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Vodafone Chair Mobile Communications Systems, Prof. Dr.-Ing. G. Fettweis
Ten Unsolved Hot Problems
in Information and Communications Technology
Gerhard Fettweis – Green Visiting Professor – UBC
– Vodafone Chair Professor – TU Dresden
– IEEE SSCS Distinguished Lecturer
Trickle / Pipeline of Technology
Vodafone Chair Funding
• e.g. fundamental limits of cellular
DFG funding
• e.g. fundamentals of interference cancellation
BMBF
• e.g. EASY-C
• e.g. Cool Silicon
EU
• e.g. Artist4G
• e.g. EARTH
TU Dresden Gerhard Fettweis Slide 2
Vodafone Chair Mobile Communications Systems, Prof. Dr.-Ing. G. Fettweis
Broadband
TU Dresden Gerhard Fettweis Slide 4
Coverage: Cellular
1995 2000 2005 2010 2015
Short links (1m)
Cellular (100m)
GSM GPRS
HSPA
HSDPA
LTE
WiMAX
WLAN (10m)
3G R99 / EDGE
LTE Advanced
100Gb/s
10Gb/s
1Gb/s
100Mb/s
10Mb/s
1Mb/s
100Kb/s
10Kb/s
802.11ac/ad
802.11n 802.11ag
802.11
802.11b
USB 1.0
USB 2.0
USB 3.0
UWB intention
802.15.3c
TU Dresden Gerhard Fettweis Slide 5
The Wireless Roadmap
1995 2000 2005 2010 2015
Short links (1m)
Cellular (100m)
WLAN (10m)
100x
10x
ITRS Roadmap: Continues until 2020
100Gb/s
10Gb/s
1Gb/s
100Mb/s
10Mb/s
1Mb/s
100Kb/s
10Kb/s
TU Dresden Gerhard Fettweis Slide 6
Fairness
High SINR
High data rate
eNodeB eNodeB
Low SINR
Low data rate
High SINR
High data rate
Unfair !!!
TU Dresden Gerhard Fettweis Slide 7
Fairness & Data Rate
0.5
0.4
0.3
0.2
0.1
0.0
-10 -5 0 5 10 15 20 25 30
SIR in dB
Reuse 1
Power Control
E[SIR] = -0.2dB
Reuse 1
Power Control
10 Interferers cancelled
Macro/Distributed MIMO
E[SIR] = 8.1dB
pdf of SIR
fairness
Interference Cancellation: Fairness & High Data Rate
data rate
Talk at RAEng 2009-09-13 Gerhard Fettweis Slide 8
Realtime 3D Multimedia Rendering
© FHG HHI Berlin
© MPI Saarbrücken
TU Dresden Gerhard Fettweis Slide 9
CoMP: Coordinated Multi-Point
We thus believe that next generation systems will include multi-cell cooperative
signal processing (“network MIMO” or CoMP):
Backhaul
infrastructure
between sites
Cell phone jointly detected by
3 base stations
Shaded area: One site containing
three base stations (i.e. cells)
with four antennas each
TU Dresden Gerhard Fettweis Slide 10
Potential Gains of CoMP
Uplink Downlink
Okumura-Hata pathloss model, ITU pedestrian A
Link-to-system mapping (MIESM), 8 MCS schemes
Spectral eff. losses through guard bands / intervals
Assuming perfect channel est., 2 rx ant. per eNB
Linear joint transmission,
assuming perfect channel
knowledge at the eNBs
2 tx ant. per eNB
World’s Largest Operational
LTE-Advanced Algorithm Testbed
11
© Google Earth
Hbf-Süd
Karstadt
Postplatz
Lennéplatz
Mitte
Kongresszentrum
Fritz-Förster-Pl.
Strassburger Pl.
WTC
Hbf
ICC 2009 Dresden
April-16 2010 Dresden
Recent Uplink Field Trial Results Observed CoMP Gains
• Moderate average gains
Scheme Avg. gain
Conv. -
CoMP C = 2 19.0 %
CoMP C = 3 22.6 %
0 1 2 3 40
0.2
0.4
0.6
0.8
1
Rate [bpcu]
Cum
ula
tive D
ensity
UE1 (conv.)
UE2 (conv)
UE1 (CoMP C = 2)
UE2 (CoMP C = 2)
UE1 (CoMP C = 3)
UE2 (CoMP C = 3)
• Peak CoMP gains up to 150%
Slide 12 Patrick Marsch, Michael Grieger, Jörg Holfeld 4GSM-Meeting
Ines Riedel Slide 13
From EASY-C to Artist4G
Achievements Technology Evolution
Project
Meeting
14
MU-MIMO
CoSCH CoMP
EASY-C identified major challenges concerning
Synchronization requirements,
Multi channel estimation,
Feedback compression,
Backhaul requirements,
UE Complexity
and proposed efficient solutions
09.1
0.20
10
Key Learnings Key CoMP Challenges Identified
The EASY-C consortium has gained vast experience in the implemen-
tation and challenges connected to coordinated multi-point (CoMP):
System Partitioning Reducing Backhaul /
Infrastructure Aspects
Scheduling
Synchronization in
time / frequency channel estimation &
obtaining transmitter
side CSI at eNBs
Impact of network MIMO
on higher protocol layers
Vodafone Chair Mobile Communications Systems, Prof. Dr.-Ing. G. Fettweis
Network Architecture
Cooperative Multi-Point (CoMP):
Power Efficient or Waste of Power?
TU Dresden Gerhard Fettweis Slide 17
Does the increase in spectral efficiency make up
for the additional energy for signal processing?
Metrics
Data transported per unity energy
Bit/J
Data delivered per unit area per unit energy
Bit/J/km2
Given site setup: site distance
Bit/J with site distance as paramenter
TU Dresden Gerhard Fettweis Slide 18
Bit per Joule Efficiency of
Cooperating Base Stations
TU Dresden Albrecht Fehske Slide 19
Extended linear power
model
Consideration of effective
rates taking into account
additional pilots and
feedback
Backhauling according to
centralized processing
per cluster
A. Fehske, J. Malmodin, G. Biczok, and G. Fettweis,
„Bit per Joule Efficiency of Cooperating Base Stations in Cellular Networks“
3rd Workshop on Green Communications, December 2010, Miami Florida, to appear
0 500 1000 1500 200010
20
30
40
50
60
70
Inter site distance in m
Bit p
er
Jo
ule
Effic
ien
cy in
kb
it/J
N
c = 1
Nc = 2
Nc = 3
Nc = 4
Nc = 5
Nc = 7
Base line processing: 128W
MIMO processing: 10%
UL Channel est.: 10%
Clustersize: 1 to 7
Transmit power 100 mW…40W
Bit per Joule Efficiency of
Cooperating Base Stations
500 1000 1500 200010
20
30
40
50
60
70
Inter site distance in m
Bit p
er
Jo
ule
Effic
ien
cy in
kb
it/J
Nc = 1
Nc = 2
Nc = 3
Nc = 4
Nc = 5
Nc = 7
TU Dresden Albrecht Fehske Slide 20
Base line processing: 128W
MIMO processing: 1%
UL Channel est.: 10%
Clustersize: 1 to 7
Transmit power 100 mW…40W
Extended linear power
model
Consideration of effective
rates taking into account
additional pilots and
feedback
Backhauling according to
centralized processing
per cluster
A. Fehske, J. Malmodin, G. Biczok, and G. Fettweis,
„Bit per Joule Efficiency of Cooperating Base Stations in Cellular Networks“
3rd Workshop on Green Communications, December 2010, Miami Florida, to appear
Processing determines whether cooperation
increases or decreases Energy Efficiency!
Network Optimization:
Micro/Macro Setup
TU Dresden Gerhard Fettweis Slide 21
The link budget is the part to watch out for
in terms of Energy Efficiency!
Example (cont„d)
Problem:
Minimize (weighted) number of BSs s.t. coverage constraints
Macro BS
Micro BS
Ptx,macro = 46 dBm
Ptx,micro = 33 dBm
Rx-Sensitivity = -97 dBm
Empirical path loss model
(WINNER II)
Based on LTE link budget
Slide 22 Ines Riedel
Optimal Area Power Consumption
2010-05-18, Taipei, Taiwan Fred Richter Slide 23
0 2 4 6 8 10 12 14 16 400
600
800
1000
1200
1400
1600
1800
2000
Target 10%-ile area spectral efficiency (bit/s/Hz/km2)
Op
tim
al a
rea
po
we
r co
nsu
mption
(W
/km
2)
Homogeneous macro
Heterogeneous, 1 micro site
Heterogeneous, 2 micro sites
Heterogeneous, 3 micro sites
Heterogeneous, 5 micro sites
Almost linear increase in area power consumption
Each deployment (hom., het.) optimal for a certain spectral efficiency range
Heterogeneous deployment beneficial for higher spectral efficiencies
High load
Access Via Alternatives
Overlapping Coverage = 39.0%
Macro BS
Micro BS
Multiple coverage:
• 1: 60.4%
• 2: 16.1%
• 3: 16.5%
• 4: 6.1%
Slide 24 Ines Riedel
TU Dresden, Peter Rost and Gerhard Fettweis Slide 25
Motivation
Base
Station
Relay
Node
Relay
Node
Relay
Node
BS coverage area
RN
coverage
areasSource: WINNER II
Network Optimization:
Micro/Macro Setup
TU Dresden Gerhard Fettweis Slide 26
How to manage and optimize the radio access over so many alternatives?
SON: self optimizing networks
Mathematical framework (honey comb, reality, stochastic geometry)
Vodafone Chair Mobile Communications Systems, Prof. Dr.-Ing. G. Fettweis
Dirty RF
TU Dresden Gerhard Fettweis Slide 28
Dirty RF
Picking up “dirt” Nonlinear LNA
Feedthrough
Coupling
Phase noise
Aperture Jitter Ambiguity I/Q Imbalance RRC mismatch Flicker Noise Digital noise Nonlinear PA
DSP:
Living
With
Dirty
RF
easy
TU Dresden Gerhard Fettweis Slide 29
LOUL
LODL
AD
LNA
PA
Du
ple
xe
r
baseband
processing
Matched
Filter
Impulse
shaping
filter
Channel
select filter
AD
sBB[k]
sUL[k]
Power
Sens.
VGA
Limited Tx-Rx Isolation due to miniaturization and frequency agility
Tx Leakage (TxL)
Strong out-of-band interference in Rx branch
Transmit Leakage in FDD and Direct Conversion
Approaches for Solution:
Problem: Tx Leakage in FDD Terminals
Additional bandpass filter
Adaptive filter in parallel to duplexer
Dirty RF: Compensation of analog impairments by digital signal processing
+ Reconfigurable / standard independent / relaxed RF requirements
- Limited reconfigurability - Increased analog complexity
DL signal
-
+
AF
Tx Leakage
TxL
Estimation
-
+
sCMP[k]
TU Dresden Gerhard Fettweis Slide 30
Du
ple
xe
r AD
Power
Sens.
VGA
LOUL
LODL
Tx LeakageChannel
select
Filter
LNA
PA
sBB[k]
sUL[k]
AD
Tx Leakage in Zero-IF Receivers
Nonlinearity of I/Q down converter
Intermodulation product 2nd order of Tx Leakage (TxL-IM2)
Low freq. TxL-IM2 interfering with down converted DL signal of interest
Tx Leakage intermodulation products 2nd order (TxL-IM2)
DL signal
0
TxL
IM2Desired
signal
f
TxL
Desired
signal
fDLfUL
TxL-IM2
TxL channel
DC-
TU Dresden Gerhard Fettweis Slide 31
Antenna nearfield distorted by
metal interrupter moved within
distance [0.036, 0.303] m
to the antenna
Tx-Rx Isolation
Measurement with UMTS SAW Duplexer B7632 (EPCOS AG)
EPCOS
UMTS
Duplexer
B7632
Monopol -
Antenna
Metal
InterrupterNetwork -
Analyser
RX
TX
ANT
d
UL-Band DL-Band
B
Frequenz [GHz]
Tx-R
x I
sola
tionsdäm
pfu
ng [
dB
]
1.9 1.95 2 2.05 2.1 2.15 2.2
-70
-65
-60
-55
-50
mean
min / max
Frequency [GHz]
Tx-R
x isola
tio
n [d
B]
Tx-Rx isolation affected by nearfield
distortions
Only small variations in UL-Band
(ca. 2.2 dB)
TxL Estimation must be adaptive
TU Dresden Gerhard Fettweis Slide 32
SNR loss due to TxL
-5 0 5 10 15 20 25 30
0
2
4
6
8
10
12
14
16
18
20
~ ° TxL [ d B ]
¢ ~ ° w
. r . t w
/ o T
x L
[ d B
]
w/o TxL Cmp
Genie TxL Cmp
ML, t =3.5
LMS
t =3.5
meas.
t =1.5
dig. TxL Cmp required
TxL
negligible analog.
TxL
Cmp req.
OFDM susceptible to TxL for TxL < 30dB
digital TxL compensation
suitable for TxL > 0dB
TxL can be mitigated digitally up to 30 dB
Simulation Parameters:
DL / UL: 802.11a, 16QAM
DL channel: HiperLAN A
CSF EQ in TxL Est.
analog DC-1
ADC: 8-bit lin. mid-rise, =2
TxL: - TxL ch: 6-tap exp PDP
and meas. IR
- TxL SIR TxL = 10dB
- no TxL I/Q mismatch
TxL Est: - NB=4000,
ML reaches almost Genie Cmp. limit,
but suffers from high min. SNR loss
LMS performance strongly
depending on TxL channel property
TU Dresden Gerhard Fettweis Slide 33
Phase Noise and Clippping
-1.5 -1 -0.5 0 0.5 1 1.5-1.5
-1
-0.5
0
0.5
1
1.5
In
Qu
a
Phase Noise:
• Kalman Tracking
• Complete Sync
-1.5 -1 -0.5 0 0.5 1 1.5-1.5
-1
-0.5
0
0.5
1
1.5
In
Qu
a
Clipping:
• Analogue PAPR
• Saleh Model
• AM/PM distortion
-1.5 -1 -0.5 0 0.5 1 1.5-1.5
-1
-0.5
0
0.5
1
1.5
In
Qu
a
Clipping & PN:
• Rate
TU Dresden Wolfgang Rave Slide 34
Phase Noise in OFDM
TU Dresden Steffen Bittner Slide 35
BER Prediction
BER prediction
• Rayleigh fading with
exponential PDP
• 16-64-QAM
• Transmitter nonlinearities
and PN
• performance behavior
can be accurately
predicted
∆𝑓3𝑑𝐵 / 𝑓𝑠𝑢𝑏 = 1%
0 5 10 15 20 25 30 35 40
10-2
10-1
Average Subcarrier SNR in dB
BE
R
Simulation
Numerical
Reference
IBO = 0dB
16-QAM IBO = 3dB
64-QAM
TU Dresden Gerhard Fettweis Slide 36
Setup:
IEEE 802.11a SISO
Exponential PDP, 8 taps
64-QAM f = 4GHz, IBO = 0dB
∆𝑓3𝑑𝐵 / 𝑓𝑠𝑢𝑏 = 1%
Rate under Phase Noise & Clipping
-10 0 10 20 30 40 0
1
2
3
4
5
6
SNR [dB]
Rate
[bits]
no distortion
no compensation
I a
= 0.2, ICI 1
I a
= 0.4, ICI 2
I a
= 0.6, ICI 3
I a
= 0.8, ICI 4
TU Dresden Gerhard Fettweis Slide 37
Many Further Issues
Dirty RF: “Estimation and Detection” algorithm design
Nonlinearities
Phase noise
I/Q imbalance
Sampling jitter
Transmit leakage
Need understanding that analog chain is channel
Vodafone Chair Mobile Communications Systems, Prof. Dr.-Ing. G. Fettweis
M2M Sensors
WWRF Vision
“7 trillion wireless devices for 7 billion people by 2017”
TU Dresden Gerhard Fettweis Slide 39
Current Paradigm of Cellular
TU Dresden Gerhard Fettweis Slide 41
sensor
Slave 1000x Energy Problem ! Master
Required Paradigm of Cellular
TU Dresden Gerhard Fettweis Slide 42
sensor
slave master
Changes in GFDM
43 Ines Riedel
20 25 30 35 40 45 -50
-40
-30
-20
-10
0
10
PSD
RRC
RECT
ma
gn
itu
de
normalized frequency
RECT
(OFDM)
RRC
(GFDM)
time domain frequency domain
Possible matched filter pulse shapes:
GFDM System ...
10 June 2010 Rohit Datta 44
...
fading
channel
up-
conversion
tail biting
transmit filter
symbol
mapping
...
binary
data
cyclic prefixtail biting
receive filter
down-
conversiondetection
remove
cyclic prefixequalization
binary
data1
HCP -CP ..
.
...
ma
gn
itu
de
[d
B]
0 10 20 30 40 50 60 -80
-70
-60
-50
-40
-30
-20
-10
0
10
20
normalized frequency
PSD
OFDM
primary
OFDM
secondary
0 10 20 30 40 50 60 -80
-70
-60
-50
-40
-30
-20
-10
0
10
20
normalized frequency
ma
gn
itu
de
[d
B]
PSD
OFDM
primary
GFDM
secondary
GFDM system can adjust out of band interferences, with suitable pulse shapping.
Vodafone Chair Mobile Communications Systems, Prof. Dr.-Ing. G. Fettweis
Multi-Processors on Chip
Data Locality
1,0 Ni ikik xay
input output
Data Locality
memory
×
+
ia k ix
1,0 Ni ikik xay
Wireless Communications:
Processing Under Energy Constraint
Parallel Processing example: FIR filter
oddi iki
eveni ikik
xa
xay
,
,
Task parallelism within equation
4 memory reads per cycle
Parallel vector processing of task
2 memory reads per cycle
i ikik
i ikik
xay
xay
11
1z
Vector processing reduces memory I/O-bandwidth: Low-Power
1,0 Ni ikik xay
Data Locality
memory
×
+
×
+
memory
×
+
×
+
Data Locality
TU Dresden, 10/9/2010 Gerhard Fettweis Slide 50
Demonstrating Low Power & Die-Size
Source: T. Noll, RWTH Aachen
SAMIRA Core Measurement
8-SIMD, 32-bit float
130nm UMC
Tomahawk Cores 4-SIMD, 16-bit fixed-pt, 130nm
GP-CPU
Conventional DSP
FPGA
ASIC
Physically optimized
NXP OnDSP Measurement
8-SIMD, 16-bit fixed
90nm
Sandblaster Cores 4-SIMD, 16-bit fixed-pt, 90nm
full-custom
Data Locality
TU Dresden, 10/9/2010 Gerhard Fettweis Slide 51
Heterogeneous MP-SoC: Task Scheduling
DSP
LMem
DSP
LMem
DSP
LMem
DSP
LMem
FLB
LMem
FLB
LMem
GPP
Shared
Memory
FLB
LMem
Task Task
Task Task
Task
Task
Task
Task
Task
Task
Task
Task
Task
Task Task
Task
Software
AD/DA
LMem
Scheduler
Router
Scheduler
Router
DSP
LMem
DSP
LMem
DSP
LMem
DSP
LMem
Scheduler
Router
GPP
GPP
GPP
Data Locality
TU Dresden, 10/9/2010 Gerhard Fettweis Slide 52
Task:
Composed of Atomic Tasks
Data Flow Program
Contains control flow and “atomic” task calls
Task
Consumes and produces chunks of data
Contains a terminating program that operates on input data
Data locality exploited by operating on local copy of data
Resulting data committed to global memory after execution of task
Task A
Task B
Task C
Task D
Glo
bal m
em
ory
Task execution
Data transfer to core (Fetch)
Data transfer from core (put)
LMEM
TU Dresden, 10/9/2010 Slide 53
Software Scheduling
Design Time versus Runtime
Operating System
SoC
Program
Thread Thread
t1 t2
t3
t4 t5
t6
t1 t2
t3 t4
t6
t5
CP
PE1 PE2 PE3
PE4 PE5 PE6
PE7 PE8 PE9
Operating System
SoC
Program
Thread Thread
t1 t2
t3
t4 t5
t6
t1 t2
t3 t4
t6
t5
CP
PE1 PE2 PE3
PE4 PE5 PE6
PE7 PE8 PE9
Co
reM
an
ag
er
Data Locality
TU Dresden Gerhard Fettweis Slide 54
LTE FDD/ TDD/ WiMAX Single-Chip SDR:
Tomahawk - Die Photo
STA
LDPC Decoder/
Deblocker
ASIP
STA Vector DSP
2x STA SIOUX
Core
Manager
3x DDR Controller
10 mm
10 mm
2x Xtensa
DC212GP
Scratchpad
PLL
PLL
PLL
Silicon on
Jan 18 2008
In 45 nm CMOS:
Complete LTE BB
< 20mm2
< 200mW
130nm UMC
57M transistors
TU Dresden Gerhard Fettweis Slide 55
Heterogeneous MP-SoC: Task Scheduling
DSP
LMem
DSP
LMem
DSP
LMem
DSP
LMem
FLB
LMem
FLB
LMem
GPP
Shared
Memory
FLB
LMem
Hardware
Task Task
Task Task
Task
Task
Task
Task
Task
Task
Task
Task
Task
Task Task
Task
Software
AD/DA
LMem
Scheduler
Router
Scheduler
Router
DSP
LMem
DSP
LMem
DSP
LMem
DSP
LMem
Scheduler
Router
GPP
GPP
GPP
Data Locality
Embedded parallel computing:
how to architect hardware and software?
Silicon chip design cost:
how to architect the chips of the future as deep
sub-micron has driven design costs above the
$100M boundary?
TU Dresden Gerhard Fettweis Slide 56
Vodafone Chair Mobile Communications Systems, Prof. Dr.-Ing. G. Fettweis
Conclusions
Sensors
WAN for sensors
Energy autonomy
RF
Dirty RF
Flexible RF
Broadband
Sheer data rate
3D rendered
apps
Computing
HW/SW architect.
Templates beyond today‟s
Networks of hierarchy
Energy metrics & coverage
Self-X
TU Dresden Gerhard Fettweis Slide 58
No modulation buys us
orthogonality anymore !!!
Time to rethink for 5G !?!
A Piece of The Bigger Picture 3dim PHY / 3dim Traffic / 3dim design space
TU Dresden Gerhard Fettweis Slide 59
space
time
frequency
capacity
energy
fair coverage
today’s
traffic mix
m2m
rendered
multimedia
Vodafone Chair Mobile Communications Systems, Prof. Dr.-Ing. G. Fettweis
Thanks to UBC for inviting me as Green Visiting Professor !
Thanks to Vodafone for 16 years of continued support !
www.vodafone-chair.com