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Smart antennas and MAC protocols in MANET

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Comparison of resolution performance of MVDR and MUSIC ... 3) MUSIC. Part II: Schemes using directional antennas. in MAC layer of ad hoc network ... – PowerPoint PPT presentation

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Title: Smart antennas and MAC protocols in MANET


1
Smart antennas and MAC protocols in MANET
  • Lili Wei
  • 2004-12-02

2
Contents
  • Smart 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

3
Part I Smart antennas-- basic concepts and
algorithms
4
Background Knowledge
  • Basic challenge in wireless communication
  • ---- finite spectrum or bandwidth
  • Multiple access schemes
  • FDMA
  • TDMA
  • CDMA

5
SDMA
  • Spatial Division Multiple Access
  • ---- Uses an array of antennas to provide control
    of space
  • by providing virtual channels in an angle
    domain

6
Directional Antennas
  • Sectorised antenna
  • Smart 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

7
System Model
  • Uniform Linear Array of M elements

d
8
System Model
Narrow Band array processing Assumption
Array response vector
9
System Model
The Beam-former Structure
10
A simple example
Design a beamformer with unit response at 600
and nulls at 00, -300, -750
11
Optimum Beamformer Design
  • Signal in AWGN and Interference

12
Optimum Beamformer Design
Under different criterions
  • Maximum SINR beamformer
  • Mean-Square-Error optimum beamformer

13
Optimum Beamformer Design
Under different criterion
  • Minimum-Variance-Distortionless-Response
    beamformer
  • Maximum Likelihood optimal beamformer

14
Practical Issues
Issues
  • In practice, neither R nor RIN is available to
    calculate the optimal weights of the array
  • In practice, direction of arrival (DOA) is also
    unknown.

Solution
  • Adaptive beamforming algorithms the weights are
    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

15
Adaptive Beamforming Algorithms
Block diagram of adaptive beamforming system
16
Adaptive Beamforming Algorithms
  • SMI Algorithm (Sample Matrix Inverse)
  • LMS Algorithm (Least Mean Square)
  • RLS Algorithm (Recursive Least Square)
  • CMA (Constant Modulus Algorithm)

17
Adaptive Beamforming Algorithms
  • 1. SMI Algorithm (Sample Matrix Inverse)

Estimate R using N samples
Use matrix inversion lemma
Then
18
Adaptive Beamforming Algorithms
2. LMS Algorithm (Least Mean Square)
According to orthogonality principle (data
error) of MMSE beamformer
Solution
  • 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, unconstraint LMS, normalized LMS,
    constraint LMS.

19
Adaptive Beamforming Algorithms
3. RLS Algorithm (Recursive Least Square)
Given n samples of received signal r(t),
consider the optimization problemminimize the
cumulative square error
Solution
  • In some situation LMS algorithm will converge
    with very slow speed, and this problem can be
    solved with RLS algorithm.

20
Adaptive Beamforming Algorithms
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
Solution
  • This is a blind online adaptation, i.e., dont
    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

21
DOA Estimation Method
  • MF Algorithm (Matched Filter)
  • MVDR Algorithm
  • MUSIC Algorithm (MUltiple SIgnal Classification)

22
DOA Estimation Method
  • MF Algorithm (Matched Filter)

The total output power of the conventional
beamformer is
  • 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

23
DOA Estimation Method
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.
Solution
  • By computing and plotting pMVDR over the whole
    angle range, the DOAs 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

24
DOA Estimation Method
Comparison 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
25
DOA Estimation Method
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
Step 2 Perform eigen decomposition
26
DOA Estimation Method
3. MUSIC Algorithm (MUltiple SIgnal
Classification)
Step 3 Estimate the number of signals based on
the fact
  • 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
find the largest peaks of Pmusic to
obtain estimates of DOA
27
DOA Estimation Method
Comparison 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
28
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

29
Part II Schemes using directional antennas
in MAC layer of ad hoc network
30
RTS/CTS mechanism in 802.11
A
B
C
D
E
RTS
RTS
CTS
CTS
DATA
DATA
ACK
ACK
31
RTS/CTS mechanism in 802.11
  • Nodes are assumed to transmit using
    omni-directional antennas.
  • Both RTS and CTS packet contain the proposed
    duration of data transmission
  • The area covered by the transmission range of
    both the sender(node B) and the receiver (node C)
    is reserved during the data transfer
  • This mechanism reduce collisions due to the
    hidden terminal problem
  • However, it waste a large portion of network
    capacity.

32
Vaidya Scheme 1
  • Assumption
  • Each node knows its exact location and the
    location of its neighbors
  • Each node is equipped with directional antennas
  • If 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
    completed
  • That 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

33
Vaidya Scheme 1
A
B
C
D
E
DRTS
OCTS
OCTS
DRTS
OCTS
OCTS
DATA
DATA
ACK
ACK
34
Vaidya Scheme 1
  • 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.

35
A possible scenario of collisions
A
B
C
D
DRTS
OCTS
DRTS
OCTS
DATA
DRTS
ACK
36
Vaidya Scheme 2
  • 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.

37
Vaidya Scheme 2
A
B
C
D
F
ORTS
ORTS
OCTS
DRTS
OCTS
DATA
ACK
38
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
  • Simulation mesh Topology (5X5)

39
But what if we have no location information ?
40
Nasipuri Scheme
  • Node A that wishes to send a data packet to B
    first sends an omni-directional RTS packet
  • Node 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 packet
  • Similarly, 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.

41
Nasipuri Scheme
CTS
CTS
4
3
B
1
2
CTS
CTS
RTS
RTS
Data
4
3
A
1
2
RTS
RTS
42
Nasipuri Scheme
43
Bagrodia 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 unlocking
  • 3) the use of Directional Network Allocation
    Vector (DNAV)

44
Bagrodia Scheme
  • 1. AOA caching
  • Each node caches estimated AOAs from neighboring
    nodes whenever it hears any signal, regardless of
    whether the signal is sent to it or not
  • When 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.

45
Bagrodia Scheme
  • 2. Beam locking and unlocking

(2)CTS
(3)Data
A
B
(4)ACK
B
(1)RTS
  • When a node gets an RTS, it locks its beam
    pattern towards the source to transmit CTS
  • The 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.

46
Bagrodia Scheme
  • 3. DNAV setting
  • DNAV 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.
  • 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

Available directions for transmission
47
Bagrodia Scheme
  • A network situation where DVCS can improve the
    network capacity with DNAVs

A
C
F
E
B
D
48
Bagrodia Scheme
  • Performance

49
Summary of Part II
  • Comparison of four schemes

50
Conclusion
  • smart 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 direction
  • smart antennas enable spatial reuse and they
    increase the communication range because of the
    directivity of the antennas
  • smart antennas can be beneficial for wireless ad
    hoc networks to enhance the capacity of the
    network
  • To best utilize directional antennas, a suitable
    MAC protocol must be designed
  • If the locations are unknown , DOA estimation may
    be needed before sending directional signals

51
reference
  • J.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)
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