cognitive elements in rrm and dsa.pdf

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Tutorial - CROWNCOM, Hannover, 23rd June 2009 Slide 1

Jordi Pérez-Romero

Francisco Bernardo

Universitat Politècnica de

Catalunya (UPC)

Barcelona, Spain

[email protected]

[email protected]

Cognitive Elements in RRM

and DSA




Outline

Introduction

Identification of challenges in future wireless

networks

Cognitive radio and cognitive networks

Identification of problems

Part I.- Cognitive Pilot Channel

Part II.- Secondary Spectrum Access

Part III.- Decentralised RRM

Part IV.- Dynamic Spectrum Assignment




Introduction

Trends that characterise future wireless scenarios

Wireless technologies are rapidly evolving towards higher bitrates (100 Mb/s, 1Gb/s) (HSPA, UTRAN-LTE, IEEE 802 family, etc.)

Multiple services with different QoS constraints

Heterogeneity of wireless devices

Co-existence of multiple RATs

• HSPA, HSPA+, UTRAN, LTE, LTE-A, IEEE 802.11x, IEEE 802.16, etc.

• Users can be served through the RAT that best fits their requirements

IP-based

Core Network

UTRAN LTEWLAN

Services

(e.g. IMS)




Introduction

Increase in the complexity of the management and network operation

Trend towards automatisation of operation procedures:• Self-x operation (self-management, self-healing, etc.)

Trend towards decentralisation:• Reduces signalling

• Improves latencies

• Bring intelligence and decision capabilities to edge nodes (e.g. base

stations) and even to terminals

COGNITIVE capabilities at the network side

COGNITIVE capabilities at the terminal side




Introduction

Spectrum scarcity?

There is no spectrum

available !!!!




Introduction

But...

Measurements in different places around the world have revealed that

there is actually a low spectrum utilisation !!!

Example:

- Urban measurements in Barcelona between 75MHz and 7 GHz ->

roughly 18% of spectrum usage!!!


http://slidepdf.com/reader/full/cognitive-elements-in-rrm-and-dsapdf 7/176Tutorial - CROWNCOM, Hannover, 23rd June 2009 Slide 7

Introduction

So...

New trend towards more flexibility in spectrum management

• Spectrum refarming – Reassign bandwidths currently allocated to a given technology to

another one (e.g. band of GSM900 to UMTS)

• Dynamic spectrum assignment (dynamic frequency planning)

– Decide an efficient assignment of frequencies to cells/RATs

• Dynamic spectrum access - Primary and secondary spectrumusage

More efficiency is in fact needed in how the spectrum is utilised !!!

RECONFIGURABILITY capabilities




Fixed Spectrum Allocation (FSA) (current policies)

Spectrum management controlled by regulatory bodies

eases spectrum management and controls the interference

between RATs

limits the flexibility of spectrum and leads to large pieces of

the spectrum wasted due to the time and space varyingtraffic distribution.

Future wireless networks will demand dynamic spectrum

access models

cope with spectrum scarcity and its underutilization

Dynamic Spectrum Access




Dynamic Spectrum Access models (I)

Exclusive Usage Rights model• Assignment of exclusive licenses under which technology, services and

business models are at the discretion of the licensee.

• Harmful interference is avoided through technology-neutral restrictions

• Licenses may be traded in an open market

Spectrum Commons model• Promotes shared access to available spectrum for a number of users

• Responsibility for interference avoidance is devolved from the regulator tothe networks

• Cognitive networks are key in providing the awareness and adaptabilityrequired

• Public commons: spectrum is open to anyone for access with equal rights(e.g. current wireless standards in license-free ISM band)

• Private commons: the license holder enables multiple parties to accessthe spectrum in existing licensed bands. Access rules set by primarylicense holder.





Dynamic Spectrum Access models (II)

Opportunistic use• Secondary users are allowed to independently identify unused spectrum

at a given time and place and utilize them while not generating harmfulinterference to primary license holders.

• Opportunistic spectrum access may take the form of:

• Underlay access: – use of signal powers below the noise floor (e.g. UWB)

• Overlay access: – detection and use of spectrum white spaces

– IEEE 802.22: new air interface for unlicensed operation of radios in the TVbroadcast bands if no harmful interference is caused to incumbent services





R

≈ ≈ f ≈

Envisaged framework

Transmitters may be from

different operators, use different

RATs and different frequency

bands at different times

− Primary network exploits DSA (Dynamic Spectrum Assignment), i.e.

frequencies assigned to different cells may vary along time.− Secondary network operates on an Opportunistic Access basis.

Exploitable bands can be those unused by primary network (e.g. thanks to

efficient DSA) or available from other sources (e.g. broadcaster).




Dynamic Spectrum Assignment (DSA)

-Decide spectrum band assignment to cells/RATs

Joint Radio Resource Management (JRRM)

-Allocation/deallocation of radio resources to servicesto ensure QoS

Long/mediumterm

Short term

Cognitive

functionalities

Measurements

Measurements

Triggeractions

Triggeractions

KPIs Spectrumallocation

Envisaged framework

The envisaged framework can only be fully accomplished by further enhancing

the Radio Access Networks (RANs) towards Cognitive Networks complemented

with Cognitive Radio-based technologies.




Cognitive Radio

Allows for Real Time Spectrum Management:

Allows individual radios or groups of radios to make

choices about their frequency and RAT use based upon

their location and the radio use environment

• Dynamic Frequency Selection

Potential to utilize the large amount of unused

spectrum in an intelligent way while not interfering with

other incumbent devices in frequency bands already

licensed for specific uses

• Increases Spectrum Efficiency




Several definitions of cognitive networks have been proposed up to

date, but all of them have one thing in common:they present a vision of a network which has the ability to think,

learn and remember in order to adapt in response to conditions orevents based on the reasoning and prior acquired knowledge, withthe objective of achieving some end-to-end goals

PERCEIVE network conditions

PLAN, DECIDE and ACT

LEARN from adaptations to make future decisions

References:R. W. Thomas, L.A.DaSilva, A.B. MacKenzie , “Cognitive Networks”, First IEEE InternationalSymposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN 2005),Baltimore,2005

Q. Mahmoud (editor), Cognitive Networks: Towards Self-Aware Networks, Ed. John Wiley andSons, 2007

Cognitive Network




OODA (Observe, Orient, Decide and Act) loop

used in very different applications ranging from business managementto artificial intelligence

in order to learn, a feedback loop is created in which past interactionswith the environment guide current and future interactions.

It guides a decision maker when choosing an appropriate action based oninteraction with the environment to achieve a specific goal.

similar to the cognition cycle described in the context of cognitive radios

Cognitive Network

Observe

Orient

Decide

Act

Environment




Cognitive Network

Cognitive Network in the context of a wireless network




Perspective of this tutorial

Problems discussed here:

How to orient and guide the terminals in their operation ?

What approaches for secondary usage are feasible?

How to select a proper RAT?

How to perform Dynamic Spectrum Assignment?

Part I.- Consider solutions based on Cognitive Pilot Channel

Part II.- Modeling of the problem and identify trade-offs

Part III.- Autonomic decision making at terminals

Part IV.- Exploit Cognitive Network features for DSA




Cognitive Pilot Channel (CPC)




Framework

Heterogeneous wireless networks

Room for improved radio resource and spectrum usage strategies(e.g. Joint Radio Resource Management, Advanced Spectrum

Management)

“Intelligence” towards edge nodes (e.g. 3GPP >R5, LTE) and towards

autonomic decision making at the mobile terminal (e.g. IEEE P1900.4)

Next Generation WiFi(IEEE 802.11n or similar)

Latest Generation WiFi(IEEE 802.11a or similar)

WiMAX3GPP

CPC: Motivation




Cognitive radio as enabler

Hidden terminal problem (conceptu al barr ier )It could be difficult to control the spectrum usage of an autonomous

cognitive radio terminal (regulatory b arr ier )

Difficulties in implementing cognitive radios on a broad frequency

band (techno log ical barr ier )

The convenience of introducing policy/control mechanisms whenmanaging secondary markets through brokerage-based spectrum

sharing (bus iness b arr ier )

Key barriers in exploiting a pure Cognitive Radio

Information channels have been proposed to circumvent these

drawbacks (e.g. Common Spectrum Coordination Channel,

Spectrum Information Channel)

M. Buddhikot, P. Kolodzy, S. Miller, K. Ryan, J. Evans “ DIMSUMNet: New Directions in Wireless Networking Using Coordinated Dynamic Spectrum

Access”, IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (IEEE WoWMoM), Taormina/Giardini Naxos,

Italy, June, 2005.

D. Raychaudhuri, X. Jing “A Spectrum Etiquette Protocol for Efficient Coordination of Radio Devices in Unlicensed Bands”, 14th IEEE 2003

International Symposium on Personal,lndoor and Mobile Radio Communication Proceedings (PIMRC), Beijing, September, 2003.




Cognitive Pilot Channel (CPC) as enabler

Medium WaveRadio

30 3 30300

Long WaveRadio

FMRadio

GSM

3G

MicrowaveRadio Links TV

VLF LF MF HF VHF UHF SHF EHF

kHz MHz GHz

330300 300

DECT WiFi

Bluetooth TETRA

LMDS

Decreasing Range

Increasing Bandwidth

Increasing Range

Decreasing Bandwidth

3

Medium WaveRadio

30 3 30300

Long WaveRadio

FMRadio

GSM

3G

MicrowaveRadio Links TV

VLF LF MF HF VHF UHF SHF EHF

kHz MHz GHz

330300 300

DECT WiFi

Bluetooth TETRA

LMDS

Decreasing Range

Increasing Bandwidth

Increasing Range

Decreasing Bandwidth

3

CPCRadio-enabler for distributed decision making process

Cognitive Network-BasedMechanisms supporting

Joint optimisation of Coverage, capacity and quality

RAT

Frequency

Cell

Power

3GPP WiFi

Cognitive Pilot Channel (CPC) concept wasconceived as a solution for conveying the necessary

information from the network side to let the terminal

know e.g. the available frequency bands, RATs,

operators, etc. at a given time and place




Radio enabler of reconfiguration management in cognitive networks.

It helps the mobile terminal (user):• Select the proper network depending on the specific conditions

(desired services, RAT availability, interference conditions, ...)

Support to JRRM

• Reconfigure the terminal capabilities to adapt to the available RATsSupport to Reconfigurability

• Avoid time and battery consuming spectrum scanning processes todiscover the available RATs and operators

Support to Context Awareness

It helps the network operator:

• Facilitate dynamic changes in the deployment by informing theterminals of the availability of new RATs/frequencies

Support to Dynamic Planning and Spectrum Management

It helps the spectrum regulator:

• Enable a secondary use of the spectrum temporary unused toincrease its usage

Support to improve spectrum utilisation

Cognitive Pilot Channel (CPC) as enabler




CPC: regulatory framework

Regulatory framework for the implementation of

cognitive radio systems and introduction of flexiblespectrum management:

actions initiated within ITU-R WP 5A contributing tothe inclusion of the CPC concept in the workingdocument on Cognitive Radio Systems (CRS),

focusing on the radio environment discovery atswitch on of the mobile terminal.

Additional standardization activities have also beenlaunched within ETSI:

CPC pre-standardization is currently under

development in the framework of the ETSI RRS(Reconfigurable Radio Systems) TechnicalCommittee.




CPC: references

D. Bourse et al. “The E2R II Flexible Spectrum Management (FSM) Framework and

Cognitive Pilot Channel (CPC) Concept – Technical and Business Analysis andRecommendations”, White Paper of the E2R-II Project, November, 2007.

P. Houzé, S. Ben Jemaa, P. Cordier “Common Pilot Channel for Network Selection”,IEEE VTC in Spring Conference, Melbourne, May, 2006.

J. Pérez-Romero, O. Sallent, R. Agustí, L. Giupponi “A Novel On-Demand CognitivePilot Channel enabling Dynamic Spectrum Allocation”, Second IEEE InternationalSymposium on New Frontiers in Dynamic Spectrum Access Networks, (DySPAN),

Dublin, April, 2007. O. Sallent, J. Pérez-Romero, R. Agustí, P. Cordier "Cognitive Pilot Channel Enabling

Spectrum Awareness", IEEE International Conference in Communications (ICC2009), Dresden, June, 2009.

O. Sallent, J. Pérez-Romero, A. Trogolo, E. Buracchini, K. Tsagkaris, P.Demestichas, "Cognitive Pilot Channel: A Radio Enabler for Spectrum Awarenessand optimized Radio Resource Management", ICT Mobile Summit, Santander, June,2009.




Cognitive Pilot Channel (CPC) use cases

UE (User Equipment) Switch-on (mainly in case ofdynamic spectrum management)

Secondary spectrum usage

Radio Resource selection policies (complementary to

JRRM)

Information provisioning (e.g. Software Download)




CPC concepts in a nutshell




CPC Mapping onto Physical Resources

Out-band CPC:CPC is transmitted using a channel outside the bands assigned to

component RATsCPC frequency to be fixed and harmonized at global/regional basis,consortium of access providers basis or only at internal level within agiven access provider domain.• Any terminal can easily connect to the CPC no matter the country and

supported technologies

More appropriate to support switch-on case in flexible spectrum scenarios• It avoids long scanning process

It may use new infrastructure or reuse sites from other RATs

CPC

ManagerOut-band CPC

RAT 1

RAT 2

RAT 3

RAT n

CPC





In-band CPC:

CPC information is conveyed using logical channels of the same RATsthat are used for the user data transmission.

It does not require a new frequency to be agreed

It uses existing infrastructure.

It is not appropriate for the support to switch-on

More appropriate for the support to RRM procedures

CPC

ManagerRAT 1

RAT 2

RAT 3

RAT n

CPC

CPC

CPC

CPC





Combined operation out-band and in-band CPC:

An out-band CPC transmitting the minimum information required tosupport the switch-on process is used. It can contain a pointer to the

RATs/frequencies where in-band CPC is located.

More detailed information (e.g. full list of RATs, policies to support RRM,

etc.) is transmitted through the in-band CPC.

CPC

ManagerRAT 1

RAT 2

RAT 3

RAT n

in-band CPC

in-band CPC

in-band CPC

in-band CPC

Out-Band CPC

out-band CPC




CPC Delivery Mode

Broadcast CPC:

It uses a downlink Broadcast CPC (DBCPC) channel where theinformation on the RATs/frequencies is broadcast periodically andcontinuously.

t

TB

DBCPC (DL)

CPC

Content

#1

CPC

Content

#2

CPC

Content

#3

CPC

Content

#N

CPC

Content

# 1

CPC

Content

#2

CPC

Content

#N




CPC Delivery Mode implementations

On-Demand CPC:

It only transmits the CPC information when required by acertain terminal.

Logical channels:

• Random Access CPC (RACPC): Uplink slotted channel wherethe mobiles operating with CPC send requests to retrieve theCPC information. Each request can optionally contain anindicator of the geographical coordinates of the mobile terminal.

• Acquisition Indicator CPC (AICPC): This DL channel is devotedto indicate that a request has been successfully received. Thechannel consists in Acquisition Indicators (AI) each oneindicating the identifier of the terminal whose request has been

received• Downlink On-Demand CPC (DODCPC): This downlink logical

channel is used to transmit the CPC information for a giventerminal.

CPC Delivery Mode




CPC Delivery Mode implementations

On-Demand CPC:

Thanks to the interaction, this approach allows having a higher control ofthe terminals using CPC facilities, thus being easier to fit CPC operationwithin specific business models and exploitation plans, e.g. for acontrolled secondary use of the spectrum.

CPC Delivery Mode

MT1 MT2

t

t

TS

Tm,OD

RACPC (UL)

DODCPC+AICPC (DL)

A I 1

CPC

for

MT 1 A I 2

CPC

for

MT 1

CPC

for

MT 1 A I N u l l CPC

for


for


for

MT 2 A I N u l l

#1 #2 #3 #4

MT3

MT4

Collision

MT3

#k #k+1#k-1

TS

CPC

for

MT 2 A I 3

#k+2




Reconfigurable scenario where frequencies allocated to the different

technologies in the different geographical regions can change during thetime.

The range of spectrum may go from Fmin to Fmax with a frequency raster off (e.g. a range of 10 GHz with f =200 kHz).

The allocation of frequencies to RATs in each region is decided by anetwork reconfiguration manager and supported by procedures, like e.g.

JRRM, DNP and ASM Wireless terminals with reconfigurable capabilities and supporting different

technologies exist. They do not know the specific frequencies of eachaccess technology.

A scanning procedure in the whole band from Fmin to Fmax to identify theexisting RATs, operators and frequencies available in each region would

last a long time and therefore it should be avoided. Terminals can optionally have a means to know the geographical

coordinates of their location thanks to some positioning system.

UE Switch on use case: considerations

S




UE Switch on use case: considerations

Number of transmitters, with possibly time-varying

assignment of RATs and operating frequencies Stand-alone CPC transmitter separated from the rest of

transmitters

• The CPC transmitter, which is physically realized on a given

RAT, operating frequency and associated bandwidth, is in

charge to convey spectrum awareness information related toits coverage area.




Mesh-based approach:

A mesh is defined as an area where certain radio electrical commonalities can beidentified (e.g. a certain frequency that is detected with a power above a certain

level in all the points of the mesh, etc.).

Mesh #i: Location information

Operator #1

RAT #1Frequency Range #1

Frequency Range #n

RAT # j

Operator #m

Mesh #i Geographic area

Frequency Range #n

Secondary Use

CPC Contents: Mesh-based approach

CPC C t t b d h




CPC Contents: coverage-based approach

Definition of CPC message:

Operator information: operator identifier

RAT list: for each operator, provide information on available RATs• RAT type: could be “GSM”, “UMTS”, “CDMA2000”, “WiMAX”, “LTE”…

• Coverage extension: could be GLOBAL (i.e. wherever the CPC is received) or LOCAL

(i.e. in an area smaller than CPC coverage) – Coverage area: could be provided in case of LOCAL coverage (e.g. reference geographical

point)

• Frequency information: provide the list of frequencies used by the RAT (e.g. the

operating band)

RAT_TYPE = GSM, UMTS,

WiMAX, LTE…

COVERAGE_EXTENSION

= LOCAL/GLOBAL

FREQ_LIST

COVERAGE_AREA

(optional)

OPERATOR_INFO

RAT_LIST

This information

is repeated for

each Operator to

be advertised by

the CPC

This information

is repeated for

each RAT of i-th

Operator




Coverage-based vs Mesh-based

[RAT2(t), f 2(t)]

[RAT 1(t), f 1(t)]

[RAT3(t), f 3(t)]

1

2

4

3

[RAT, f, BW]

CPC TRx

Coverage based:

3 regions signalled through CPC:

- RAT 1

- RAT 2

- RAT 3

Mesh-based:

4 regions signalled through CPC:

1) RAT1, RAT 2 available

2) RAT 2 available

3) RAT 3 available

4) RAT2, RAT3 available

Example:

In general, less information is required to be sent with the coverage area-

based, which will reduce the bit rate requirements for a broadcast CPC.




Switch-on procedure through CPC

The CPC radio interface, either being a newly defined accesstechnology or the adaptation of an existing one, will be operatingat a certain frequency f and occupying a certain bandwidth BW.

It is assumed a mobile terminal that:

Has implemented all related L1/L2 CPC aspects

Is aware of pre-defined f for CPC

Note: If CPC uses a narrowband RAT, it may be necessary to pre-define more than one f to allowa proper CPC frequency planning along different CPC TRx

UMTS

f1

WIFIf2

CPC

LTE

f3





Broadcast CPC / Coverage-area based

1) CPC-L1 detection and synchronization over pre-defined f If no CPC signal detected, start-up is only possible through general scanning across RATs

and frequencies

If 1 CPC signal is detected, proceed with next steps

If more than 1 CPC is detected, perform “CPC TRx selection” and proceed with next steps

2) (Optionally) determine terminal’s geographical position

3) Retrieve CPC information (list of RATs/frequencies and correspondingcoverage indication)

4) From 3), generate “Initial RAT/frequency candidate list” If no positioning is available, the list includes all RATs/frequencies conveyed by the CPC

If positioning is available, the list only includes those RATs/frequencies conveyed by theCPC whose coverage indication includes the mobile terminal’s position





5) From 4) and according to some criteria available at the terminal, generate“Ordered Initial RAT/frequency candidates list” Possible criteria for ordering: based on pre-defined list of preferences, starting from global

coverage candidates….

6) Starting with i=1, perform L1 detection and synchronization over the i-thcandidate in the “Ordered Initial RAT/frequency candidates list” If L1 detection and synchronization succeeds, follow standardized procedures over the

corresponding RAT If L1 detection and synchronization fails, go back to 6) with i=i+1

If no more candidates on the list, perform a general scanning across RATs and frequencies

CPC

CPC LIST

Operator

RAT

Frequency

ORDERED

CANDIDATES

LIST

Operator

RAT

Frequency

R l f i i i i CPC




Role of positioning in CPC

Broadcast / Mesh-based

If positioning is available, the terminal reads the contentscorresponding to its mesh

If positioning is not available, the terminal must (smartly)process information from all meshes and scan across thesignalled RATS/frequencies

• E.g. the terminal could start with the RAT/frequency that isassociated to the highest number of meshes

UMTS

f1

WIFI

f2

CPC

LTE

f3

R l f iti i i CPC





Broadcast / Coverage area-based

If positioning is available, the terminal reads the list providedand verifies if the terminal is located within the signalledcoverage area for the component of interest within the list

If positioning is not available, the terminal must (smartly) scanacross the signalled RATS/frequencies

• E.g. the terminal could start with the RAT/frequency that issignalled as global coverage

R l f iti i i CPC





On-demand

If positioning is available at the mobile terminal and has beentransmitted to the network in the request message, the CPCdirectly provides the list of RATs/frequencies available in thatposition

If positioning is available at the mobile terminal but has notbeen transmitted to the network in the request message:

• the CPC provides the list of RATs/frequencies within the CPCcoverage area, the terminal reads the list provided and verifies if the terminal is located within the signalled coverage area for thecomponent of interest within the list

If positioning is not available, the CPC provides the list of

RATs/frequencies within the CPC coverage area and theterminal must (smartly) scan across the signalledRATS/frequencies

• E.g. the terminal could start with the RAT/frequency that issignalled as global coverage

f C C




Errors in info conveyed by CPC

Coverage areas in practice show very irregular forms

The specification of a geographical area through the CPC (e.g.mesh or coverage area) is an inherent source of errors

Accuracy in the specification of a geographical area could beimproved if the number of bits is increased





If a coverage area is characterized by a circle with R the cell radius,

missdetection and false detection errors will occur An error may impact on the switch-on latency

R

CPC causes false

detection

(i.e. RAT is not availablebut CPC is indicating that

it is available)

CPC causes missdetection

(i.e. RAT is available but CPC is

indicating that it is not available)


E i i f d b CPC




On-demand CPC with positioning may exhibit better robustness tosuch type of errors, particularly if the information conveyed by theCPC is obtained from a planning tool or a real propagationdatabase.


CPC as a s pport to s itch on S mmar




CPC as a support to switch-on: Summary

Switch-on process in mobile terminals is based on a scanning

process, where the terminal looks for a signal with knowncharacteristics on a given frequency band

In a full DSA context, switch-on process can be a time and power consuming task

The set of potentially available RATs/frequencies can be large

CPC helps to reduce the latency in the switch-on process

CPC points to the terminal to a more reduced and accurate setof available RATs/frequencies

Positioning is not a requirement for CPC operation but it helps tofurther reduce the latency in the switch-on process

Positioning allows more detailed determination of the set of available RATs/frequencies

CPC dimensioning methodology




Methodology for dimensioning the out-band CPC in the start-up

phase:Both broadcast and on-demand possibilities for the CPC

information delivery and the mesh-based and coverage-area

based strategies for the CPC contents definition are

considered.

The objective is to identify those parameters that play arelevant role in the CPC dimensioning to determine the

required downlink capacity per CPC transceiver in accordance

with a given CPC deployment.

This methodology can be used as a reference to establish a

comparison between the different approaches in differentscenarios.







Offered CPC downlink capacity:

The CPC physical and link layer design will end up with acertain downlink capacity to convey CPC information:

• For each CPC transceiver (TRx): R bits/s/TRx.

Deployment strategy characterized by a certain density ofCPC transceivers over a given geographical area:

• Transceiver density: T TRx/km2.

Resulting offered downlink capacity is R T bits/s/km2

A proper dimensioning strategy will match the demanded CPCcapacity with the offered CPC capacity to determine R and T.

Demanded CPC downlink capacity:

Dependant on the CPC delivery strategy:• Broadcast/on-demand

and on the CPC contents approach:• mesh-based / coverage-area based





Demanded CPC capacity / on-demand-mode:Information about the operators/RATs/frequencies is provided upon request.

• Density of requests by L requests/s/km2

.• Let assume the most general case in which positioning information is not known.


CPC will provide, for each request, the information corresponding to all the meshes inthe CPC transmitter coverage area (i.e. a total of Xm /T meshes/request).

• Total amount of information to be delivered per request is Xm Mm /T bits/request

• CPC demanded capacity for on-demand mode and Mesh-based approach = Mm Xm L/T

bits/s/km2

Coverage area-based approach:

CPC will specify for each request the information corresponding to all coverage areasin the CPC transmitter range (i.e. a total of Xc /T areas/request)

• Total amount of information to be delivered per request is Xc Mc /T bits/request

• CPC demanded capacity for on-demand mode and Coverage area-based approach =Mc Xc L/T bits/s/km2

When information on positioning is included in the request, the amount of informationof the on-demand CPC can be reduced .

• Only Mm bits/request corresponding to the list of RATs/frequencies in the position wherethe terminal is located could be transmitted

• CPC demanded capacity for on-demand mode and coverage area-based approach withpositioning= Mm L bits/s/km2

CPC dimensioning methodology:




g gy

examples

Scenario 1:

Cellular technology with radius RC=1 km is deployed in the whole area

Density of hotspot transmitters Tx=0.18 transmitters/km2, each with

radius RH=200m is scattered randomly in the scenario.

Scenario size: 10 km x 10 km.

Four frequencies (three for the cellular and one for the hotspots).





Resulting number of zones to be signalled:

Mesh-based approach: Xm1

=2.54 zones/km2.

Coverage area based approach: Xc1=0.6 zones/km2

Demanded capacity broadcast:

examples

RC=1km, RH=200m, Tx=0.18 Tx/km2

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5

P (s)

D e m a n d e d C a p a c i t y ( k b / s / k m 2 )

M=100 bits. Coverage

M=400 bits. Coverage

M=100 bits. Mesh

M=400 bits. Mesh

P=1s, M=400 bits, mesh-based

Req. capacity:

1 kb/s/km2





Trade-off between the CPC transmitter density and the CPC

transmitter bit rate for different demands :

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35 40 45 50

R(kb/s/Tx)

T ( T x / k m 2 )

Demand=1 kb/s/km2

Demand=2 kb/s/km2

Demand=5 kb/s/km2

Demand=10 kb/s/km2

Demand=15 kb/s/km2

examples

CPC deployment using

cellular infrastructure

T=0.42 TRx/km2

To get 1 kb/s/km2

It is required:

R=2.4 kb/s

CPC dimensioning methodology:l




Scenario 2:

Same as scenario 1 but increasing the radius of the hotspottransmitters to RH=700m

examples

Resulting number of zones:

Mesh-based approach:Xm2=3.64 zones/km2.

Coverage area- based

approach: Xc2=0.6 zones/km2 (i.e.

the same as in Scenario 1 since

in fact the total number of

transmitters is the same).





RC=1km, RH=700m, Tx=0.18 Tx/km2

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5P (s)

D e m a n d e d C a p a c i t y ( k b / s / k m

2 )

M=50 bits

M=100 bits

M=200 bitsM=400 bits

M=1000 bits

Demanded capacity Broadcast:

examples

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5

P (s)

D e m a n d e d C a p a c i t y ( k b / s / k m

2 )

M=400 bits. Coverage (Sc 1 and 2)

M=400 bits. Mesh (Sc 2)


Mesh-based

Mesh-based demanded

capacity has increased wrt

scenario 1 while coverage-

based case has remained the

same





Broadcast approach, P=1s and M=400 bits.

Deployment of CPC using cellular infrastructure in the scenario: T=0.42 TRx/km2

examples

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10

R(kb/s/Tx)

T ( T x / k m 2 )

Demand=1 kb/s/km2

Demand=1.46 kb/s/km2

Demand=0.24 kb/s/km2

Sc 1, mesh-based

R=2.4 kb/sSc 1 and 2, coverage-based

R=0.57 kb/s

Sc 2, mesh-based

R=3.5 kb/s





Scenario 3:

Same as scenario 1 but now the radius of the cellular technology is

RC=200 m

examples

Resulting number of zones:


Xm3=54.71 zones/km2

Coverage area-based

approach: Xc3=10.04 zones/km2





examples

0

5

10

15

20

25

30

0 1 2 3 4 5

P (s)

D e m a n d e d C a p a c i t

y ( k b / s / k m

2 )

M=400 bits. Coverage (Sc 3)


M=400 bits. Coverage (Sc 1)


Mesh

x 21.5

Cov. x

16.7

Assuming broadcast

approach, there is an

increase in demanded

capacity in both meshand coverage-area

based wrt scenario 1





0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12

R(kb/s/Tx)

T ( T x / k m 2 )

Demand=4 kb/s/km2

Demand=22 kb/s/km2

Broadcast approach, P=1s and M=400 bits.

Mesh-based requires demanded capacity around 22 kb/s/km2

Coverage area-based requires demanded capacity around 4 kb/s/km2

examples

Coverage-based with cellular

infrastructure (T=9.86 TRx/km2)

R=0.4 kb/s

Coverage-based with dedicated

CPC infrastructure (T=2 TRx/km2)

R=2 kb/s

Mesh-based with cellular

infrastructure (T=9.86 TRx/km2)

R=2.2 kb/s

Mesh-based with dedicated CPC

infrastructure (T=2 TRx/km2)

R=11 kb/s

CPC as enabler for decentralisedl ti ti i ti




resource selection optimization

Traditionally, RRM has been mainly centralized, based

on the fact that a central network node has a morecomplete picture of the radio access status than a

particular node.

With the increase of complexity in the scenarios, the

use of a centralized manager would impose large

communication signalling overhead due to fastcontext changes.

Complexity can be relaxed by moving part of RRM

into more decentralized solutions.

CPC appears as an appealing possible future solution for

enabling such a network-assisted decentralized radio resource

management.

CPC as enabler for decentralised




Handover Assistance

Load Balancing

Context information

RR Policies

Mainly via In-band Solution


CPC as enabler for decentralisedl ti ti i ti





In-band CPC solution seems to be the most convenient one,

allowing to bear information to both UL and DL.

• Information categories:

- Context information (either in UL or DL):

- Network capabilities

- Measurements.- Device capabilities

- Policies:

- Operator can keep the control

- Radio Resource Selection (RRS) policies

• The terminal, driven by RRS policies and network context information

can implement the action(s) of the optimization of its operation e.g. for

the (re-)configuration of its operating parameters like RAT, frequency,

modulation type etc.

CPC In-Band: Current Solutions




Mapping on existing standards:

Possible CPC In-band solution focusing on the legacysystems GSM and UMTS: identify possible “freespaces” in the exchange of the protocol messages (e.g.System Information or RR/RRC messages) in terms of “not used bits” in order to send additional information

(e.g. related to the CPC) using these bits and withoutheavy new investments in the network.




Secondary Spectrum Access

Secondary Spectrum Access




y p

Objective :

Maximize spectrum reuse amongst users while ensuring that

mutual interference between them remains at acceptable levels.

Motivation:

Sporadic use of particular spectrum bands while others are

profusely used.Fixed spectrum assignment to a licensee can lead to spectrum

underutilisation.

New technical advances are needed to develop strategies and policies

aiming to the utmost and efficient access to spectrum resources:

Spectrum Sharing mechanismsCognitive Radio Networks

Functionalities to support SecondarySpectrum Access




Spectrum Access

Spectrum Sensing:

Detecting unused spectrum (spectrum holes) that can be used forsecondary spectrum access without generating harmful interference toothers

Spectrum Management:

Selecting the best available spectrum based on secondary networkcommunication needs and on characteristics of the available bandwidths(e.g. bit rates, etc.)

Spectrum Mobility:Maintaining seamless secondary communication requirements during thetransition to a better spectrum portion

Spectrum Handoff

Evacuation mechanisms once a primary user is detected

Spectrum Sharing:

Providing a fair spectrum scheduling among coexisting secondary usersDevelop appropriate MAC protocols

Ref: I.F. Akyildiz, W.-Y. Lee, M.C. Vuran, S. Mohanty, “Next generation/dynamic spectrum access/cognitive radio wireless

networks: a survey”, Comput. Networks (Elsevier) 50 (13) (2006) 2127–2159

Considered Framework




Compound network formed by:

Primary Network (PN), serving Primary Users (PUs).Secondary Network (SN) serving Secondary Users (SUs).

Spectrum resources are in the form of C channels which partition a

given spectrum range.

PUs are licensed to use the spectrum (priority over SUs).

SUs may seek and make use of spectrum opportunities as long asthey do not interfere with PUs.

P

S

P

S

C

c1 c2 c3 c4 c5

SpectrumCollision

Considered Framework




According to the bandwidth relation between primary andsecondary users 3 possibilities envisaged.

frequency

Model Abstraction

P

S S

P

S S

P

S S

P

S S

P P

S

P P

S

Model Abstraction Model Abstraction

a) Wideband Primary /

Narrowband Secondary

c) Narrowband Primary /

Wideband Secondary

b) Equal band Primary /

Secondary

Spectrum #1 Spectrum #2 Spectrum #3

P

S

P

S

P

S

P

S

Secondary Network Procedure




Secondary Network Procedure

Identification of a specificchannel to support SUcommunication.

Configuration of secondarytransmit and receive ends toenable communication over theidentified channel MAC

Detection of primary presencecommunication while

maintaining the secondarycommunication, in which casethe secondary communicationmust evacuate the channel.

Spectrum handover (SpHO)mechanisms, if available, will

intend to find a proper alternative channel where thecommunication can becontinued in order to avoid theinterruption of the secondarycommunication.

Identification of frequency bandfor secondary communication

Configuration TRX

Start of Communication

End of Communication

Monitoring ofprimary’s

presence

SpectrumHandover

procedure

Spectrum Sensing




Spectrum sensors perform a binary hypotheses (H 0 or H 1) test over

a given band W:H 0 : Primary signal is not present

H 1 : Primary signal is present

Energy detection:

Optimal detector if not sufficient information about the primary

signal is available at the receiver Measure the energy of the received signals during an interval T

• Y : decision statistic (energy estimation)

• λ : decision threshold.

H 0 is decided if Y < λ

H 1 is decided if Y > λ

Sensing mechanisms are negatively affected by channel conditions,hidden terminal, limited sensitivity, etc.

p g

Spectrum Sensing




Errors in the sensing procedure:

Values of ε and δ mainly depend on:

The time-bandwidth product: m=T ·W

The average SNR (γ).

Usually express ε against δ in Receiver

Operating Characteristics (ROC) curves.

Improved ε and δ can be obtained by

means of sensing cooperation.

False Alarm probability Spectrum underuse

Missed Detection probability Spectrum collision

p g

Pr( | is true)Y 0 H

Pr( | is true)Y 1

H

10-4

10-3

10-2

10-1

100

10-4

10-3

10-2

10-1

100

False-Alarm Probability ( )

M i s s

e d - D e t e c t i o n P r o b a b i l i t y ( )

ROC Curve for =10 dB

m=5

m=10

m=50

m=100

m=150

m=500

m=1000

m

Modelling Framework Assumptions




A total of C channels (bands) are available for both PUs and SUs. One useroccupies only one channel.

PUs and SUs request channels in accordance with session activity:Session arrival rate of PUs and SUs follow a Poisson distribution with rates λ p and λ s (arrivals per second).

Session duration are exponentially distributed with average 1/ μ p and 1/ μ s(seconds) for PUs and SUs correspondingly.

Network operation:The PN operates autonomously (i.e. channels are assigned regardless the number

of SU users).No coordination exists between PN and SN

SN gathers info about PUs occupation to decide on SU assignments based onSpectrum Sensing.

SpHO mechanisms: a SU is re-allocated when a PU appears in the same channel

Spectrum sensing is not error-free, causing:Missed detection spectrum collision.

False alarm spectrum underutilization.

Spectrum awareness periodicity of ΔT (sensing periodicity)Observe the system at discrete time instants and make decisions based upon thisinfo

g p

Modelling framework




g

Markov models become an important aid in modelling problems

dealing with the dynamic access to shared spectrum resources.

Continuous Time Markov Chain (CTMC) models.

Observe the system state upon arrival/departures.

Assume up-to-date spectrum occupation information upon

arrival/departures.

Discrete Time Markov Chains (DTMC):

The system state is observed at discrete periodic time instants.

The DTMC will assume that spectrum occupancy information is

provided at discrete time instants with fixed periodicity ΔT.

Thus decouples traffic generation processes from spectrum

occupancy information up-dates higher flexibility and broaderapplicability.

State Space Definition




A state in the Markov model is defined by the pair (i, j) indicating the

number of PUs (i) and SUs ( j) in the compound network. For a correct spectrum use (i.e. with no spectrum collisions), the

number of PUs (i) plus the number of SUs ( j) must not exceed the

total number of available channels (C ). We define the non-col l is ioncond i t ion as:

( , )i j f i j C

( , )

: ,i j

S i C j C S =

( , ) ( , )

:i j i j

S f C nc

S = ( , ) ( , )

:i j i jS f C

c S =

Non-collision statesCollision states

S

i

S (3,0) S (3,1) S (3,2) S (3,3)

S (2,0) S (2,1) S (2,2) S (2,3)

S (1,0) S (1,1) S (1,2) S (1,3)

S (0,0) S (0,1) S (0,2) S (0,3) j

cS ncS

Spectrum Awareness Model




The probability to detect k PUs when there are in fact i PUs is

given by:

with ε and δ the false-alarm and missed-detection probabilitiesrespectively.

The SN operation will be based on

the knowledge of the detected statespace as opposed to the true state space.

min( , )

( , )

max(0, )

i C k m k i C m k m i m

k i

m i k

C i ib

m k i m

i

j

k

T r u e

S t a t

e S p

a c e

Detected State Space

( , )k ib

S (i,j)

S (k,j)

Transition Probabilities




The transition S (i,j) S (i+N,j+M) probabilities can be computed with – i

≤ N ≤ C-i and – j ≤ M ≤ C-j :

Probabilities P (i+N,j+M|i,j) are entries of the Transition Probability

Matrix P from which we may compute the steady state

probabilities P (i,j) .

We can also determine the steady state probabilities of the

detected states (i.e. including possible sensing errors) as:

( , | , ) ( , , , ) ( , , ) ( , , , ) ( , , )

max( ,0) max( ,0)

ji P P S S

i N j M i j i j N k k i j k i j M k k i j k

k N k M

P a d a d

( , ) ( , ) ( , )0

C

i j i n n jn P b P

( , , , ) ( , )

( , , ) ( , )

Pr[ = | , ]

Pr[ = | ] Pr[ ] P

P P P

i j k l a n i j d

P P PD PDi j k d n i j k

a N k S N l

d N k S N k

X

X

Key Performance Indicators




Several performance metrics can be defined from the steady

state probabilities:

Average Served Traffic

(Average number of users)( , )

( , )i j

P i jS N i P

S

( , )( , )

i jS i jS

N j P S

( , )( , )

i j P i jS

N i P S

Blocking Probability( , )0

C P

B C j j

P P

( , )0

C C S

B i ji j C i P P

Interruption Probability served

1 11 1

S S

D S S S S B

B

S

T N P

T P P

Interference Probability( , )

( , )i j c

I i jS P P

S

Throughput( , ) ( , )

( , ) ( , ) ( , )( )i j i j

P P

i j i j c i j

S S

P i R P S S

( , ) ( , )

( , ) ( , ) ( , )( ) (1 )i j i j

S S sens

i j i j c i j

S S

T P j R P

T S S

Performance Evaluation Set-Up




p

Sensing parameters

W= 200 KHz.γ = 10 dB (Rayleigh)

T sens =C·T

ε and δ extracted from ROCcurves.

Ideal case

Assume that every ΔT thereis error-free available info i.e.ε = δ = 0

Errors due to out-of-datespectrum occupancyinformation ( ΔT ).

10-4

10-3

10-2

10-1

100

10-4

10-3

10-2

10-1

100


M i s s e d - D e t e c t i o n P r o b a b i l i t y ( )

ROC Curve for =10 dB

m=5m=10

m=50

m=100

m=150

m=500

m=1000

m * * T m W

5 0.01 0.7277 0.000025

10 0.01 0.6278 0.00005

50 0.01 0.3378 0.00025100 0.01 0.2073 0.0005

150 0.01 0.1391 0.00075

200 0.01 0.0974 0.001300 0.01 0.0511 0.0015400 0.01 0.0281 0.002

Other assumptions:

C=16 channels.

1/ μp =1/ μs = 120 secs.

ΔT= 100 ms

Sample Results: spectrum occupancy




Model validated against a system-level simulator.

Better spectrum occupancy information better spectrumopportunities are exploited:

High values of m in the sensing case.

Average Number of Users

0

2

4

6

8

10

12

0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00

Offered Secondary Traffic Load (Ts) [Erlangs]

N

u m b e r o f U s e r s

Num PUs

Num SUs (CPC)

Num SUs (m=5)

Num SUs (m=10)

Num SUs (m=50)

Num SUs (m=100)

Num SUs (m=150)

Num SUs (m=200)

Num SUs (m=300)

Num SUs (m=400)

(Ideal)

Sample Results: Blocking probability




For low m high false-alarm thusnumber of sensed PUs is higherthan the true number of PUS.

Increasing m means more reliableinfo, thus more SUs are admitted.

Blocking Prob. is due to bothsensed PUs and admitted SUs.

Blocking is higher in ideal casegiven that better spectrumoccupancy info is known (moreSU).

When m there is convergencetowards ideal case.

Average Number of Users (SNR=10dB; Tp=10 Erlangs)

0

2

4

6

8

10

12

14

16

0 200 400 600 800 1000

Time-Bandwidth Product (m)

N u m U s e r s

Num PUs Sensed Num PUs

Num SUs Ts=5 Erlangs Num SUs Ts=2 Erlangs

Num SUs CPC (Ts=5 Erlangs) Num SUs CPC (Ts=2 Erlangs)

Blocking Probability (SNR=10dB)

0

0.05

0.1

0.15

0.2

0.25

0 200 400 600 800 1000


P r o b a b i l i t y

Ts=5 Erlangs Ts=2 Erlangs

CPC (Ts=5 Erlangs) CPC (Ts=2 Erlangs)

Ideal Ideal

IdealIdeal



Sample Results: Throughput




Increasing m increase

in throughput.

We cannot increase m

indefinitely since we also

increase the time devotedto sensing (T sens ).

Throughput degradation

rises when T sens gets

comparable to ΔT.

Secondary Throughput

0

50

100

150

200

250

300

350

400

0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00

Offered Secondary Traffic Load (Ts) [Erlangs]

T h r o u g h p u t ( k b p s )

SUs (CPC)

SUs (m=5)

SUs (m=10)

SUs (m=50)

SUs (m=100)

SUs (m=150)

SUs (m=200)

SUs (m=300)

SUs (m=400)

SUs (m=1000)

Throughput-Sensing Tradeoff

0

20

40

60

80

100

120

140

0 200 400 600 800 1000


A v e r a g e S e c

o n d a r y T h r o u g h p u t

( k b p s )

Ts=2 Erlangs; SNR=10dB



Ts=5 Erlangs;SNR=5dB

(Ideal)

Sample Results: Interference probability




Interference Probability

(Tp=5 Erlangs, Ts=20 Erlangs)

0

0.05

0.1

0.15

0.2

0.25

0.01 0.1 1 10

ΔT (s)


CPC

m=50

m=200

m=400

Interference Probability

(Tp=10 Erlangs, Ts=20 Erlangs)

0

0.05

0.1

0.15

0.2

0.25

0.01 0.1 1 10

ΔT (s)


CPC

m=50

m=200

m=400

High values of ΔT out-of-date primary spectrumoccupancy informationhigher interferenceprobabilities:

High m values highspectrum utilization

higher chances that SUsinterfere with PUs. Theideal case produceshighest interference.

Low m values higher ε

which prevents from

assigning SUs lessinterference is observed.

Ideal Ideal

Discussion




Spectrum utilization subject to errors in the sensing

procedure. Up-to-date spectrum occupancy info is required.

Trade-off with time devoted to sensing must be considered.

For large bandwidth sensing could result inefficient.

Operating Point of Spectrum Sensing




Spectrum Sensing capabilities at the SU side is assumed.

Spectrum sensing may be affected by errors:

False-Alarm (FA):

• A free channel is erroneously sensed to be occupied.

Miss-detection (MD):

• An occupied channel is erroneously sensed to be free.

Unfortunately a tradeoff exists between FA and MD.

One must decide an adequate Operating Point (FA,MD) for the

detection mechanism:

• Usually determined by adjusting a decision threshold (e.g. in

energy detector case).

Operating Point of Spectrum Sensing




ROC curves (red), MD

probability (δ) is plotted

against FA probability (ε).

Each blue point over the

ROC curves determines a

particular Operating Point

(OP).

OP parameterization

through parameter 0≤Θ≤1

given function (in green):

1

1

min

min

Low MD (High FA) benefits PUs: Low interference.

Low FA (High MD) benefits SUs: High utilization.

Objective: To find appropriate value for

Θ such that both PUs and SUs are

“satisfied”.

10-4

10-3

10-2

10-1

100

10-4

10-3

10-2

10-1

100


M

i s s e d - D e t e c t i o n P r o b a b i l i t y (

)

ROC Curve for =10 dB and m=100

ROC Curve

Operating Point=0.4

=0.3=0.2=0.1

=0.5

=0.6

(εmin, δmin)High FALow MD

Θ→1

Θ→0

High MD

Low FA

Satisfaction measure of PUs and SUs




Adopt the classical definition of Grade of Service (GoS):

1 P P

B P I P GoS P P 1S S S

B S D S GoS P P

Primary GoS Secondary GoS

: Blocking prob. PUs

: Interference prob.

: Weight factor PUs

P

B P

P

I P

: Blocking prob. SUs

: Interruption prob.

: Weight factor SUs

S

B P

S

S

D P

“Interference is worse than blocking” “Interruption is worse than blocking”

Aggregate GoS accounting for PUs and SUs:

1 A S P

A AGoS GoS GoS

The value of ωA

will indicate the “willingness” of primary networktowards secondary operation:

• High ωA

: primary is highly protected (strict SU operation).

• Low ωA

: primary is willing to share (relaxed SU operation).

Results




Primary GoS is concerned withkeeping interference low, thismeans low MD, which implies OPvalues Θ→1.

However, if SU traffic is low, theninterference is low, such thatrelaxed values of Θ→0 can also beplausible.

Secondary GoS is concerned with

keeping interruption of SUs low,this means low FA, which impliesΘ→0.

Fixed PU traffic (5 Erlangs)

0 0.2 0.4 0.6 0.8 10

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Operating Point ( )(a)

G o S

Primary GoS

Ts=0.56

Ts=1.25

Ts=2.14

Ts=3.33

Ts=5

Ts=7.5

Ts=11.67

Ts=20

Ts=45

B A D

G O

O D

Increasing

SU traffic

Primary GoS

Low MD

High FA

Low FA

High MD

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Operating Point ( )(b)

G o S

Secondary GoS

Ts=0.56

Ts=1.25

Ts=2.14

Ts=3.33

Ts=5

Ts=7.5

Ts=11.67

Ts=20

Ts=45

B A D

G O

O D

Increasing

SU traffic

Secondary GoS

Low MD

High FA

Low FA

High MD

An appropriate selection of the OP Θ must

consider an aggregate GoS measure

Results




Suitable values for the OP arethose that minimize the aggregateGoS:

Minimum values.

Minimum GoS regions.

0 0.2 0.4 0.6 0.8 10

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Operating Point ( )(c)

G o S

Aggregate GoS

Ts=0.56

Ts=1.25

Ts=2.14

Ts=3.33

Ts=5

Ts=7.5

Ts=11.67

Ts=20

Ts=45

Aggregate GoS

B A D

G O

O D

Low MD

High FA

Low FA

High MD

Increasing

SU traffic

Define Feasible OP Region such that the

aggregate GoS is at most ΔΘ percent

higher than the minimum GoS.

0.4 0.6

0.4 0.6

0.4 0.6

Minimum

GoS values

Minimum GoS

regions

ΔΘ%

Discussion




An appropriate election of the sensing OP can achieve good balancebetween primary and secondary GoS performances.

Selection of the OP is sensitive to:

secondary traffic load (Ts): OP values should be increased and narrowedwith Ts.

• Low Ts : OP values between [0.4-0.6] satisfy both PUs/SUs

• High Ts : OP should be close to 0.6 in order to protect PUs.

signal-to-noise ratio (SNR): OP values should be increased with SNR.• Low SNR: OP values close to 0.5

• High SNR: OP values should be increased to 0.55

time-bandwidth product (m): OP values should be increased with m.• Low m: OP values close to 0.55

• High m: OP values increased to 0.6

Priority factor (ωA): According to the “tolerance” of PUs towards SUs.• Low OP values admitted if secondary traffic is low or ωA values are low.

• High OP values required if secondary traffic is high and if ωA values are high.

References




O. Sallent, L. Giupponi, J. Nasreddine, R. Agustí, J. Pérez-Romero,"Spectrum and Radio Resource Management in Cognitive HeterogeneousWireless Networks," Vehicular Technology magazine, May 2008.

J. Nasreddine, O. Sallent, J. Pérez-Romero, R. Agustí "Positioning-basedFramework for Secondary Spectrum Usage", Physical Communication Journal(PhyCom), Elsevier, June 2008, Vol. 1, pp. 121-133.

J. Pérez-Romero, X. Gelabert, O. Sallent, R. Agustí "A Novel Framework forthe Characterization of Dynamic Spectrum Access Scenarios", 19th PersonalIndoor and Mobile Radio Communications conference (PIMRC’2008),Cannes, France.

X. Gelabert, O. Sallent, J. Pérez-Romero, R. Agustí "Exploiting the OperatingPoint in Sensing-Based Opportunistic Spectrum Access Scenarios", ICC2009, Dresden, June 2009.

Xavier Gelabert, Ian F. Akyildiz, Oriol Sallent, Ramon Agustí “Operating point

selection for primary and secondary users in cognitive radio networks”,Computer Networks, Elsevier, February, 2009.




Decentralised RRM

Basics on RRM




Radio Network Planning:

Radio Resource Management functions are in charge of allocating

and managing RRUs as efficiently as possible at a short time basisRRM mechanisms may overcome long term reactivity in radionetwork planning.

RRM strategies are not subject to standardizationdifferentiationamong operators and manufacturers

Radio Resource Management (RRM)Deployment of the network in a target coverage area and Provisionof enough RRUs (Radio Resource Units) with sufficient quality given

• Radio Access Technology (RAT)

• Number of users to be supported

• the service area• desired QoS (bit rate, blocking, delay, etc.)

Provide a network topology and a given configuration of the cell sites

Evolving process with sustained and long term variations

Basics on RRM




COBERTURACOVERAGE

CALIDADQUALITY

COVERAGE

CALIDADQUALITY

CAPACIDADCAPACITY

COVERAGE

QUALITY

EFFICIENCY

CAPACIDADCAPACITY

COVERAGE

QUALITY

EFFICIENCY

COBERTURACOVERAGE

CALIDADQUALITY

COBERTURACOVERAGE

CALIDADQUALITY

(a) Contradictory objectives

in a radio network

(b) Thick tuning provided by

Radio Network Planning

(c) Fine tuning provided by

Radio Resource

Management mechanisms

RADIO

NETWORK

PLANNING

Set of requirements RRUs provision

RADIO

RESOURCE

MANAGEMENT

RRUs allocation

RRM-functions




Timescale activation

and execution periodsof RRM

MAC ALGORITHM

PACKET SCHEDULING

Serving

cell

Instantaneous

bit

rate

Code

sequence

Transmitted

Power

level

CODE

MANAGEMENT

HANDOVER

MANAGEMENT

ADMISSION

CONTROL

POWER

CONTROL

Maximum

bit

rate

CONGESTION

CONTROL

1 slot

1 frame

10ths to 1000ths

of frames Serving

cell

Maximum

bit rate

HANDOVER

CONGESTION

CONTROL

ADMISSION

CONTROL

RRM in Heterogeneous Networks




Core Network

Data Network

(Internet)PSTN/ISDN

UTRAN/FDD LTE GERAN

(GSM/EDGE)Public WLAN

Private

WLAN

Private

WPAN

Macrocell

Microcell

Multi-mode Terminal

RAT selection

- Coordination between RATs

- Inter-working between RATs

Introduction of new algorithms

operating from a common

perspective: CRRM

Multiple RATs available

Which is the best one?

ABC: Always Best Connected concept

Joint Radio Resource Management




JRRM: set of functions devoted to ensure an efficient use of available

resources in heterogeneous networks scenarios by means of a proper

coordination between the different radio access networks.

f GERAN

ttt

cUTRAN WLAN

JRRM

f

GERANttt

c

UTRAN WLAN

RRM-GERAN RRM-UTRAN RRM-WLAN

Pool of resources managed together,

leading to TRUNKING GAIN

Joint Radio Resource Management




Different terminologies in the literature to refer to the

same concept:

CRRM - Common Radio Resource Management

JRRM - Joint Radio Resource Management

MRRM - Multi Radio Resource Management

JRRM Functions




RRM functionalities in a single-RAT context:

Admission control

Congestion control

Horizontal (intra-system) handover

Packet scheduling

Power control

When these functionalities are coordinated among different RATsin a heterogeneous scenario, they can be denoted as “common”or "joint" (i.e. thus having the common admission control,common congestion control, etc.)

In a heterogeneous scenario two main specific additionalfunctionalities arise:

Initial RAT selection

Vertical (inter-system) Handover

Problems in JRRM




The problem is in general very complex since it is influenced by very different

aspects:

Measurements belonging to different RATs are of different nature, sothey are not always comparable (e.g. received power, Ec/Io, etc.)

Multiplicity of services and QoS degrees

Decision some times is not only related with technical aspects, but also

economical considerations can be included (e.g. WLAN can offer a

cheaper service, trade-off QoS cost, due to infrastructure investments an

operator can favour one RAT with respect to another, etc.)

Features such as user mobility may favour

some RATs with respect to others (e.g. WLAN may

not be appropriate for a high speed user)

Scenarios can exhibit a high degree of variabilityin terms of traffic load, services, etc.,

JRRM solutions need to be ADAPTIVE.

Terminal

characteristicsServices

& QoS

RATs

supported Cell load

conditions

UE interference

conditions

User

profile

Operator

preferences

RAT & Cell

Selection

Guiding principles of RAT selection




Service-based RAT selection:

Direct mapping services-RATs (e.g. voice services throughGSM and data services through UMTS)

Load Balancing:

Try to keep a similar load level in all RATs, e.g. by assigningthe RAT with the lowest load appropriate if the consideredRATs can provide similar QoS levels to a given service

by reducing load, interference can be reduced and overloadsituations can be more easily avoided

Interference-based strategies:

Exploit the different sensitivity to interference in theconsidered RATs:

• In case of CDMA/TDMA networks, CDMA interference impacts oncapacity, so terminals with the worst propagation conditions (e.g.at the cell edge, that generate the largest interference to othercells) can be assigned to TDMA.

Decentralisation of JRRM




Traditionally, (J)RRM functions in a wireless cellular network are mainly

centralized, implemented in a central node (e.g. RNC) which may have a

more complete picture of the radio access status than a particular node.

Drawbacks:

signalling load and transfer delay towards the central node

prevents an efficient implementation of short term (J)RRM functions

The terminal keeps also relevant information of interest to (J)RRM

functions

Clear trend towards decentralising functions (either to nodes B or even to

the terminal)

Limitation in the amount of information available at the terminal

Network can provide some information to assist the terminal in its

decisionsExample: standardisation of IEEE P1900.4

Functionalities for autonomousRAT selection




Distr. decision making

for RAT selection andprotocol configuration

Awareness

networking- Infrastructure to

terminal

Acquisition and learning

context information

Acquisition and maintaining

policy information


user information


context information

Acquisition and maintaining

policy information

Awareness

networking- Terminal to terminal

Example 1: Decentralised RAT selectionin CDMA/TDMA networks




The mobile terminal takes the decision on which RAT to connect based onits own path loss measurements and the information broadcast through a

radio enabler (e.g. IEEE P1900.4, CPC, etc.), in terms of a certain maximumpath loss.

( ) p L t

t

th PL

VHO TO

TDMACDMA

Session start

Terminal makes

measurements of

L p(t)

( ) p L t

t

th PL

VHO TO

TDMACDMA

Session start

Terminal makes

measurements of

L p(t)

p th L PL

RAT selection policy :

p th L PL

Select CDMA

Select TDMA

P 1 9 0

0 . 4

( P L t h, Δ

, M u p, M

d o w n )

RAT IS SELECTED AT THE TERMINAL(DECENTRALIZED DECISION MAKING)

p th L PL


p th L PL

Select CDMA

Select TDMA

p th L PL


p th L PL

Select CDMA

Select TDMA

P 1 9 0

0 . 4

( P L t h, Δ

, M u p, M

d o w n )

RAT IS SELECTED AT THE TERMINAL(DECENTRALIZED DECISION MAKING)

Example 1: Decentralised RAT selectionin CDMA/TDMA networks




The optimum PLth setting is that providing almost load balancing between CDMAand TDMA:

However, the smarter distribution of users with the autonomic policy (i.e. usersclose to the cell site choose UTRAN and users far from the cell site chooseGERAN) provides better performance than a “blind” load balancing scheme

1.5

2

2.5

3

3.5

4

4.5

5

400 500 600 700 800 900 1000 1100 1200

Users

D L T h r o u g h p u t ( M b / s )

PLth=115 dB

PLth=120 dB

PLth=125 dB

LB strategy

24% gain in

maximum

throughput

Example 2: Fuzzy-Neuraldecentralised JRRM




By means of adequateinformation and proper policies

sent through a radio enabler (e.g.

CPC), the mobile terminal is able

to take JRRM decisions in a

decentralized manner. Both intra

and inter-operator levels can beconsidered.

RAT 1

RAT 2

RAT 3

CPC

min

L

RAUMTS

M H

Layer 1

Layer 2μH(RAUMTS)

μH(SSUMTS)μL(MS)

μ N(DGERAN) jμY(DUMTS) j

μ N(DWLAN) j

F U Z Z I F I C A T I O N

I N F E R E N C E E N G I N E

D E F U Z Z I F I C A T I O N

Layer 5

Layer 4

Layer 3

sum

L

SSUMTS

H

SSGERAN

HL

SSWLAN

HL L

RAGERAN

M H L

RAWLAN

M H L

MS

H

j

μL(SSGERAN)

μL(SSWLAN)μH(RAGERAN)

μH(RAWLAN)

N PN PY Y

FSDUMTS

μ N(DUMTS)

μY(DUMTS)

μPY(DUMTS)

μPN(DUMTS)

N PN PY Y

FSDWLAN

μ N(YWLAN) μY(DWLAN)

μPY(DWLAN)

μPN(DWLAN)

1 432

μY(DUMTS)1 μY(DUMTS)432

μH (BGERAN) μ

L(BGERAN)

L M H

BGERAN

μM(BGERAN)

μH(BUMTS)

L M H

BUMTS

μM(BUMTS )

μL(BUMTS)

μY(DGERAN)

Y

μPY(DGERAN)

N PN PY

FSDGERAN

μ N(DGERA N)

μPN(DGERAN)

Y

r(t)

Measurements

Fuzzy-Neural JRRM

CPC info

Operator

RAT

Bit rate

Example 2: Fuzzy-Neural decentralised JRRM- Motivation




Hybrid Fuzzy-Neural methodology is a

candidate to cope with JRRM

Fuzzy logic-based methodology has been proved to be good t expl ining how to

re ch suit ble decisions because it is able to simplify a large state space of solutions

by means of reasonable rules, but is not so good in pattern recognition.

Heterogeneous Inputs of JRRM algorithm are transformed in homogeneous

inputs of a Fuzzy Controller

Neural networks methodology is good t recognizing p tterns by me ns of le rning

procedures, but lacks the capabilities of making decisions. It could implement theCognitive Concept of a brainy radio being able to learn,

Outputs: selected RAT and bandwidth for each user

Example 2: Fuzzy-Neural decentralised JRRM- Elements




5-Layered Structure

Fuzzy Based Decision BlockFuzzifier

Inference Engine

Defuzzifier

Reinforcement Learning

Example 2: Fuzzy-Neural decentralised JRRM- Layered structure




min

L

RA UMTS

M H

Layer 1

Laye r 2μH(RA UMTS )

μH(SS UMTS )μL(MS)

μ N(DGERAN ) jμY(DUMTS) j

μ N(DWLAN ) j

F U Z Z I F I C A T I O

N

I N F E R E N C E E

N G I N E

D E F U Z Z I F I C A T I O N

Layer 5

Layer 4

Layer 3

sum

L

SSUMTS

H

SSGERAN

HL

SS WLAN

HL L

RAGERAN

M H L

RAWLAN

M H L

MS

H

j

μL(SS GERAN ) μL(SS WLAN )μH(RA GERAN )

μH(RA WL AN )

N PN PY Y

FSD UMTS

μ N(D UMTS )

μY(D UMTS )

μPY (DUMTS )

μPN (D UMTS )

N PN PY Y

FSD WLAN

μ N(YWLAN ) μY(D WLAN )

μPY (DWLAN )

μPN (D WLAN )

1 432

μY(DUMTS )1μY(DUMTS)432

μH (BGERAN )μL(BGERAN )

L M H

BGERAN

μM(B GERAN )

μH(BUMTS )

L M H

BUMTS

μM(B UMTS )

μL(BUMTS )

μY(D GERAN )

Y

μPY (DGERAN )

N PN PY

FSD GERAN

μ N(D GERAN )

μPN(D GERAN )

Y

r(t)

Example 2: Fuzzy-Neural decentralised JRRM- Step 1.- Fuzzification




It assigns, for each input linguistic variable, a value between 0 and 1 of a

so-called membership function characterizing a given Fuzzy Set. In ourscheme these functions are adjusted properly.

0

1

SSGERAN (dBm)-123 -121 -120 -100

L(SSGERAN) H(SSGERAN)

Input variables:

SSUMTS, SSGERAN, SSWLAN: Received Signal

Strength for each of the considered RATs.

Fuzzy Subset: L (low), H (high)

RAUMTS,RAGERAN,RAWLAN:Resource Availability

in each of the considered RATs (provided by

radio enabler)

Fuzzy Subset: L (low), M (medium), H (high)

MS: Mobile Speed.

Fuzzy Subset: L (low), H (high)

Example 2: Fuzzy-Neural decentralised JRRM- Step 2.- Inference Engine




For each combination of fuzzy subsets from step 1, the inference engine makes use

of some predefined fuzzy rules to indicate, for each RAT, the suitability of selecting it.

IF THEN

SSUMTS SSGERAN SSWLAN RAUMTS RAGERAN RAWLAN MS DUMTS DGERAN DWLAN BUMTS BGERAN

H L L H H M L Y N N H L

H L L M H M L PY N N M L

H L L L H M L PN PN N L L

Example:

POLICY PROBLEM: Inference rules will follow specific policies that need to be predefined

Each one of the combinations is associated to a metric given by the minimum (i.e.

commitment) of the membership values of the corresponding input variables

Inference engine outputs:

At the output the total membership value will be the sum of the numerical outputs from

each combination pointing to a given RAT Fuzzy set or 1 when this sum exceed 1.



Example 2: Fuzzy-Neural decentralised JRRM- Reinforcement Learning




This procedure is determines appropriate membership functions

parameters in the fuzzification and defuzzification steps.

Parameters are dynamically adjusted according to the propagated

measured reinforcement signal r (t ) that captures target metrics (e.g

the Non-Satisfaction User Probability, GoS, Average delay, etc)

It is a Key Concept in this JRRM framework enabling to set a given

QoS by learning first from the scenario and acting later.

In the layered fuzzy-neural structure this reinforcement signal is

introduced at layer 5 and is propagated from top to bottom

Example 2: Fuzzy-Neural decentralised JRRM- Reinforcement Learning




The goal of the reinforcement learning is to minimize the error function given by:

Each general adjustable parameter w (e.g. any of the mean and dispersion

values of the membership functions at layers 5 and 2) is obtained as that the

minimizes the error function :

where γ is the learning rate

t wt E t wt w 1

where is the current measured target and Is the desired target value.

r(t) is the REINFORCEMENT SIGNAL

22 *1 1

( )

2 2 T E t r t Q Q t

*

T QQ t

Examples of signals Q(t):

Satisfaction probability (prob. that the user throughput is above a threshold)

User acceptance (balance user throughput with economical considerationsin terms of price paid)

Example 2: Fuzzy-Neural decentralised JRRM

- Results




Reinforcement Learning ensures that the selected metric is kept at its

desired value. The target rate can be set to any desired rate e.g. P*=1,3,10 %

0

2

4

6

8

10

12

14

16

18

0 500000 1000000 1500000 2000000Simulation Frames

N o t S a t i s f i e d U s e r s ( % )

80

90

100

110

120

130

140

150

160

170

N u m

b e r o f U s e r s i n t h e s c e n a r i o

P*=1 P*=3 P*=10 Number of Users

Example 2: Fuzzy-Neural decentralised JRRM

- Results




Example: scenario with three RATs (UMTS, GERAN, WiFi)

Alg. #1 :users can autonomously select any bit rate supported by the

selected RAT. Alg. #2: the CPC provides indications on the maximum allowed bit rate for

UMTS and GERAN depending on the existing load.

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

5

50 70 90 110 130 150 170 190 210 230 250

Number of users

%

Alg. #1 - Blocking (%)

Alg. #1 - Overload (%)

Alg. #2 - Blocking (%)

Alg. #2 - Overload (%)

Example 3: Exploitation of the time dimension in theautonomous RAT selection




Non-real time data users can also utilize the flexibility in their

transmission / reception timing to wait for more suitable

communication opportunities (time dimension added to theRAT/operator selection).

RAT1, Op1: Low bit

rate. Higher price

RAT2, Op2: High

bit rate. Cheaper

price

Should I start

transmission

now or wait

until arriving

close to RAT2

?

?

Decision is based on Context [t0,Te], the context information fromthe current instant up to the data delivery deadline. This contextincludes e.g.:

RATs available and achievable bit rates in the interval, which inturn depend on:

• Mobile position, speed and direction

Amount of information to be transmitted

Example 3: Exploitation of the time dimension in theautonomous RAT selection




Sample results compared with respect to a strategy that does notaccount for time dimension

NRT Delay is increased but without exceeding maximumdeadline.

Total throughput of RT users can be very significantlyincreased, due to the interference reduction and capacity left by

NRT users.L=2MB, 100 NRT users

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 50 100 150 200 250 300 350 400

Delay (s)

C D F

Reference

Proposed

L=2MByte

0

20

40

60

80

100

120

140

160

180

20 30 40 50 60 70 80 90 100

Num NRT users

R T T h r o u g h p u t I m p r o v e m e n t

( % )

Scenario 1 Scenario 2

References




IEEE P1900 Home Page, http://www.ieeep1900.org

O. Holland et al. “Development of a Radio Enabler for Reconfiguration Managementwithin the IEEE P1900.4 Working Group” Second IEEE International Symposium on NewFrontiers in Dynamic Spectrum Access Networks, (DySPAN), Dublin, April, 2007.

J. Pérez-Romero, O. Sallent, R. Agustí, J. Nasreddine, M. Muck “Radio AccessTechnology Selection enabled by IEEE P1900.4”, Procs. IST Summit, Budapest, June,2007.

J. Pérez-Romero, O. Sallent, R. Agustí, L. Giupponi “Decentralized Spectrum and RadioResource Management Enabled by an On-demand Cognitive Pilot Channel”, Annales deTelecommunication, Springer, Vol. 63, No. 5-6, pp.281-294.

J. Pérez-Romero, O. Sallent, "A Novel Autonomous RAT Selection Algorithm for NonReal Time Services", Procs. ICT Summit, Santander, June, 2009.

K. Kalliojärvi, G. Acar, E. Patouni, V. Stavroulaki, J. Pérez-Romero "AutonomousFunctionalities for Cognitive Radio", Procs. ICT Summit, Santander, June, 2009.




Dynamic Spectrum Assignment

Outline of the block




Introduction

DSA and Cognitive Networks

Case Study 1: DSA in WCDMA networks

Algorithms

Metrics

Results

Case Study 2: DSA in OFDMA networks

Fixed Frequency reuse factors

General DSA framework

DSA algorithms based on Heuristics

DSA algorithms based on Reinforcement Learning

Conclusions

Objective





Objective

Introduce cognitive network functionalities in order to provide thenetwork with the ability to decide automatically the adequate spectrum

assignment to the different transmitters.

f 1, f 2, f 3 f 1Traffic distribution

variation

DSANetwork observation

and analysis

Detection of Traffic

distribution variation

Decision :

Find a better assignment

f 1, f 2f 1, f 2

f3 is released

Execution :

New frequencies

assigned/released

f2 is assigned





Network observation and analysis:

Identify smart KPIs reflecting networkbehavior:

• Spectral efficiency

• Spatial spectrum usage: howfrequencies are spatially distributed

• Degree of QoS fulfilment

• Intercell interactions

Main objective

Find the best spectrum allocation in a primary network

Release of some blocks of spectrum in large geographic zones whileguaranteeing the QoS levels of primary users

Spare frequencies could be exchanged between different RATs oroperators without a risk of high interference





Decision algorithms:

To determine the total number of frequencies in each cell and inthe scenarioThey operate to reach a certain target

Possibilities:

• Heuristic algorithms

• Machine learning-based algorithms

• ...

High-level vision of a DSA algorithm




Multiple possibilities:

- simulated annealing

- genetic algorithms

- other heuristic

-...

Illustrative Example




The same QoS is ensured

Some carriers could be released for

other RATs/operators or secondary

market

Use DSA

methodology

With frequency reuse 1

All carriers are used

Carrier 1

Carrier 3

Carrier 2




Tutorial - CROWNCOM, Hannover, 23rd June 2009Slide 129

Introduction



Algorithms

Metrics

Results






Conclusions



Coupling Matrix

C li M t i




where

,

,

1 ,

1

1

l

l

l l l

l

ni l

l jii i j

b

o i

LS

SF L

E

N

Coupling Matrix

Smart radio indicator that is able to reflect both macroscopic andmicroscopic properties of the radio network

it represents the sensitivity of the total received power by one cell I j to thevariations of the total received power by a neighboring cell I l

,,

,

0 if

otherwise1

l j j l

j j

l j

S C

S

Spectral radius of coupling

matrix (i.e. max eigenvalue) is

a representative indicator ofoutage conditions (i.e. QoS

degradation).





Introduction



Algorithms

Metrics

Results






Conclusions

Target metrics

Same system




Uniform Allocation

Same system

Allocation 1

Index 1: spectrum efficiency in

bps/Hz/cell

Index 2: Account also for spatial

distribution

Allocation 2

Target: Max spectral efficiency

Useful Released Surface (URS)

For a given frequency allocation:




For a given frequency allocation:

W (f ) : the bandwidth of carrierf ,

C (f ) : the set of non-contiguousareas where f could be used

by another network,

: the surface of area c where f could be used byanother network,

: the weight given to areac depending on the expectednumber of other network’users in this area

1 1

f F C

f f f

c c f cURS W S

f

cS

f

c





Introduction



Algorithms

Metrics

Results






Conclusions

Outage Probability threshold 0.05

Results for DSA in WCDMA




g y

System with 3 carriers

DSA based on Simulated Annealing

Compare algorithms:Uniform algorithm

DSA optimizing Spectral

Efficiency: SA-SE

DSA optimizing URS: SA-URS

Both DSA lead

to high Spectral

Efficiency

SA-URS gives

the highest URS

especially for

high traffic load

Outage probability constraint is

satisfied

Results for DSA in WCDMA




Released areas (in white) for a given traffic (the third carrier is

fully utilized)

SA-SE

SA-URS

Carrier f1 Carrier f2





Introduction



Algorithms

Metrics

Results






Conclusions

OFDMA (Orthogonal Frequency Division Multiple Access) has been

OFDMA as a radio access technology




OFDMA (Orthogonal Frequency Division Multiple Access) has been

seen as the candidate technology for future systems (LTE, WiMax)

Builds a time-frequency grid Resource Block (RB) is the minimum resource that can be

allocated to a user.

In frequency, the whole available bandwidth is divided into groups

of adjacent subcarriers or chunks

In each subcarrier different bit rates can be achieved thanks toadaptive coding and modulation in accordance with SINR

C h u n k

ResourceBlock

· · ·· · ·· · ·

· · ·· · ·· · ·

Time

Frequency

123

N-2N-1N

T1 2 3 4Frames

OFDMA as a radio access technology




Benefits of OFDMA Allows exploitation of multiuser diversity

High spectral efficiency (narrow separation between

subcarriers)

High flexibility

• Various modulation and coding schemes per subcarrier

• Assignment of subcarriers to users is flexible

• Assignment of subcarriers to cells is flexible

Main drawbackIntercell interference

Fixed Frequency Reuse Schemes




Frequency Reuse Factor (FRF)distributes regularly the total

spectrum band of the system over different cells.

FRF=1 where all chunks are available at any cell and are

transmitted with the same power.

High Intercell interference for users at cell’s border

FRF=3 the total band is divided into 3 equal subbands, and is

distributed over groups of 3 contiguous cells (or clusters)

repeating this pattern in the cellular system.

Reduces by 3 the potential cell capacity

Partial Freq enc Re se (PR) di ides the total band bet een a





Partial Frequency Reuse (PR) divides the total band between acentral and an edge subband.

The central subband is available in all cells (FRF1)The edge subband is further divided and distributed regularly(FRF3) over cells

Edge users have priority in using the Edge band, although

Central users can use it when is not used in the edge area.

Soft-Frequency Reuse (SR) divides the frequency band into threesubbands

The edge subband is transmitted with greater power than theother two central subbands that are only available for thecentral users.

The edge subband alternate their position into the system’sband following a FRF3 scheme.Central users can use it when is not used in the edge area






Fixed FRF schemes limit system’s performance




Fixed FRF schemes limit system s performance.

the assignment of frequency resources to cells is homogeneous and cannot

be changed online.the frequency deployment may not be adapted to heterogeneous spatial

traffic distributions and their variation in time.

it is difficult to find a group of cells where the same spectrum band is not

used, which prevents it from being offered to a spectrum secondary market.

Dynamic Spectrum Assignment (DSA) allows better usage of the spectrum.

Spectrum assignment

FRF1 FRF3 DSA





Introduction



Algorithms

Metrics

Results






Conclusions

Obj ti D l DSA f k bli d

DSA framework for OFDMA




Objective: Develop a DSA framework enabling secondary

cognitive radio usage in a multicell OFDMA system within

Private Commons initiative.

Cell-by-cell spectrum adaptation

• Decide appropriate chunk-to-cell allocation in accordance

with traffic needs

• Mitigate intercell interferenceTargets:

• (1) improve spectral efficiency,

• (2) maintain users’ QoS satisfaction and,

• (3) enable opportunistic spectrum access by releasing some

frequency bands in large geographical areas.





OFDMA multicell deployment

Short-Term Scheduler : Schedulesusers’ transmissions by exploitingmultiuser channel diversity

DSA Controller : Cell-by-cell dynamicspectrum assigment in the long-term

(i.e., hours)

Secondary

Cognitive RadioMarket

Residential

Subarea

Business

Subarea

STS

STS

STS

STS

STS

STS

STS

STS

STS

STS

STS

STS

Primary

users

Secondary

users

ASM

Controller

Primary

Operator

Cognitive Radio

Opportunities

Short-Term Scheduler





Short Term Scheduler

Located at the base station low latencies and high speedchannels

Each RB of the time-frequency grid is given fairly to each user.

Several options for the scheduling strategy:

• Round Robin

• (Generalized) Proportional Fair

• Maximum Throughput





Generalized Proportional Fair Scheduler:

α≥0 and β≥0 are the weighting values for the GPF scheduler. The greater α and the lower β, the greater the

probability of scheduling users with good channel condition and viceversa. PF is obtained with α=1 and β=1.

• R m,n(t) represents the instantaneous achievable rate that user mcan get at chunk n.

• W m,n(t) is the window-averaged version of R m,n(t) as follows:

,*

,

( )( ) arg max

( )

m n

mm n

R t m t

W t

20Instataneous SINR

1


Similar channel qualities for all users




0 50 100 150 200 250-30

-25

-20

-15

-10

-5

0

5

10

15

d B

user 1

user 2

user 3

0 50 100 150 200 250-30

-25

-20

-15

-10

-5

0

5

10

15

20Scheduled user Proportional Fair

user 1

user 2

user 3

0 50 100 150 200 250-30

-25

-20

-15

-10

-5

0

5

10

15

20Scheduled user Max-Rate

user 1

user 2

user 3

Max Rate:

User with best channel is

selected

Proportional Fair:

Users alternate the

channel

Similar channel qualities for all users

40Instataneous SINR

user 1


Different channel qualities




0 50 100 150 200 250-30

-20

-10

0

10

20

30

d B

user 2

user 3

Max Rate:

Some users get very few

times the channel

Proportional Fair:

All users have

opportunities to access

the channel

0 50 100 150 200 250-30

-20

-10

0

10

20

30

40Scheduled user Max-Rate

user 1user 2

user 3

0 50 100 150 200 250-30

-20

-10

0

10

20

30

40Scheduled user Proportional Fair

user 1

user 2

user 3

Different channel qualities

DSA controller (1/2)






Decides the chunks to beallocated to each cell byexecuting the DSA algorithm.

Located in a network node withthe ability to control a set of

cells.

Adapts the system to trafficvariations in time and space inthe medium-long term

Allows efficient spectrum usage

Execution Trigger

DSA Algorithm

Spectrum

Allocation

I n p u t s ON/OFF

Real Network Area

Centralized DSA controller

STS

STS

STS

STS

Chunk Allocation






Inputs:• from each cell under its control

• the number of users per cell, the SNR values, , and some QoS

indicators (e.g. dissatisfaction probability)

Triggers:

• periodically (in periods of e.g. tenths of minutes given the slow trafficvariation envisaged)

• each time the dissatisfaction probability rises above a given

threshold.

Outputs:

• the specific chunks assigned to each cell

,m n





Introduction

DSA and Cognitive Networks Case Study 1: DSA in WCDMA networks

Algorithms

Metrics

Results






Conclusions



DSA-heuristic Algorithm




Step 1. Computes the number of chunks to assign to agiven cell (N j)

Step 2. Allocates the chunks to each cell taking intoaccount the potential intercell interference and cellload (costs matrix A)

max

min ,max 1,/

j th

j

U T N N f

W N

0

ji

ij j i

if i j

U U R A(i, j)otherwise

DU U

Simulation Model

19 Cells and 12 chunks3 80 %

Cell #16

3 80 %

Cell #17

3 80 %

Cell #18(b)

5 26 %

Cell #16

5 26 %

Cell #17

5 26 %

Cell #18(a)

C ll l d




SINR per chunk taking

into account fast fading Adaptive Coding and

Modulation per chunk

Homogeneous and

Heterogeneous spatial

traffic distributions 300 active users

Spatial distribution of the users in the scenarios.

9.00 %

Cell #1

9.00 %

Cell #2

22.00 %

Cell #3

9.00 %

Cell #4

2.00 %

Cell #5

2.00 %

Cell #6

2.00 %

Cell #7

2.00 %

Cell #8

9.00 %

Cell #9

9.00 %

Cell #10

9.00 %

Cell #11

2.00 %

Cell #12

2.00 %

Cell #13

2.00 %

Cell #14

2.00 %

Cell #15

2.00 %

Cell #16

2.00 %

Cell #17

2.00 %

Cell #18

2.00 %

Cell #19

(d)

6.00 %

Cell #1

6.00 %

Cell #2

10.00 %

Cell #3

6.00 %

Cell #4

5.00 %

Cell #5

5.00 %

Cell #6

5.00 %

Cell #7

5.00 %

Cell #8

6.00 %

Cell #9

6.00 %

Cell #10

6.00 %

Cell #11

5.00 %

Cell #12

5.00 %

Cell #13

3.80 %

Cell #14

3.80 %

Cell #15

3.80 %3.80 %3.80 %

5.00 %

Cell #19

5.26 %

Cell #1

5.26 %

Cell #2

5.26 %

Cell #3

5.26 %

Cell #4

5.26 %

Cell #5

5.26 %

Cell #6

5.26 %

Cell #7

5.26 %

Cell #8

5.26 %

Cell #9

5.26 %

Cell #10

5.26 %

Cell #11

5.26 %

Cell #12

5.26 %

Cell #13

5.26 %

Cell #14

5.26 %

Cell #15

5.26 %5.26 %5.26 %

5.26 %

Cell #19

CoI#6

CoI#3

7.83 %

Cell #1

7.83 %

Cell #2

15.00 %

Cell #3

7.83 %

Cell #4

4.00 %

Cell #5

4.00 %

Cell #6

4.00 %

Cell #7

4.00 %

Cell #8

7.83 %

Cell #9

7.83 %

Cell #10

7.83 %

Cell #11

4.00 %

Cell #12

4.00 %

Cell #13

2.00 %

Cell #14

2.00 %

Cell #15

2.00 %

Cell #16

2.00 %

Cell #17

2.00 %

Cell #18

4.00 %

Cell #19

(c)

Cell load

percentage

KPIs

Results presented in terms of spectral efficiency, users’di ti f ti b bilit d t ll




dissatisfaction probability and spectrum usage per cell.

Spectral efficiency

Dissatisfaction Probability

Useful Released Surface (URS)

bits/s/Hz/cell

Total cell throughput

Cell Bandwidth

1

1 1th th

m

T T

t m U

P t U

1 ( )0 ( )

th

m

T m th

m th

th t T t

th t T

2 MHz km( ) ( ) ( )

1 1

n N n n n

a a

n a

URS B S

Sample results DSA-heuristic

12

(a)Average Number of chunks per cell

FRF1 35(b) Average Dissatisfaction

FRF1




5.26% 10.0% 15.0% 22.0%0

2

4

6

8

10

12

Percentage of the total load of the most loaded cell

C h u n k s

FRF3

SR

PR

DSA1DSA2

DSA3

DSA4

5.26% 10.0% 15.0% 22.0%0

5

10

15

20

25

30


%

FRF3

SR

PR

DSA1DSA2

DSA3

DSA4

5.26% 10.0% 15.0% 22.0%2

2.2

2.4

2.6

2.8

3

3.2(c) Average Spectral Efficiency


b

i t s / s / H z / c e l l

FRF1

FRF3

SR

PR

DSA1

DSA2

DSA3

DSA4

5.26% 10.0% 15.0% 22.0%

0

2

4

6

8

10(d) URS


M H z * k m

2

FRF1

FRF3

SR

PR

DSA1

DSA2

DSA3

DSA4

OK

KO





Introduction

DSA and Cognitive Networks Case Study 1: DSA in WCDMA networks

Algorithms

Metrics

Results Case Study 2: DSA in OFDMA networks



DSA algorithms based on HeuristicsDSA algorithms based on Reinforcement Learning

Conclusions

Reinforcement Learning

Dynamic Spectrum Assignment requires a flexible radio interface




(OFDMA) and adaptation mechanisms/algorithms to variable networks

conditions in terms of traffic demands.

Reinforcement Learning (RL) shows adequate adaptation by

learning the best actions to apply to a variable environment that

returns a reward for each action.

The final goal of the learning system is to maximize the received

reward in the long-term

REINFORCEMENT

LEARNING

ENVIRONMENT

Next

ActionNext

Reward

System Model

Same system model




y

Decision

Maker RL

Trigger

RL-DSANetwork

Characterizarion

Entity (NCE)

Spectrum Assignment

Events NCEInputs

ON

Reward

Action

Network Area

Centralized DSA controller

RL Agents

Status

STS

STS

STS

STS

STS

STS

STS

OFF Cell Loads

C h u n k

ResourceBlock

· · ·· · ·· · ·

· · ·· · ·· · ·

Time

Frequency

123

N-2N-1N

T1 2 3 4Frames

DSA controller functional blocks

RL-DSA Network

Characterization Action




RL-DSA is the core functionality of the DSA controller Actions of RL-DSA represent candidate spectrum assignments.

Reward reflects the suitability of a given spectrum assignment.

Network characterization mimics the response of the real network

RL Trigger runs RL-DSA when average QoS in the network area is

unacceptable (Dissatisfaction Probability) Decision Maker decides when the RL-DSA has found a new

spectrum assignment.

Decision

Maker RL

Trigger

Spectrum

AssignmentEvents

Model

Inputs

ON

Reward

RL Agents

Status OFF Cell Loads

RL-DSA Algorithm

RL-DSA relies on the Bernoulli

To Environment




Logistic Units of the REINFORCE

methods. REINFORCE methods maximize a

given reward in the long term.

Two possible actions: Output “y”

is a Bernoulli random variable.

Internal Probability “p” contains

current knowledge of the unit.

Learning is enforced in the

weights “w” taking into account,

the reward “r ” and the status ofthe environment “x”.

Randomness allows exploration of

new solutions.

( ) ( )T

M i i

i ij ij

j z w x w x

1

( )i

i i i z p f z

e

1

1

i p

( , ) ii i

i

p g p

p if =0

if =11

Pr( )/

Pr( )i i

i

i i

y p y

y p1

0 1

B e r n

o u l l i L o g i s t i c U n i t

i z

iw

1 iw

2 iM w...

...i

x1 i

x2 iM

xr

From Environment

( )( )ij i i jw r r y p x

RL-DSA Algorithm

K cells and system bandwidth divided into N chunks.




RL system composed of KN RL agents based on the REINFORCE

methods. RL-DSA Algorithm

Reward should reflect network performance objectives since is

maximized by RL-DSA

y11

y12 N y

1 K y1 K y

2 KN y

CELL #1 CELL #K

Reward r

RLAgent

RLAgent

RLAgent

RLAgent

RLAgent

Action

RLAgent

( ) N x

1 ( ) K x

1( )x

12( )x

11 ( ) K x

2 ( ) KN x

R L A g e n t s S t a t u s

( ) N w

1 ( ) K w

1( )w

12( )w

11 ( ) K w

2 ( ) KN w

r r r r r r

p11

p12 N p

1 K p1 K p

2 KN p

CellK CellK CellKCell1 Cell1 Cell1

Cell loads

RL-DSA Algorithm

RL DSA l ith k h k t ll i t th t i i




RL-DSA algorithm seeks chunk-to-cell assignments that maximize

spectral efficiency per cell provided that the QoS per cell is assured.

RL-DSA maximizes a given reward in the long term:

1) Spectral Efficiency ( ) only: 2) and bandwidth free for CR usage:

k

0, if ( )( )

( ), otherwise

k th

k

k

th t T r t

t

k th Average throughput per cell

Spectral efficiency per cell k n

N Total number of chunks

Used chunks per cell

0, if ( )( )

( ) ( ) , otherwise

i th

k

k k

th t T r t

t N n t

RL-DSA Algorithm

ILLUSTRATIVE BEHAVIOUR RESULTS




0 2000 4000 6000 8000 10000 12000

0

0.5

1

Probabilities evolution per cell

0 2000 4000 6000 8000 10000 12000

0

0.5

1

0 2000 4000 6000 8000 10000 120000

0.5

1

RL Steps

Chunk #1

Chunk #2

Chunk #3

Chunk #4

Chunk #5

Chunk #6

Chunk #4

Chunk #3

Chunk #1

Chunk #5

Chunk #6

Chunk #2

0 2000 4000 6000 8000 10000 12000

3.45

3.5

3.55

3.6

3.65

3.7

3.75

3.8

3.85

3.9

3.95

Average Reward evolution

RL steps

Cell#1

Cell#2

Cell#3

Cell #1Cell #3

Cell #2

RL internal probabilities evolution per cell Average reward evolution

ILLUSTRATIVE BEHAVIOUR RESULTS

Sample results RL-DSA

Scenario Layout:




Four different load spatial

distributions

Compared Strategies

Fixed FRF1 and FRF3

Dynamic heuristic

RL-DSA

Metrics:

Dissatisfaction Probability

Spectral efficiency(bits/s/Hz)

Useful Released Surface

(MHz* Km2)

9.00 %

Cell#1

9.00 %

Cell#2

22.00 %

Cell#3

9.00 %

Cell#4

2. 00 %

Cell#5

2.00 %

Cell#6

2.00 %

Cell#7

2.00 %

Cell#8

9.00 %

Cell#9

9.00 %

Cell#10

9.00 %

Cell#11

2. 00 %

Cell#12

2. 00 %

Cell#13

2.00 %

Cell#14

2. 00 %

Cell#15

2. 00 %

Cell#16

2.00 %

Cell#17

2.00 %

Cell#18

2.00 %

Cell#19

(d)

6.00 %

Cell#1

6.00 %

Cell#2

10.00 %

Cell#3

6.00 %

Cell#4

5. 00 %

Cell#5

5.00 %

Cell#6

5.00 %

Cell#7

5.00 %

Cell#8

6.00 %

Cell#9

6.00 %

Cell#10

6.00 %

Cell#11

5. 00 %

Cell#12

5. 00 %

Cell#13

3.80 %

Cell#14

3. 80 %

Cell#15

3. 80 %

Cell#16

3.80 %

Cell#17

3.80 %

Cell#18

5.00 %

Cell#19

(b)

5.26 %

Cell#1

5.26 %

Cell#2

5.26 %

Cell#3

5. 26 %

Cell#4

5.26 %

Cell#5

5. 26 %

Cell#6

5.26 %

Cell#7

5.26 %

Cell#8

5.26 %

Cell#9

5.26 %

Cell#10

5.26 %

Cell#11

5.26 %

Cell#12

5.26 %

Cell#13

5.26 %

Cell#14

5.26 %

Cell#15

5.26 %

Cell#16

5.26 %

Cell#17

5.26 %

Cell#18

5.26 %

Cell#19

(a)

7.83 %

Cell#1

7.83 %

Cell#2

15.00 %

Cell#3

7. 83 %

Cell#4

4.00 %

Cell#5

4. 00 %

Cell#6

4.00 %

Cell#7

4.00 %

Cell#8

7.83 %

Cell#9

7.83 %

Cell#10

7.83 %

Cell#11

4.00 %

Cell#12

4.00 %

Cell#13

2.00 %

Cell#14

2.00 %

Cell#15

2.00 %

Cell#16

2.00 %

Cell#17

2.00 %

Cell#18

4.00 %

Cell#19

(c)

Cell LoadPercentage

Scenario: 19 cell scenario with 190 users homogeneously distributed





demanding 256kbps each ( ). Speed 3 km/h. Reward 1)

Dynamism: From minute 25 to 35, 3 sessions per minute are started inone cell and 1 session per minute is stopped in other cell.

thT

0 10 20 30 40 50 600

10

20

A v e r a

g e o v e r a l l c e l l s

[ % ]

Average dissatisfaction probability

FRF1

FRF3

DSA_heuristic

RL-DSA

0 10 20 30 40 50 600

50

100

M o s t l o a d e d c e l l

[ % ]

FRF1

FRF3

DSA_heuristic

RL-DSA

0 10 20 30 40 50 600

2

4

time [minutes]

L e a s t l o a d e d c e l l

[ % ]

FRF1

FRF3

DSA_heuristic

RL-DSA

0 10 20 30 40 50 60

4.6

4.8

5

A l l c e l l s

[ b i t s / s / H z ]

Average Spectral Efficiency

FRF3

DSA_heuristic

RL-DSA

0 10 20 30 40 50 60

4.6

4.8

5

M o s t l o a d e d c e l l

[ b i t s / s / H z ]

FRF3

DSA_heuristic

RL-DSA

0 10 20 30 40 50 603.5

4

4.5

5

L e a s t l o a d e d c e l l

[ b i t s / s / H z ]

time [minutes]

FRF3

DSA_heuristic

RL-DSA

Results Best spectral

efficiency

Reduced

dissatisfaction

probability

Good adaptability

to dynamic

changes


Scenario: 19 cell scenario with 300 static users. 4 spatial

distributions tested (homogeneous slightly heterogeneous




5.26% 10.0% 15.0% 22.0%0

5

10

15(d) Average Num ber of chunks per cell

C h u n k s

5.26% 10.0% 15.0% 22.0%0

5

10

15

20

25(a) Average Dissatisfaction

%

5.26% 10.0% 15.0% 22.0%2.5

3

3.5

4(b) Average Spectral Efficiency

b i t s / s / H z / c e l l

5.26% 10.0% 15.0% 22.0%0

5

10

15

20(c) Useful Released Surface

M H z * k m

2

5.26% 10.0% 15.0% 22.0%0

5

10

15(e) Average Number of chunks (mos t loaded cell)

C h u n k s

Percentage of the total load of the mos t loaded cell

5.26% 10.0% 15.0% 22.0%0

5

10

15(f) Average Numbe r of chunks (least loaded cell)

C h u n k s

FRF1

FRF3

DSA-heur

RL-DSA

distributions tested (homogeneous, slightly heterogeneous,

moderate heterogeneous, highly heterogeneous). Reward 2)

Results:

Best tradeoff between spectral efficiency and QoS requirements

High Useful Released surface





Introduction



Algorithms

Metrics

Results






Conclusions

Conclusions

DSA can bring major improvements to cellular net ork in terms of spectral efficienc




network in terms of spectral efficiency.

DSA can be successfully applied to WCDMA andOFDMA radio access technologies.

Different methodologies to incorporate decision

and learning mechanisms to the network can befollowed:

Heuristic algorithms

Meta-heuristics (Simulated Annealing)

Machine Learning (Reinforcement Learning)

Conclusions

DSA algorithms adapt the spectrum assignment to thedifferent spatial and temporal traffic variations




different spatial and temporal traffic variations.

DSA algorithms show a good trade-off betweenspectral efficiency and satisfaction probability.

Ameliorate overall system’s spectral efficiency withrespect to the total reuse of frequency resources

Maintain or improve the satisfaction probability metricfor users at the edge of the cell.

DSA algorithms enable the releasing of spectrumbands in large geographical areas so that thisspectrum can be exploited by secondary cognitive radiousers.

References SPECTRUM ACCESS MODELS:

Hoffmeyer, J.A. Regulatory and Standardization Aspects of DSA Technologies – Global Requirements and

Perspectives 1st IEEE International Symposium on in New Frontiers in Dynamic Spectrum Access Networks




Perspectives. 1 IEEE International Symposium on in New Frontiers in Dynamic Spectrum Access Networks

(DySPAN) 2005; pp.700-705. DOI: 10.1109/DYSPAN.2005.1542699

FCC Spectrum Policy Task Force. Report of the spectrum efficiency working group 2002. [online]

<http://www.fcc.gov/sptf/reports.html>. [date of access] 06/01/2008

Akyildiz, I. F., Lee, W.-Y., Vuran, M. C., Mohanty, S. Next generation/dynamic spectrum access/cognitive radio

wireless networks: a survey. Computer Networks 2006; 50(13); 2127-2159. DOI: 10.1016/j.comnet.2006.05.001

Zhao, Q., Sadler, B. M. A Survey of Dynamic Spectrum Access: Signal processing, networking, and regulatory

policy. IEEE Signal Processing Magazine, 2007; 24(3); 79-89. DOI: 10.1109/MSP.2007.361604

Buddhikot, M.M. Understanding Dynamic Spectrum Access: Models, Taxonomy and Challenges. 2 nd IEEE

International Symposium on in New Frontiers in Dynamic Spectrum Access Networks (DySPAN) 2007, pp. 649-

663. DOI: 10.1109/DYSPAN.2007.88

FCC. Promoting Efficient Use of Spectrum Through Elimination of Barriers to the Development of Secondary

Markets. Second Report and Order on Reconsideration and Second Further Notice of Proposed Rule Making ,

2004. [online] <http://wireless.fcc.gov/licensing/index.htm?job=secondary_markets>. [date of access] 06/01/2008

Weiss, T.A., Jondral, F.K. Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency.

IEEE Communications Magazine 2004; 42(3); 8- 14. DOI: 10.1109/MCOM.2004.127376

Brito, J. The Spectrum Commons in Theory and Practice 2007. [online] <http://stlr.stanford.edu/pdf/brito-

commons.pdf>. [date of access] 06/01/2008.

FREQUENCY PLANNING SCHEMES:

Sternad, M., Ottosson, T., Ahlen, A., Svensson, A., Attaining both Coverage and High Spectral Efficiency with

Adaptive OFDM Downlinks, IEEE Vehicular Technology Conference 2003-Fall;

Huawei TR1-050507. Soft Frequency Reuse Scheme for UTRAN LTE, 3GPP TSG RAN WG1 2005.

References

REINFORCEMENT LEARNING:

R. S. Sutton and A. G. Barto, “Reinforcement Learning: An Introduction”, A Bradford Book, The MIT Press, 1998, ISBN

http://www.fcc.gov/sptf/reports.html

http://www.fcc.gov/sptf/reports.html




R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction , A Bradford Book, The MIT Press, 1998, ISBN

0-262-19398-1

Ronald J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning” Journal

Machine Learning Publisher Springer Netherlands. Volume 8, Numbers 3-4 / May, 1992

SHORT TERM SCHEDULING STRATEGIES:

Wengerter, J. Ohlhorst, A.G.E. von Elbwart, Fairness and throughput analysis for generalized proportional fair frequency

scheduling in OFDMA, 61st IEEE Vehicular Technology Conference 2005-Spring; 3; pp. 1903- 1907. DOI:

10.1109/VETECS.2005.1543653

DYNAMIC SPECTRUM ASSIGNMENT STRATEGIES: Nasreddine, J.; Sallent, O.; Perez-Romero, J.; Agusti, R., "Novel Inter-Cell Interaction Approach for WCDMA-based

Cognitive Networks," Communications, 2007. ICC '07. IEEE International Conference on , vol., no., pp.4573-4580, 24-

28 June 2007

Nasreddine, J., Pérez-Romero, J., Sallent, O., Agustí, R. A primary spectrum management solution facilitating

secondary usage exploitation. 17th ICT mobile and wireless communications summit 2008.

F. Bernardo, R. Agustí, J. Pérez-Romero, and O. Sallent “Dynamic Spectrum Assignment in Multicell OFDMA Networks

enabling a Secondary Spectrum Usage”, Special Issue on “Cognitive Radio and Advanced Spectrum Management”,

Wireless Communications and Mobile Computing (WCMC) Journal, 2009.

F. Bernardo, R. Agustí, J. Pérez-Romero, O. Sallent “Temporal and Spatial Spectrum Assignment in Next Generation

OFDMA Networks through Reinforcement Learning”, Vehicular Technology Conference (VTC)-2009 Spring.

Francisco Bernardo, Ramón Agustí, Jordi Pérez-Romero and Oriol Sallent “A Self -organized Spectrum Assignment

Strategy in Next Generation OFDMA Networks providing Secondary Spectrum Access”, International Conference on

Communications (ICC) 2009.



Thank you!

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