2.MIMO- Multiple Antenna Schemes

download 2.MIMO- Multiple Antenna Schemes

of 29

Transcript of 2.MIMO- Multiple Antenna Schemes

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    1/29

    M ULTI ANTENNA SYSTEM

    By

    Pradip Paudyal

    [email protected]

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    2/29

    I NTRODUCTION TO D IVERSITY T ECHNIQUES

    Diversity= state of being varied, variety[Oxford Advanced Learners Dictionary]. The basic concept of diversity: transmit the signal viaseveral independent diversity branches to getindependent signal replicas via

    time diversityfrequency diversityspace diversitypolarization diversity.

    High probability: all signals not fade simultaneously.High probability: the deepest fades can be avoided.Protection against fading.

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    3/29

    ADAPTIVE ANTENNA

    An adaptive antenna system consists of severalantenna elements, whose signals are processedadaptively in order to exploit the spatialdimension of the mobile radio channel

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    4/29

    A DAPTIVE A NTENNA O PERATION

    Conventional BTS: radiation pattern covers thewhole cell areaSmart Antenna BTS:

    adaptive radiation pattern, "spatial filter

    transmission/reception only to/from the desired userdirectionminimize antenna gain to direction of other users

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    5/29

    SISO, SIMO, MISO

    Single-Input, Single-Output channel suffers fromfadingSingle-Input, Multiple-Output channel: RX diversityMultiple-Input, Single-Output channel: TX diversity,

    beam-forming.

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    6/29

    TRANSMIT D IVERSITY

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    7/29

    RECEIVE DIVERSITY

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    8/29

    D EFINITION OF MIMO Multiple-Input, Multiple-Output channelMapping of a data stream to multiple parallel data streamsand de-mapping multiple received data streams into asingle data stream

    Aims at high spectral efficiency / high data rate

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    9/29

    MIMO- M ULTIPLE A NTENNA S CHEMES

    The transmitting end as well as the receiving ends are equippedwith multiple antenna elements.Transmission of several independent data streams in parallelover uncorrelated antennas .

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    10/29

    MIMO-M ODE OF OPERATION

    Spatial multiplexing :used to increase the data rate spatial diversity mode: to maximize range or reliability

    Figure : Spatial multiplexing and spatial diversity mode

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    11/29

    MIMO-M ODE OF OPERATION CONT ..

    Figure: Basic operation of MIMO System

    Theoretical maximum rate increase factor = Min(NTx , N Rx) in a rich scattering environment andno gain in a line-of-sight environment.

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    12/29

    MIMO-G AINS

    The use of multiple antennas can provide gaindue to

    antenna gainmore receive antennas; more power is collected

    interference gaininterference nulling by beam-forming (array gain)interference averaging (to zero) due to independentobservations

    diversity gain against fadingreceive diversitytransmit diversity.

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    13/29

    C APACITY A NALYSIS OF MIMO S YSTEM

    The classic Shannon-Hartley law suggests that theachievable channel capacity increaseslogarithmically with the transmit power. Bycontrast, the MIMO capacity increases linearlywith the number of transmit antennas, providedthat the number of receive antennas is equal tothe number of transmit antennas. With the

    further proviso that the total transmit power is

    increased proportionately to the number oftransmit antennas, a linear capacity increase isachieved upon increasing the transmit power,which justifies the spectacular success ofMIMOs ....

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    14/29

    C APACITY A NALYSIS OF MIMO S YSTEM

    Shannons capacity Formula The maximum achievable transmission rate for agiven channel with band width B , transmittedsignal power P and in AWGN channel thecapacity of the system is given by :

    In practice, this is considered as the SISO (singleinput single output). This equation gives theupper limit for achievable error free SISOtransmission rate.

    20

    1 P

    C B log N B

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    15/29

    C APACITY A NALYSIS OF MIMO S YSTEM

    General Capacity formula:Consider the antenna array with elements attransmitter and antenna array with element atreceiver. The impulse response of the channel betweenthe j th transmitter element and i th receiverelement is denoted as . ,ijh t

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    16/29

    C APACITY A NALYSIS OF MIMO S YSTEM

    MIMO Channel Matrix

    1 1 1 2 1

    2 1 2 2 2

    1 2

    t

    t

    r r r t

    , , ,n

    , , ,n

    n , n , n ,n

    h ,t h ,t .h ,t

    h ,t h ,t .h ,t H ,t

    h ,t h ,t .h ,t

    y t H ,t s t u t

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    17/29

    C APACITY A NALYSIS OF MIMO S YSTEM

    The matrix elements are complex numbers thatcorresponds to attenuation and phase shift thatwireless channel introduced to the signal.Transformation of MIMO Channel into n SISOSub-channels by SVD technique .

    min ,r t n n n

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    18/29

    C APACITY A NALYSIS OF MIMO S YSTEM

    The output of MIMO system can be expressed as:

    If we assume that transmitted signal bandwidthis narrow enough that the channel response canbe treated as flat across frequency, and thendiscrete time equation is:

    y t H ,t s t u t

    y Hs u

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    19/29

    C APACITY A NALYSIS OF MIMO S YSTEM

    MIMO capacity can be estimated by the followingequation:

    Where H is the channel matrix, R SS is the convexmatrix of transmitted vector S , H H is the

    transpose conjucate of the H matrix and P ismaximum normalized transmit power. Byconverting MIMO channel to SISO sub-channel we can fiend simplified results.

    2( )max log [det( )]

    ss

    H

    sstr R P C I HR H

    min ,r t n n n

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    20/29

    C APACITY A NALYSIS OF MIMO S YSTEM

    Then total capacity of n SISO sub-channels is thesum of individual capacities and as a result totalcapacity of MIMO channel is:

    Where, P k is the power allocated to the k th sub-

    channel and is its power gain. According to thesingular value decomposition algorithm , k=1,2n are the eigenvalues of matrix, which arealways non negative.

    22

    1

    log (1 )n

    k k

    k C p

    2

    k 2k

    H HH

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    21/29

    C APACITY A NALYSIS OF MIMO S YSTEM

    The power allocation algorithm must satisfy

    We noticed that achieved capacity depends on thealgorithm used for allocating power to each sub-channel. The theoretical analysis assumes thechannel state known at the receiver.When channel sate is not known at transmitter,

    then,

    1

    n

    k k

    p P

    /k t p p n 2log det( ) H t

    pC I HH n

    22

    1

    log (1 )n

    k t k

    pC n

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    22/29

    C APACITY A NALYSIS OF MIMO S YSTEM

    When channel state is known, it can performoptimum combining methods during the powerallocation process.One method to calculate the optimum powerallocation to n sub-channel is to employ thewater-filling algorithm .

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    23/29

    C APACITY A NALYSIS OF MIMO S YSTEM

    Consider the assumption of CSI at thetransmitter; we can proceed to the followingcapacity formula.

    Where is the amount of power that is assignedto k th sub-channel.

    1

    22

    .log 1

    nk

    k k

    t

    pC n

    k

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    24/29

    I NTRODUCTION TO S PATIAL M ULTIPLEXING

    The basic concept of multiplexing: divide(multiplex) transmit a data stream severalbranches and transmit via several (independent)channels in

    Time :time division multiplexing (TDM)frequency : frequency division multiplexing (FDM)

    typical example: orthogonal FDM (OFDM)

    space : space division multiplexing (SDM) or spatial

    multiplexingdifferent bits from different antennarequires independent channels

    code : code division multiplexing (CDM)applied in 3G systems.

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    25/29

    L INEAR D ISPERSION C ODING

    Linear dispersion coding (LDC) offers aframework to combine spatial multiplexing andtransmit diversity.Code design consists of finding optimumdispersion matrices.

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    26/29

    L AYERED S PACE T IME A RCHITECTURES

    Bell Labs Layered Space Time (BLAST) architecture was oneof the first spatial multiplexing systems.

    Called also layered space time (LST).Detection originally based on linear and decision feedbackequalization.

    Vertical LST (V LST) : Basic scheme with no coding involved Horizontal LST (H LST) : Coding included. Diagonal LST (D LST) : Coding and spatial interleavingincluded and Spatial interleaving to improve performance viaspatial diversity.

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    27/29

    S COPE

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    28/29

    REFERENCES T. M. Cover & J. A. Thomas, Elements of Information Theory. JohnWiley & Sons, 1991. ISBN: 0-471-06259-6G. J. Foschini& M. J. Gans, On limits of wireless communications in afading environment when using multiple antennas. Wireless PersonalCommunications , vol. 6, pp. 311-335, Nov.-Dec. 1999.D. Gesbert, M. Shafi, D. Shiu, P. J. Smith, and A. Naguib , Fromtheory to practice: An overview of MIMO space-time coded wirelesssystems, IEEE J. Select. Areas Commun., vol. 21, no. 3, pp. 281 302,2003.J. C. Liberti. and T. S. Rappaport, Smart Antennas for WirelessCommunications: IS-95 and Third Generation CDMA Applications,Prentice Hall, Upper Saddle River,NJ, 1999.

    A. J. Paulraj, D. A. Gore, R. U. Nabar, and H. Bolcskei , An overviewof MIMO communications A key to gigabit wireless, Proc. IEEE, vol.92, no. 2, pp. 198 218, Feb. 2004.E. Telatar, Capacity of multi-antenna Gaussian channels. EuropeanTransactions on Telecommunications, vol. 10, no. 6, pp. 585-595,Nov.-Dec. 1999.B. Vucetic & J. Yuan, Space Time Coding. John Wiley and Sons,2003. ISBN 0-470-84757-3

  • 8/13/2019 2.MIMO- Multiple Antenna Schemes

    29/29

    Thank you.