Transcript Slide 1

3GPP TSG RAN IMT Advanced Workshop
Shenzhen, China, April 7-8, 2008
LTE - IMT advanced Candidate Technologies
REV-080045
Content
New technologies for PHY Multi-antenna Processing & Scheduler
SON Realization and Evolution
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LTE - IMT advanced New Technologies for PHY Multi-antenna
Processing
Targets
LTE/WiMAX performance results
 DL Cell edge rate should be improved
 UL Cell average/edge rate should be improved
ALU preferred Requirements ( towards IMT Advanced)
• DL peak spectral efficiency
---> 10 b/s/Hz/sector
• UL peak spectral efficiency
----> 5 b/s/Hz/sector
• DL average spectral efficiency - 3-4 b/s/Hz/sector
• UL average spectral efficiency - 1.5-2 b/s/Hz/sector
• DL cell edge spectral efficiency ---> 0.12 b/s/Hz/sector
• UL cell edge spectral efficiency ---> 0.06 b/s/Hz/sector
– Sector: 120°
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Outlook on candidate technologies
 Novel MU-MIMO algorithms (PHY, MAC)
• Adaptive switching between single-/multi-user/multi-site modes
• Combination of spatial multiplexing and beamforming
 Network MIMO concepts and algorithms (PHY, MAC) for FDD/TDD
• Coherent/non-coherent solutions
• Centralized (e.g.RRH) and distributed (collaboration among Node Bs) solutions
 Dynamic ICIC concepts
• Dynamic exchanges of resource blocks ultiziation among Node Bs
• Beam Coordination between cells in collaboration
 Schedulers for exploitation of the advanced MIMO and multi-site features
• Cross-layer optimal resource allocation with advanced MU-MIMO/IFCO features
• Multi-site scheduler with exploitation of the multi-site features
• Interworking and optimization between UL/DL scheduler
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MIMO recommendations for LTE advanced
FDD
In General
Overall MIMO recommendations for LTE advanced (FDD):
 Place greatest emphasis on MU-MIMO, since it has the most attractive
performance-complexity tradeoff
 SU-MIMO should be pursued to deliver high peak user rates for IMT-Adv
requirements
 Increase of DL cell edge rates by
• Multi-site Collaborative MIMO (constructive data instead of interference)
• Complemented by a combination
– Spatial Interference Coordination (beam coordination)
– Fractional frequency/time reuse Interference Coordination
 Further gains in spectral efficiency are desired on uplink,
• network MIMO with coherently coordinated bases.
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MIMO Configurations
MIMO
Multiple bases
(Network MIMO)
Single base
Co-located
antennas
Distributed
antennas
SU-MIMO,
MU-MIMO
Macroscopic
MIMO
Noncoherent
Coherent
(Magnitude only) (Magnitude/phase)
Collaborative
MIMO
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Coherent
Network
MIMO
Single-site MIMO evolutions (FDD)
DL MU-MIMO based on one or both of the following approaches depending on
antenna configuration, cell size and mobility:
 Fixed-beams (e.g., grid-of-beams approach): Suitable for high mobility, can
operate without dedicated pilots (but would benefit from them), works best
with closely spaced antennas. >= 4*x xpol. Tx-antennas or 1*4 Tx antennas
 User-specific beams (e.g., ZF): Suitable for low mobility, requires dedicated
pilots, but potentially better interference suppression. 2*2 Tx antennas
With closely spaced antennas the same given beam could be applied over the
whole bandwidth, reducing uplink feedback requirements.
Techniques (e.g., hierarchical feedback) to reduce CSI feedback requirements.
 MU-MIMO with user-specific beams should be revisited with the target of
reduced feedback bandwidth.
UL MU-MIMO
 Performance improvement with more than 1 transmit antennas at UE (2-4)
 ensure that signaling supports co-channel transmission by multiple users.
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Evolved MIMO for IMT-Advanced
Extended Precoding
 Combinations of Beamforming and Diversity Transmission
• Beamforming for Multi-User Transmission (SDMA), based on closely spaced
antenna elements (0.5 lambda)
• Diversity for link enhancement and/or spatial multiplexing, based on
cross-polarized antenna elements
 Requires appropriately optimized codebooks for the antenna weights
 For up to 8 antenna elements in a 4x2 X-pol. configuration ( compact
housing)
data stream 1 / 2
MS 1
Base-
station
MIMO channel
data stream 3 MS 2
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Grid of fixed beams system level results based on 3GPP LTE parameters
5
x 10
"spectral efficiency" vs cell border TP
Used here
Next improvement
step
MU-MIMO or SU-MIMO
only
Combination of
SU-MIMO and MU-MIMO
4 Tx, 2 Rx
4 Tx, 4 Rx
5-percentile Throughput in bit/s
7
6
1x2
5
2x2 SU MIMO
(PARC + TxDiv)
4
2x2 GoB
3
4x2 GoB
4x2 SU MIMO
8 Tx (4*2 xpol.), 2 Rx
4x2 GoB + SDMA
8 Tx, 4 Rx
2
MRC receiver
IRC receiver
No sector coord.
Sector coordination
1
0
0
0.5
1
1.5
bit/s/Hz/sector
2
2.5
•
7x3 cells with wrap around, av. 10 users per cell
•
10 MHz BW
•
Control and pilot overhead considered
•
Score based proportional fair scheduling
•
NGNM case 1 parameter set:
• 500m ISD, 3km/h, 20 dB Penetr. loss
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Multiuser MIMO and scheduling for limited feedback
• In Multiuser (MU) MIMO, multiple streams
can be allocated among different users.
Relative Sum throughput gain
Mobile speed (kmph)
• MU eigenmode transmission (MET) uses
channel knowledge at the Tx to form noninterfering user-specific beams.
- Design codebooks whose codewords are
indexed using uplink feedback bits.
- Aggregate B feedback bits per signaling
interval for hierarchical feedback.
3
50
Unitary beamforming
(baseline)
1.0 1.0
MET with
hierarchical feedback
1.4 1.28
K = 20 users per sector, 1 rx ant per user,
B = 4, M = 4 tx ants (10l spacing)
Note: Baseline values normalized to 1 for different velocities
MET block diagram
A
3
B
User
data
streams
User
Beamselection 2 forming
C
D
Channel state feedback
1
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11 Users estimate channel and
feedback quantized state.
22 Base selects users to serve and
calculates beam weights to
maximize sum rate while
addressing fairness.
3
3 Data is transmitted.
Multiple-site MIMO (FDD)
For UL, application of Network MIMO coherently coordinates a
reasonable number of base stations in Rx
• Standardization issues: pilot structure, signaling and X2 Interface
• Issues like backhaul bandwidth and architecture, channel estimation
overhead should be investigated
Coherent network MIMO for DL only in TDD mode.
• Feedback requirements in FDD are likely to be too high.
 Possible FDD Solution for DL as a adaptive combination of:
• Multiple Site Non-coherent Collaborative MIMO to leverage Cell edge
rate
• Single Site MU-MIMO to leverage the cell average rate
• Single site SU-MIMO (+ Tx-Div) to leverage the user rate
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Collaborative/Network MIMO overview
Coordinate transmission and
reception of signals among
multiple bases.
Reduces intercell
interference and improves
cell-edge performance and
overall throughput.
Collaborative MIMO: share
user data and long-term
noncoherent channel
information.
Coherent network MIMO:
share user data and shortterm coherent channel
information.
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Multi-Mode Adaptive MIMO for DL/UL
Use adaptive MIMO to accommodate demand of
higher data rate and wider coverage in next
generation broadband wireless access
• SU MIMO for peak user data rate improvement
• MU MIMO for average data rate enhancement
• Collaborative/Network MIMO for cell edge user
data rate boost
MAC layer
A
uniform
MIMO
platform
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adaptive selection
Cross-layer
design
SU-MIMO
MU-MIMO
Collaborative/
Network
MIMO
Key technologies in Multi-mode Adaptive MIMO
Data + Sync Protocol for DL (Extension of eMBMS protocol); Data + Channel Estimates for UL
MIMO channel
Serving eNB/
per User
Multi-dimension adaptation
•Adaptation strategy
•Multi-variable channel measurement
•Low-rate feedback mechanism
Cellular system
SU-MIMO
SU-MIMO enhancement
•Closed-loop MIMO
•Iterative MIMO receiver
Multicast
Anchor
Collaborative/Network
MIMO
Collaborative/Network
MIMO/Beam Coordination
•Implementation of multi-BS
collaboration with channel
information
MU-MIMO
eNBs have to be synchronized !!!
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MU-MIMO optimization
•MU precoding algorithm
•Trade-off design of scheduler between
complexity and performance
Collaborative MIMO DL: First Simulation Results
Candidate Co-MIMO user decision
Cell deployment
19-cell wrap around
A serving sector surrounded by 6 adjacent
10 users per sector
BS: 4 Tx ant. per sector; MS: 2 Rx antennas
MIMO channel: i.i.d (next step SCM)
Random scheduling (next step proportinal fair like)
Each candidate Co-MIMO user can be served by
N (N=2,3) sectors
A user is candidate for Co-MIMO mode if
PRxs – PRx1 < Co-MIMO threshold
The i-th neighboring sector satisfying
PRxs – PRxi < Co-MIMO threshold
is possible to cooperatively serve the user.
SU-MIMO Co- MIMO
2BS (Co-MIMO Thr.
2dB)
Cell average rate
gain
Cell edge rate gain
Co- MIMO
Co- MIMO
3BS (Co-MIMO Thr. 3BS (Co-MIMO
2dB)
Thr. 4dB)
1
1.25
t.b.a.
1.24
1
1.69
t.b.a
1.67
Code book based feedback
Co-MIMO includes SU-MIMO for users not in collaboration
The multiple resource usage for the collaboration case is taken into account
Without control & Pilot overhead
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Coherent Network MIMO for UL
What is it: Interference reduction via coherent receiver coordination between
multiple bases.
How does it work: Coordinating base stations compute beamforming weights that
maximize SINR (MMSE) for each user.
Potential performance gains of Network MIMO for S-sector coordination
Baseline: single-sector (1Tx 4Rx)
3-sector
(no FFR)
Equal user rates
Unequal user rates
avg. throughput
1.2x
avg.throughput
1.15x
“cell-edge”
1.6x
3-sector
(w/ FFR)
9-sector
(no FFR)
9-sector
(w/ FFR)
1.9x
1.15x
1.25x
1.25x
1.9x
2.7x
3.4x
What’s needed to make it happen:
 Short-term coherent channel knowledge and user data shared among
coordinating bases.
 Backhaul traffic increases by factor S (if all users are In collaboration)
 10% channel knowledge, 90% user data .
 Time and phase-synchronized transmission among coordinating BSs.
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Efficient Channel Quality Feedback for IMT-Advanced
UL feedback channel is a bottleneck for the system performance in an FDD
system. A more efficient feedback scheme provides
 lower resource usage in the uplink and/or
 higher downlink performance through finer granularity of the channel
state information knowledge at the basestation
Compression / sourcecoding of channel state information feedback
 based, e.g., on Wavelets (or other transformations)
• allows variable frequency resolution over the bandwidth
• e.g., UE adaptively provides high resolution of CQI on good
subcarriers / resource blocks, & low resolution on bad resource blocks
Hierarchical Feedback approach
• successive refinement of the quantization with imperfect channel
state information at the Tx.
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Conclusion
 Mix of these technologies allows to meet the IMT adv performance
requirements
 The introduction/improvement of MU-MIMO in DL and UL has a high
potential to boost the cell average rate
Co-MIMO for DL can be applied to FDD system to improve cell edge
performance and average cell capacity
• About 70% improvement for cell edge rate rate compared with SU-MIMO
• 25% improvement in average sector capacity compared with SU-MIMO
 Network MIMO can be applied for the UL (FDD) for the DL/UL(TDD)
• 25% improvement for cell average rate compared to MU-MIMO (further
improvements from single site MU-MIMO)
• Factor 3.4 gain for cell edge rate
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SON for IMT-Advanced Networks:
Self Organizing and Optimizing Networks
Target: Simplified Network Operation
Self-Organizing-Network (SON) technologies
 100% Plug and Play
 Fully decentr. OMC less Network Management (prio for pico/femto layer)
• Self-protection against malicious resource usage (multi-vendor problem)
Multi-RAT operation (intra 3GPP and inter 3GPP)
 Self-configuration / optimization for heterogeneous networks
(3GPP / non 3GPP)
 Generic protocols and measurements
• Generic parameters for
– Handover decisions
– Load balancing
– QoS optimization
Multi-operator networks
• RAN sharing
• Equipment sharing
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Evolution: Phased Approach for Self-x (SON) introduction
 First step (LTE-R8):
• focus mainly on configuration use cases needed for first
deployments
• NEM centric automated configuration and tool based optimisation
• Self-x support functions decentralized in eNB (for configuration
and optimisation use cases)
• tight control and surveillance in OMC
 Second step (beyond LTE R8):
•
•
•
•
decentralised “NEM less” architecture (Pico & Femto Layer)
Complete Self-x functions put to eNB
NM/OSS: performance and alarm management,
NM/OSS: control/tuning of Self-x use cases requiring
– deeper system performance analysis and simulation,
– further standardisation required
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Evolution: Phased Approach for Self-x (SON) introduction
Release 8:
 RAN configuration use cases:
–Add/Remove cell incl. power saving cell (Auto download of initial
radio parameters from OMC)
–Neighbourhood relation configuration and optimisation for LTE
tools for RAN
planning,
configuration
and
optimisation
Release 9 and +:
 RAN optimization use cases
–Cell outage compensation
–LTE handover parameter optimisation
–Interference optimisation for LTE conventional
parameter
configuration
–Load balancing for LTE
 QoS optimization use cases
–Scheduler operation optimisation for LTE
–MIMO Mode Selection Optimisation for LTE
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deployment
new site,
add new cell,
capacity
upgrade
failure cases
self-configuration
performance
optimisation
self-optimisation
Use Case “LTE Handover Parameter Optimisation”
Self-optimisation of initially configured HO parameters
 Optimisation goals
• Minimisation of HO failure rates for intra-LTE
• Avoidance of ping-pong effects
• Enhancement to Multi-RAT HO
 Optimisation approach: Self-optimisation of
HO parameters leading to UE handover request
• HO thresholds, hysteresis, Cell Individual Offset
(CIO),time to Trigger (TTT) after analysis of
handover
 Challenge: user throughput at HO (cell edge)
• Considering QoS at cell edge during handover as
constraints
Signal
strength
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Source cell
HH
HA
Neighbor cell
TTTA
TTTH
Addition
Event
Handover
Event
Time
Use Case “Interference Coordination in UL and DL”
Dynamic or semi-static interference
coordination of radio resources (example:
frequency case)
 Possible optimisation goals
• Cell edge bitrate, improved fairness, load balancing,
increased number of real time users, network
capacity
 Power restriction scheme and power
attenuation
• Indication of upper limit of Tx power per PRB
relative to the rated output power
• Exchange of upper limits of the Tx power per PRB
and resource restrictions between neighbour eNBs
• over X2 interface in intervals of 200 ms to 1 s
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resource grant (Tx pwr on
certain frequency subsets)
resource
request
eNB #2
eNB #1
eNB #3
Use Case “Scheduler optimisation”
Optimisation goals
 self-optimisation of user -, cell-, cell edge throughput & delay according to
operator preferences with weightings and fairness parameter
 self-optimisation of network service availability per QoS label
Optimisation approach for QoS and scheduler configuration parameters
 indication of estimated impact on performance and resulting QoS based on
target derived from Off-line System Simulations
 adaption of scheduler operation to actual traffic mix
 PFMR (Proportional Fair with Minimum Rates) scheduling for tuning of cell
edge bit rate and cost versus fairness proportional to experienced radio
conditions
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Use Case “MIMO Mode Selection Optimisation for LTE”
Optimisation goal of MIMO modes switching
• Optimum service provisioning among attached UEs
• Cell edge data rate and total cell throughput
• Optimisation of network due to insufficient radio condition (SINR) at cell
edge, and service availability per QoS label
Optimisation approach
 Evaluation of mapping of link characteristic (rank, SINR) to MIMO modes
 Configuration of MIMO thresholds and MIMO-mode switching criterions
(diversity, beamforming, spatial multiplexing for SU MIMO and MU MIMO)
supported by targets derived from Off-line System Simulation
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Today
Self-X Architecture Evolution (priority Pico & Femto Layer) (1)
1st step
self-config
OMC/NEM
centric
automated
configuration
Evolution
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Fully
decentralised in
eNBs &
Multivendor NM
Self-X Architecture Evolution (prio for Pico & Femto Layer) (2)
Vision of fully decentralised self-optimisation
 Network management in NM OSS
• network planning
• alarm and performance
monitoring
• high level performance tuning
• open Itf-N
 “NEM less” network management
 Fully autonomous, distributed RAN
optimisation
 Self-x functions in UE and eNB
• measurements, UE location info
• alarms, status reports, KPIs
• distributed self-x algorithms
 Self-x information exchange
via X2
 Multi-vendor interoperability
supported via X2 (to support Pico
& Femto deployments)
NM OSS
Itf-N
X2-Itf
Network Management
 performance
monitoring
 KPIs
 alarms
 high level network
performance tuning
LTE RAN
self-x
eNB
self-x
self-x
RAN selfoptimization
eNB
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eNB
Conclusion
Significantly improved radio network management
by SON:
 emphasis on performance tuning and supervision
 100% plug and play
 continuous, automated radio network optimisation
w.r.t. operator preferences
 innovative techniques for performance optimisation
(scheduler, MIMO modes)
 considerable effort reduction for operators
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