wireless mesh networks: first part: an overview 2-nd workshop 2-nd workshop on on women project...

Wireless Mesh Networks:Wireless Mesh Networks:

First part:First part:an Overviewan Overview

2-nd Workshop2-nd Workshop

on on

WOMEN ProjectWOMEN Project

Rome September 8-th, 2006

University of Rome “La Sapienza”, INFOCOM Dept. (Faculty of Engineering)

OutlineWireless Mesh Network: Definition and Characteristics

MAC layer solutions currently adopted

Wireless Mesh Networks: application scenarios

Slotted Seeded Channel Hopping (SSCH)

Dynamic Source Routing (DSR)

Conclusions and future researches

Network layer solutions currently adopted

I.F. Akyildiz, X. Wnag, W. Wang, “Wirless Maesh Netowrks: a survey” , Computer Netwroks I.F. Akyildiz, X. Wnag, W. Wang, “Wirless Maesh Netowrks: a survey” , Computer Netwroks No.47, pp. 445-487, 2005.No.47, pp. 445-487, 2005.

Wireless Mesh Networks: Definition

A Wireless Mesh Network is a multi-hop distributed mesh topology system, with self-configuration and self-organization capabilities, where each node is potentially able to forward Informative Units toward other nearby nodes

Wireless Mesh Networks: Characteristics (1/2)

1. Auto-configuration: all network nodes are designed to self-discover their neighbors and paths without needing of any centralized network entity

2. Auto-organization: nodes can autonomously resolve Out-of-Service events, due to temporary off or congested radio links, by exploiting the Mesh Topology

3. Scalability: the covered area can be extended by simply adding new nodes to the current Mesh Network

4. Mobility: the nodes can move on a limited area and keep the connectivity with (at least) a network node

5. Mesh Clients: mobile and peripheral nodes able to communicate with other nodes only through radio interfaces. Minimal routing functions are solved by them. Moreover, they are power constrained, typically low cost and developed on already existing Wireless Cards (e.g., 802.11a/b/g Network Interface Cards (NIC) )

6. Mesh Routers: nodes with minimum (or null) mobility, constituting the network backbone, with radio interfaces towards the mesh clients and mesh routers and wired interfaces towards the outside network. They are not power constrained, can process the most of network traffic and results more expensive than the mesh clients.

7. Additional features of the Wireless Mesh Networks: Currently there is no standard, and open questions are related to the security aspects and to proper MAC protocol developments

Wireless Mesh Networks: Characteristics (2/2)

This architecture is composed by mesh routers which are employed for the wireless backbone and mesh clients are excluded by the mesh topology

Connections among the mesh routers are realized with IEEE802.16 technology

Mesh routers function also as gateway for Internet access

Wireless Mesh Networks: architectures (1/3)(Infrastructure/backbone)

IEEE

802.16

Wireless Mesh Networks: architectures (2/3)(Client-Mesh)

This architecture is composed by self-configured Mesh Clients with routing functions

It represents the mesh network operating in ad-hoc mode

Currently wireless links are IEEE 802.11 based

IEEE 802.11

This architecture given by combing the two previous ones

Mesh Clients can access at the network through mesh routers as well as directly with other mesh clients

IEEE

802.16

IEEE 802.11

Wireless Mesh Networks: architectures (2/3)(Hybrid-Mesh)

Application Scenarios (1/2)

Low cost alternative to link difficult areas to be cabled

Broadband home networking Community Networking

Alternative to

IEEE 802.11 and Bluetooth standards

They can be view as a low cost solution of wide band access networks

Metropolitan Wireless Mesh Networks

Application Scenarios (2/2)

MAC layer solutions

Mac protocol for Wireless Mesh Networks has to consider several differences with those employed by the WLANs:

1) The multi-hop environment 2) All the architectures are distributed and each node is involved to the cooperation of

the network traffic management

Currently, the proposed MAC protocols are mainly based on two methods:

“Virtual” MAC protocols working on the top of existing MAC protocols

P.Bahl, R. Chandra, “SSCH: Slotted Seeded Channel Hopping for Capacity

Improvement in IEEE 802.11 Wireless ad-hoc networks”

Innovative MAC protocols with features similar to those proposed for ad-hoc wireless networks

J.W. Kim, N. Bambos, “Power Efficient MAC scheme using Channel Probing in Multirate Wireless Ad-hoc Networks“

Slotted Seeded Channel Hopping(SSCHSSCH)

SSCH is a protocol for ad-hoc wireless networks using IEEE 802.11 standard and exploits IEEE 802.11 MAC layer service

It can be simply implemented via software into a device equipped with a Wireless Network Interface Card (NIC) and IEEE 802.11 standard compliant

Its task is to extend the channelization of the IEEE 802.11a (13 channels), IEEE 802.11 b/g (11 channels) standard to the ad-hoc networks so to increase the throughput of each node

Each node is equipped with a Channel Scheduler for the channel/frequency hopping

Each node is equipped with a Packet Scheduler where the flow management is given by per-neighbor FIFO queues which are maintained in a priority queue ordered by perceived neighbor reachability

As the number of flows increases, SSCH considerably exceeds the IEEE 802.11a performances

SSCH Performances

Non-disjoint flows

Disjoint flows

Dynamic Source Routing (1/2)

Routing protocol for ad-hoc wireless networks with low mobility nodes

Differently to “Distance vector” or “Link State” based protocols, Dynamic Source Routing does not use periodic routing advertisement messages

It is based on Source Routing technique: before transmitting, each node evaluates the nodes’ sequence (hop) through which the packets are forwarded toward the destination node

Route Discovery

A control procedure is adopted for the correct packet reception and is based on data link acknowledgement between two adjacent nodes

Route Maintenance

DSR Performances

Routing length between a factor 1.01 and 1.09 from the optimal case

Overhead: ratio from 1.01 to 2.6 from the optimal case

ConclusionsConclusions

Wireless Mesh Networks are considered as a flexible, performing and low cost alternative to current WLANs

Currenlty, the proposed solutions are of proprietary type (MIT, Roofnet, Nokia, Mesh Connectivity Layer) and are essentially based on the IEEE 802.11 a/b/g standards

The MAC (SSCH) and routing (DSR) protocols currently adopted result to be extremely simple to be implemented

By the end of 2006 IEEE 802.11s Mesh standard is expected to be ratified

Wireless Mesh Networks:Wireless Mesh Networks:

Second Part:Second Part:PublicationsPublications

Title of Paper : “Optimized Power Allocation for Multi-Antenna Systems impaired by Multiple-

Access Interference and Imperfect Channel-Estimation”

Accepted on IEEE Tr. On Vehicular Technology

Authors: E.Baccarelli, M.Biagi, C.Pelizzoni, N.Cordeschi

WOmEn Project First Publication

Outline

• System Model (Wireless MIMO channel)• Mean Mutual Information• Power-Constrained Maximization of the Mean Mutual Information• System Nodes Interaction: the Game Theory Approach

• Spatial-Power Allocation Multi-Antenna (SPAM) Game for Ad -hoc

networks • SPAM game-vs.-collision-free Access strategies• Conclusions

System Model-(MIMO Wireless Ad-Hoc Network) (1/2)

Tx0 Rx0

Tx2

Tx1 Rx2

Rx1

System Model (2/2)

Spatial Covariance Matrix.

Channel Matrix.

K

H

d

Multiple Access Interference (MAI)

Tx0-Rx0 Reference link

• The overall observed signal vector during the payload phase

T

L tr

1( ) ( ) ( ), T T 1 Tn n n n

t y H d

†whereTra{ } tP, E{ (n) (n)}R R

• The information stream is power constrained as:

1[ ] d T

Tpay

t I H

������������������������������������������y

†E{ ( ) } payT Pt

Payload Phase (Tx0-Rx0 reference link)

User Information Throughput and Capacity

The choice of is finalized to reach the system capacity { ( ) }n

†

pay: { } T pay

1( ) sup I( ; | )

TE t P

C

H y H

}

pay

1( ) sup I( ; | )

TG GPttra{R

H y H

T

•

•

We adopt Gaussian distributed input signals for computing the following information throughput:

( ) ( )G CH HT

Under some conditions we have derived the Gaussian Throughput is equal to the Capacity

Mean Mutual Information

T *-1/2 -1/2 2 -1

pay r d d d

2

-1 *pay

tr d

1I( ; | ) T lg det + + P

t

Tlg det + ( )

t[( )]

([ )]

-

G

y H I K H H

R

KR K

I K

Such expression is valid under some conditions we have derived and reported into the Paper

Maximization of the User Information Throughput

Problem: evaluate and

(opt)Rˆ( )G HT

†( ) U *(1),..., *( ),0 UA t s Aopt diag P P s R

• It has been derived the Power Allocation Algorithm in order to find the optimal expressions of P*(1)….P*(s). It reduces to the Water Filling Approach when perfect Channel estimation is considered.

*-1/2 †

d d A A

t-sA 1 s

,

=diag{k ,...,k , } , s min{r,t}.

A A H K U A = U D V

D 0

•

2

*

1 1 1

1ˆ(H) lg 1 lg 1 ( ) lg 1 *( )T

r s r

G m lm m lm pay

PP m P m

T

Modelling of the Nodes Interaction (1/2) Game Theory Approach

F.R. Farrokhi, etc…

“Link-Optimal Space-Time Processing with Multiple Transmit and Receive Antennas”

IEEE Commmunications Letters, Vol.5 March 2001.

The Game Theory is adopted in order to consider the node interaction and the dynamic ad –hoc network topology

Modelling of the Nodes Interaction (2/2)Game Theory Approach

MIMO ad-hoc network may be modelled as Noncooperative Strategic Game

• - set of pair; (players set)

• - Action Set of node ;

• - Utility Function of node .

( ) ( ) *

{ : 0 [ ] }, 1,...,g g

g g gA Tra t P g n R R

I( ; |1

( ) ( ,..., ,..., ) )Gg g

pay

u uT

(1) (g) (n*) (g) (g)gy Ha R R R

����������������������������

x(g) x(g)T -R

xgT

xgT

{1,...,n*} N

There have been found Existence and Uniqueness Conditions for the Nash Equilibrum

1. Setup Phase (Eigenvalues and Nash Equilibrium )

2. While

Evaluate

3. Shape

4.

5. If go to 6

else go to 2

6. Evaluate the Throughput

Spatial Power Allocation for Multi-Antenna (SPAM) Systems

* budgetm

P m P

I

2 -1m

t1,...,s: 1+ K +d

m

Pm k tra

I

( )newR

( ) ( )new old Ψ R R

+

220.05 ( )

E EoldΨ R ( ) : ( )new oldR R

*( ), P m m I

SPAM Game-vs.-collision free access strategies

(Examples of Throughput Regions for an hexagonal network)

SNR=5dB, t=r=4.F.R. Farrokhi, etc…

“Link-Optimal Space-Time Processing with Multiple Transmit and Receive Antennas”

IEEE Commmunications Letters, Vol.5, March 2001.

1. The information throughput has been expressed in closed form for the general case of imperfect channel estimations and spatially colored MAI.

2. The power allocation and spatial shaping have been accomplished via the SPAM Game.

3. The SPAM game is fully distributed, asyncronous, scalable access schemes.

4. The SPAM game allows point-to-point throughput higher than those attainable via conventional orthogonal (e.g., collision-free) access schemes.

Conclusions

Title of Paper : “Interference Suppression in MIMO

Systems for ThroughputEnhancement and Error Reduction”

Proc. of IEEE International Wireless Communications and Mobile Computing Conference, 3-6 July 2006, Vancouver, pp.611-616.

Authors: E.Baccarelli, M.Biagi, C.Pelizzoni, N.Cordeschi

WOmEn Project Second Publication

Outline

• Reference MIMO Model• Transmission Rate and Error Rate in multi-user environment• Throughput enhancement and error reduction via interference cancellation• Performances• Impact on MAC and Routing of the pursued aproach•Conclusions and work in progress

Reference MIMO Model

• We consider a scenario where a mesh router receives information bits in the presence of multi-user interference from different mesh clients

Reference MIMO Model• The packet structure

Ttr (header) Tpay (payload)

T (packet)

.......

TL (learning)

Reference MIMO Model

• The received sequence, once acquired information about channel state (perfect CSI assumed) and interference is (under no CSI at the Tx)

0 ( ) ( )0 0

10

n

Us sl l

n nn n

E E

t t

Y Φ H Φ H N

V

Reference MIMO Model• The interference is “generally” spatially colored since it depends on topology, so this last heavily influences the behavior of the system

( )

1

n

Us l

n nn n

E

t

V Φ H N

Orthogonal STBCof n-th user

Possible spatial colorationInduced by the channel

spatially white noise

Transmission Rate and Error Rate in multi user environment

(1 )EP R T

Double goal:

High throughput with

Low BER

Requirement:High interference suppression capability since the transmission is simultaneous (collision)

• The final goal should be to transmit at high rate with very low bit error rates• By defining the “net throughput” also known as “gooput”

We have to maximize throughput and minimize error probability

Transmission Rate and Error Rate in multi user environment

• The throughput enhancement in the sense of “information rate” can be achieved by estimating interference at receiver side and by subtract it since interference reduces the capacity region

At the same time, the error reduction can be obtained by reducing the effect of interference that, generally, increases BER

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+00

0 1 2 3 4 5 6 7 8 9 10

SNRdB

BER MIMO interference free

MIMO interfered

Throughput enh. and error red. via interference cancellation

• The transmitter avoids to transmit for TL slots so the receiver can estimate the statistical feature of V (multi-user interference) and the linear estimator is given by

†1

0v v r h

V AY

A K K I RN

interferencenoise Reference signal

And channel

Throughput enh. and error red. via interference cancellation

• The performances in terms of estimation error variance can be evaluated in the following way

†12

0

†1

0

( )

2

v v v v r h

v v r h v

Tra Tra

Tra

K K K K I R

K K I R K

N

N

Throughput enh. and error red. via interference cancellation

• The error variance reduces itself to

for high noise and/or high level of reference signal

for low noise and low

level of reference signal

2 ( )vTra K

2 0

Throughput enhancement and error via interference cancellation

• The parameter that influences the performance is the SIR after cancellation

0 0( )

0 020 0

s scE E

SIR SIR

N N

Throughput enhancement and error via interference cancellation

• To increase network rate means

0

( )0 1

0

1 †0 0

1 † 10

,..., max log det

( ( ) )

2( ( ) ) ] ]

tots sn n

n U

n n

n

Utot

max rE E n

h r v v v v r h

v v r h v

H H I

R I K K K K N I R

K K N I R K

mT

N

Throughput enhancement and error via interference cancellation

• to reduce the error probability means to minimize

0

20

2

4 ( )

1 2

r

E qS

tP

E

Nq takes into account for the cardinality of modulation format

Performances

performances in terms of BEP for different q (modulation formats)

Performances

Net throughput for t=2, and different values of r

How interference Suppression can aid MAC?

• Approaches as CSMA usually tries to avoid collisions

i.s.

i.s.

The packet is dropped (SISO)

The packet is NOT dropped (MIMO)

Impact on MAC

• For sure the effect of interference suppression SIMPLIFIES the MAC procedures and the architecture• The MAC may operate in severe interference suppression conditions that means when interference is comparable with the main signal

Impact on Routing

• Preliminary results show that MIMO system allows power saving strategies (SISO same performance with low power emission) and interference suppression allows us to consider quasi-orthogonal transmission• This suggests that multi-hop approach is unnecessary in this operating conditions

Conclusions

• Interference suppression allow the system to e more simple at upper layers• Without decreasing transmission rate (due to multi-user interference) we are able to assure good performances in terms of BER• This does not require severe hardware complexity.

Fast Downloading of Large Files via Multi-Channel Wireless

Mesh Networks

Enzo Baccarelli, Mauro Biagi, Nicola Cordeschi Cristian Pelizzoni,

WOmEn Project Third Publication

First International Workshop on "Wireless mesh: moving towards applications" ( WiMeshNets 2006 ), (Co-located with QShine 2006, Waterloo, Ontario, Canada) August 10, 2006

Outline

• System Model and Problem Setup

• The constrained Minimization Problem, the fundamental trade-off: Time-minimization, Budget Constraints, and QoS client Requirements

• The possible strategies: Single-channel v.s. Multi-channel, Optimal and Sub-optimal approach: the On-Off policy

• Optimal energy-allocation policy and Throughput performance of the system: the general framework and two specific rate-functions of practical interest

• An application example. The Broadcast MIMO systems: the Dirty Paper strategy

• Conclusions and Works in Progress

• The last-hop of Wireless Mesh Networks,.

• Broadcast-type multi-channel Wireless Application scenarios: fading affected-links

• Energy-limited (battery powered) Wireless Mesh Router

• Multi-flows and Multimedia applications: the large (and increasing) size of multimedia objects.

• Clients demand for fast-downloading: Guaranteed QoS and maximum allowed Download-Time.

• Conventional current proxies inadeguate to solve the problem.

Problem Setup

Target:

Design of the Optimal Energy-allocation-Policy: ‘’How Much’’ and ‘’How Distribute’’

The Minimization of Download-Time of huge-size data.

System Model – Budget Constraints and QoS Requirements

Multi-channel system constituted by orthogonal sub-channels: a mesh-router serves clients requiring the download

1PM

Information Units (IU) to transfer to the clients

tot][Joule

max

mini

]/[ SlotJoule

P

1mm

tot

maximini

Overall available energy

Upper bound on the allowed peak-energy per slot

Minimum energy to be radiated over i-th sub-channel

][Joule

maxi iT TQoS Client requirements:

The Considered Family of Rate-Fuctions

( ) ( ( ); ( ))i i iIU t R t t ]/[ SlotIU

We assume: is in over non decreasing both for and strictly concave over non decreasing for

))();(( ttR 2C 00

0)0,(),0( RR),( R ),( R

ddRR ),(),( 0

( ) ( )( ) ε ( ); ( ); ( )r ri it t t t

budget state download state

channel state

How to choose?

1 2( ) ( ), ( )..., ( ) , for 1,2,...T

Mt t t t t

( ) ( )( ) ( ( ( ); ( )); )ir r

i i tt t t

)(ti)(tIU i

)(t)(tIU

iσ

totmax

1( ) [ ( ),.., ( )]TMt t t σ

Multi-Channel Optimal DesignSingle-Channel Optimal Design ( )i t

How much Energy

to radiate

How much Energy

and

How distribute it

The optimal allocation of

( ) ( ( ); ( )), 1,...,i i iIU t R t t i M The System Rate-Function:

The Throughput performance of the considered System

( ) ( )( )( ) ( ; ( ); ( ))r ri i ttt t σ

Related Works

• Single Client • No QoS constraints are accounted for• Single channel discrete state link• Linear rate-function

“Energy Allocation and Trasmission Scheduling in Satellite and Wireless Networks”, A. Fu, Phd Thesys, Massachusetts Institute of Technology, January 2003

Our Work: Multiple traffic-flows, multiple Clients QoS Requirements Continuous state multiple-orthogonal sub channels The rate-function: a general framework

, R 1 2, ,..., N I

0 1 2 3 4 5 6 7 8 9 10

0

0.2

0.4

0.6

0.8

1

1.2

Th

eu

The On-Off Policy

?The Optimal Policy

max

min

max ( )

. .

( ) ( )

) )

( )

( (T

i i

IU t

s t

t

t

U t

t

t K I

1

A

1

max

min

min ( , )

. .

( ) , 1

( ) ( ) , 1

( ) , 1

TOT

t

i

T

i i

E

s t

i K t

t t t

t t

1

T

The Optimization Problem and its Restatement

For , we have the following equivalent, simpler form:

( ) ( )( , , ) ; ( )r r A ε ε

(Low of Large Number)

The Optimal Energy-allocation Policy (1/3)

HL KKK

)(

)()(

)(

H

HL

L

10 0

max

0

( ) ( ; ) ,1 K

R

0

1

sup ( 0, )R

HK K 0max

1

( )M

ii

• •

Form of the Optimal Policy strongly depends on the Average available energy for the download of a single IU

•

totK

][ IUJoule

totmax

Total energy not sufficient to meet QoSLK Kmax

max

0

( , ) ( )R p d

“Maximal

energy” policy

Single channel Optimal Policy

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

1

2

3

4

5

6

7

8

9

0()(

Jou

le)

K=4K=3K=2K=1 )log();( 1R

)(

)()(

)(

H

HL

L

max

11

0

L0

The Logarithmic rate-function - 1

(Shannon capacity)

)()( 0N0eNp

(Rayleigh channel)

8max

1N0 34.4HK

0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.210

2

104

106

108

1010

1012

1014

K

Tem

po

me

dio

(s

lot)

Politica OttimaPolitica Euristica

0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

K

Th

rou

gh

pu

t (n

ats/

slo

t)

Politica OttimaPolitica Euristica

The Logarithmic rate-function - 2

On-Off policy only sub-optimal:very poor performance in strongly energy-limited application scenarios

2max191KH .

1N0

0 1 2 3 4 5 6 7 8 9 10

0

0.2

0.4

0.6

0.8

1

1.2

Th

eu

)(Th

Ave

rag

e d

ow

nlo

ad t

ime

(slo

t)

Optimal Policy

On-Off Policy

Optimal Policy

On-Off Policy

The Optimal Energy-allocation Policy (2/3)tot

max

)(ti)(tIU i

)(t)(tIU

iσ

min

0 1 1( ; ) ;

MAX

i

i i iR K

min

0 1( ; ) ( ; (( ) ))i

i i iRx x σσ

0max

1

| ( ) ( ) ( )M

ii

x

σ σ σ σB

Full Allocation Sub-Region

Not-Full Allocation Sub-Region

0max

1

1| ( ) ( )

M

ii

x K

σ σ σA

Case of the Logarithmic Rate-function

( , ) log(1 )i i i iR

00 )(:AA 0 σ max)(:B σ0

A

2max

2M 51K .max

50K . 80K . 21K .

};{)(};;{)(:B maxmax 00 02

01 σσ

1N0

†

;

;

pre - subtractor

y Hs n

H RQ

s = Q s

R

1)

2)

3)

An Application Example: the Broadcast MIMO System – (1/3)

)(ti)(tIU i

)(t)(tIU

iσ

Mesh Clients Mono-Antenna

Mesh Router Multi-Antenna

Dirty-Paper Strategy:

1) Channel Matrix QR-factorization,

2) Orthogonal Pre-coding,

3) Iterative Interference pre-subtraction

Interference-free

Space-Orthogonal Sub-Channels

,, 1, ,i i ii i

y R s n i M

( , ) log(1 )i i i iR

†

,

; ;

, 1, ,i i ii iy R s n i M

y Hs n H RQ s = Q s

An Application Example: the Broadcast MIMO System – (2/3)

42K .31K .

8max

2M

22K .max

1N0

0.8 0.85 0.9 0.95 1 1.05 1.1 1.15 1.2 1.25 1.3140

150

160

170

180

190

200

210

K

Tem

po

med

io (

Slo

t)

An Application Example: the Broadcast MIMO System – (3/3)

2max

4M 1K

8max

4M

191K .max

1N0

1N0

Ave

rag

e d

ow

nlo

ad t

ime

(slo

t)

Conclusions and Works in Progress

• The Multi-channel Optimal Energy-allocation Policy has been derived for the general framework we considered

• An efficient Algorithm for its computation has been derived, and comparisons with sub-optimal approaches has been carried out

• Performance evaluations in single-link systems and broadcast system application scenarios have been carried out

• How can the Considered Strategy take into account for real-time multimedia applications (not-elastic traffic)?

Thinking about….

WOMEN project:providing QoS with channel state dependent scheduling in WMNs

University of Rome La Sapienza

Presented by Tiziano Inzerilli8/9/2006

Contents

• Models and basic assumptions• Approach for QoS provision: traffic control & scheduler design• Statistics

Contents

• Models and basic assumptions• Approach for QoS provision: channel state dependent

scheduling• Statistics

MODELS & BASIC ASSUMPTIONS

Network model

MR

BS

MC

• MR (mobile routers) & BS (base stations) :– quasi static

nodes– Point-to-

multipoint transmission

– IEEE802.16/ IEEE802.11

• MC (mobile nodes)– Fast moving– Point-to-point

transmission– IEEE802.11

link

channel

Link model

Boff-1

Trans.node

Rec.Node 1

Brec-1

Rec.Node 2

Rec.Node N

Channel 1

Channel 2

Channel N

Boff-2

Boff-

N

Brec-2

Brec-

N

C1,, BER1

C2,, BER2

CN,, BERN

k

totk CtC )(

totkkeff CtCtC )()(

Bandwidth allocation

•

•

Varibility due to• channel impairments• MAC

Errors in differentChannels statistically independent

Channel model

Gilbert channelmodel

Traffic control portion

Boff Btrans

Transmittingnode

Receivingnode

Brec

sink sink

Bqueue-loss Bchannel-loss

Channel model: Gilbert Channel

• Every Tslot a transition occurs.

Good Channel

BadChannel

r

q1-r1-q

rq

qP

rq

rP BG

,

• Rayleigh fading channel: average fade duration (AFD) and average non-fade duration (ANFD) vs. the fade margin M and the Doppler spread fd.

• Transition Probabilities

1

2

1

M

d

ef

MAFD

df

MANFD

2

BPAFDANFD

AFDBLER

1

slotT

AFDr q r

P

PB

B

1

Channel model: types of channels

0 200 400 600 800 1000 1200 1400 1600 1800 20000

1

2

Idea

l cha

nnel

0 200 400 600 800 1000 1200 1400 1600 1800 20000

1

2

GE

cha

nnel

0 200 400 600 800 1000 1200 1400 1600 1800 20000

1

2

CG

E c

hann

el

No MAC contentionNo channel errors

No MAC contentionChannel errors

MAC contentionChannel errors

Channel State Dependent (CSD) & Independent (CSI) Scheduling Model

CSD Packet Scheduling

CSI Packet Scheduling

Error Correction: FEC, ARQ, interleaving

Flo

w 1

Flo

w 2

Flo

w 3

Flo

w 4

Flo

w 5

Flo

w N

Flo

w 1

Flo

w 2

Flo

w 3

Flo

w 4

Flo

w 5

Flo

w N

Cha

nnel

1

Cha

nnel

2

Cha

nnel

3

Cha

nnel

K

Wireless Link Wireless Link

WirelessLink

MonitoringWireless

LinkMonitoring

Contents

• Models and basic assumptions• Approach for QoS provision: channel state dependent

scheduling• Statistics

APPROACH FOR QOS PROVISION:traffic control & scheduler design

Metrics & Constraints

Delay Constraint (only real-time traffic)

maxjj DtD

Bandwidth Allocation Metrics

)(

totj

recj

totj

j Ct

tBC

tBAI

N

tBAI

tBAI jj

2

Data Loss Metrics

1)()(

)(

)(

)(

)(

)()()(

ttB

tB

tB

tB

tB

tBtt tot

k

offk

k

reck

k

transk

k

reck

k

offk

k

transk

channcong

Link Utilization Metric

1)()(

)(00

tot

k

transk

tot

k

reck

linkCtt

tB

Ctt

tBt

Design of traffic control

Regulation

Flow Classification

SchedulingLink

monitoring

C(t)

S(t)

Mean link Capacity overtime

Instantaneous channel state vector

Classification

Offeredtraffic

Real-timeNon-

Real-time

video1

video2

voice1

voice2

Web br.

File transf

email

With timeconstraints

Without timeconstraints

Other design options

• Assessment for channel estimation– SNR/SIR, Monitoring ACK reception, RTS/CTS frame exchange,

BER/BLER measured at destination, Ad-hoc probing frames, …

• Regulation strategies– Algorithms: Dual leaky buckets, markers, droppers, …– Strategies: never drop, drop after deadline, deadline only for

real-time traffic, …

• Scheduling strategies– Algorithms: Round robin, priority queuing, weighted fair

schedulers, deadline-based schedulers

• SELECTION CRITERIAS– Computational cost– Optimize metrics

Contents

• Models and basic assumptions• Approach for QoS provision: channel state dependent

scheduling• Statistics

STATISTICS

Simulation Scenario

BS

MC1

• Main features– Multiple real-time and non-

real-time flows to MC1, MC2, MC3

– Total capacity: 4Mbps– Link load: 99,1%

• Three subscenarios– Error-prone channel– Bandwidth variability per MC

due to MAC– Comparison with WFS

• Mapping of Flows:– MC1: video, voice– MC2: video, email, FTP– MC3: HTTP, email, FTP

MC2 MC3

Error-prone channel subscenario

10 20 30 40 50 60 70 80 900.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

Theoretical curveExperimental curve

% data loss

Link

Util

izat

ion

Effi

cien

cy

Variable bandwidth subscenario

0 10 20 30 40 50 60 70 80 90 1000.7

0.75

0.8

0.85

0.9

0.95

1

% of variable bandwidth

Link

Util

isat

ion

Effi

cien

cy

Comp. with WFS subscenario:average BAI statistics

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

50.00%

Correct DLB Setting Partially IncorrectDLB Setting

Totally IncorrectDLB Setting

Static DLB Alg. Dyn. Reg. with DLB Dyn. Reg. with LB WFS

Comp. with WFS subscenario:Link Utilization Efficiency

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

90,00%

100,00%

Correct DLB Setting Partially IncorrectDLB Setting

Totally IncorrectDLB Setting

DLB Alg. Dyn. Reg. with DLB Dyn. Reg. with LB WFS

Future Ways

• To evaluate the potentials offered by MIMO-UWB at physical layer

• To consider the joint effect of beamforming and interference cancellation in order to aid MAC ad routing

• Performance Analysis of an innovative scheduling algorithm for OFDMA based IEEE 802.16a systems

• Performance analysis of an innovative algorithm of Connection Admission Control for IEEE 802.16 systems

• To take into account for real-time multimedia applications (not-elastic traffic)