Postgraduate course on "Communications in wireless MIMO channels: Channel models, baseband algorithms, and system design" Lectures given by • Prof. Markku Juntti, University of Oulu • Prof. Tadashi Matsumoto, University of Oulu/ Elektrobit • Docent Ian Oppermann, University of Oulu/ Southern Poro Comm. • Docent Juha Ylitalo, Nokia/University of Oulu
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Postgraduate course on
"Communications in wireless MIMOchannels: Channel models,
baseband algorithms, and system design"
Lectures given by
• Prof. Markku Juntti, University of Oulu
• Prof. Tadashi Matsumoto, University of Oulu/ Elektrobit
• Docent Ian Oppermann, University of Oulu/ Southern PoroComm.
• Docent Juha Ylitalo, Nokia/University of Oulu
Course description
1. Introduction (2h) --Juha 17.10
2. Capacity limits of MIMO channels (4h) –Markku 22.10, 24.10
3. MIMO radio channel models (4h) –Juha/Ian 29.10, 31.10
• Prerequisites:Necessary: Signals and systems, Digital Filters, Random Signals,Digital Communications I, Digital Communications II,Coding Methods, Radio Communication Channels.
Recommended: Statistical Signal Processing.
Useful background: Information Theory.
• Requirements: Final exam and a few homework problems
• Credit units: To be determined
Course description, cont'd
• As a part of the course an optional homework project willbe arranged. To receive extra credit units a student maydesign and perform a simple study using Matlab.
• The study may consist of Monte-Carlo simulations forShannon capacity or design of a simple CDMA transmitter-channel-receiver chain with multiple antennas and itsperformance evaluation compared to a single antennatransmitter/receiver.
• A report of the study shall be written. Report could be in aform of a 5-page conference paper.
- Rx/Tx diversity: combining of decorrelated signals
- MIMO: increasing spectral efficiency/ data rates
• Simple example: SINR improvement
• Definition of MIMO
• Spatial correlation matrix
• Example: Diversity & MIMO in WCDMA
Single signal through
correlated channels
Single signal through
decorrelated channels
Multiple parallel
signals through
decorrelated channels
Historical Note
• Multiple antenna transmission used by Marconi in1901
• Four 61m high tower antennas (circular array)
• Morse signal for "S" from England to Signal Hill,St. John, Newfoundland, distance 3425km
• Submarine sonar during 1910's
• Acoustic sensor arrays 1910's
• RF radars 1940's
• Ultrasonic scanners from 1960's
Advantages of Multiple AntennaTechniques
• Resistivity to fading (quality)
• Increased coverage
• Increased capacity
• Increased data rate
• Improved spectral efficiency
• Reduced power consumption
• Reduced cost of wireless network
Some challenges:
- RF: Linear power amplifiers, calibration
- Complex algorithms: DSP requirements, cost
- Network planning & optimisation
Demonstration by Lucent
with 8 Tx /12 Rx antennas:
1.2 Mbit/s in 30kHz
• A smart antenna system consists of several antennaelements, whose signals are processed adaptively in orderto exploit the spatial dimension of the mobile radiochannel.
• It is not the antenna that is smart, but the antenna system !
What are Smart Antennas ?
R F I F
R F IF
R F IF
+
Baseband processing
Weight
Adaptation
Introduction - Beamforming
• Conventional BTS:
radiation pattern covers the whole cell area
• Smart Antenna BTS:
adaptive radiation pattern, "spatial filter"
transmission/reception only to/from the desired userdirection
Foschini and Gans, "On limits of wirelesscommunications in a fading environment whenusing multiple antennas", Wireless PersonalCommunications, vol. 6, no.3, 1998
s2
s3
s4
s1
s1, s2, s3, s4
V1
V2
V3
V4
V1
V2
V3
V4
a)
b)
Same signal on allantennas, i.e. conventional
Tx diversity
Different signals on Txantennas. i.e. true MIMO
Maximum Gain: Transmit Diversity
Maximum Capacity: Parallel channel transmission
Introduction to MIMO
BLAST (PARC) type of tranmission scheme is considered as MIMO, whereasSTTD is a hybrid, considered as a Tx diversity scheme
Channel capacity (Shannon)
• Represents the maximum error-free bit rate
• Capacity depends on the specific channel realization,noise, and transmitted signal power.
• Single-input single-output (SISO) channel
• Multi-input multi-output (MIMO) channel
- Q is the covariance matrix of the transmitted vector
��
�
�
��
�
�+= 2
22 1log ασ
n
PC
)()()( tntxty +⋅= α
���
�
���
�
��
�
�
�+= H
n
C HQHI22
1detlog
σ)()()( ttt nHxy +=
Power allocation strategies- Uniform power distribution
• Transmission power has to be properly distributed over theantennas to maximize the capacity
• For unknown channel uniform power distribution over theantennas can be applied
which gives
• For fading channel ergodic capacity can be found byMonte-Carlo simulations
IQTn
P=
���
�
���
�
��
�
�
�+= HT
n
nPC HHI
22
/detlog
σ
Power allocation strategiesWater-filling
• For known channel optimum power distribution using the“water-filling” technique can be applied
• The “water-filling” algorithm can be derived afterconverting the MIMO channel into a set of L parallelchannels using a SVD of the channel matrix
yielding the following optimum power allocationk
nk Kp
λσ 2
−=
Lktntxty kkkk
H
≤≤+==
1)(~)(~)(~ λUDVH
Capacity resultsUncorrelated Rayleigh MIMO channel (I)
0 1 2 3 4 5 6 7 80.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99
1Capacity CDFs for uncorrelated flat-freq. Rayleigh channels (21.000000 dB)
Capacity in bits per second per Hertz
Pro
babi
lity(
capa
city
>ab
cisa
)
SISOMIMO(1,2)Unknow n MIMO(2,1)Know n MIMO(2,1)MIMO(1,4)Unknow n MIMO(4,1)Know n MIMO(4,1)
Capacity resultsUncorrelated Rayleigh MIMO channel (II)
0 2 4 6 8 10 12 14 16 18 20 220.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99
1Capacity CDFs for uncorrelated flat-freq. Rayleigh channels (21.000000 dB)
Capacity in bits per second per Hertz
Pro
babi
lity(
capa
city
>ab
cisa
)
SISOUnknow n MIMO(2,2)Know n MIMO(2,2)Unknow n MIMO(2,4)Know n MIMO(2,4)Unknow n MIMO(4,2)Know n MIMO(4,2)Unknow n MIMO(4,4)Know n MIMO(4,4)
Capacity resultsFully correlated Rayleigh MIMO channel (I)
0 1 2 3 4 5 6 7 80.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99
1Capacity CDFs for correlated flat-freq. Rayleigh channels (21.000000 dB)
Capacity in bits per second per Hertz
Pro
babi
lity(
capa
city
>ab
cisa
)
SISOMIMO(1,2)Unknow n MIMO(2,1)Know n MIMO(2,1)MIMO(1,4)Unknow n MIMO(4,1)Know n MIMO(4,1)
Capacity resultsFully correlated Rayleigh MIMO channel (II)
0 1 2 3 4 5 6 7 80.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99
1Capacity CDFs for correlated flat-freq. Rayleigh channels (21.000000 dB)
Capacity in bits per second per Hertz
Pro
babi
lity(
capa
city
>ab
cisa
)
SISOUnknow n MIMO(2,2)Know n MIMO(2,2)Unknow n MIMO(2,4)Know n MIMO(2,4)Unknow n MIMO(4,2)Know n MIMO(4,2)Unknow n MIMO(4,4)Know n MIMO(4,4)
C=log2(1+SNR) [b/s/Hz]
MIMO with N Tx and M Rx antennas, unknown channel:
MIMO versus Rx/Tx Diversity(theoretical)
Spectral efficiency of one channel, no diversity:
Rx & Tx diversity: N Tx and M Rx antennas, known channel:
C=Nlog2(1+SNR*M/N) [b/s/Hz]
M=N=> C= Nlog2(1+SNR) [b/s/Hz]
C=log2(1+SNR*M*N) [b/s/Hz]
MIMO vs. diversity approaches
True MIMO has a theoretical potential at high SNRs, whileconventional Rx schemes are more attractive at low SNRs