lte - imt advanced - candidate technologies 3gpp tsg ran imt advanced workshoprev-080045 shenzhen,...

LTE - IMT advanced -

Candidate Technologies

3GPP TSG RAN IMT Advanced Workshop REV-080045 Shenzhen, China, April 7-8, 2008

All Rights Reserved © Alcatel-Lucent 2006

Content

New technologies for PHY Multi-antenna Processing & Scheduler

SON Realization and Evolution

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°


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

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.


MIMO Configurations

MIMO

Single base Multiple bases(Network MIMO)

Co-locatedantennas

Distributed antennas

Noncoherent(Magnitude only)

Coherent(Magnitude/phase)

MacroscopicMIMO

CollaborativeMIMO

CoherentNetwork

MIMO

SU-MIMO,MU-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.


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)

Evolved MIMO for IMT-Advanced

MIMO channelBase-

station

data stream 1 / 2

data stream 3

MS 1

MS 2


Grid of fixed beams system level results based on 3GPP LTE parameters

• 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

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

8 Tx (4*2 xpol.), 2 Rx

8 Tx, 4 Rx

MRC receiver IRC receiver

No sector coord. Sector coordination0 0.5 1 1.5 2 2.50

1

2

3

4

5

6

7

x 105 "spectral efficiency" vs cell border TP

bit/s/Hz/sector

5-pe

rcen

tile

Thr

ough

put

in b

it/s

1x2

2x2 SU MIMO(PARC + TxDiv)

2x2 GoB

4x2 GoB

4x2 SU MIMO

4x2 GoB + SDMA


Multiuser MIMO and scheduling for limited feedback

• In Multiuser (MU) MIMO, multiple streams can be allocated among different users.

• MU eigenmode transmission (MET) uses channel knowledge at the Tx to form non-interfering user-specific beams.- Design codebooks whose codewords are

indexed using uplink feedback bits. - Aggregate B feedback bits per signaling

interval for hierarchical feedback.A

C

B

D

Beam-forming

User data

streams

Userselection

Channel state feedback

1 Users estimate channel and feedback quantized state.

2 Base selects users to serve and calculates beam weights to maximize sum rate while addressing fairness.

3 Data is transmitted.

MET block diagram 1

2

3

1

2

3

MET withhierarchical feedback

Relative Sum throughput gain

K = 20 users per sector, 1 rx ant per user,B = 4, M = 4 tx ants (10spacing)

Note: Baseline values normalized to 1

for different velocities

1.28

3 50Mobile speed (kmph)

1.4

Unitary beamforming

(baseline)1.01.0


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


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 short-term coherent channel information.


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

A unifor

m MIMOplatfor

m

A unifor

m MIMOplatfor

m

SU-MIMO

MU-MIMO

Collaborative/Network

MIMO

adapti

ve s

ele

ctio

n

MAC layer

Cross-layer design


Key technologies in Multi-mode Adaptive MIMO

Cellular system

Collaborative/Network MIMO MU-MIMO

SU-MIMO

MIMO channel

SU-MIMO enhancement•Closed-loop MIMO•Iterative MIMO receiver

MU-MIMO optimization•MU precoding algorithm•Trade-off design of scheduler between complexity and performance

Collaborative/Network MIMO/Beam Coordination•Implementation of multi-BS collaboration with channel information

Multi-dimension adaptation•Adaptation strategy•Multi-variable channel measurement•Low-rate feedback mechanism

Multicast Anchor

Serving eNB/per User

Data + Sync Protocol for DL (Extension of eMBMS protocol); Data + Channel Estimates for UL

eNBs have to be synchronized !!!


Collaborative MIMO DL: First Simulation Results

SU-MIMO

Co- MIMO

2BS (Co-MIMO Thr. 2dB)

Co- MIMO


Co- MIMO


Cell average rate gain

1 1.25 t.b.a. 1.24

Cell edge rate gain

1 1.69 t.b.a 1.67

Code book based feedbackCo-MIMO includes SU-MIMO for users not in collaborationThe multiple resource usage for the collaboration case is taken into accountWithout control & Pilot overhead

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 antennasMIMO channel: i.i.d (next step SCM)Random scheduling (next step proportinal fair like)

Candidate Co-MIMO user decision

Each candidate Co-MIMO user can be served by N (N=2,3) sectorsA user is candidate for Co-MIMO mode if PRxs – PRx1 < Co-MIMO thresholdThe i-th neighboring sector satisfying PRxs – PRxi < Co-MIMO thresholdis possible to cooperatively serve the user.


Coherent Network MIMO for ULWhat 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)

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.

Equal user rates

Unequal user rates

avg. throughput 1.2x 1.9x

avg.throughput 1.15x 1.15x 1.25x 1.25x

“cell-edge” 1.6x 1.9x 2.7x 3.4x

3-sector 3-sector 9-sector 9-sector(no FFR) (w/ FFR) (no FFR) (w/ FFR)


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.


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

SON for IMT-Advanced Networks: SON for IMT-Advanced Networks: Self Organizing and Optimizing NetworksSelf 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


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


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

Release 9 and +:

RAN optimization use cases– Cell outage compensation– LTE handover parameter optimisation– Interference optimisation for LTE– Load balancing for LTE

QoS optimization use cases– Scheduler operation optimisation for LTE– MIMO Mode Selection Optimisation for LTE

Evolution: Phased Approach for Self-x (SON)

introduction

deployment new site,

add new cell, capacity upgrade

self-configuration

performance optimisation

self-optimisation

tools for RAN planning,

configuration

and optimisation

conventional parameter

configuration

failure cases


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

TTTA

TTTH

Hand-overEvent

Signalstrength Source cell

Neighbor cell

AdditionEvent

Time

HA

HH


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

resource grant (Tx pwr on certain frequency subsets)

resource request

eNB #2

eNB #3

eNB #1

resource grant (Tx pwr on certain frequency subsets)

resource request

eNB #2eNB #2

eNB #3eNB #3

eNB #1eNB #1


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


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


Self-X Architecture Evolution (priority Pico & Femto Layer) (1)

Tod

ay Fully

decentralised in eNBs &Multivendor

NM

OMC/NEM centric

automated configurati

on

Evolution1st step

self-config


Self-X Architecture Evolution (prio for Pico & Femto Layer) (2)

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)

Vision of fully decentralised self-optimisation

eNB

LTE RAN

Network Management

eNB

eNB

self-x

NM OSS

Itf-NX2-Itf

self-x

self-x

RAN self-optimization

performance

monitoring KPIs alarms

high level network performance tuning


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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