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MIMO Wireless Communication

MIMO Wireless Communication. Per Hjalmar Lehne, Telenor. Guest lecture at UniK 1 March 2012. Agenda. What is MIMO? Different gains of multiple antenna systems Fundamental Limits of Wireless Transmission Shannon capacity of Wireless Channels Multiple antennas at one end

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MIMO Wireless Communication

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  1. MIMO Wireless Communication Per Hjalmar Lehne, Telenor Guest lecture at UniK 1 March 2012

  2. Agenda • What is MIMO? • Differentgainsof multiple antenna systems • Fundamental Limits of Wireless Transmission • Shannon capacityof Wireless Channels • Multiple antennas at one end • Capacityof MIMO Links • Data transmission over MIMO Systems • General principles • DiversityusingSpace Time BlockCodes • Spatial Multiplexing • Wireless channelmodelling • TheoreticalModels • HeuresticModels • Broadband Channels • Measured Channels • System LevelIssues • Optimum useof multiple antennas • MIMO in Mobile Broadband • MIMO TransmissionScheme for HSPA and LTE

  3. Agenda • What is MIMO? • Differentgainsof multiple antenna systems • Fundamental Limits of Wireless Transmission • Shannon capacityof Wireless Channels • Multiple antennas at one end • Capacityof MIMO Links • Data transmission over MIMO Systems • General principles • DiversityusingSpace Time BlockCodes • Spatial Multiplexing • Wireless channelmodelling • TheoreticalModels • HeuresticModels • Broadband Channels • Measured Channels • System LevelIssues • Optimum useof multiple antennas • MIMO in Mobile Broadband • MIMO TransmissionScheme for HSPA and LTE

  4. What is MIMO? • MIMO: Multiple input – multiple output • Given an arbitrarywirelesscommunication system: • ”A link for whichthetransmitting end as well as thereceiving end is equippedwith multiple antenna elements” • The signals onthetransmit antennas and receive antennas are ”combined” to improvethequalityofthecommunication (ber and/or bps) • MIMO systems usespace-timeprocessingtechniques • Time dimension is completedwiththe spatial dimension

  5. Agenda • What is MIMO? • Differentgainsof multiple antenna systems • Fundamental Limits of Wireless Transmission • Shannon capacityof Wireless Channels • Multiple antennas at one end • Capacityof MIMO Links • Data transmission over MIMO Systems • General principles • DiversityusingSpace Time BlockCodes • Spatial Multiplexing • Wireless channelmodelling • TheoreticalModels • HeuresticModels • Broadband Channels • Measured Channels • System LevelIssues • Optimum useof multiple antennas • MIMO in Mobile Broadband • MIMO TransmissionScheme for HSPA and LTE

  6. Different gains of multiple antenna systems • ”Smart antenna” gain • Beamforming to increase the average signal-to-noise (SNR) ratio through focussing energy into desired directions • Spatial diversity gain • Receiving on multiple antenna elements reduces fading problems. The diversity order is defined by the number of decorrelated spatial branches • Spatial multiplexing gain • A matrix channel is created, opening up the possibility of transmitting over several spatial modes of the matrix channel increasing the link throughput at no additional frequency, timer or power expenditure

  7. Multiple antenna fundamentals Recovered data stream Data Tx antenna ports Channel Weighting, /demapping, demodulation, decoding Coding, modulation, weigthing/mapping Data Rx antenna ports Data stream

  8. Multiple antenna fundamentals Recovered data stream Data Tx antenna ports Weighting, /demapping, demodulation, decoding Coding, modulation, weigthing/mapping N transmit antennas Data Rx antenna ports Data stream M receive antennas Channel matrix

  9. Multiple antenna fundamentals Recovered data stream Data Tx antenna ports Weighting, /demapping, demodulation, decoding A1 A2 A3 Coding, modulation, weigthing/mapping A4 Data Rx antenna ports Data stream

  10. Multiple antenna fundamentalsSpatial multiplexing Recovered data stream Data Tx antenna ports Weighting, /demapping, demodulation, decoding Coding, modulation, weigthing/mapping Data Rx antenna ports Data stream The different data streamsaredivided in space rank(H) determineshowmanystreamsarepossible to transmit

  11. Multiple antenna fundamentalsTransmit diversity Recovered data stream Data Tx antenna ports Weighting, /demapping, demodulation, decoding A1 A2 A3 Coding, modulation, weigthing/mapping A4 Data Rx antenna ports Data stream Redundancy: The data streamscontainthe same data

  12. Multiple antenna fundamentalsBeamforming Recovered data stream Data Tx antenna ports Weighting, /demapping, demodulation, decoding A1 A2 A3 Coding, modulation, weigthing/mapping A4 Data Rx antenna ports Data stream Onlythe best spatial channel is used to maximize C/N

  13. Agenda • What is MIMO? • Differentgainsof multiple antenna systems • Fundamental Limits of Wireless Transmission • Shannon capacityof Wireless Channels • Multiple antennas at one end • Capacityof MIMO Links • Data transmission over MIMO Systems • General principles • DiversityusingSpace Time BlockCodes • Spatial Multiplexing • Wireless channelmodelling • TheoreticalModels • HeuresticModels • Broadband Channels • Measured Channels • System LevelIssues • Optimum useof multiple antennas • MIMO in Mobile Broadband • MIMO TransmissionScheme for HSPA and LTE

  14. Fundamental limits of wireless transmission • Shannon capacityof Wireless Channels: • h is theunitpowercomplexGaussian amplitude ofthechannel • h is a random variable • Multiple antennas at one end: • Capacityof MIMO Links: • AveragecapacityCa • OutagecapacityCo

  15. Shannon capacity of Wireless ChannelsIdeal Rayleigh Channel

  16. Agenda • What is MIMO? • Differentgainsof multiple antenna systems • Fundamental Limits of Wireless Transmission • Shannon capacityof Wireless Channels • Multiple antennas at one end • Capacityof MIMO Links • Data transmission over MIMO Systems • General principles • DiversityusingSpace Time BlockCodes • Spatial Multiplexing • Wireless channelmodelling • TheoreticalModels • HeuresticModels • Broadband Channels • Measured Channels • System LevelIssues • Optimum useof multiple antennas • MIMO in Mobile Broadband • MIMO TransmissionScheme for HSPA and LTE

  17. Data transmission over MIMO systems • Two maincategories: • Data rate maximization • Sending as many independent signals as antennas • Spatial multiplexing • Diversitymaximization • The individualstreamscan be encodedjointly • Protectagainsttransmissionerrorscaused by channel fading • Minimizetheoutageprobability

  18. Maximizing diversity with space-time block codes • Alamouti’sscheme: • The blockof symbols s0 and s1 is codedacross time and space • Normalizationfactorensures total energy to be the same the case ofone transmitter • Reception: • The receivercollectstheobservation, y, over two symbol periods Tx0 Rx Tx1

  19. Spatial multiplexing • ExtendingtheSpace-TimeBlockCoding • Transmitting independent data over different antennas • The receiver must un-mixthechannel • Limited diversitybenefit C H Y

  20. Spatial multiplexing - decoding • Zero-forcing (ZF) • InvertingmatrixH • Simple approach • Dependent onlow-correlation in H • Maximumlikelihood (ML) • Optimum • Comparing all possible combination withtheobservation • Highcomplexity • Nulling and cancelling • Matrixinversion in layers • Estimatesone symbol, subtracts and continuesdecodingsuccessively

  21. Transmission scheme performance • Same transmission rate • Alamouti • Spatial multiplexing – zero forcing • Spatial multiplexing – maximum likelihood • Combined STBC spatial multiplexing

  22. Agenda • What is MIMO? • Differentgainsof multiple antenna systems • Fundamental Limits of Wireless Transmission • Shannon capacityof Wireless Channels • Multiple antennas at one end • Capacityof MIMO Links • Data transmission over MIMO Systems • General principles • DiversityusingSpace Time BlockCodes • Spatial Multiplexing • Wireless channelmodelling • TheoreticalModels • HeuristicModels • Broadband Channels • Measured Channels • System LevelIssues • Optimum useof multiple antennas • MIMO in Mobile Broadband • MIMO TransmissionScheme for HSPA and LTE

  23. Wireless channel modelling • The promiseofhigh MIMO capacitieslargelyreliesonthedecorrelationproperties: • Between antennas • Full-ranknessofthe MIMO channelmatrixH • E.g. spatial multiplexingbecomescompletelyinefficientifthechannel has rank 1 • Aimofchannelmodelling: • Get an understandingofwhatperformancecan be reasonablyexpected form MIMO systems • To providethenecessarytools to analyzetheimpactofselected antenna or propagation parameters • Spacing, frequency, antenna height.. • To influencethe system design in the best way

  24. Wireless channel modelling • Four approaches • Theoretical Models • E.g. the ”idealistic” channel matrix of perfectly uncorrelated (i.i.d.) random Gaussian elements • Heurestic Models • In practice, MIMO channels will not fall completely into any of the theoretical cases • Broadband Channels • Frequency selective fading is experienced a new MIMO matrix is obtained at each frequency/sub-band • Measured Channels • Validate the models, provide acceptance of MIMO systems into wireless standards

  25. Theoretical channel models • Ideal channel (i.i.d.): • Corresponds to a richmultipathenvironment • Emphasizingthe separate roles • Antenna correlation (transmit or receive) • Rank ofthechannel • UncorrelatedHigh Rank (UHR akai.i.d.) • CorrelatedLow Rank (CLR) • Antennas areplacedtooclose to eachother, or • Toolittleangularspread at both transmitter and receiver • UncorrelatedLow Rank (ULR) • ”pin-hole” model

  26. Heuristic channel models • Display a wide range of MIMO channel behaviours through the use of as few relevant channel parameters as possible, with as much realism as possible • What is the typical capacity of a MIMO channel? • What are the key parameters governing capacity? • Under what simple conditions do we get full rank channel? • The model parameters should be controllable or measurable

  27. Antenna correlation at transmitter or receiver • A MIMO channel with correlated receive antennas: • For ”large” values of the angle spread and/or antenna spacing, R will converge to the identity matrix • For ”small” values of θr, dr, R becomes rank deficient (eventually rank one) causing fully correlated fading • Generalized model includes correlation on both sides:

  28. The double scattering model – ”pinhole” channels • Uncorrelatedlow rank: • Significantlocalscatteringaroundboththe BTS and thesubscriber’s antennas • Localscatterer’sareconsidered as virtualreceive antennas • Whenthevirtualaperture is small, eitherontransmit or receive, the rank ofthe overall MIMO channelwill fall

  29. Broadband channels • Frequency selective channels are experienced • MIMO capacity benefits OFDM systems with MIMO • Additional paths contribute to the selectivity as well as a greater overall angular spread • Improving the average rank of the MIMO channel across frequencies H(f)

  30. Measured channels • Channel matrix is measuredusing multiple antennas at transmitter and receiver • Resultsconfirmthehighlevelof MIMO capacitypotential, at least in urban and suburban areas • Eigenvalueanalysis • A large numberofthe modes of MIMO channelscan be exploited to transmit data SISO 4x4 MIMO NLOS LOS 2x2 MIMO

  31. Agenda • What is MIMO? • Differentgainsof multiple antenna systems • Fundamental Limits of Wireless Transmission • Shannon capacityof Wireless Channels • Multiple antennas at one end • Capacityof MIMO Links • Data transmission over MIMO Systems • General principles • DiversityusingSpace Time BlockCodes • Spatial Multiplexing • Wireless channelmodelling • TheoreticalModels • HeuresticModels • Broadband Channels • Measured Channels • System LevelIssues • Optimum useof multiple antennas • MIMO in Mobile Broadband • MIMO TransmissionScheme for HSPA and LTE

  32. System level issues – optimum use of multiple antennas • Multiple antenna usage is not new in mobile systems: • Spatial diversity systems • Different goals: • Beamforming is optimum using a large number of closely spaced antennas: • Directional beamforming imposes stringent limits on spacing, typically a half wavelength • Best performance in line-of-sight (LOS) • MIMO algorithms focusses on diversity or data rate maximization: • Antennas will use as much space as possible to realize decorrelation between antennas • Turning rich multipath into an advantage and lose the gain in LOS cases

  33. MIMO in mobile broadband • A unfavourableaspect: • Increasedcost and sizeofthesubscriber’sequipment • Limits applicabilityon simple mobile devices • A betteropportunity: • Wireless LAN modems – tablets - laptops

  34. Agenda • What is MIMO? • Differentgainsof multiple antenna systems • Fundamental Limits of Wireless Transmission • Shannon capacityof Wireless Channels • Multiple antennas at one end • Capacityof MIMO Links • Data transmission over MIMO Systems • General principles • DiversityusingSpace Time BlockCodes • Spatial Multiplexing • Wireless channelmodelling • TheoreticalModels • HeuresticModels • Broadband Channels • Measured Channels • System LevelIssues • Optimum useof multiple antennas • MIMO in Mobile Broadband • MIMO TransmissionScheme for HSPA and LTE

  35. MIMO transmission schemes for LTE • LTE supports downlinktransmissionsonone, two or fourcell-specificantenna ports • Up to two transport blockscan be transmittedsimultaneouslyon up to fourlayers • The use of multiple antennas in the DL of LTE comprises several modes • The system adaptively switches between each mode to obtain the best possible performance as the propagation conditions vary

  36. Downlink multi-antenna support in LTE • Up to 4x4 antennas ondownlink • 8x8 onLTE-advanced • Single-userschemes • Transmitdiversity (2) • Spatial multiplexing (3, 4) • Beamforming (6) • Multi-user MIMO (5) • A commonphysicallayerarchitecture:

  37. Downlink multi-antenna support in LTE • Up to 4x4 antennas ondownlink • 8x8 onLTE-advanced • Single-userschemes • Transmitdiversity (2) • Spatial multiplexing (3, 4) • Beamforming (6) • Multi-user MIMO (5) • A commonphysicallayerarchitecture:

  38. Transmit Diversity with 2 Tx antennas • Alamouti scheme • Transmitted diversity streams are orthogonal: Subcarrier (frequency) Port (antenna) x1 x2 -x2* x1* Antenna port 0 Antenna port 1 OFDM subcarriers

  39. Downlink multi-antenna support in LTE • Up to 4x4 antennas ondownlink • 8x8 onLTE-advanced • Single-userschemes • Transmitdiversity (2) • Spatial multiplexing (3, 4) • Beamforming (6) • Multi-user MIMO (5) • A commonphysicallayerarchitecture:

  40. Downlink spatial multiplexing for 2x2 antennas • The numberofcodewordsequalsthetransmission rank and codewordn is mapped to layern • Rank oneprecodersarecolumnsubsetsofthe rank twoprecoders • Recommendationsontransmission rank and whichprecodermatrix to use is obtained via feedback from thesubscriberequipment (UE) • The base station (eNB) can override the rank recommended by the UE • Codeword to layermapping:

  41. Downlink multi-antenna support in LTE • Up to 4x4 antennas ondownlink • 8x8 onLTE-advanced • Single-userschemes • Transmitdiversity (2) • Spatial multiplexing (3, 4) • Beamforming (6) • Multi-user MIMO (5) • A commonphysicallayerarchitecture:

  42. DL peak throughputs in LTE 64QAM Modulation MIMO config

  43. Downlink MIMO for HSPA (3G) • HSPA supports downlink closed-loop MIMO rank 2

  44. Other multiple antenna schemes • Multi-user (MU-) MIMO • Spatial multiplexing to different UEs in the same cell • Also called Spatial Division Multiple Access (SDMA)

  45. Summary • MIMO is using multiple antennas at both transmitter and receiverends to set up a wireless link • MIMO gainscan be beamforming, diversity or spatial multiplexing • Wireless link capacitycan be multiplied by min(M,N) • Data transmissionexploitsthe spatial dimension by maximizingeither data rate or diversity • Wireless channelmodelling is a tool to getthenecessaryunderstandingofperfoemence and be atool to analyzetheimpactofthe design • Optimum useof multiple antennas containconflicting goals in the system design, especiallywhen it comes to antenna sizes and design • Both HSPA and LTE enablespracticaluseof MIMO

  46. Literature • David Gesbert and JabranAkhtar: ”Breaking theBarriersofShannon’sCapacity: An Overviewof MIMO Wireless Systems”. Telektronikk, 98(1), p53-54, 2002. • 3G Americas White paper: "MIMO TransmissionSchemes for LTE and HSPA Networks”, chapter 4, p19-30. 2009. • -- • Extrareading for thoseinterested: • David Gesbert etal.:” From Theory to Practice: An Overviewof MIMO Space-TimeCoded Wireless Systems”. IEEE Journal onSelected Areas in Comunications, 21(3), p281-302, April 2003. • A. Sibille, C. Oestges, A Zanella. ”MIMO: From Theory to implementation”. Academic Press, 2010. ISBN-10: 0123821940, ISBN-13: 978-0123821942

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