MIMO Wireless Communication
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Transcript of MIMO Wireless Communication
MIMO Wireless CommunicationPer Hjalmar Lehne, Telenor
Guest lecture at UniK1 March 2012
Agenda
What is MIMO?Different gains of multiple antenna systemsFundamental Limits of Wireless Transmission• Shannon capacity of Wireless Channels• Multiple antennas at one end• Capacity of MIMO LinksData transmission over MIMO Systems• General principles• Diversity using Space Time Block Codes• Spatial MultiplexingWireless channel modelling• Theoretical Models• Heurestic Models• Broadband Channels• Measured ChannelsSystem Level Issues• Optimum use of multiple antennas• MIMO in Mobile BroadbandMIMO Transmission Scheme for HSPA and LTE
01.03.20122
Agenda
What is MIMO?Different gains of multiple antenna systemsFundamental Limits of Wireless Transmission• Shannon capacity of Wireless Channels• Multiple antennas at one end• Capacity of MIMO LinksData transmission over MIMO Systems• General principles• Diversity using Space Time Block Codes• Spatial MultiplexingWireless channel modelling• Theoretical Models• Heurestic Models• Broadband Channels• Measured ChannelsSystem Level Issues• Optimum use of multiple antennas• MIMO in Mobile BroadbandMIMO Transmission Scheme for HSPA and LTE
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What is MIMO?
MIMO: Multiple input – multiple outputGiven an arbitrary wireless communication system:• ”A link for which the transmitting end as well as the receiving end is
equipped with multiple antenna elements”
The signals on the transmit antennas and receive antennas are ”combined” to improve the quality of the communication (ber and/or bps)MIMO systems use space-time processing techniques• Time dimension is completed with the spatial dimension
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Agenda
What is MIMO?Different gains of multiple antenna systemsFundamental Limits of Wireless Transmission• Shannon capacity of Wireless Channels• Multiple antennas at one end• Capacity of MIMO LinksData transmission over MIMO Systems• General principles• Diversity using Space Time Block Codes• Spatial MultiplexingWireless channel modelling• Theoretical Models• Heurestic Models• Broadband Channels• Measured ChannelsSystem Level Issues• Optimum use of multiple antennas• MIMO in Mobile BroadbandMIMO Transmission Scheme for HSPA and LTE
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Different gains of multiple antenna systems
”Smart antenna” gain• Beamforming to increase the average signal-to-noise (SNR)
ratio through focussing energy into desired directionsSpatial 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
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Multiple antenna fundamentals
7
Channel
Coding, modulation,
weigthing/mapping
Weighting, /demapping,
demodulation, decoding
Data
Data stream
Tx antenna ports
Rx antenna ports
Data
Recovered data stream
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Multiple antenna fundamentals
8
Coding, modulation,
weigthing/mapping
Weighting, /demapping,
demodulation, decoding
Data
Data stream
Tx antenna ports
Rx antenna ports
Data
Recovered data stream
343332
242322
141312
31
21
11
hhhhhhhhh
hhh
H
N transmit antennas
M receive antennas
Channel matrix
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Multiple antenna fundamentals
9 01.03.2012
A1
A2
A3
A4
Coding, modulation,
weigthing/mapping
Weighting, /demapping,
demodulation, decoding
Data
Data stream
Tx antenna ports
Rx antenna ports
Data
Recovered data stream
Multiple antenna fundamentalsSpatial multiplexing
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The different data streams are
divided in space
Coding, modulation,
weigthing/mapping
Weighting, /demapping,
demodulation, decoding
Data
Data stream
Tx antenna ports
Rx antenna ports
Data
Recovered data stream
343332
242322
141312
31
21
11
hhhhhhhhh
hhh
H
rank(H) determines how many streams are possible to transmit
Multiple antenna fundamentalsTransmit diversity
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A1
A2
A3
A4
Redundancy: The data streams contain the same
data
Coding, modulation,
weigthing/mapping
Weighting, /demapping,
demodulation, decoding
Data
Data stream
Tx antenna ports
Rx antenna ports
Data
Recovered data stream
Multiple antenna fundamentalsBeamforming
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A1
A2
A3
A4
Only the best spatial channel is used to
maximize C/N
Coding, modulation,
weigthing/mapping
Weighting, /demapping,
demodulation, decoding
Data
Data stream
Tx antenna ports
Rx antenna ports
Data
Recovered data stream
Agenda
What is MIMO?Different gains of multiple antenna systemsFundamental Limits of Wireless Transmission• Shannon capacity of Wireless Channels• Multiple antennas at one end• Capacity of MIMO LinksData transmission over MIMO Systems• General principles• Diversity using Space Time Block Codes• Spatial MultiplexingWireless channel modelling• Theoretical Models• Heurestic Models• Broadband Channels• Measured ChannelsSystem Level Issues• Optimum use of multiple antennas• MIMO in Mobile BroadbandMIMO Transmission Scheme for HSPA and LTE
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Fundamental limits of wireless transmission
Shannon capacity of Wireless Channels:• h is the unit power complex Gaussian
amplitude of the channel– h is a random variable
• Multiple antennas at one end:
• Capacity of MIMO Links:
Average capacity Ca
Outage capacity Co
)1(log
)1(log2
2
2
hC
C
)1(log2*hhC
*HHI
NC M
detlog2
%9..9.99 oCCP
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Shannon capacity of Wireless ChannelsIdeal Rayleigh Channel
)1(log 22 hC
)1(log2*hhC
*HHI
NC M
detlog2
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Agenda
What is MIMO?Different gains of multiple antenna systemsFundamental Limits of Wireless Transmission• Shannon capacity of Wireless Channels• Multiple antennas at one end• Capacity of MIMO LinksData transmission over MIMO Systems• General principles• Diversity using Space Time Block Codes• Spatial MultiplexingWireless channel modelling• Theoretical Models• Heurestic Models• Broadband Channels• Measured ChannelsSystem Level Issues• Optimum use of multiple antennas• MIMO in Mobile BroadbandMIMO Transmission Scheme for HSPA and LTE
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Data transmission over MIMO systems
Two main categories:• Data rate maximization
– Sending as many independent signals as antennas– Spatial multiplexing
• Diversity maximization– The individual streams can be encoded jointly– Protect against transmission errors caused by channel fading– Minimize the outage probability
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Maximizing diversity with space-time block codes
Alamouti’s scheme:• The block of symbols s0 and s1 is coded across time and space• Normalization factor ensures total energy to be the same the
case of one transmitterReception:• The receiver collects the observation, y, over two symbol
periods
*
01
*10
21
ssss
C
Tx0
Tx1
*10 , ss
*01, ss
Rx
nChn 10 yy0h
1h
n
nsHn ˆ*10
Tyy
Tss 10s
*0
*1
10
21ˆ
hhhh
H 10 hhh
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Spatial multiplexing
Extending the Space-Time Block Coding• Transmitting
independent data over different antennas
• The receiver must un-mix the channel
• Limited diversity benefit
NHCY
C H Y
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Spatial multiplexing - decoding
Zero-forcing (ZF)• Inverting matrix H• Simple approach• Dependent on low-correlation in H
Maximum likelihood (ML)• Optimum• Comparing all possible combination with the
observation• High complexity
Nulling and cancelling• Matrix inversion in layers• Estimates one symbol, subtracts and continues
decoding successively
YHC
NHCY1
ˆ
CHYC
C ˆˆ
minargˆ
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Transmission scheme performance
Same transmission rate
• Alamouti
• Spatial multiplexing – zero forcing
• Spatial multiplexing – maximum likelihood
• Combined STBC spatial multiplexing
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Agenda
What is MIMO?Different gains of multiple antenna systemsFundamental Limits of Wireless Transmission• Shannon capacity of Wireless Channels• Multiple antennas at one end• Capacity of MIMO LinksData transmission over MIMO Systems• General principles• Diversity using Space Time Block Codes• Spatial MultiplexingWireless channel modelling• Theoretical Models• Heuristic Models• Broadband Channels• Measured ChannelsSystem Level Issues• Optimum use of multiple antennas• MIMO in Mobile BroadbandMIMO Transmission Scheme for HSPA and LTE
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Wireless channel modelling
The promise of high MIMO capacities largely relies on the decorrelation properties:• Between antennas• Full-rankness of the MIMO channel matrix H
– E.g. spatial multiplexing becomes completely inefficient if the channel has rank 1
Aim of channel modelling:• Get an understanding of what performance can be reasonably expected
form MIMO systems• To provide the necessary tools to analyze the impact of selected antenna
or propagation parameters– Spacing, frequency, antenna height..
• To influence the system design in the best way
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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
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Theoretical channel models
Ideal channel (i.i.d.):• Corresponds to a rich multipath environment
Emphasizing the separate roles• Antenna correlation (transmit or receive)• Rank of the channel
– Uncorrelated High Rank (UHR aka i.i.d.)– Correlated Low Rank (CLR)
– Antennas are placed too close to each other, or– Too little angular spread at both transmitter and
receiver– Uncorrelated Low Rank (ULR)
– ”pin-hole” model
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**txrxtxrx gg uuH
*txrxggH
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
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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:
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02/1, HRH
rr d
2/1,0
2/1, ttrr dd RHRH
The double scattering model – ”pinhole” channels
Uncorrelated low rank:• Significant local scattering around both the BTS and the subscriber’s
antennas• Local scatterer’s are considered as virtual receive antennas
– When the virtual aperture is small, either on transmit or receive, the rank of the overall MIMO channel will fall
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Broadband channels
Frequency selective channels are experiencedMIMO 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
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H(f)
Measured channels
Channel matrix is measured using multiple antennas at transmitter and receiver• Results confirm the high level of MIMO capacity potential, at least in urban and
suburban areas• Eigenvalue analysis
– A large number of the modes of MIMO channels can be exploited to transmit data
30
0 10 20 3010
-4
10-2
100
Diversity gain, full CSI
SNR [dB]
Cum
pro
babi
lity
0 200 400 6000
10
20
30SNR mean value and difference
Route sample no.
dB
0 200 400 6000
200
400
600
800
Route sample no.
Cap
acity
Mbi
ts/s
P Kvadraturen 01 15 21
0 200 400 600 8000
0.5
1
Sum capacity, C-sum [MBits/sec]
Pro
babi
lity(
Cap
acity
< C
-sum
)
RX= 10,14,12,16 TX= 2,6,1,5
SISO
2x2 MIMO
4x4 MIMO
LOS
NLOS
Agenda
What is MIMO?Different gains of multiple antenna systemsFundamental Limits of Wireless Transmission• Shannon capacity of Wireless Channels• Multiple antennas at one end• Capacity of MIMO LinksData transmission over MIMO Systems• General principles• Diversity using Space Time Block Codes• Spatial MultiplexingWireless channel modelling• Theoretical Models• Heurestic Models• Broadband Channels• Measured ChannelsSystem Level Issues• Optimum use of multiple antennas• MIMO in Mobile BroadbandMIMO Transmission Scheme for HSPA and LTE
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System level issues – optimum use of multiple antennas
Multiple antenna usage is not new in mobile systems:• Spatial diversity systemsDifferent 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
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MIMO in mobile broadband
A unfavourable aspect:• Increased cost and size of the subscriber’s equipment• Limits applicability on simple mobile devices
A better opportunity:• Wireless LAN modems – tablets - laptops
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Agenda
What is MIMO?Different gains of multiple antenna systemsFundamental Limits of Wireless Transmission• Shannon capacity of Wireless Channels• Multiple antennas at one end• Capacity of MIMO LinksData transmission over MIMO Systems• General principles• Diversity using Space Time Block Codes• Spatial MultiplexingWireless channel modelling• Theoretical Models• Heurestic Models• Broadband Channels• Measured ChannelsSystem Level Issues• Optimum use of multiple antennas• MIMO in Mobile BroadbandMIMO Transmission Scheme for HSPA and LTE
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MIMO transmission schemes for LTE
LTE supports downlink transmissions on one, two or four cell-specific antenna ports• Up to two transport blocks can be
transmitted simultaneously on up to four layers
The use of multiple antennas in the DL of LTE comprises several modesThe system adaptively switches between each mode to obtain the best possible performance as the propagation conditions vary
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LTE Transmission modes1 Single eNB antenna2 Tx diversity (SFBC)3 Open-loop SM4 Closed-loop SM5 Multi-user MIMO6 Beamforming7 UE specific RS
Downlink multi-antenna support in LTE
Up to 4x4 antennas on downlink• 8x8 on LTE-advancedSingle-user schemes• Transmit diversity (2)• Spatial multiplexing (3, 4)• Beamforming (6)Multi-user MIMO (5)A common physical layer architecture:
Scrambling Modulation mapper
Layermapper Precoding
Resource element mapper
OFDM signal generation
Resource element mapper
OFDM signal generationScrambling Modulation
mapper
layers antenna portscode words
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1 Single eNB antenna2 Tx diversity (SFBC)3 Open-loop SM4 Closed-loop SM5 Multi-user MIMO6 Beamforming7 UE specific RS
Downlink multi-antenna support in LTE
Up to 4x4 antennas on downlink• 8x8 on LTE-advancedSingle-user schemes• Transmit diversity (2)• Spatial multiplexing (3, 4)• Beamforming (6)Multi-user MIMO (5)A common physical layer architecture:
Scrambling Modulation mapper
Layermapper Precoding
Resource element mapper
OFDM signal generation
Resource element mapper
OFDM signal generationScrambling Modulation
mapper
layers antenna portscode words
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1 Single eNB antenna2 Tx diversity (SFBC)3 Open-loop SM4 Closed-loop SM5 Multi-user MIMO6 Beamforming7 UE specific RS
Transmit Diversity with 2 Tx antennas
Alamouti scheme• Transmitted diversity streams are orthogonal:
*1
*2
2111
00
)2()1()2()1(
xxxx
yyyy
Subcarrier (frequency)
Port (antenna)
Antenna port 0
x1 x2
Antenna port 1
-x2* x1
*
OFDM subcarriers07 September 201138
Downlink multi-antenna support in LTE
Up to 4x4 antennas on downlink• 8x8 on LTE-advancedSingle-user schemes• Transmit diversity (2)• Spatial multiplexing (3, 4)• Beamforming (6)Multi-user MIMO (5)A common physical layer architecture:
Scrambling Modulation mapper
Layermapper Precoding
Resource element mapper
OFDM signal generation
Resource element mapper
OFDM signal generationScrambling Modulation
mapper
layers antenna portscode words
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1 Single eNB antenna2 Tx diversity (SFBC)3 Open-loop SM4 Closed-loop SM5 Multi-user MIMO6 Beamforming7 UE specific RS
Downlink spatial multiplexing for 2x2 antennas
The number of codewords equals the transmission rank and codeword n is mapped to layer nRank one precoders are column subsets of the rank two precoders
Recommendations on transmission rank and which precoder matrix to use is obtained via feedback from the subscriber equipment (UE)• The base station (eNB) can override the rank recommended by the UE
Codeword to layer mapping:
jj
11,
1111
,1001
Codeword 1 Codeword 2Rank 1 Layer 1Rank 2 Layer 1 Layer 2Rank 3 Layer 1 Layer 2 and 3Rank 4 Layer 1 and 2 Layer 3 and 4
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Downlink multi-antenna support in LTE
Up to 4x4 antennas on downlink• 8x8 on LTE-advancedSingle-user schemes• Transmit diversity (2)• Spatial multiplexing (3, 4)• Beamforming (6)Multi-user MIMO (5)A common physical layer architecture:
Scrambling Modulation mapper
Layermapper Precoding
Resource element mapper
OFDM signal generation
Resource element mapper
OFDM signal generationScrambling Modulation
mapper
layers antenna portscode words
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1 Single eNB antenna2 Tx diversity (SFBC)3 Open-loop SM4 Closed-loop SM5 Multi-user MIMO6 Beamforming7 UE specific RS
DL peak throughputs in LTE
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1 layer1 layer
2 layer
4 layer
64QAM Modulation
MIMO config
1.4 3 5 10 15 20Carrier Bandwidth (MHz)
5.2Mbps 13Mbps 21.6Mbps43.2Mbps
64.8Mbps
86.4Mbps10.4Mbps 25.9Mbps
43.2Mbps86.4Mbps
129.6Mbps
172.8Mbps
23Mbps49Mbps
82Mbps
163Mbps
245Mbps
326Mbps
Data rate (gross)
Peak
Thr
ough
put
1.4 3 5 10 15 20Carrier Bandwidth (MHz)
5.2Mbps 13Mbps 21.6Mbps43.2Mbps
64.8Mbps
86.4Mbps10.4Mbps 25.9Mbps
43.2Mbps86.4Mbps
129.6Mbps
172.8Mbps
23Mbps49Mbps
82Mbps
163Mbps
245Mbps
326Mbps
Data rate (gross)
Peak
Thr
ough
put
Downlink MIMO for HSPA (3G)
HSPA supports downlink closed-loop MIMO rank 2
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Other multiple antenna schemes
Multi-user (MU-) MIMO• Spatial multiplexing to different UEs in the same cell• Also called Spatial Division Multiple Access (SDMA)
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Summary
MIMO is using multiple antennas at both transmitter and receiver ends to set up a wireless link
MIMO gains can be beamforming, diversity or spatial multiplexing
Wireless link capacity can be multiplied by min(M,N)
Data transmission exploits the spatial dimension by maximizing either data rate or diversity
Wireless channel modelling is a tool to get the necessary understanding of perfoemence and be atool to analyze the impact of the design
Optimum use of multiple antennas contain conflicting goals in the system design, especially when it comes to antenna sizes and design
Both HSPA and LTE enables practical use of MIMO
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Literature
David Gesbert and Jabran Akhtar: ”Breaking the Barriers of Shannon’s Capacity: An Overview of MIMO Wireless Systems”. Telektronikk, 98(1), p53-54, 2002.3G Americas White paper: "MIMO Transmission Schemes for LTE and HSPA Networks”, chapter 4, p19-30. 2009.--Extra reading for those interested:David Gesbert etal.:” From Theory to Practice: An Overview of MIMO Space-Time Coded Wireless Systems”. IEEE Journal on Selected 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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