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Dip. Ingegneria dell’Informazione, Univ. Pisa, Pisa, Italy
MIMO & 5G Cellular Communications
Marco [email protected]
Cybersecurity
Electronic and Communication Systems
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Multiantenna Systems 1: Reception Diversity
TX RX
• 1 transmitting antenna• 2 receiving antennas with
combining• No increase in bit-rate•DIVERSITY Gain = 3 dB
( ) ( ) ( )
1 1
m m mr s n= +
( ) ( ) ( )
2 2
m m mr s n= +
( )ks
( ) ( ) ( ) ( )( ) ( ) ( ) ( )1 2 1 2
2 2
+ += = + = +
m m m mm m m mr r n n
z s s n
At time mT…
( )mz
The noise variance is reduced by a factor 2
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Multiantenna Systems 2: Independent Channels
TX RX
• 2 transmittingantennas
• 2 receiving antennas• No mutual interference• No diversity gain•MULTIPLEXING GAIN x 2
The bit-rate is DOUBLED
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
MIMO Channel Modeling
TX RX
2 x 2 simple example with narrowband signals on a
frequency-flat channel at time mT, neglecting noise
(1)
ms
(2)
ms
(1)
mr
(2)
mr
11h
22h
21h
12h
The terms hij are just complex coefficients that modify the amplitude/phase of the transmitted symbols because the channel is non-frequency-selective !
( ) ( ) ( )
1 11 1 12 2
( ) ( ) ( )
2 21 1 22 2
m m m
m m m
r h s h s
r h s h s
= +
= +
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
MIMO-OFDM 1/2
How can we possibly havea narrowband channel at the high signaling rates of modern communications?
( ) ( ) ( )
1 11 1 12 2
( ) ( ) ( )
2 21 1 22 2
m m m
m m m
r h s h s
r h s h s
= +
= +
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
MIMO-OFDM 2/2
The flat MIMO channel isactually experienced on each single frequency
k/TMC of the subcarriersraster in OFDM
communications !
( ) ( ) ( )
1, 11 1, 12 2,
( ) ( ) ( )
2, 21 1, 22 2,
0,..., 1
= +
= −
= +
m m m
k k k
MC MC
m m m
k k k
MC MC
k kr H s H s
T Tk N
k kr H s H s
T T
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
General MIMO (Multiantenna) System
TX RX
H• NT transmitting antennas• NR receiving antennas• low-rate channels with
flat-fading• NR x NT channel matrix H
NTNR
The channels are “different” in SPACE
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
The complex-valued MIMO channel at time m
m m m= +r Hs w
( )
1
( )
2
( )
R
m
m
m
m
N
r
r
r
=
r
( )
1
( )
2
( )
T
m
m
m
m
N
s
s
s
=
s
Transmitted symbols vector Received (soft) symbols vector
( )
1
( )
2
( )
R
m
m
m
m
N
w
w
w
=
w
White noise vector
11 1
1
T
R R T
N
N N N
h h
h h
=
H
ChannelMatrix
• Independent (equi-power Rayleigh) Channels
• Spatially White equi-power noise
• Stationary channel(long coherencetime)
TX RX
H
NTNR
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Simple Examples
• Diagonal matrix (NR=NT): independent channels, max. multiplexing gain, no diversity gain
• NR x 1 matrix: conventional diversity reception, max. diversity gain, no multiplexing gain
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
MIMO Reception (spatial MUX)
m m m= +r Hs w
( )
1
( )
2
( )
R
k
k
k
k
N
r
r
r
=
r
( )
1
( )
2
( )
T
k
k
k
k
N
s
s
s
=
s
TX RX
H
NTNR
( )1 1 1ˆ ˆ ˆ− − − = = + = + = +m m m m m m m mz H r H Hs w s H w s w
•Need to know the channel matrix H
• H has to be non-singular•Possible noise enhancement→need to reduce constellation size
T RN N=
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
The MIMO Channel
• DIVERSITY gain by combination of multiple RX copies of the same signal with independent noise components(improves SNR)
• MULTIPLEXING gain by using multiple independentchannels with multiple TX/RX antennas (improves rate) but increases noise and increases bit-rate less thanexpected
SNR/Rate relation through capacity theorem
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
MIMO Capacity – Perfect CSI and Feedback Info 1/4
Compute the SVD of H:†=H UDV
=NR
NT
0
0
NT
Unitary Matrix
Unitary
Matrix
are the NT and the channels are DECOUPLED †eigen( )HH
†
RN=UU I
NR >NT
NR
†
TN=VV I
, uplink-like
H D
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
MIMO Capacity – Perfect CSI and Feedback Info 2/4
† † )(m m m m m m m m m= + = + = + = +U U UV VD D Dr Hs w s w s w s w
I am left with an equivalent set of NT independent (parallel) channels whose capacity is evaluated via the Shannon formula for
the AWGN channel
( )† †
m m m m m m = = + = +r r s Dw sDU U wU
and also
( ) ( ) ( ) , 1,..., = + =m m m
i i i i Tr d s w i N
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
MIMO Capacity – Perfect CSI and Feedback Info 3/4
S/P P/SV †U
1 0
0TN
d
D
d
=
If we want to actually “exploit” such parallel channels, we have to pre-code sm by V
before transmitting, and then post-process rm by U disregarding the excess NR-NT
useless channels
†=H UDV
sm
rm
†
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
MIMO Capacity – Perfect CSI and Feedback Info 3/4
2 2
2 2 21
log 1 , .
=
= + + =
TN
k kk
k k
d PC P const
d
Now, if the TX has knowledge of D then it can i) allocate different power on the different “virtual channels”, and ii) allocate it in an optimal fashion by WATER FILLING !
(independent channels)
S/P
sm
1P
TNP
1
TN
k TOT
k
P P=
=
Constraint:
V
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
MIMO Capacity – Perfect RX CSI & no TX Feedback
2 2
2 22 21 1
log 1 log 1 = =
= + = +
TT NN
k TOT k TOT
k kT T
d P d PC
N N
Cannot do water-filling – I just distribute power evenly, Pk=PTOT/NT:
22 † †
2 2eigen ( ) 1 eigen
= + = +
T
k TOT TOTk k k N
T T
d P Pd
N NHH I HH
BUT
† †
2 22 21
log eigen log det =
= + = +
T
T T
N
TOT TOTk N N
k T T
P PC
N NI HH I HH
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Ergodic MIMO Capacity (Telatar)
The entries hij of channel matrix are actually
ZERO-MEAN GAUSSIAN (Rayleigh Fading) independent of each other
What we have computed up to now has to be averaged over the channel realizations:
†
2 min( , ) 2E log det
= +
R TE N N
T
PC
NH I HH
†
2 2log det
= +
T
TOTN
T
PC
NI HH
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
An Example (Schlegel)
•vmobile = 50 km/h - mobile
receiver speed
•Ts = 1 ms – sampling
time
•R = 50 m - radius of
circle containing the
scatterers
•L = 2 km – separation
of TX and RX
•d = 5l - separation of
antenna elements
•f0 = 2.4GHz - carrier
frequency
•Es/N0 = 10dB - symbol
signal to noise ratio
2 x 2
8 x 8
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Time-, Space-, and Space-Time coding
COD/MOD
DEM/DEC
S/P P/SH
TX RX
Redundant (parity-check symbols) can be added in time (interleaved with information symbols), in space, by multiplexing them on the TX antennas with
information symbols, or BOTH: space-time codes.
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Space-Time Block Codes
ST
EncoderST
DecoderS/P P/S
H
= +R HS W
( ) 2 ( ) 2 2| | 1 , | | 2m m
i iE s E w = = =
(1) (2) ( )
1 1 1
(1) (2) ( )
2 2 2
(1) (2) ( )
T T T
M
M
M
N N N
s s s
s s s
s s s
=
S
TIME
SP
AC
E
Space-TimeCodeword
ksnd kr nz
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
(1) (1)*
(1) (2) 1 2
1 2 1 2(1) (1)*
2 12
− = = +
Sd dP
r r h h w wd d
R
The mother-of-all ST Block Codes (Alamouti)
*(1) (1) (2) (1)
1 1 1 2
*(1) (1) (2) (1)
2 2 2 1
s d s d
s d s d
= = − = = =
S
Designed for a NR=1 x NT=2 MIMO system (single RX antenna !!!)Coding rate: 1 in space, ½ in time, overall ½
BUT no bandwdith increase wrt 1 x 1 system with no coding
TX RX
1h
2h2 2
1 2| | | | 2+ =h h
Ps is the TOTAL transmitted power from the two antennas
Variance of noise terms: 2 2 2 2
1 2 02 / = = + =I Q sN T
Unit-power Constellation
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
The mother-of-all ST Block Codes (Alamouti)
*(1) (1) (2) (1)
1 1 1 2
*(1) (1) (2) (1)
2 2 2 1
s d s d
s d s d
= = − = = =
S
Received code block:
( ) ( )
(1) (1)*
(1) (2) 1 2
1 2 1 2(1) (1)*
2 1
(1) (1) (1)* (1)*
1 1 2 2 1 1 2 2 1 2
2
2 2
− = = +
= + + − + +
S
S S
d dPr r h h w w
d d
P Ph d h d w h d h d w
R
TX RX
1h
2h
2 2
1 2| | | | 2+ =h h
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Decoding the Alamouti code
The decoder needs (perfect) CSI to compute the sufficient statistics(soft symbols) Z for data detection
( ) ( )
(1) (1(1) (1) * (1)*
1 1 1 2
* *
(1) (1)* 1 2
1 2
2 2 (1)
1 2
) * (1)
2
2 1
*
2 1 2 1 2
2 2 (1)
1 2 21 1 2| | | | | |2
| |2
= = − = −
=
+
+ + +
−
+S S
z r hz r h h r
Ph h d
h r
Ph h d ww
h hr r
h hZ
where
* *
1 1 1 2 2w w h w h = + * *
2 1 2 2 1w w h w h = −
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Decoding the Alamouti code
The decoder needs (perfect) CSI to compute the sufficient statistics(soft symbols) for data detection
(1) (1) * (1)*
1 1
* *
(1) (1)* 1 2(1) (1) * (1)*
2 1 2 1 2
(1)
2 2
1 2 2
(
1 2
2 1
1)
1 122
22
+
= = − = −
=
−
+ + SS
z r h h rz r h h r
P
h hr r
h h
Pwd w d
Z
Where the new noise variances are
( )2 2 2 2 2
1 2 1 2 1 0| | | | 2 2 / = = + = sh h N T
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Where is the gain ?
• The signal power increases by
• The noise power increases by
•There are no CROSS TERMS between channels !
•TWO-FOLD GAIN DIVERSITY (3dB gain) as in a
conventional receive-diversity 2 x 1 system (requires
CSI !)
( )2
2 2
1 2| | | | 4+ =h h
2 2
1 2| | | | 2+ =h h
Can we GENERALIZE?
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
With two RX antennas (2 x 2 code)
* * * *
(1) (1) (1) (2)* (1) (2)*11 12 21 22
1 2 1 1 2 2
12 11 22 21
= = − + − − −
Zh h h h
z z r r r rh h h h
(1) (2) (1) (1)*1 1 11 12 1 2
(1) (1)*(1) (2)21 22 2 12 2
2
− = = +
Sr r h h d dP
h h d dr rR W
TIMETIME
SP
AC
ES
PA
CE
Show that we have FOUR-FOLD
diversity gain (but no mux gain)
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
MU MIMO / Space-Division Multiple Access (SDMA)
When the number of antennas is much larger than the number of users in the cell, we can also implement Multiple Access/Multiplexing by «synthesizing» multiple beams directed to spatially separated users: Multi-User MIMO (MU-MIMO)
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Massive MIMO
Lead to MASSIVE MIMO (very many antennas at the BTS or AP)
†
2 2E log det
= +
T
TOTE N R T
T
PC N N
NH I H H
†
2 2E log det
= +
R
TOTE N R T
T
PC N N
NH I HH
NT→ (Downlink): MUX gain only
NR→ (Uplink): MUX and DIV gain
† →RT NNHH I
† →TR NNHH I
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Massive MIMO
Lead to MASSIVE MIMO (very many antennas at the BTS or AP)
NT→ (Downlink): MUX gain only
NR→ (Uplink): MUX and DIV gain
2 2log 1
= +
TOTE R
PC N
2 2log 1
/ ( / )
= +
TOTE T
R T
PC N
N N
†
2 2E log det
= +
T
TOTE N R T
T
PC N N
NH I H H
†
2 2E log det
= +
R
TOTE N R T
T
PC N N
NH I HH
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Beyond-4G technologies (1/3)
Basics of beyond-4G technologies
Do we really need 5G?
o over 50%/year growth in data traffic
o at least, a 1000× increase every decade
o in 2018, global traffic will reach more
than 1/100 of a zettabyte (1 ZB=1021 B)10
×in
cre
ase
in f
ive
ye
ars
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Beyond-4G technologies (2/3)
Basics of beyond-4G technologies
According to CISCO, mobile data traffic will reach the following milestones within
the next five years:
o monthly global mobile data traffic will
surpass 15 exabytes by 2018
o the number of mobile-connected devices
will exceed the world’s population by 2014
o the average mobile connection speed will surpass 2 Mb/s by 2016
o due to increased usage on smartphones, smartphones will reach 66% of
mobile data traffic by 2018
o tablets will exceed 15% of global mobile data traffic by 2016
o 4G traffic will be more than half of the total mobile traffic by 2018
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Beyond-4G technologies (3/3)
Basics of beyond-4G technologies
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Technology drivers
Basics of beyond-4G technologies
o network densitification
o massive MIMO
o mm-wave technology
and many more: full-duplex antennas, spectrum sharing, advanced PHY and
interference management, device-to-device (D2D) communications, etc.
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Network densification (1/3)
Basics of beyond-4G technologies
Cooper’s “law”: the wireless capacity has doubled every
30 months over the last century in the last fifty years,
capacity has increased about a million times!
5× 5× 25×
1600×
The right path to pursue is
network densification: very
dense deployment of BTSs
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Network densification (2/3)
Basics of beyond-4G technologies
Shannon’s law:
number of antennas
bandwidth
adaptive coding and modulation, interference
management
With extreme network densification, we can reuse Shannon’s law
everywhere, thus increasing the area spectral efficiency (in b/s/Hz/m2)
heterogeneous, multi-tier
dense networks: small cells,
relays, etc.
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Network densification (3/3)
Basics of beyond-4G technologies
Open challenges include:
o self-organization, exacerbated by
random/unplanned deployment of small cells
o coverage and performance predicition:
stochastic geometry and random matrix
theory could serve as powerful tools
o interference management and resource
allocation, also considering the presence of
multiple tiers
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Massive MIMO (1/2)
Basics of beyond-4G technologies
Massive MIMO (a.k.a. as multiuser MIMO) technology:
antennas
(hundreds)
single-
antenna terminals
The massive MIMO concept relies on the law of large numbers, to average
out frequency selectivity and thermal noise:
o spatial multiplexing gain
o array gain
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Massive MIMO (2/2)
Basics of beyond-4G technologies
In the uplink, the BTS:
o acquires CSI from pilot symbols
o detects the information symbols
o since , adopts linear
processing techniques (MRC, ZF,
MMSE), which are nearly optimal
In the downlink, the BTS:
o uses CSI obtained in the uplink
o applies multiuser MIMO precoding,
using low-complexity precoders
o Thanks to the unused degrees of freedom, massive MIMO can obtain:
• hardware-friendly waveform shaping
• PAPR reduction due to multiuser precoding
o The major challenge is getting accurate CSI estimation
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
mm-wave technology (1/2)
Basics of beyond-4G technologies
Increasing the carrier frequency increases the path loss
However, network densification includes small cells with coverage radius
path loss becomes acceptable
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
mm-wave technology (2/2)
Basics of beyond-4G technologies
o mm-waves can provide a brand-new, very wide frequency band, with
very high-gain steerable antennas at both the MS and the BTS sides
o we can augment the currently saturated 700 MHz – 2.5 GHz radio
spectrum, moving to 60 GHz
o due to very low wavelengths, we can
accommodate the antennas required
by massive MIMO
Wireless channel modeling is not valid anymore: research challenges
include new propagation models for mm-wave communications
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
MMIMO Antennas
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Structure of the LTE-A frame
4G systems
15=scf kHz
2048 , 847= =vN N
1201 15 18= =RFB MHz
Nominal Channel BW: 20 MHz
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
Summary of LTE PHY Parametrs
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Dip. Ingegneria dell’Informazione
University of Pisa, Pisa, Italy
New Radio: the Radio Interface of 5G
• CP-OFDM format, both UL and DL, plus SC-FDMA for UL
• QAM constellations up to 256-QAM
• Multiple carrier spacing: Df=2m x 15 kHz, where m=0,1,2,3,4 is the so-callednumerology
• 1 slot=14 OFDM symbols (twice as many as in LTE)
• (I)FFT size 2048 or 4096