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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