Advances in Coordinated Multi-Cell Multi-User MIMO Systems

Advances in Coordinated Multi-Cell Multi-User MIMO
Systems
Li-Chun Wang
Department of Electrical Engineering
National Chiao Tung University
Hsinchu, Taiwan
http://lichun.cm.nctu.edu.tw
lichun@cc.nctu.edu.tw

Information for presentation slides
• http://www.ieee-globecom.org/2011/private

Outline
1 Introduction to MIMO Antenna
Techniques
2 Single-cell Multi-user MIMO Systems
3 Single-cell Multi-user MIMO Broadcast
Systems
4 Multi-cell Network MIMO systems
5 Updates of Network MIMO Techniques in
3GPP LTE-A Updates
6 Conclusions

Main Story
• Story 1: Scheduling in diversity-based MIMO
• Story 2: Scheduling for multiplexing-based MIMO
• Story 3: A robust multi-user MIMO broadcast
system against huge feedback channel
variations
• Story 4: Channel assignment for multi-cell
Network MIMO
• Story 5: Viewpoints of 3GPP on network MIMO

What happened in 1895?

1895 in Taiwan

1895 in Italy
It is dangerous to put limits on wireless! (1932)

Guglielmo Marconi (1874 ~ 1937)
• In 1895 he began laboratory experiments and
succeeded in sending wireless signals over a
distance of one and a half miles.
• In 1896, he was granted the world's first patent for a
system of wireless telegraphy.
• On 12 December 1901, using a 150 meter (500ft) kitesupported
antenna for reception, the message fwas
received at Signal Hill in Newfoundland of Canada by the
company's new high-power station at Poldhu, which was
transmitted about 3,500 kilometres (2,200 mi).
• Guglielmo Marconi and Ferdinand Braun won the
Nobel Prize in Physics 1909.

Wireless 101
The more things changes, the more remain the same.
(Alphonse Karr, 1808)

What is the fundamental issue for wireless
communications?
Spectrum Efficiency

Some Milestones in Telecommunications
• Fundamental resources (degrees of freedom) in
communication system engineering
– Power (pre-1948),
– Bandwidth (1948, Shannon Capacity)
– complexity (1980, TCM),
– Space (1998, Space-Times Processing )
11

An Open Issue
What is the next possible degree of freedom
that can be exploited for communications
systems?
12

Research Trends in Wireless Networks
• The Past Two Decades: Key Developments at the Link
Level :
– MIMO; MUD; Turbo
• Today: An Increased Focus on Interactions Among Nodes
– Competition
» Cognitive radio
» Information theoretic security
» Game theoretic modeling, analysis & design
– Collaboration
» Network coding
» Cooperative transmission & relaying
» Multi-hop transmission & coalition games
» Collaborative beam-forming
» Collaborative inference
– Competition & Collaboration in Wireless Networks

Hint from this Talk
How can we effectively exploit the degree of
freedom in the user domain to design multiuser
MIMO and multi-cell network MIMO
systems?
14

1. Introduction to MIMO Antenna
Techniques
(Story 1: Scheduling in diversity-based
MIMO)

Multi-Input Multi-Output (MIMO)
Antenna Techniques
• Independent parallel transmit and receive antenna
pair
• MIMO antenna techniques
– boost channel capacity
– enhance link reliability
– reject strong interference
N t
N r
Tx
…
…
Rx
16

Diversity-Multiplexing Tradeoff
• Diversity -> improve link reliability by replicas
a
Tx
a
a
a
a
Rx
a
• Multuplexing -> enhance data rate by multiplexing
ba
Tx
a
b
a+b
b+a
Rx
ba
subchannel interference
canceling is required
17

Scheduling Techniques
• Through periodically selecting the best user to serve, the
system performance is improved by exploiting multiuser
diversity or cooperative diversity.
• Ordered Statistics is the fundamental mathematical
techniques for analyzing the scheduling wireless systems.
user 1
user 1
user 2
user 3
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
18

A Multiuser MIMO Scheduling System
• Perfect SNR estimation and noiseless feedback
• H k =[ h ij
(k)
], each h ij
(k)
is subject to Nakagami fading
• The BS selects the target user with highest effective
SNR
19

The Benefit from Multiuser Scheduling
multiuser diversity or cooperative diversity

Nakagami-m Fading Channel
Which one delivers the highest capacity ?

Impact of Channel Fading on System Capacity
m=1
m=1
Two totally different stories for K=1 and K >1

A Multiuser MIMO Scheduling System
• Perfect SNR estimation and noiseless feedback
• H k =[ h ij
(k)
], each h ij
(k)
is subject to Nakagami fading
• The BS selects the target user with highest effective SNR

A Generic Diversity-Based MIMO System
The maximum diversity gain = N t N r

Some Diversity-Based MIMO Schemes
• Four existing diversity-based MIMO schemes are
considered
– ST/SC
– ST/MRC
– MRT/MRC
– STBC
ST : selective transmission
SC : selective combining
MRT : maximum ratio transmission
MRC : maximum ratio combining
STBC : space time block code
• All of them can deliver full antenna diversity gain

When Diversity-Based MIMO Meets
Multiuser Scheduling
• In general, scheduling is a MAC layer technique to
deliver multiuser diversity gain by exploiting
independent channel fluctuations among users
• By contrast, antenna diversity is a physical layer
approach to offer reliable transmissions with the
major goal of mitigating channel fading
Induced link variation by
diversity-based MIMO
SISO

A Closer Look for their Interplay
Diversity-based
MIMO Link
“peak”
AF
Array gain
AF
SISO

An Analytical Upper Bound
• In [Chen & Wang ’04], we show that
system capacity
selection order
AF gain
array gain
SNR
Nakagami fading parameter

ST/SC over Multiuser Scheduling System
SISO
ST/SC
MS 1
MS 1
virtual users
MS 4
virtual antennas
MS 2
MS 2
MS 5
Tx
Rx-
SC
MS 3
K=3, N t =1, N r =2
MS 3
MS6
K=6, N t =1, N r =1
K=1, N t =1, N r =6

MRT/MRC over Multiuser Scheduling System
SISO
SC
MRC
MRC
SISO

STBC over Multiuser Scheduling System
STBC (N t ,1)
SISO
STBC
channel damping
Fading
channel
m
MS 1
MS 2
=
Fading
channel
4*m
MS 1
MS 2
<
Fading
channel
m
MS 1
MS 2

System Capacity with Joint Antenna
and Multiuser Diversity

Main Point in Story 1
• Multiuser scheduling and the diversity-based MIMO
with the multiuser scheduling system may not be a
good marriage.
• Why?
– User population, i.e. K, has contributed a large
amount of diversity in the whole system
– More important is their intrinsic conflicts – one
prefers variation, while the other creates
tranquility

2. Single-cell Multi-user MIMO
Systems
(Story 2: Scheduling for multiplexing-based
MIMO)

Issue for a Spatial Multiplexing-based MIMO
• Diversity-multiplexing tradeoff in a point-to-point MIMO system
– multiplexing gain comes at the price of diversity gain [Zheng &
Tse ’03]
– may translate into smaller coverage areas if the SM MIMO
scheme is used [Catreux & Greenstein ‘03]
MS
• The coverage is defined as the maximum distance at which
the link suffices for maintaining a required receive SNR γ th with
a probability, say 90%, at least
35

Scheduling Techniques
• Through periodically selecting the best user to serve, the
system performance is improved by exploiting multiuser
diversity or cooperative diversity.
• Ordered Statistics is the fundamental mathematical
techniques for analyzing the scheduling wireless systems.
user 1
user 1
user 2
user 3
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
36

A Multiuser MIMO Scheduling System
• Perfect SNR estimation and noiseless feedback
• H k =[ h ij
(k)
], each h ij
(k)
is subject to Nakagami fading
• The BS selects the target user with highest effective
SNR
37

The SWNSF Scheduling Game
SWNSF: strongest-weakestnormalized-subchannel-first
MS 1
MS 2
MS 3
38

Effect of SWNSF Scheduling on min
SWNSF scheduling enhances the output SNR of the weakest subchannel
λ 3 > λ 2 >λ 1

s
u
i
d
a
r
Effect of SWNSF Scheduling on Coverage
e
g
a
r
e
v
o
c
d
e
z
i
l
a
m
r
o
N
1.8
1.6
1.4
1.2
1
0.8
0.6
N=2
N=3
MS
MIMO + RR Scheduling
MS
MIMO + SWNSF Scheduling
(“soft” coverage depends on K)
0.4
1 5 10 15 20 25 30
Number of users K
40

Effect of SWNSF Scheduling on Other i

Coverage Extension & Capacity Improvement
number of users
number of antennas
43

Main Point in Story 2
Multi-User Scheduling is
A Soft Coverage Extension Technique
"Enhancing Coverage and Capacity for Multiuser MIMO Systems by Utilizing Scheduling, "
IEEE Trans. on Wireless Communications, Vol. 5, No. 5, pp. 1148-1157, May, 2006.

3. Single-cell Multi-user MIMO
Broadcast Systems
Story 3: A robust multi-user MIMO broadcast system
against huge feedback channel variations

MIMO Broadcast Systems
• MIMO antenna in point-to-point scenario can boost
capacity through spatial multiplexing.
• MIMO broadcast systems (point-to-multiple) can transmit
personalized data services to multiple users concurrently.
Traditional point-to-point transmission
Point-to-multiple transmissions in spatial domain:
Multi-User MIMO broadcast systems

Challenges in Transmit Beamforming MU-MIMO
• Types of MU-MIMO Transmit beamforming systems:
– Zero-forcing (ZF) based transmit beamforming
– Block diagonalization (BD) based transmit
beamforming
– Dirty paper coding (DPC)
• Challenges of feedback channel design in transmit
beamforming MU-MIMO broadcast
» Accuracy
» Bandwidth => codebook solution

MIMO Broadcast Systems with
Transmit Beamforming
H 1
Receive
Beamforming
Transmit
Beamforming
W
H K
Receive
Beamforming
H k
M T M R complex-valued entries of channel matrix per user
• Utilize feedback information to calculate the beamforming weight W
for interference cancellation
• ZF-DPC: QR-decomposition based transmit beamforming
• ZF: channel inverse based transmit beamforming

MIMO Broadcast Systems with
Joint Transmit Precoding and Receive Equalizer
Transmit
Precoding
T k
H 1
Receive
Equalizer
Receive
Equalizer
H K
R 1
H k
R K
R k
M T M R complex-valued entries of channel matrix per user
Need return R k to each user!!
• Utilize feedback information to calculate T k and R k for interference
cancellation – Block diagonalization (BD)

A Nonconformist (Why Not) Issue
• Most MU-MIMO systems design transmit beamforming
(precoding) at the transmitter side to cancel the inter-stream
interference among serving users, but suffer from feedback
channel errors and bandwidth issues.
• Q. Can we have a different architecture to realize MU-
MIMO broadcast systems?

Our Answer
• Receive ZF beamforming MIMO broadcast systems combined
with multiuser scheduling
• An alternative MU-MIMO technique to use feedback
channel for a different purpose => user selection.
• Has advantages in terms of robustness against CSI
and feedback link errors [Wang’10]
[Wang’10] Li-Chun Wang and Chu-Jung Yeh, "Scheduling for multiuser MIMO
broadcast systems: transmit or receive beamforming?" IEEE Transactions on
Wireless Communications, vol. 9, no. 9, pp. 2779 – 2791, Sep, 2010.

New Perspective: MIMO Broadcast Systems with
Receive Beamforming
H 1
Receive
Beamforming
Transmit
Beamforming
W 1
[γ 1 , γ 2 , … ]
M T SNR values
H K
Receive
Beamforming
W K
M T real-valued scalars per user (vector feedback)
• Utilize feedback information to select the appropriate users
• The receive ZF MIMO broadcast systems: channel inverse based
receive beamforming

Example of Receive ZF Beamforming (3 x 3)
• Feedback information
– User 1: [1.25, 0.49, 0.50]
– User 2: [0.81, 0.70, 2.25]
– User 3: [0.49, 1.69, 1.21]
Select
Antenna 1 Antenna 2 Antenna 3
User 1 User 3 User 2
(Vector feedback)

Literature Survey on MU-MIMO
Tx BD Rx Note
BD: Block diagonalization
[Caire’03] o x x Concept of ZF-DPC and ZF beamforming
[Jindal’05] [Sharif’07] o x x The gain of broadcast system over point-to-point
transmission
[Vishwanath’03]
[Viswanath’03]
[Weingarten‘06]
[Tu’03][Dimi´c’05]
[Yoo’06][Bayesteh’08]
[Jindal’06][Yoo’07]
[Swannack‘06]
[Caire’10] [Ding’07]
[Zhang’09]
[Spencer’04][Chen’08]
[Shen’06,07]
[Heath’01][Airy’04]
[Chen’07]
Our work in [Wang’10]
[Wang’11a]
[Wang’11b]
o x x Theoretical capacity region of MIMO broadcast
systems
o x x Scheduling algorithms and sum-rate performance
evaluation and analysis
o x x Under limited feedback condition: sum-rate analysis,
codebook and scheduling algorithm design
x o x The concept, theoretical capacity, scheduling
algorithms of BD
x x o Concept. scaling law. sum-rate analysis under equal
power allocation (M R = M T case)
o o
Effects of feedback channel variations (Part I)
Link performance analysis (Part 2)
Effects of channel estimation error for receive ZF
beamforming (Part 3)

Key Analytic Results (M R = M T = M)
• The sum rate of the receive ZF MIMO broadcast
systems with water-filling power allocation
The water-level solution of the equation
– Utilize order statistics technique and long-term
power constraint for water-filling equation to derive
the closed form
Γ R (a,x): upper incomplete gamma function
E i (x): exponential integer function of order i

Example of an MU-MIMO System with Receive ZF
Beamforming (3 x 3)
• Feedback information
– User 1: [1.25, 0.49, 0.50]
– User 2: [0.81, 0.70, 2.25]
– User 3: [0.49, 1.69, 1.21]
Select
Antenna 1 Antenna 2 Antenna 3
User 1 User 3 User 2
(Vector feedback)

Key Analytic Results (M R ¸ M T )
• A general form of the sum rate with water-filling
power allocation
L = M R – M T
Key steps in the closed form
• Order statistics technique
• Long-term power constraint for water-filling
equation
• The integral identity provided in [Alouini’99]
The water-level solution of the equation

Sum Rate Comparison
• Under similar feedback requirement
M T values for feedback!!
Transmit beamforming: M T = {2, 4}; M R =1
Receive beamforming: M T = {2, 4} = M R
RZFSTZFS in sum rate

What happen if feedback information is perturbed
or channel estimation is inaccurate?

Two Issues on channel information accuracy
• Can receive beamforming ZF MIMO broadcast systems
tolerate feedback channel variations?
• Can receive beamforming ZF MIMO broadcast systems
channel estimation errors?
H 1
Receive
Beamforming
Transmit
Beamforming
channel estimation errors
[γ 1 , γ 2 , … ]
H K
Receive
Beamforming
[Wang’07] each entry of
E k is distributed by
feedback channel variations (Part I-1)

Feedback CSI Variations
• Adopt coefficient of variation (CV) to evaluate the impact
of feedback CSI variations
– Perturbed from the noisy estimated information, the
outdated information, and quantization errors, etc.
– For random variable X, CV = σ x / E[X]
• For the feedback CSI x, the perturbed feedback CSI
received at BS is
x’ = x +
– ~ N(0, σ x2 ) and σ x = CVx

Effects of Feedback CSI Variations
on Sum Rate (CV = 0.5)
K = 20, M T = M R = 3
• With perfect CSI feedback
– transmit beamforming is better
than receive beamforming
(1 ~ 1.5 nats/s/Hz)
– ZF-DPC is the best one
• With perturbed CSI feedback
– Large sum rate degradation
for transmit beamforming
(Reduce 19.7% ~ 33.8%)
– Slight sum rate degradation for
receive ZF beamforming
(Reduce 3.5%)

Effects of Feedback CSI Variations
on Sum Rate (CV = 1.5)
K = 20, M T = M R = 3
• Receive ZF beamforming is
more robust to feedback CSI
variations
– 7% sum rate reduction
– Keep the sum rate slope
• Transmit beamforming is more
sensitive to feedback CSI
variations
– 38.8 ~ 61.1% sum rate
reduction
– Interference dominated sum
rate performance

Summary
• The MU-MIMO with receive beamforming combined with
multiuser is still an interesting alternative MU-MIMO subject to
feedback channel errors.
• Imperfect CST at the receiver (CRI-R) will cause serious sum-rate
performance degradation and cause the sum-rate floor.
– Sum rate capacity will no longer linearly increase with
SNR in decibel and be bounded.

Main Point in Story 3
Multi-user broadcast MIMO systems DO NOT
require transmit beamforming precoders.
“Scheduling for Multiuser MIMO Broadcast Systems: Transmit or Receive
Beamforming?” IEEE Trans. on Wireless Communications, Vol. 9, No. 9.
pp. 2779~2791, Sep. 2010.

4. Multi-cell Network MIMO
systems
Story 4: Channel assignment for multi-cell
Network MIMO

What and Why Network MIMO?
• Inter-cell interference: In conventional cellular systems, the
solution is using larger frequency reuse among cells
– Poorer spectral efficiency!!
Inter-stream interference free!!
MIMO broadcast system
CSI exchange through high-speed backbaul
Inter-cell interference free!!
Network MIMO system
The concept is also used in 3GPP LTE-A (CoMP) and IEEE 802.16m (Co-MIMO)

key Issues for Network MIMO
2nd-tier neighboring cells
1st-tier neighboring cells
Conventional omni-cell approach
IGI causes serious signal
quality degradation
14 dB degradation for 7-cell network MIMO
23 dB degradation for 3-cell network MIMO

Literature Survey
FFR?
Sector or
omni-cell?
Network
MIMO?
Note
[Lei’07],
[Fujii’08]
[Chiu’08]
[Shamai’01]
[Karakayali’06]
o sector x SINR improvement and soft
handoff scheme
x omni o The concept of multi-base
station cooperation
[Somekh’06]
[Jing’07]
x
Wyner’s
model
o
Performance analysis based
on Wyner’s circular cell model
[Zhang’09] x omni o BD-based network MIMO
performance
[Boccardi’07]
[Huang’09]
x sector o Effect of network MIMO
+ 3, 6, 12 sector per cell
Our work o sector o Design a 3-cell network MIMO
in FFR tri-sector cell

Basic Fractional Frequency Reuse (FFR) Concept

FFR in Tri-Sector Cell*
(Regular Approach)
Each cell has the same frequency partition!!
*F. Khan, LTE for 4G Mobile Broadband: Air Interface Technologies and Performance, 1st ed. Cambridge University Press, 2009.

Proposed Solution
Coordinated group under
certain frequency band
FFR: fractional frequency reuse

Regular versus Rearranged Frequency Partitions
A cell can simultaneously forms 3 groups with neighboring 6 cells

Benefit of Rearranged Frequency Partition
• Extra 8.5 dB SINR improvement!!
(compared to traditional tri-sector FFR)
10 dB improvement for sectoring FFR
Extra 2.7 dB gain under regular
partition + 3-cell network MIMO
Extra 8.5 dB gain under rearranged
partition + 3-cell network MIMO
90% percentile

Proposed Architecture with 60 o Sectoring

Benefit of Proposed Architecture
over Conventional Approach
• A smaller coordination, 3-cell network MIMO architecture, can
even outperform conventional 7-cell network MIMO system
– 2 dB SINR improvement for 60 o sectoring
– 3 dB SINR improvement for 120 o sectoring
Traditional 7-cell approach
Proposed 3-cell approach
with rearranged partition

Benefit of Multiple Antennas at the Base Station
• Each sector BS equips with t transmit antennas to serve t
users simultaneously.
• 3-cell network coordination with proposed rearranged trisector
frequency partition.
– Total 3t transmit antennas serve 3t user terminals
Almost linear sum rate improvement
as number of antennas t increases
Proposed network MIMO architecture
achieves extra gain over conventional
approach

Summary
• By exploiting geographic cell distribution and frequency
partition, a 3-cell network MIMO can outperform
conventional 7-cell network MIMO with omni-directional cell
• This kind of 3-cell coordinated network MIMO architectures
is particularly useful
– The default number of neighboring cells in the Co-MIMO
transmission is three for IEEE 802.16m
– In LTE-A RAN 1 meeting, the number of coordination in
CoMP is also three
CoMP: coordinated multi-point
CoMIMO: collaborative multiple-input multiple-output

Main Point in Story 4
Network MIMO systems only need 3 cell site
to cooperate
• Li-Chun Wang and Chu-Jung Yeh, “3-Cell network MIMO architectures
with sectorization and fractional frequency reuse,” IEEE Journal in
Selected Area in Communications, Vol. 29, No. 6, pp. 1185~1199,
June, 2011
• Li-Chun Wang and Chu-Jung Yeh, “Antenna architectures for network
MIMO,” Cooperative Cellular Wireless Networks,” Cambridge University
Press, ISBN-13: 9780521767125.
• IEEE C802.16m-09/2280 “Frequency Planning for Inter-Cell
Interference Reduction in 3-Cell Collaborative MIMO Systems”

5. Updates of Network MIMO
Techniques in 3GPP LTE-A
Updates

3GPP LTE/HSPA Evolution
HSPA Series Evolution
(CDMA-based)
• DL: 2x2 MIMO
• DL: 2x2 MIMO+64QAM
or DC+64QAM
• DL: DC+64QAM+MIMO
• UL: DC
• DL: 4-Carrier
LTE Series Evolution
(OFDM-based)
Note: Dates refer to the first completed (full) specifications
81
• DL: 4x4 MIMO in 20MHz
3GPP Concept for
IMT-A (4G)

LTE-A Key Technologies
• Carrier Aggregation
– Aggregate bandwidth up to 100MHz
• Downlink transmission scheme
– Improvements to LTE by using 8x8 MIMO
– Data rates of 100Mb/s with high mobility and 1Gb/s
with low mobility
• Uplink transmission scheme
– Improvements to LTE by using 4x4 MIMO
– Data rates up to 500Mb/s
• Relay Functionality
– Improving cell edge coverage
– More efficient coverage in rural areas
82

Advanced MIMO Techniques in DL
• Extension up to 8-stream transmission
– Increased from 4 streams in Rel-8/9
– Satisfy peak SE requirement (i.e., 30 bps/
Hz)
• Support for enhanced MU-MIMO
– Not more than 4 UEs are co-scheduled
– Not more than 2 layers per UE
83

Advanced MIMO Techniques in UL
• Introduction of SU-MIMO up to 4-stream
transmission
– Rel. 8 LTE does not support SU-MIMO
– Satisfy peak SE requirement (i.e., 15 bps/
Hz)
• Introduction of UL transmit diversity for
PUCCH
– Improved signaling robustness and celledge
performance
84

Enhanced ICIC
• Enhanced ICIC for non-CA based deployments
of heterogeneous networks for LTE
– WI proposed by CMCC in RAN#47 (Mar. 2010)
– Identify and evaluate non-CA based ICIC strategies
of heterogeneous network deployments
– HetNet use cases are priorities as follows:
» Indoor HeNB clusters
» Outdoor Hotzone cells
» Indoor Hotzone scenarios
85

R10 LTE-A Performance Improvement
• Rel-8 LTE vs. IMT-A requirements
– DL: Rel-8 LTE fulfills IMT-A requirements
– UL: Need to double from Rel-8 to satisfy
IMT-A requirement
FDD Rel-8 LTE (1) Rel-10LTE-A (2) IMT-Advanced
Peak spectrum efficiency
[bps/Hz]
DL 16.3 30.6 15
UL 4.3 16.8 6.75
TDD Rel-8 LTE (1) Rel-10LTE-A (2) IMT-Advanced
Peak spectrum efficiency
[bps/Hz]
DL 16 30 15
UL 4 16.1 6.75
Note:
(1)
4x4 MIMO in DL, SIMO in UL
(2)
8x8 MIMO in DL, 4x4 MIMO in UL
Source: 3GPP TR 36.912
86

R11 LTE-A New Technical Issues
• CoMP (Coordinated Multipoint Transmission and Reception)
• MTC (Machine-Type Communications )
• Network Energy Saving
• MODAI (Mobile Data Impact)
87

CoMP (Coordinated Multipoint Transmission and Reception)
• New study Item in Rel-10
– Approved in RAN#47 (Mar. 2010)
• DL CoMP Transmission Scheems
– Joint Processing
» Joint transmission/dynamic cell selection
– Coordinated Scheduling/Beamforming
• Better cell edge performance
– Less co-channel interference / more signal
88

Joint Processing (JP)
The coordinated cells will serve the user together. The base
station will exchange the channel state information (CSI)
and data to each other.
CSI and data exchange between each base station
Coordinated transmission to the
user

Coordinated Beamforming (CB)
If there are two users close to each other but in different cells.
By setting the appropriate beaming weight, we can decrease the
interference from different base stations.
Only CSI exchange between each base station
Only one base station will transmit to the user

Coordinated Scheduling (CS)
Use the coordinated scheduling to decide which user will be
serve. Even without interference nulling, we can avoid the
interference from the other base stations.
Only CSI exchange between each base station
Only one base station will transmit to the appropriate user

Mulit-Cell Co-operations
Joint Processing
for User Data
Shared CSI in
Multi-Sites
Joint
DataTransmission
Interference
Nulling
JP CB CS
☺ X X
☺ ☺ ☺
group 1 1
☺ ☺ X
Beamforming ☺ ☺ ☺
JP : Joint Processing
CB : Coordinated
Beamforming
CS : Coordinated Scheduling

4 Scenarios for CoMP in 3GPP LTE-A
• According to R1-110564 in 3GPP, CoMP (coordinated
multipoint transmission) techniques are applied in the
following 4 scenario
Scenario 1 : Homogeneous network with intra-site
Scenario 3 : Heterogeneous network with low power
RRHs, which create cells with their own cell ID.
Scenario 2 : Homogeneous network with high Tx power
RRHs
Scenario 4 : Heterogeneous network with low power
RRHs, which create cells with the same cell ID as the macro.

NCTU has built a LTE-A System-Level Simulator
• In order to propose new techniques for LTE-A systems, we must
establish the LTE-A system-level simulator and calibrate the
performance metrics in 3GPP TR36.814.
Step(1a) : wideband
SINR
Path-loss, Shadowing, Antenna Pattern
Step(1c) : DL SINR and cell
spectral efficiency
Spatial Channel Model, OFDMA, MRC Antenna
Combining, Adaptive MCS , HARQ
SU/MU-MIMO
Codebook-based Precoder, MMSE receiver, Rank
Adaptation, SU/MU Mode Switching
Hierarchical Network
MIMO
RRH Selection, Cooperation/Non-cooperation
mode switching

MIMO Physical Layer Simulation Flow Chart

NCTU LTE-A System-Level Simulator Interface

Hierarchical Base Station Cooperation with
Single Cell ID (HBSC-S)
• If the RRH nodes share the same cell ID with the corresponding
macro-BS, the RRH nodes can be regarded as the distributed
antennas of the macro-BS. We call the system as Hierarchical
Base Station Cooperation with the Single cell ID (HBSC-S).

Hierarchical Base Station Cooperation with
Multiple Cell IDs (HBSC-M)
• We apply the cooperation technique to mitigate the intra-cell
interference.
• We call the system as Hierarchical Base Station Cooperation with
Multiple cell IDs (HBSC-M).

Wideband SINR for Calibration Step 1a
Our calibration result is consistent with those in
3GPP.

Spectral Efficiency for Calibration Step 1c
Our calibration result is consistent with those in
3GPP.

Spectral Efficiency for MIMO Systems
• All our simulation results are consistent with those in
3GPP.
Spectral
Efficiency
2x2 SU-MIMO
Cell-Average
2x2 SU-MIMO
5% Cell-Edge
4x2 SU-MIMO
Cell Average
4x2 SU-MIMO
5% Cell-Edge
2x2 MU-MIMO
Cell-Average
2x2 MU-MIMO
5% Cell-Edge
Min-value of
others in 3GPP
(bits/s/Hz)
Our Work
(bits/s/Hz)
Max-value of
others in 3GPP
(bits/s/Hz)
2.14 2.37 2.47
0.072 0.079 0.100
2.34 2.52 2.66
0.085 0.089 0.110
2.56 2.62 2.77
0.070 0.086 0.110

Spectral Efficiency for HBSC-M Systems in Multi-Cell
Case
• HBSC-M systems outperform both the HBSC-S systems and the SU-
MIMO system in the spectral efficiency.
• The best position for each RRH node is 0.6R~0.7R.
+12%
+ 20%

Energy Efficiency for HBSC Systems
• When considering the energy efficiency (bits/
Joule), the HBSC-S system with 1 RRH selected
outperforms the HBSC-M system.
+ 9%

Main Point in Story 5
LTE-A system-level simulator supporting
hierarchical network MIMO is important.
3GPP R1-111528, “Simulation Evaluation for CoMP Scenarios 3 and 4”,
May 2011
“Performance Calibration and Simulation Methodology for
3GPP LTE-A Systems.” IEEE VTS APWCS, Aug. 2011
2011/7

Conclusions
• We have discussed multiuser MIMO communications
systems from the perspective of radio resource arrangement
and place emphasis on user scheduling and channel
assignment correctly.
• Main point: If we utilize the degree of freedom in the user
domain cleverly, we can deliver many advantages to MU-
MIMO and network MIMO:
– enhancing coverage and capacity simultaneously;
– Improving robustness to channel variations;
– Reducing the necessity of large number of cooperative
cell site.

Future Research Directions
• Channel state feedback for downlink network MIMO
• Asynchronous uplink network MIMO
• Hierarchical network MIMO

Final remark
Coming together is a beginning.
Keeping together is progress.
Working together is success.
~ Henry Ford

References
• C. J. Chen and Li-Chun Wang, "Enhancing Coverage and Capacity for
Multiuser MIMO Systems by Utilizing Scheduling, ” IEEE Trans. on Wireless
Communications, Vol. 5, No. 5, pp. 1148-1157, May, 2006.
• C. J. Chen and Li-Chun Wang, “A Unified Capacity Analysis for Wireless
Systems with Joint Multiuser Scheduling and Antenna Diversity in
Nakagami Fading Channels,” IEEE Trans. on Communications, vol. 54, No.
3, pp. 469~478, Mar. 2006.
• Li-Chun Wang and Chu-Jung Yeh, “Scheduling for Multiuser MIMO
Broadcast Systems: Transmit or Receive Beamforming?” IEEE Trans. on
Wireless Communications, Vol. 9, No. 9. pp. 2779~2791, Sep. 2010.
• Li-Chun Wang and Chu-Jung Yeh, “3-Cell network MIMO architectures
with sectorization and fractional frequency reuse,” IEEE Journal in
Selected Area in Communications, Vol. 29, No. 6, pp. 1185~1199, June,
2011
• Li-Chun Wang and Chu-Jung Yeh, “Antenna architectures for network
MIMO,” Cooperative Cellular Wireless Networks,” Cambridge University
Press, ISBN-13: 9780521767125.
• IEEE C802.16m-09/2280 “Frequency Planning for Inter-Cell Interference
Reduction in 3-Cell Collaborative MIMO Systems”

References
• Y. J. Liu, T. T. Chiang and Li-Chun Wang “Performance Calibration and
Simulation Methodology for 3GPP LTE-A Systems.” IEEE VTS APWCS, Aug.
2011
• 3GPP R1-111528, “Simulation Evaluation for CoMP Scenarios 3 and 4”,May
2011
• -------------------------------------------------------------------------------------

Reference
[1] TR 36.814 : 3rd Generation Partnership Project ; Technical Specification
Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-
UTRA); Further advancements for E-UTRA physical layer aspects (Release 9)
[2] TR 36.819 : 3rd Generation Partnership Project;Technical Specification
Group Radio Access Network; Coordinated multi-point operation for LTE
physical layer aspects(Release 11)
[3] M.2135 Report ITU-R M.2135-1(12/2009) Guidelines for evaluation of radio
interface technologies for IMT-Advanced M Series Mobile,
radiodetermination, amateur and related satellites services
[4] S. Shamai and B. M. Zaidel, “Enhancing the cellular downlink capacity via
co-processing at the transmitting end,” IEEE Vehicular Technology
Conference Spring, vol. 3, pp. 1745 – 1749, May 2001.
[5] H. Zhang and H. Dai, “Cochannel interference mitigation and
cooperative processing in downlink multicell multiuser MIMO networks,”
EURASIP Journal on Wireless Communications and Networking, vol. 2004, no.
2, pp. 222–235, 2004.

Reference
[6] G. J. Foschini, K. Karakayali, and R. A. Valenzuela, “Coordinating multiple
antenna cellular networks to achieve enormous spectral efficiency,” IEE
Proceedings-Communications, vol. 153, no. 4, pp. 548 – 555, Aug. 2006.
[7] S. Jing, D. N. C. Tse, J. B. Soriaga, J. Hou, J. E. Smee, and R. Padovani,
“Multicell downlink capacity with coordinated processing,” EURASIP
Journal on Wireless Communications and Networking, 2008.
[8] H. Huang, M. Trivellato, A. Hottinen, M. Shafi, P. Smith, and R. Valenzuela,
“Increasing downlink cellular throughput with limited network MIMO
coordination,” IEEE Transactions on Wireless Communications, vol. 8, no.
6, Jun. 2009.
[9] F. Richter, A. J. Fehske, and G. Fettweis, “Energy efficiency aspects of
base station deployment strategies for cellular networks,” IEEE Vehicular
Technology Conference Fall, Sep. 2009.
[10] A. J. Fehske, P. Marsch, and G. P. Fettweis, “Bit per Joule efficiency of
cooperating base stations in cellular networks,” IEEE GLOBECOM
Workshops, Dec. 2010.

Reference
[11] J. Salo, G. Del Galdo, J. Salmi, P. Kyosti, M. Milojevic, D. Laselva, and C.
Schneider. (2005, Jan.) MATLAB implementation of the 3GPP Spatial
Channel Model (3GPP TR 25.996). [Online]. Available: http://www.tkk.fi/
Units/Radio/scm
[12] 3GPP, R1-110867, “CoMP with lower Tx power RRH in heterogeneous
network,” NTT DOCOMO, Feb. 21-25, 2011.
[13] L. Thiele, T. Wirth, M. Schellmann, Y. Hadisusanto, and V. Jungnickel, “MU-
MIMO with localized downlink base station cooperation and downtilted
antennas,” IEEE International Conference on Communications
Workshops, pp. 1–5, 2009.
[14] N. Jindal, “Antenna combining for the MIMO downlink channel,” IEEE
Transactions on Wireless Communications, vol. 7, no. 10, pp. 3834–3844,
Oct. 2008.
[15] S. Fang, G. Wu, Y. Xiao, and S. Q. Li, “Multi-user MIMO linear precoding
with grassmannian codebook,” International Conference on
Communications and Mobile Computing, vol. 1, pp. 250–255, 2009

Reference
[16]. M. Sawahashi, Y. Kishiyama, A. Morimoto, D. Nishikawa, and M. Tanno,
“Coordinated multipoint transmission/ reception techniques for LTE-
Advanced,” IEEE Wireless Commun. Mag., pp. 26-34, June 2010.
[17]. S. A. Jafar, “Interference Alignment: A New Look at Signal Dimensions in
a Communication Network,” Foundations and Trends in Communications
and Information Theory, 2011.