural /remote services

Broadband wireless access

Unlicensed devices

Ref: Cordeiro et al., IEEE 802.22: The First Worldwide

Wireless Standard based on Cognitive Radio, IEEE, 2005

Electrical EngineeringIIT M d

IEEE 802.22 Specifications


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IIT MadrasIEEE 802.22 Specifications

Target specifications Spectral efficiency 0.5 b/s/Hz 5 b/s/Hz

Average: 3 b/s/Hz 18 Mbps in 6 MHz

Assuming 12 simultaneous users 1.5 Mbps (DL) and 384 Kbps (UL)

Range: 33 Km (extend to 100 Km) CPE Tx power 4W EIRP @ CPE

Air interface

Requirements Flexibility and quick adaptibility

Link adaptation based on SINRAdapt modulation and Coding option

Frequency agility

OFDM(A) based UL and DL

Transmit Power Control : 30 dB with steps of 1 dB Channel Bonding Utilizing more than one TV channel

System can use larger BW to support higher throughput

Electrical EngineeringIIT M d

IEEE 802.22 MAC


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IIT MadrasIEEE 802.22 MAC

Medium Access Control (MAC)

Design tailored for Cognitive Radio functionality

Channel sensing

Channel classification

Operating currently being used (sensing every 2 sec)

Back-up Cleared for becoming operating channel (sense every 6 sec)

Candidate likely to become back-up channel (every 6 sec, for 30 sec)

Protected incumbent detected through sensing (every 6 sec)

Disallowed due to operational or regulatory issues

Unclassified not yet sensed

Maintenance of channel information

Status of TV channels from external geo-location database List of available TV channels + permissible EIRP

MAC deals with protection of incumbent not addressed in traditional systems

Electrical EngineeringIIT MadrasATSC Signal Sensing


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IIT MadrasATSC Signal Sensing

Spectrum Shape and Signal Characteristics

More efficient than Analog in Bandwidth and Power

VSB spectrum is flat

noise-like randomized data

SRRC pulse shaping is used (=0.115) Half Power freqs are 5.381 MHz apart

Low-Level Pilot Carrier

At lower band edge

Adds 0.3 dB to total avg power

Data Structure and Moduln Scheme

8 data levels (leads to 3 bits/symbol) Has SYNC symbols occurring (4 out of every 832 symbols)

Can be exploited for signal detection

Electrical EngineeringIIT MadrasDetection Techniques


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IIT MadrasDetection Techniques

Proposed Approaches :

1. Energy Detection

2. Detection Using Tones

3. Thomsons Multitaper Method (Haykin, Reed and Thomson)4. Cyclostationary Detection

Tone Detection

Extensively used in GSM

BCCH detection methods

Robust detection techniques like DTMF


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Electrical EngineeringIIT MadrasMulti Taper Method


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IIT MadrasMulti-Taper Method Spectral Concentration Problem

Find finite-duration sequence with Max Spectral Concentration in given band

Spectral Concentration is defined as

A finite sequence - cannot give finite bandwidth spectrum

Formulated as an eigenvector problem

Eigenvectors are called Discrete Prolate Spheroidal Sequences

ftN

n

newfU2

1

)(

=

=

=5.0

5.0

2

2

)(

)(

),(

dffU

dffU

WN

W

W

1),(0


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IIT MadrasPlots of First Four DPSS

First four Slepian DPSS seqs

First four Slepian DPSS seqs C0=6.5, K=11 tapers, N=2200

20 sets of data

10.72 Msymbols per sec

Electrical EngineeringIIT MadrasMTM


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Multitaper Method

Check for Line spectra within

Bias-Variance problem is replaced with Bias-Resolution problem

Time-BW product limits number of tapers used are the eigenvalues associated with the kth eigenspectrum

give a measure of leakage in the kth eigenspectrum

A natural spectral estimator is

NWK 2

)2/,2/( WfWf +

NWC=

0

k

)1( k

=

==1

0

1

0

2)(

)(K

k

k

K

k

kk fX

fS



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Interference Mitigation - Relays

Electrical EngineeringIIT MadrasIndoor Personal Relay (IPeR)


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y ( )

Conventional Femto basestation not feasible in Indian context Lack of wireline broadband for backhaul wireless femto

Relay has directional antenna or multi-antenna link to eNodeB

High-SINR link IPeR is user-deployed

Tx power controlled by BS Interference must be minimized

Potential forhigh bit rate indoors x2, x3 throughput for indoor users

Near-Transparent L1 relay With selective forwarding

Low latency

Cost, complexity, and power similar to terminal

Electrical EngineeringIIT MadrasInterference Case I


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Interference Case I

Electrical EngineeringIIT MadrasInterference Case II


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Interference Case II

Electrical EngineeringIIT MadrasInterference Case III


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Interference Case III

Electrical EngineeringIIT MadrasManaging Interference


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g g

Relays use the same spectrum as macro-cell in uplink and downlink

Typically a relay serves only a few users in a small area

Interference management can play an important role in ensuring that

Gains from deployment of relays are maximized

Spectrum is utilized efficiently

Current solutions orthogonalize interference

static (or semi-static) partitioning of resources between eNB and RNs Potentially under-utilization of resources

Cognitive Interference Management (CIM) a dynamic technique

Cognitive adapts or learns from environment

Completely avoiding interference from relays

Improving spectrum re-use

Electrical EngineeringIIT MadrasCognitive Interference Management


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g g

Objective:

Dynamically allocate resources

Interfering links become orthogonal

The cognitive framework comprises two elements UE Classification

To identify interfering links

Scheduling of transmissions such that

eNB-UE and RN-UE links that do not interfere can use same resources

eNB-UE and RN-UE links that interfere use orthogonal resources

Allows any part of frame to be used for eNB RN and RN UE

RNs do not have to transmit and receive at the same time Interference constraints are satisfied

RN power control used - additional tool to change interference profile

Electrical EngineeringIIT MadrasUE Classification


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Identify interfering links, eNB classifies UEs w.r.t to each RN Relay-cell UE - Served by the RN

Victim UE Sees significantly strong interference from the RN

Safe UE All other UEs (not affected by interference from the RN)

Victim or Safe UE is being served by the eNB or another RN

Electrical EngineeringIIT MadrasExample: Classification


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p

RSUE5

SSUE4

VSUE3

SVUE2

SRUE1

RN2RN1

R Relay-cell UE

S Safe UE

V Victim UE



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CR Testbed


CR Testbeds


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Many universities have implemented CR testbeds VA Tech, Berkeley, U Kansas, Rutgers-Winlab, Georgia Tech,

In India, CDAC Thiruvananthapuram developing CR Testbed

Focus on spectrum sensing

Algorithms developed by IISc Bangalore Testbed needed for algorithm validation, prototype development


VA Tech CORNET


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VA Tech CORNET (Cognitive Radio Network) as a case study Focus on SDR and CR capability

Flexibility in PHY and MAC layers - Enable processing intensive research

48 nodes

Each node has two main componets

General purpose computing platform (Intel Xeon processor based server)

Flexible RF front-end

RF Component

Ettus Research USRP2 (Universal Software Radio Peripheral)

Supports rapid design and implementation of SDR systems Motherboard VirtexSpartan3 FPGA (high-speed signal processing)

14-bit, 100 Msps Analog Digital,16-bit, 400 Msps Digital Analog

Daughter board Motorola RFIC4

Continuous tuning in 100 MHz 4 GHz, Instantaneous BW 10 KHz 20 MHz Gigabit Ethernet connection between USRP2 and host processor



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IITM Testbed


Generic Wireless Transceiver


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DAC LPF RF up-converter

Basebandprocessing

Basebandprocessor

ADC CSF RF down-converter

Basebandprocessing


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Baseband Processing


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Remote Radio Head-end


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Electrical EngineeringIIT MadrasSetup for 4x4 Antenna Configuration


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Electrical EngineeringIIT MadrasSummary


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A broad overview of Cognitive Radio

A paradigm shift in wireless communications

Potential of significant increase in spectrum availability

Opportunistic access

Spectrum sensing a key element in CR

Cooperative sensing is attractive IEEE 802.22 standard an interesting opportunity

CR techniques in 4G Indoor Personal relays

Overall, CR is an exciting field



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My best wishes

to all participants ofthe IWCR 2010 Workshop

Thank You !


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Documents

Koilpillai CR Keynote