application of cognitive radios in centralized and in … of cognitive radios ... software radio,...

KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association

COMMUNICATIONS ENGINEERING LAB (CEL)

www.kit.edu

Application of Cognitive Radios in Centralized and in Ad-hoc Overlay SystemsFriedrich Jondral

VirginiaTech, May 30, 2012

Communications Engineering Lab (CEL)2 09.05.2012

0. Introduction

Prof. Dr.rer.nat. Friedrich Jondral

Communications Engineering Lab (CEL)3 09.05.2012 3

Karlsruhe

Karlsruhe

● 8° 24’ 18” E, 49° 00’ 54” N

●114.9 m above sea level

● founded: 1715

● 173.49 km2

● 285 000 inhabitants

● German Federal Constitutional Court


Karlsruhe Institute of Technology


• University of the State of Baden-Wuerttembergand National Research Center of theHelmholtz Association

• merger of the Universität Karlruhe (TH) and the Forschungszentrum Karlsruhe(2009)

• No. of students: ~ 22 500

• No. of employees: ~ 9 000


Communications Engineering Lab


People11 researchers6 administrative and technical staff6 adjunct lecturers

10 student assistants

Research FieldsSoftware Defined Radio, CognitiveRadio, Dynamic Spectrum Management, Cooperative Communication, Ultra Wide Band, Sensors, Signal Analysis

EducationProbability, Communications Engineering, Applied Information Theory, Coding, Signal Processing, Software Radio, Satellite Communications, Multi Carrier Transmission, Spectrum Management, Multi Rate Technology and Filter Banks,Laboratory, Bachelor and Master Projects


Friedrich K. Jondral1970-79 Technische Universität Braunschweig

Education in Mathematics and Physics

1975 Diploma in Mathematics

Research Associate at the Applied Mathematics Lab

WS 77/78 Visiting Researcher to Nagoya University, Nagoya (Japan)

1979 Doctorate in Natural Sciences (Dr.rer.nat.)

1979-92 AEG-Telefunken (and Successor Companies) Ulm

Various Positions in Research, Development and Systems Engineering

1984 Habilitation (State Doctorate) in Applied Mathematics (Universität Ulm)

1991 Adjunct Professor (Universität Ulm)

1993- Karlsruhe Institute of Technology

Full Professor,

Director Communications Engineering Lab

SS 2004 Visiting Faculty to the Virginia Polytechnical

Institute and State University,

Blacksburg (Virginia)



Synopsis

1. Historic Aspects and Introduction to Cognitive Radio

1.1 Historic Aspects

1.2 Software Defined Radio, Cognitive Radio

1.3 Regulatory Aspects

2. OFDM Based Overlay Systems

2.1 Motivation

2.2 Detection of Primary User Signals by Secondary User Systems

2.3 Wireless Ad Hoc Networks

3. Vision Meets Reality

4. Summary


Communications Engineering Lab (CEL)8 09.05.2012 Prof. Dr.rer.nat. Friedrich Jondral

1. Historic Aspects and Introduction to Cognitive R adio


1.1 Historic Aspects


Some important events

GSM, SDR

digital signal processing, DR

audio broadcast

Marconi's experiments

Hertz's experiments

Maxwell equations

1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030

Shannon, television

transistor

UMTS, WLAN, CR



The Starting Point


James Clerk Maxwell, 1831 – 1879

There is nothing as practical as a convincing theory.

Maxwell's equations (1865)

magnetic field

electric field

electric displacement

magnetic flux density

current density

volume charge density

rot H J D= +

rot E B= −

div D = ρ

div B 0=

H

E

D

B

ρJ


Generation of Electromagnetic Waves


Heinrich Hertz, 1857 – 1894

Karlsruhe 1887:

Electromagnetic wavespropagate through freespace

Metallic walls reflectelectromagnetic waves

Electromagnetic wavesexhibit the properities oflight waves (reflection, diffraction, refraction, polarization, interference, ...)

Hertzian dipole

z

x

θ r


Pioneering Radio Experiments


Guglielmo Marconi 1874 – 1937

First radio transmissionexperiments

• 1899 over the English Channel

• 1901 transatlantic from Poldhu(Cornwall) to Signal Hill / St. John‘s (Newfoundland)


Modulation


a amplitude, fc carrier frequency

ϕ phase, fi information frequency

( ) ( ) ( ) ( ){ } c is t a t cos 2 f f t t t= π + + ϕ

analog digital multi carrier spread spectrum

� AM� SSB� RSB� FM

� ASK� FSK� PSK� QAM

� FFT based- OFDM- DMT

� Filter Bank

� DS� FH� TH


Digitalization / Coding


Claude E. Shannon, 1916 – 2001

Source coding

Channel coding (FEC)

Cryptographic coding

Sampling theorem, 1949

If the real signal s(t) is

• integrable over the whole real axis

• band limited by B, i.e.

then

s(t) is determined by its values s(k∆t ),

k ∈ Ζ, periodically taken at the time

difference

( ) ( ) ( ) s t S f : S f 0 f B• = ∀ ≥

( ) ( ) ( )( )k

sin2 B t k ts t s k t

2 B t k t

∞

=−∞

π − ∆= ∆

π − ∆∑

:1

t2B

∆ =


Networking

Radios are normally integrated into networks→ Cognitive Radio is a networking subject

In many cases radio networks serve as access networ ks to fiber optic backbones

We have distinguish commercial, security, and milit ary networks

Autarky is of special interest for military and sec urity networks

Military applications may call for low probability of intercept and for advanced crypto requirements



Radios in Past and Present


Apple i-Phone, 2007

TELEFUNKENTFK801, ca. 1935


Software Defined Radio / Cognitive Radio


Joseph Mitola III


1.2 Software Defined Radio, Cognitive Radio


Hierarchical Cells


Source: UMTS Task Force Report


Mobile Standards


user data rate

mobility/range

high velocity

long distance traffic

urban traffic

walking

nomadic

fixed

inhouse

personal environment

0.1 1 10 100 Mbit/s

vehi

cle

stat

iona

ry

EDGE

HSPA

3G/UMTS

GSM/GPRS

DECT

Bluetooth

flashOFDM

WLAN(IEEE 802.11x)

WiMAXIEEE

802.16-2004

IEEE802.16e

3Gsuccessorsystems

>2014

pede

stria

n

Source:Klaus-D. Kohrt: 3G und WIMAX –Konkurrenten oder Partner?ntz, Heft 1, 2007, S. 12-15


Mobile Spectrum in Europe


890

915

935

960

. . . f [MHz]

GSM DECTUTRA-TDD

UTRA-FDD MSS ISM WLAN WiMAX

FutureWiMAX

1600 1700 1800 1900 2000 2100 2200 2300 2400 2500

800 900 1000 1100 1200 1300 1400 1500 1600

2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500 3600

2690

1710

1785

1805

1880

1900

1920

1980

2010

2025

2110

2170

2200

2400

2483

,5

2305

2320

2345

2360

5100 5200 5300 5400 5500 5600 5700 5800 5900

5150 5350 5470 5725 5850


The Key Question


What does a subscriber need?One specific device for each and every situation

or one device that serves all situations?

Personal Area Networks Pico Cells Voice

Wireless Local Area Networks Micro Cells Data

Cordless Phones Macro Cells Video

Cellular Networks Global Cells Multimedia

Broadcast NetworksLocation & Navigation

Satellite Networks Infotainment


Standards


Definition :A communications standard is a set of documents that describesthe functions of a communication system in such a way tha t a manufacturer can develop terminals or infrastructure equi pmenton this basis.

Remarks:(i) Standardization is one necessary condition for making a com-

municationsystem successful on the market.

(ii) Today, standardization encompasses all kinds of comm uni-cation networks.

Will standards continue to play an outstanding role in future communication systems ?


Standards Summary


Radio communication standards define transmission s ystemsw.r.t. specific services like voice, video, data, m ultimedia, broadcast, location, navigation etc.

The accompanying transmission modes and protocols d epend on data rate bandwidth, velocity, type of service etc.

Mobile radio communication starts with the channel properties.


Definitions 1)


Software Radio (SR): An ideal SR directly samples the antenna output.

1) According to J. Mitola, 2000

Digital Radio (DR): The baseband signal processing is invariably implemented on a DSP.

Software Defined Radio (SDR): An SDR is a realizable version of an SR: Signals are sampled after a suitable band selection filter.

radio frontend

radio

frequency

RF

baseband

processing

analog-to-digital

conversion

A/D

data

processing

control(parametrization )

tran

smit

rece

ive

from

user

tous

er


Radio Types since 1980

Time Frame

Radio TypeSignal

ProceedingHardware

AdaptivityFeatures

Services

1980µP-controlledAnalog Radio

Analog circuits, µP

AGC, AFC Voice

1990Digital Radio

(DR)DBP

ALE with ACA, adaptive filters,

equalizersVoice, data, SMS

2000Software Defined

Radio (SDR)FGPA

Adaptive coding and modulation,

multiband, multistandard,

multirole

Multimedia

2010Cognitive Radio

(CR)GPP

Location, spectral

environment, velocity

Location based applications, sensor networks, internet of

things



Radio Evolution


1980 1990 2000 2010

104

105

106

108

109

1010

Adaptivity increases

No.

of t

rans

isto

rs o

n an

IC

Moore‘s law:The packing density of transistors on an integrated circuit increases by a factor of two every two years.

Digital Radio

µP-controlledAnalog Radio

Software DefinedRadio

Cognitive Radio


Multi Standard Terminal


ZigBee

Bluetooth

UWB

NFC

GPSGSM, GPRS,

EDGE, UMTS,HSPA, S3G, LTE

WiMax

DVB-H

WLAN


Multi Band, Multi Standard Approach


� Integration

• Use technology development (Moore’s law) to miniatu rize current solution

• Examples:- On-chip VCOs- Integration of passive components in RF ICs- WCDMA and GSM on general baseband IC

� Architecture

• Facilitate multi-band multi-standard

• Examples:- Use homodyne instead of heterodyne receivers- SDR- Modular and expandable software architecture

The rightarchitecture

is key


Reuseable Radio Architecture


� General radio architecture

� All platforms use the same open and stable APIs

� Hardware depends on product configuration (WCDMA, E DGE, GPRS, ... )

Customer Applications

Product specific Hardware

NetworkAccessService

DataCommunication

Services

MMI andMultimedia

Services

ApplicationPlatformServices

OperationServices

Hardware Abstraction Layer (HAL)

Middleware Service Platform


Cognitive Radio: Definitions

Joseph Mitola / Gerald Maguire, 1991 ( IEEE Pers. Comm., vol. 6, no. 4, 1999):“Radio etiquette is the set of RF bands, air interfaces, protocols, and spatial and temporal patterns that moderate the use of radio spectrum. Cognitive radio extends the software radio with radio-domain model-based reasoning about such etiquettes.” Simon Haykin, 2005 ( IEEE J. Select. Areas in Comm., vol. 23, no. 2, 2005):“Cognitive radio is an intelligent wireless communication system that is aware of its surroundingenvironment (i.e. its outside world), and uses the methodology of understanding-by-building to learn from the environment and adapt its internal states to statistical variations in the incoming RF stimuli by making corresponding changes in certain operating parameters (e.g. transmit power, carrier-frequency and modulation strategy) in real-time, with two primary objectives in mind:

- highly reliable communications whenever and wherever needed;- efficient utilization of the radio spectrum.”

Friedrich K. Jondral, 2005 ( EURASIP J. on Wireless Comm. and Networking, 2005, no. 3):“A cognitive radio (CR) is an SDR that additionally senses its environment, tracks changes, and reactsupon its findings. A CR is an autonomous unit in a communications environment that frequently exchanges information with the networks it is able to access as well as with other CRs.”BNetzA, 2006 :Cognitive radio is a radio or system that senses and is aware of its operational environment and can dynamically and autonomously adjust its radio operating parameters.Note: Cognitive radio may benefit from SDR implementation techniques.



Cognitive Radio: Tasks

Evaluate the actual transmission request: Data rate , BER, delay, …, own location, partner’s location, time

Choose the suitable transmission mode: Modulation, coding, MIMO, transmit power, …, w.r.t. the hardware available, the interfe rence temperature limit

Look for a transmission resource: Spectrum holes

Get in touch with the communications partner: Negot iate about the resource to be used, agree upon possible alternatives and upon the transmission mode for the reverse link, exchange channel state information (C SI)

Choose the suitable receiver adjustment



Cognitive Characteristics

Awareness (with respect to the transmitted waveform, RF spectrum, communication network, localization and geography, available services, user needs, language, situation, security policy, …)

Intelligence

Learning

Adaptivity

Reliability

Efficiency



Mitola's Cognitive Cycle


A necessary condition for highest flexibility in mobile communications is a general rethinking in spectrum allocation: Open access

In order to make open access feasible Cognitive Radios are necessary.

Immediate Urgent Normal

NewStates

PriorStates

GenerateAlternatives

EvaluateAlternatives

Receive a MessageRead Buttons

Send a Message

Register toCurrent Time

Save Global States

Set Display Source:Joseph Mitola III: Cognitive Radio – An Integrated Agent Architecture for Software Defined Radio. KTH Stockholm, 2000

Pre-ProcessParse

Infer on ContextHierarchie

Initiate Process(es)

Allocate Resources

ORIENTEstablish Priority

ACT

LEARN PLAN

DECIDE

OutsideWorld

OBSERVE


CR Properties


Mitola's cognition cycle is very general. The proper ties of cognitive radios may be divided into two groups

user centric properties (support functions like finding an appropriate restaurant, recommendation of a travel route, supervision of apointments, . . .)

technology centric properties− spectrum monitoring− localization− awareness of processing capabilities (partitioning and scheduling of

processes)− information and knowledge processing− time− …


Technology Centric Cognitive Radio


Station BStation A

Transmission

Channel measurement and modeling

Reception

Monitoring

SpectralEnvironment

AvailableChannelCapacity

TransmissionPower

and SpectrumManagement

InterferenceTemperature

SpectrumHoles

Noise StatisticsTraffic Volume

RF Signals

Monitoring

SpectralEnvironment

AvailableChannelCapacity

TransmissionPower

and SpectrumManagement

InterferenceTemperature

RF Signals

SpectrumHoles

Noise StatisticsTraffic Volume

Reception

Channel measurement and modeling


1.3 Regulatory Aspects


Regulation


Today “spectrum“ is regulated by governmental agencies , e.g. the American Federal Communications Commission (FCC) or the Bundesnetzagentur (BNetzA)

“Spectrum“ is assigned to users or licensed to them on a long term basis normally for huge regions like whole countries

This may lead to wasting of resources

Vision : Resources are assigned where and as long as they are needed, spectrum access is organized by the netw ork (i.e. by the end users)


Cooper‘s Law

Over the last 45 years, information transmission by electromag-netic waves increased by a factor of 10 6. This was made possible by

• Implementation of frequency division multiplex (factor of 5)

• Introduction of enhance technologies like time division multiplex, digital modulation, coding etc. (factor of 5)

• Enlargement of the useable frequency range (factor of 25)

• Utilization of space multiplex (factor of 1600)


Spectrum Utilization 30 MHz – 10 GHz

2% 8%4%

19%

1%

13%19%

5%

5%

24%Radar

Other Aeronautical

Broadcasting

PMR

Defence Systems

Fixed AccessRadio Relay and Fixed Satellite

Satellite Mobile

Land Mobile

(GSM, UMTS, DECT)

Source: Dr. Klaus-D. Kohrt,CROWNCOM 2009


Spectrum Utilization Measurements


density of thetime between

arrivalsdBs-1

electricfield strength

dBmV/m

Lichtenau (Germany), September 2001


Self Regulation


Wireless LANs (IEEE 802.11x)ISM band: 2400 – 2483.5 MHzWLAN band: 5150 – 5350 MHz and 5470 – 5725 MHz

Ultra Wide Band

UW

B E

IRP

Em

issi

on L

evel

in d

Bm

Frequency in GHz

Part 15 Limit

fc greater than3.1 GHz

−40

−45

−50

−55

−60

−65

−70100 101

fc less than960 GHz


Advanced Spectrum Management


Spectrum reallocation : The reallocation of bandwidth from government or other long-standing users to new serv ices such as mobile communications, broadband internet access, a nd video distribution.Spectrum leases : The relaxation of the technical and commercial limitations on existing licensees to use their spec trum for new or hybrid (for example, satellite and terrestrial) ser vices and granting most mobile radio licensees the right to lease thei r spectrum to third parties.Spectrum sharing : The allocation of an unprecedented amount of spectrum that could be used for unlicensed or share d services.

Source:G. Staple, K. Werbach: The End of Spectrum Scarcity. IEEE Spectrum, March 2004, pp. 41-44


Cognitive Radio: Spectral Efficiency *)


CR technology is perfectly suited to opportunistically employ the wireless spectrumFlexible spectrum utilization is allowed by

• frequency agility• dynamic frequency selection• adaptive modulation• transmit power control• location awareness• negotiated use

CRs could skillfully navigate their way through interference and greatly improve spectral efficiencyFCC and other regulators are altering their rules i n order to allow for more flexible use of the licensed wireless spectrum

*) from N. Devroye, P. Mitran, V. Tarokh: Limits on Communications in a Cognitive Radio Channel.IEEE Communications Magazine, June 2006, pp. 44-49


Secondary Frequency Markets *)


Spectrum leasing: Allowing unlicensed users to lease any part or all of the spectrum of a licensed user

Dynamic spectrum leasing: Temporary and opportunistic usage of spectrum rather than a longer-term sublease

Private commons: A licensee could allow unlicensed users access to his spectrum without a contract, optionally with an access fee

Interruptible spectrum leasing: Suitable for a lessor that wants a high level of assurance that any spectrum temporarily in use, or leased, to an incumbent CR could be efficiently reclaimed if needed**)

*) from N. Devroye, P. Mitran, V. Tarokh: Limits on Communications in a Cognitive Radio Channel.IEEE Communications Magazine, June 2006, pp. 44-49**) e.g. T. A. Weiss, F.K. Jondral: Spectrum Pooling: An Innovative Strategy for the enhancement ofSpectrum Efficiency. IEEE Communications Magazine, Radio Communications Supplement, March 2004, pp. S8-S14


2. OFDM Based Overlay Systems


2.1 Motivation


Motivation


Situation:

• resource ‘frequency’ is limited

• large amount of spectrum unused

⇒ increasing efficiency in spectrum use is desirable

Approaches:

• cooperative systems (e.g. GPRS)

• underlay systems (e.g. ultra wideband)

• overlay systems m

easu

rem

ent t

ime

1810 MHz frequency 1880 MHz

signal strength (dBm)


OFDM Overlay Systems


Coexistence of two independent systems in the same frequency band

Licensed / primary user (PU) system:

• TDMA / FDMA

• no CSMA

• has priority

• allocation is time and location dependent

⇒⇒⇒⇒ OFDM is a flexible and efficient technology for ove rlay systems

Overlay / secondary user (SU) system:

• OFDM

• has to continuously monitor the PU system’s allocation


OFDM Overlay Systems


PU system: Busy and idle subbandsSU system: Allocation vector

PU system: Resource exploitation in the time frequency-plane

SU system: Detection period andupdate interval


Radio Access Networks


Radio access networks connectmobile users to a communicationinfrastructure (backbone).

Only the last hop is wirelessUsually the networking protocol is TCP/IP (mobile IP)

Examples:Cellular systems of second and third generationWireless local area networksWiMax

Backbone

Backbone

Cell


2.2 Detection of Primary User Signals by secondary User SystemsMotivation


SU Detection of PU Signals

Reliable detection of PU signals by the SU system is of paramount importance

Only if this reliability is satisfactory, the PUs will tolerate the SU system sharing their spectrum

Consequence : The SUs‘ probability of detection PD for PU signals must be high (e.g. 99.9%)

The SU system‘s efficiency decreases with increasing false alarm probability PF

PD and PF cannot be independently optimized

According to its importance, first PD is specified and then PF is minimized



Detector Model (SU Receiver)


If s(k) and n(k) are zero mean complex Gaussian randomvariables, z is a zero-mean complex Gaussian random vector

s(k) signal from PU systemn(k) noisePU primary user is detected

M complexnumbers


Receiver Operating Characteristics


Numerically calculatedreceiver operatingcharacteristics ( ROCs),λ0 threshold of LR test ,M = 8

( ) ( )( ) ( )

− −− − −

= =

−λ = σ λ + σ λ − σ σ −∑ ∏

mM 1 m 12 M 1 2 M m 10F N 0 N 02M2 m 1 l 1N

N

1P exp 2 2 M l

22 M 1 !

( ) ( ) ( ) ( ) ( )( ) ( )− −− − −

= =

−λ = σ + σ λ + σ + σ λ − σ + σ σ + σ − ∑ ∏

mM 1 m 10 2 2 M 1 2 2 M m 12 2D S N 0 S N 0MM 2 2 S N m 1 l 1

S N

1P exp 2 2 M l

22 M 1 !


Distributed Detection


Tolerating a SU overlay system is attractive for PUs only if the detection probability PD of PU signals by the SU system is sufficiently high (e.g., 99.9%).

According to the ROCs, our detector cannot achieve this PD at an acceptable false alarm probability PF.

Increasing M will not really help, because larger numbers of FFT cycles cause a larger detection duration.

Distributed detection by all SU stations mayhelp to solve the problem.


Distributed Detection - SU Access Network


SU system's detection probability

SU system's false alarm probability

number of stations involved in thedetection process

( ) ( )= − − LSF FP L 1 1 P

( ) ( ) ( )= − − ⇒ = − −LS SLD D D DP L 1 1 P P 1 1 P L

( ) ( )( ) = − − − −

S SLF F DP L 1 1 P 1 1 P L

( )SFP L

( )SDP L

L

SU System'sfalse alarmprobability PS (L)

(M = 8, SNR = 2dB,

PS (L) = 0.999)

F

D


2.3 Wireless Ad Hoc Networks


Wireless Ad Hoc Network


Gupta, Kumar (2000): " ... the throughput furnished to each user diminishes to zero as the number of users is increased, ..."*)

In ad hoc networks all links are wireless.

Network layer: Routing (time-varying network topology, power constraints, mobile radio channel)

Radio access: TDMA is complex (no centralized control), FDMA is inefficient, CDMA is difficult to implement (user mobility, time-varying neighborhood) ⇒ random access,but "hidden stations" and "exposed stations" (CTS/RTS) exist

Physical layer: Power control

*) P. Gupta, P.R. Kumar: The Capacity of Wireless Networks. IEEE Transactions on Information Theory, Vol.46, No.2, March 2000, pp. 388-404


Wireless Ad Hoc Network - System Model


Random geometric graph theory *):

Ad hoc network is represented by undirected graph G = G(X; r0)

X : set of nodes with N = |X|

r0 : transmission range of a node

nodes are distributed randomly in system area A

Two nodes u, v ∈ G areneighbors if |u - v| ≤ r0

*) For the motivation to use random geometric graphtheory cf. J. Andrews et al.: Rethinking Information Theory for Mobile Ad Hoc Networks. IEEE Comm. Mag., Dec. 2008

Network with N = 250 andr0 = 0.2∧


Number of Neighbors: Borderless Scenario


Assumption: Infinite system area, i.e. , with constant as

1. Number of nodes in every finite subarea follows a Poissondistribution

2. Numbers of nodes in disjoint subareas are indepe ndent random variables�� the number of nodes forms a homogeneous Poisson poi nt

process

With 1. also the number of neighbors D follows a Poisson distribution:

with the location independent expectation

( ) ( ) −ν∞ ∞

ν= = ν =d

P D d P ;d ed!

ρ = NA

{ } = ν = ρπ 20E D r

→ ∞N→ ∞A


Numbers of Neighbors: Scenario with Borders


The location independent probability is derived by integration

Assumption: The system area is a disk with radius a

The probability that a second node is placed in the transmission range of a given node x depends on its location and is given by p0(x).

( ) ( ) ( )( )N d 1d

0 0

N 1P D d | x p x 1 p x

d

− −− = = −

( ) ( ) ( ) ( )= = = =∫∫ XP D d P d P D d | x f x dx


Number of Neighbors: Scenario with Borders


With a uniform node distribution in the system area

and using polar coordinates, the location independent probability of having d neighbors is

Area A

x

a

xr0

( ) X

1for x A

f x A0 otherwise.

∈=

( ) ( ) ( )( )1

N d 1d

0 00

N 1ˆ ˆ ˆ ˆP d 2r p r 1 p r drd

− −− = ⋅ −

∫


Number of Neighbors: Scenario with Borders

The probability that a second node is placed in the transmission range of a node is given by 1)

with

(normalized radial component)

(normalized transmission range);

1) C. Bettstetter: On the Connectivity of ad hoc networks. The Computer Journal, vol. 47, no. 4, 2004, pp. 432-447


Detector Model (SU Receiver) revisited


If s(k) and n(k) are zero mean complex Gaussian randomvariables, z is a zero-mean complex Gaussian random vector

M complex numbers

s(k): signal from PU systemn(k): noisePU: primary user is detected M complex

numbers


Receiver Operating Characteristics SU accessnetwork, revisted


Numerically calculatedreceiver operating

characteristics (ROCs),λ0 threshold of LR test,

M = 8

( ) ( ) ( ) ( ) ( )( ) ( )mM 1 m 1

2 2 M 1 2 2 M m 10D S N 0 S N 0M 2 2M 2 2 m 1 l 1N NS N

1P exp 2 2 M l

22 M 1 !

− −− − −

= =

−λ = σ + σ λ + σ + σ λ − σ + σ σ + σ − ∑ ∏

( ) ( )( ) ( )

mM 1 m 12 M 1 2 M m 10F N 0 N 0M 22 m 1 l 1NN

1P exp 2 2 M l

22 M 1 !

− −− − −

= =

−λ = σ λ + σ λ − σ σ −∑ ∏


Distributed Detection Wireless Ad Hoc Networks


The detection probability can be increased by combi ning the detection probabilities of several nodes in a cluster (L: number of contributing nodes):

The network false alarm probability is defined in a similar way.

( ) ( )LcellD DP L 1 1 P= − −

( ) ( )LcellF FP L 1 1 P= − −

Application of distributed detection to ad hoc netw orks leads to the definition of the network detection probability :

Borderless scenario: ( ) ( ) ( )net cellD D

d 0

P P ;d P d 1∞

∞

∞=

ν = ν ⋅ +∑

Scenario with borders: ( ) ( ) ( )N 1

net cellD 0 0 D

d 0

P N;r P r ;d P d 1−

=

= ⋅ +∑


Borderless Scenario


0 2 4 6 8 10

1

0.99

0.98

0.97

0.96

0.951 3 5 7 9

simulationtheory: PD = 0.9

PD = 0.8PD = 0.7PD = 0.6

PDnet∞

ν

( ) ( )( ) d

d 1netD D

d 0

P e 1 1 Pd!

∞

∞+−ν

=

νν = ⋅ − −∑

( ) ( )( ) d

d 1netF F

d 0

P e 1 1 Pd!

∞

∞+−ν

=

νν = ⋅ − −∑


Borderless Scenario


Network false alarm probability depending on ν for a required network detection probability of 0.999

ν should be greater than 9 for a good performance (e.g., ρ = 2.9 km-2

if r0 = 1 km)


Scenario with Border Effects


simulationtheory: PD = 0.9

PD = 0.8PD = 0.7PD = 0.6

r0

0 0.2 0.4 0.6 0.8 1

1.0

0.99

0.98

0.97

0.96

0.950.1 0.3 0.5 0.7 0.9

PDnet

Network detection probability depending on the norm alizedtransmission range with N = 40.

( ) ( ) ( )( ) ( )( ) 1N 1 N d 1d d 1net

D 0 0 0 Dd 0 0

N 1ˆ ˆ ˆ ˆP N;r 2r p r 1 p r dr 1 1 Pd

− − − +

=

− = ⋅ ⋅ − ⋅ − −

∑ ∫




r0

N = 150N = 80N = 40N = 20N = 10

0 0.2 0.4 0.6 0.8 1

1.0

0.99

0.98

0.97

0.96

0.1 0.3 0.5 0.7 0.9

PDnet

0.95

Network detection probability depending on the norm alizedtransmission range with PD = 0.8.




Possible combinations of N and r0 resulting in different givennetwork detection probabilities (PD = 0.8).

0 0.2 0.4 0.6 0.8 1

200

180

160

140

120

100

80

60

40

20

= 0.999

= 0.99

= 0.9

= 0.82

N

r0

PDnet

PDnet

PDnet

PDnet


Receiver Operating Characteristic


Receiver operating characteristics for M = 4, SNR = 0 dB, r0 = 0.2 and different numbers of nodes in the network.

PFnet

00 0.2 0.4 0.6 0.8 1

1

0.8

0.6

0.5

0.3

0.1

single detectionN = 40

PDnet

N = 80N = 150

0.2

0.4

0.7

0.9


Overlay Frame


Coordination of detection periods in ad hoc SU systems:

What is the present allocation in the PU system?

Are there any other SUs with whom a communicationslink may be established?

Block 1 Block 2 Block 3

Overlay frame

Detection Beaconing ACK Data


SU Entering an Ad Hoc Network


Association

Find an idle slot

Send RTS

RTStransmissionsuccessful?

Receive CTS

Send

yesno

First estimate of the AV

Listen for beacons

Beacon received?

Send beacon

Synchronize frameto other SU's frame

SU associated

noyes

Communication


Boosting and Collection


Key fact in ad hoc networks :No central node, no central knowledge, all SUs are equal

PU signal detection and informationcollection

Slot A: Every SU performs detectionof PU signals

Slot B: Boosting or collection inthe SUs' system. Each SUdecides randomly ( w.p. 0.5) whether to boost or to collect

Slot C: Each SU performs theoperation he did not duringslot B.

A B C

SU1

boosting

collectionSU2

A BC

. . .

. . .

. . .

. . .

B C

Detection Beaconing ACK Data

A


Boosting and Collection


Key fact in ad hoc networks :No central node, no central knowledge, all SUs are equal

PU signal detection and information collectionSlot A: Every SU performs detection

of PU signals

Slot B: Boosting or collection in theSUs' system. Each SU decidesrandomly ( w.p. 0.5) whetherto boost or to collect

Slot C: Each SU performs the operationhe did not during slot B.


Summary


The introduction of cognitive radio systems is not a revolution but an evolution

The scarce resource “spectrum“ is underutilized

New (self) regulation strategies are necessary

OFDM overlay systems may help to achieve higher efficiency in spectrum usage

Distributed detection of PU signals in SU systems

Radio access networks and wireless ad hoc networks must be distinguished: Different signaling strategies are t o be employed

Advanced spectrum utilization (and regulation) is a hot topic

but should be handled with care!


3. Vision Meets Reality



Immediate Urgent Normal

ACT

OutsideWorld

NewStates

PriorStates

Generate Alternatives

EvaluateAlternatives

ORIENT

Establish Priority

Receive a MessageRead Buttons

Send a Message Initiate Process(es)

Register toCurrent Time

Pre-ProcessParse

Save Global States

Allocate Resources

Set Display

Infer on ContextHierarchie

Joseph Mitola III: Cognitive Radio – An Integrated Agent Architecture for Software Defined Radio. KTH Stockholm, 2000

CR: Vision

OBSERVE

DECIDE

LEARN PLAN


CR: Definition

“Cognitive Radio is an intelligent wireless communication system that is aware of its surrounding environment (i.e. its outside world), and uses the methodology of understanding-by-building to learn from the environment and adapt its internal states to statistical variations in the incoming RF stimuli by making corresponding changes in certain operating parameters (e.g. transmit power, carrier-frequency and modulation strategy) in real-time, with two primary objectives in mind:

- highly reliable communications whenever and wherever needed;- efficient utilization of the radio spectrum.”

Simon Haykin: Cognitive Radio: Brain-Empowered Wireless Communications. IEEE J. Select. Areas in Comm., vol. 23, no. 2, 2005, pp. 201-220


Reality


CR is not a revolution in radio communications, it is merely the way ahead to more automation and adaptation

• in finding the optimum frequency and• in using the optimum transmission power

With these properties

• higher spectrum efficiency• lower costs and• more environmental acceptability

are achieved.

The CR paradigm makes sense only in networks.


Meaning of "Spectrum"

A material quantity that may be partitioned

or an immaterial mediumthat may be accessedwithout regulation?


Spectrum Utilization

M. McHenry: NSF Spectrum Occupancy Measurements. The Shared Spectrum Company, Tech. Rep., 2005, http://sharedspectrum.com/?sectio=nsf_measurementsFundamental Statement:Even in crowded frequency regions not more then 15 percent of the (theoretical) capacity is actually used.

However:A hundred percent usage of the transmission resourceis utopistic (interferences)But: Struggling is promising.

Photo: The Shared Spectrum Company


Dynamic Spectrum Access (DAS)

Dynamic Spectrum AccessDynamic Spectrum Access

DynamicExclusiv Use Model

DynamicExclusiv Use Model

HierarchicalAccess ModelHierarchical

Access Model

Open Sharing Model(Spectrum

Commons Model)

Open Sharing Model(Spectrum

Commons Model)

SpectrumPropertyRights

SpectrumPropertyRights

DynamicSpectrum Allocation

DynamicSpectrum Allocation

SpectrumUnderlay

(Ultra WideBand)

SpectrumUnderlay

(Ultra WideBand)

Spectrum Overlay

(OpportunisticSpectrumAccess)

Spectrum Overlay

(OpportunisticSpectrumAccess)

from: Qing Zhao, Brian M. Sadler: A Survey of Dynamic Spectrum Access.IEEE Signal Processing Magazine, May 2007, pp. 79 - 89


DSA: Questions

What is the meaning of “Spectrum Access”?To enhance the efficiency in the usage of spectrum (briefly: spectral efficiency) in a specific geographic region, CRs access spectrum holes left by the licensed users’ system (primary users) as secondary users.

I.e.: Spectrum Access happens in time, frequency, and space.

What is the meaning of “Dynamic”?Nobody knows …On which scale is DSA based upon? Milliseconds, seconds, minutes, …? Change in primary users’ behavior?


Dynamic / Detection Time

DynamicDetection

Time

Burst

TV White Space

high short

low long


Time/Frequency Plane

GSM 1800No. of Channels: 374Bandwidth: ≈270 kHzDistance: 200 kHzBurst Duration: 0.577 ms


Energy Detector

s(t) Transmitter Signal

u(t) Baseband Representation of s(t)

r(t) Received Signal

v(t) Baseband Representation of r(t)

T Duration of s(t)

Radio Frontend Decision∫ |v(t)|2dtT

0

r(t)


Matched Filter Detector





T Duration of s(t)

Radio Frontend Decision∫ v(t)u(T−t) dtT

0

r(t)

u(t)


Pattern Recognition Detector





T Duration of s(t)

Radio Frontend DecisionFeature

Extraction

r(t)

u(t)

PatternRecognition

FeatureExtraction

. . .

. . .


Signal Detection


DetectorA Priori

Knowledge

Detection Time/ Computational

ComplexityApplicability Robustness

Energy Nothing low universal high

MatchedFilter

Signal medium specific medium

Pattern Recognition

Signal Features

high highly specific low


Energy Detector

Detection Time: AWGNFalse Alarm Rate: 10-4

Detection Probability: β(σ2: normalized noise variance)

β = 0.9999n

β = 0.999 β = 0.99 σ2SNR[dB]

111 93 74 2 -356 47 37 1 028 24 19 1/2 314 12 10 1/4 67 6 5 1/8 94 3 3 1/16 122 2 2 1/32 152 2 2 1/32 15

1 1/37 15.71 1/47 16.7

1 1/56 17.5


Energy Detector

D = duration for one scan over the 374 channels of GSM 1800false alarm rate: 10-4

detection probability: 0.999SNR: 9 dB

Monitoring of the GSM band on burst basis by one scanning energy detector withfalse alarm rate 10-4 and detection probability 0.999 at an SNR of 9 dB isimpossible!

And: What about the power needed in the mobile radio for permanent scanningand detection?

D = 6 x No. of Channels x = 6 x 374 x s = 8.31 ms1

Bandwidth1

270000

D = =14.4 bursts8.310.577


Proposed Solution 1


Distributed DetectionFor networks with access point :Timo Weiß: OFDM-basiertes Spectrum Pooling. Dissertation, Forschungsberichte aus dem Institut für Nachrichtentechnik der Universität Karlsruhe (TH), Band 13, Karlsruhe 2004

For ad hoc networks:Ulrich Berhold: Dynamic Spectrum Access Using OFDM-based Overlay Systems. Dissertation, Forschungsberichte aus dem Institut für Nachrichtentechnik der Universität Karlsruhe (TH), Band 21, Karlsruhe 2009

MAC frame MAC frame MAC frame

detectionphase

boostingphase

broadcastphaseP P

2 ms


Distributed Detection and Boosting


With Access Point Ad Hoc

b) Boosting and Collection


Proposed Solution 2


Off-line Sensing, Data Base Query, and Instantaneou s Measurement

During idle times• The radio senses all potential transmission channels1)

• The sensing results for each channel, together with the time of the day when the sensing took place, are stored in a data base in order to establish channel utilization statistics depending on time and frequency

When a communications request occurs1. The radio queries the data base for a channel that is idle with highest

probability at the current time of the day and that has not been sensed yet2. The radio instantaneously senses the chosen channel3. If the channel is idle, the radio starts operation.

If not, it goes back to 1.

1) The power problem for this remains unsolved.


Data Base Query

16:05

16:10

16:15

16:20

Time Channel Utilization Statistics

16:17

Channel No. Priority

1 2

2 5

3 4

4 5

5 1

6 3

1 2 3 5 64

1 2 3 5 64

1 2 3 5 64

1 2 3 5 64

. . .

. . .


Don‘t forget


CoordinationA channel idle at station A must not be idle at station B (agreement necessary).

Continuous SensingAs long as a SU station is active, it must permanently sense it‘s channel (look through).

Automated Frequency Change If a PU signal is detected on the currently used channel, communication partners must identify a new usable frequency and jointly switch to it.

Hidden Stations

Multicast / Broadcast


Summary


As of January 31, 2012 there are • 8 079 papers on Cognitive Radio,• 7 777 papers on Spectrum Sensing, and• 2 519 papers on Dynamic Spectrum Accesslisted in the IEEE Xplore Digital Library. Many of them do not observe any constraints imposed by physics.

All notions that we use in communications need to be well defined.

Detection time depends on SNR, false alarm rate, detection probability, and further conditions imposed by wave propagation.

CR and DSA bear high potential for theoretical and practical research work.


References


Linda E. Doyle: Essentials of Cognitive Radio. Cambridge University Press, Cambridge (U.K.), 2009

Bruce Fette (Ed.): Cognitive Radio Technology, 2nd Edition. Academic Press, Burlington (MA), 2009

Ekram Hossain, Vijay K. Bhargava (Ed.): Cognitive Wireless Communication Networks. Springer, New York, 2007

Preston Marshall: Quantitative Analysis of Cognitive Radio and Network Performance. Artech House, Norwood (MA), 2010

Joseph Mitola III: Cognitive Radio – An Integrated Agent Architecture for Software Defined Radio. Ph.D. Dissertation, Department of Teleinformatics, KTH Stockholm (Sweden), 2000

Alexander M. Wyglinski, Maziar Nekovee, Thomas Hou ( Ed.): Cognitive Radio Communications and Networks: Principles and Practice. Academic Press, Burlington (MA), 2010


References


Timo A. Weiss, Friedrich K. Jondral: Spectrum Pooling: An Innovative Strategy for the Enhancement of Spectrum Efficiency. IEEE Communications Magazine, March 2004, Radio Communications Supplement, pp. S8 - S14

Jörg Hillenbrand, Timo A. Weiss, Friedrich K. Jondral: Calculation of Detection and False Alarm Probabilities in Spectrum Pooling Systems. IEEE Communications Letters, April 2005, Vol. 9, No. 4, pp. 349 - 351

Friedrich K. Jondral: Software Defined-Radio – Basics and Evolution to Cognitive Radio. Invited paper,EURASIP Journal on Wireless Communications and Networking, 2005, No. 3, pp. 275 - 283

Friedrich K. Jondral: Cognitive Radio: A Communications Engineering View. IEEE Wireless Communications, August 2007, pp. 28-33

Ulrich Berthold, Sinja Brandes, Michael Schnell and Friedrich K. Jondral: OFDM-Based Overlay Systems: A Promising Approach for Enhancing Spectral Efficiency. IEEE Communications Magazine, Vol. 45 No. 12, December 2007, pp. 52-58

Ulrich Berthold, Friedrich K. Jondral: Distributed Detection in OFDM based Ad Hoc Overlay Systems. Proceedings of the IEEE Vehicular Technology Conference VTC2008-Spring, Singapore, May 11-14, 2008, CD-ROM


Thank you!

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