Smart antennas and MAC protocols in MANET Lili Wei 2004-12-02.

Smart antennas and MAC protocols in MANET

Lili Wei2004-12-02

ContentsSmart antennas – basic concepts and algorithms

• Background knowledge• System model• Optimum beamformer design• Adaptive beamforming algorithms• DOA estimation method

Schemes using directional antennas in MAC layer of ad hoc network

• Vaidya scheme1• Vaidya scheme2• Nasipuri scheme• Bagrodia scheme

Part I : Smart antennas-- basic concepts and algorithms

Background KnowledgeBasic challenge in wireless communication:---- finite spectrum or bandwidth

Multiple access schemes:FDMATDMACDMA

SDMASpatial Division Multiple Access

---- Uses an array of antennas to provide control of space by providing virtual channels in an angle domain

Directional AntennasSectorised antenna

1) switched beam system

•Use a number of fixed beams

•Select one of several beams to enhance receive signals

2) adaptive array system

•Be able to change its antenna pattern dynamically;

Smart antenna

System Model

c

d

c

l sin

tfj cetmtx 21 )()(

dd

)(22 )()( tfj cetmtx

))1((2))1(()( MtfjM

ceMtmtx

Uniform Linear Array of M elements

System Model)()( tmtm

tfj cetmtx 21 )()(

c

dj

etxtx sin2

12 )()(

Narrow Band array processing Assumption:

c

dMj

M etxtx sin)1(2

1 )()(

sin)1(2

sin22

sin2

1

c

c

c

dMj

dj

dj

e

e

e

S

Array response vectorArray response vector

System ModelThe Beam-former Structure

)(

)()(1

*

tXw

txwty

H

i

M

ii

Mw

w

w

w

2

1

)(

)(

)(

)( 2

1

tx

tx

tx

tX

M

)(1 tx

)(2 tx

)(txM

1

2

M

*1w

*1w

*Mw

)(ty

A simple example Design a beamformer with unit response at 600 and nulls at

00, -300, -750

Optimum Beamformer Design

)()()( 111 tntitx

)()()( 222 tntitx

)()()( tntitx MMM

1

2

M

*1w

*1w

*Mw

)(ty

Signal in AWGN and Interference

)()()()( tntitXtr

SetmtX tfj c2)()(

HtrtrER )()(

H

NI tntitntiER )()()()(

)()( trwtyH


Maximum SINR beamformer

SRS

SRw

NI

HNI

SINR1

1

max

Under different criterions

Mean-Square-Error optimum beamformer

2)(tmEP

SPRwMMSE1


Minimum-Variance-Distortionless-Response beamformer

SRS

SRw HMVDR

1

1

Under different criterion

Maximum Likelihood optimal beamformer

SRS

SRw

NI

HNI

ML1

1

Practical Issues

In practice, neither R nor RI+N is available to calculate the optimal weights of the array;In practice, direction of arrival (DOA) is also unknown.

Issues

SolutionAdaptive beamforming algorithms – the weights ar

e adjusted by some means using the available information derived from the array output, array signal and so on to make an estimation of the optimal weights;DOA estimation methods

Adaptive Beamforming Algorithms

Block diagram of adaptive beamforming system


1. SMI Algorithm (Sample Matrix Inverse)2. LMS Algorithm (Least Mean Square)3. RLS Algorithm (Recursive Least Square)4. CMA (Constant Modulus Algorithm)


1. SMI Algorithm (Sample Matrix Inverse)

N

i

H

iiN rrN

R1

1ˆ

Estimate R using N samples:

n

rrR

n

nR

H

nnnn

1

ˆ1ˆ

nn

H

n

n

H

nnnnn

rRrn

RrrRR

n

nR

11

11

111

11

ˆ)1(

)ˆˆˆ1ˆ

,....2,1

0

ˆ 10

k

c

cIR

Use matrix inversion lemma:

Then:

SRw nn1ˆ


2. LMS Algorithm (Least Mean Square)

**1 )( nnnnn

H

nnnn erwdwrrww

nn

H

nn drwe

• Need training bits and calculate the error between the received signal after beamforming and desired signal;

• The step size u decides the convergence of LMS algorithm;• Based on how to choose u, we have a set of LMS algorithm, “u

nconstraint LMS”, “normalized LMS”, “constraint LMS”.

According to orthogonality principle (data| error) of MMSE beamformer: 0)()()( * tdwtrtrE H

Solution:

3. RLS Algorithm (Recursive Least Square)


)()(ˆ *1

11 nn

H

nnnn dwrrnRww

Given n samples of received signal r(t), consider the optimization problem—minimize the cumulative square error

n

kk

kn e0

2min 10

Solution:

• In some situation LMS algorithm will converge with very slow speed, and this problem can be solved with RLS algorithm.


4. CMA (Constant Modulus Algorithm) Assume the desired signal has a constant modulus, the

existence of an interference causes fluctuation in the amplitude of the array output. Consider the optimization problem:

2

22

)(2

1min AtrwE

H

Solution:

)( 22

1 Arwwrrww nH

nn

H

nnnn

• This is a blind online adaptation, i.e., don’t need training bits• CMA is useful for eliminating correlated arrivals with different

magnitude and is effective for constant modulated envelope signals such as GMSK and QPSK

DOA Estimation Method

1. MF Algorithm (Matched Filter)2. MVDR Algorithm 3. MUSIC Algorithm (MUltiple SIgnal Classificatio

n)

DOA Estimation Method1. MF Algorithm (Matched Filter)

The total output power of the conventional beamformer is:

wRwwtrtrEwtrwEtyEPH

HHH

)()()()(2

2

• The output power is maximized when • The beam is scanned over the angular region say,(-900,900), in

discrete steps and calculate the output power as a function of AOA

• The output power as a function of AOA is often termed as the spatial spectrum

• The DOA can be estimated by locating peaks in the spatial spectrum

• This works well when there is only one signal present• But when there is more than one signal present, the array

output power contains contribution from the desired signal as well as the undesired ones from other directions, hence has poor resolution

0Sw

2. MVDR Algorithm


This technique form a beam in the desired look direction while taking into consideration of forming nulls in the direction of interfering signals. wRwtyE

Hmin)(min

2 1SwtosubjectH

Solution:

SRS

P HMVDR1

1)(

• By computing and plotting pMVDR over the whole angle range, the DOA’s can be estimated by locating the peaks in the spectrum

• MVDR algorithm provides a better resolution when compared to MF algorithm

• MVDR algorithm requires the computation of a matrix inverse, which can be expensive for large arrays

DOA Estimation MethodComparison of resolution performance of MF and MVDR

algorithms

Scenario: Two signals of equal power at SNR of 20dB arrive at a 6-element uniformly

spaced array at angles 90 and 100 degrees, respectively

3. MUSIC Algorithm (MUltiple SIgnal Classification)


MUSIC is a high resolution multiple signal classification technique based on exploiting the eigenstructure of the input covariance matrix.

Step 1: Collect input samples and estimate the input covariance matrix

N

i

H

ii rrN

R1

1ˆ

Step 2: Perform eigen decomposition

VVR̂

},,,{ 21 Mdiag M 21

MqqqV ,,, 21

3. MUSIC Algorithm (MUltiple SIgnal Classification)


Step 3: Estimate the number of signals based on the fact :

DMK ˆ

• The first K eigen vectors represent the signal subspace, while the last M-K eigen vectors represent the noise subspace

• The last M-K eigen values are equal and equal to the noise variance find the D smallest eigen values that almost equal to each other

Step 4: Compute the MUSIC spectrum

SVVS

PHnn

HMUSIC

1)( MKKn qqqV ,,, 21

find the largest peaks of Pmusic to obtain estimates of DOAK̂

DOA Estimation MethodComparison of resolution performance of MVDR and MUSIC

Scenario: Two signals of equal power at SNR of 20dB arrive at a 6-element uniformly

spaced array at angles 90 and 95 degrees, respectively

Summary of Part I• System model• Optimum beamformer design• Adaptive beamforming algorithms 1) SMI 2) LMS 3) RLS 4) CMA• DOA estimation method 1) MF 2) MVDR 3) MUSIC

Part II: Schemes using directional antennas

in MAC layer of ad hoc network

RTS/CTS mechanism in 802.11

A B C D E

RTS RTS

CTS CTS

DATA DATA

ACK ACK

Nodes are assumed to transmit using omni-directional antennas.Both RTS and CTS packet contain the proposed duration of data transmissionThe area covered by the transmission range of both the sender(node B) and the receiver (node C) is reserved during the data transferThis mechanism reduce collisions due to the hidden terminal problemHowever, it waste a large portion of network capacity.

RTS/CTS mechanism in 802.11

Vaidya Scheme 1Assumption:

Each node knows its exact location and the location of its neighborsEach node is equipped with directional antennasIf node X received RTS or CTS related to other nodes, then node X will not transmit anything in that direction until that other transfer is completedThat direction or antenna element would be said to be “blocked”While one directional at some node be blocked, other directional at the same nodes may not be blocked, allowing transmission using the unblocked antenna

Vaidya Scheme 1

A B C D E

DRTS

OCTS OCTS

DATA

ACK

DRTS

OCTS

DATA

ACK

OCTS

Utilize a directional antenna for sending the RTS (DRTS), whereas CTS are transmitted in all directions (OCTS).

Data and ACK packets are sent directionally.

Any other node that hears the OCTS only blocks the antenna on which the OCTS was received.

Vaidya Scheme 1

A possible scenario of collisions

A B C D

DRTS

OCTS OCTS

DATA

ACK

DRTS

DRTS

A node uses two types RTS packets: DRTS and ORTS according to the following rules:

1) if none of the directional antennas at node X are blocked, then node X will send ORTS;

2) otherwise, node X will send a DRTS provided that the desired directional antenna is not blocked.

Vaidya Scheme 2

A B C D

ORTS

OCTS OCTS

DATA

ACK

Vaidya Scheme 2

F

ORTS

DRTS

Performance

5 10 15 20 25

4 9 14 19 24

3 8 13 18 23

2 7 12 17 22

1 6 11 16 21

Connections

802.11 Scheme1

Scheme2

1 21 157.50 146.73 165.892 22 89.90 85.31 81.303 23 22.00 91.39 105.034 24 89.29 82.30 82.835 25 157.94 153.30 163.37Throughput 516.63 559.03 598.42

Simulation mesh Topology (5X5)

But what if we have no location information ?

Node A that wishes to send a data packet to B first sends an omni-directional RTS packetNode B receives RTS correctly and responds by transmitting a CTS packet, again on all directions.In the meanwhile, B can do DOA estimation from receiving RTS packetSimilarly, node A estimates the direction of B while receiving the CTS packet.Then node A will proceed to transmit the data packets on the antenna facing the direction of B.

Nasipuri Scheme

Nasipuri Scheme

A4

1

3

2

RTSRTS

RTSRTS

B4

1

3

2

CTSCTS

CTSCTS

Data

Nasipuri Scheme

Directional Virtual Carrier Sensing(DVCS)

Three primary capabilities are added to original 802.11 MAC protocol for directional communication with DVCS:

1) caching the Angle of Arrival (AOA)2) beam locking and unlocking3) the use of Directional Network Allocation Vector

(DNAV)

Bagrodia Scheme

1. AOA cachingEach node caches estimated AOAs from neighboring nodes whenever it hears any signal, regardless of whether the signal is sent to it or notWhen node X has data to send, it searches its cache for the AOA information, if the AOA is found, the node will send a directional RTS, otherwise, the RTS is send omni-directionally.The node updates its AOA information each time it receives a newer signal from the same neighbor.It also invalidates the cache in case if it fails to get the CTS after 4 directional RTS transmission.

Bagrodia Scheme

2. Beam locking and unlockingBagrodia Scheme

A B

B(1)RTS

(2)CTS(3)Data

(4)ACK

When a node gets an RTS, it locks its beam pattern towards the source to transmit CTSThe source locks the beam pattern after it receives CTS .The beam patterns at both sides are used for both transmission and reception, and are unlocked after ACK is completed.

3. DNAV settingDNAV is a directional version of NAV(used in the original 802.11 MAC), which reserves the channel for others only in a range of directions.

Bagrodia Scheme

DN

AV

(30

0 )

DNAV(750)DNAV(3000)

Available directions for transmission

In the fig:Three DNAVs are set up towards 300, 750 and 3000 with 600 width.Until the expiration of these DNAVs, this mode cannot transmit any signals with direction between 0-1050 or 270-3300 , but is allowed to transmit signals towards 105-2700 and 330-3600

A network situation where DVCS can improve the network capacity with DNAVs

Bagrodia Scheme

F

BD

E

A C

Performance

Bagrodia Scheme

Summary of Part II

RTS CTS Data ACK

802.11 omni omni omni omni

Vaidya 1 dir. omni dir. dir.

Vaidya 2 dir./omni omni dir. dir.

Nasipuri omni omni dir. dir.

Bagrodia dir./omni dir. dir. dir.

Comparison of four schemes

Conclusionsmart antenna is a technology for wireless systems that use a set of antenna elements in an array. The signal from these antenna elements are combined to form a movable beam pattern that can be steered to a desired directionsmart antennas enable spatial reuse and they increase the communication range because of the directivity of the antennassmart antennas can be beneficial for wireless ad hoc networks to enhance the capacity of the networkTo best utilize directional antennas, a suitable MAC protocol must be designedIf the locations are unknown , DOA estimation may be needed before sending directional signals

referenceJ.C.Liberti, T.S.Rappaport, “Smart antennas for wireless communications: IS-95 and third generation CDMA applications”L.C.Godara, “Application of antenna arrays to mobile communicaitions, part I: performance improvement, feasiblility, and system considerations”L.C.Godara, “Application of antenna arrays to mobile communications, part II: beam-forming and direction-of-arrival considerations”Y.b Ko, V.Shankarkumar and N.Vaidya, “Medium access control protocols using directional antennas in ad hoc networks”A.Nasipuri, S.Ye, J.You and R.Hiromoto, “A MAC protocol for mobile ad hoc networks using directional antennas”M.Takai, J.Martin, A.Ren and R.Bagrodia, “Directional virtual carrier sensing for directional antennas in mobile ad hoc networks”S.Bellofiore, J.Foutz, etc.. “Smart antenna system analysis, integration and performance for mobile ad-hoc networks (MANETs)

Smart antennas and MAC protocols in MANET Lili Wei 2004-12-02.

Documents

Transcript of Smart antennas and MAC protocols in MANET Lili Wei 2004-12-02.