LTE Advanced Description

LTE Advanced Description

All Rights Reserved © Alcatel-Lucent 20092 | Advanced LTE Description | June 2009

Contents

Summary on single site studiesCOMP Classification Positioning of COMP technologies Requirements on the X2

Enabling technologies Efficient feedback

Feedback compression Discrete feedback: Best companion

Receivers: SIC and IRC Pilot design and concepts

Summary and way forward Outlook: ARTIST-4G project proposal


Downlink study for 6 sectors versus 3 sctors Same number of users per site, same number of antenna elements Spectral efficiency increased by 50 to 60 % for 6 sectors 6 sectors were evaluated for 30,35,40 deg. Of hor. Beam width

Cell edge data rates decreases with beam width Cell edge data rate stays equal in best case

Downlink Study of Closed loop TX diversity vs. Open loop SFBC At low velocities

CL wins : Spectral efficiency is increased by 20% and cell edge thpt. by 40% At high velocities 30 km/h to 250 km/h

CL wins for the cell edge data rate ( it is equal or better ) OL wins for the spectral efficiency (slight gain at 30km/h, 50% gain at 250 km/h)

Recent single site studies on R8


LTE Advanced


Targets for LTE, LTE advancedLTE

(3GPP,)

LTE advanced

Peak data rate DL 100Mb/s @20MHz

(2RX)

1Gb/s (low mobility)

100 Mb/s (high mobility)

Peak data rate UL 50 Mb/s(1TX)

500 Mb/s

Peak spectrum efficiency DL

5 bit/s/Hz

(2RX)

30 bit/s/Hz

(8x8 ant)

Peak spectrum efficiency UL

2.5 bit/s/Hz

(2RX)

15 bit/s/Hz

(4x4 ant)

Average spectrum efficiency DL

3 ..4 times R6 1.69 bits/Hz

(2x2 ant)

3.7 bit/s/Hz (4x4 ant)

Average spectrum efficiency UL

2 ..3 times R60.028 bits/Hz

(1x2 ant)

2 bit/s/Hz

(2x4 ant)

Cell edge spectrum efficiency DL

2 .. 3 times R6 0.05 bits/Hz

(1x2 ant)


Cell edge spectrum efficiency UL

2 .. 3 times R60.028 bits/Hz

(1x2 ant)


LTE-A:

High spectrum flexibility, e.g. spectrum allocations up to 100 MHz

Service: 60 VoIP channels / MHz

Backward compatibility to LTE

Performance targets in 3GPP TR 36.913 v8.0.0 “Requirements for Further Advancements for E-UTRA (LTE-Advanced)”, June 2008


How to reach LTE-A targets?Following the Cellular paradigm

Increase the re-use of spectrum per km² (using smaller cells) => Move the traffic from the air into the fixed part

But there are economic constraints: Base station sites are expensive ( Mainly OPEX for acquisition, backhaul, maintenance, energy )

The approach does not improve the situation at cell edge

Alternative: increase sectorization


Options within one cell

Keep the number of antennas: Improve the bits / Hz for single link

Improve receivers, channel feedback, link adaptation, scheduling … Introduce layered transmission (RDMA)

Increase the number of antennas Improve the link budget ( by Beamforming )Increase the re-use of spectrum per km² (using MU MIMO / SDMA)Take contribution from SU MIMO operation (for UEs in good radio conditions)


Multi-cell situation: two approaches

Reduce / Avoid inter-cell interference (using space and frequency) Interference co-ordination Coordinated scheduling Use beamforming

Use signals from neighbor cell constructively Network MIMO

Interference avoidance Network MIMO


Candidate Technologies for Performance Improvements with LTE Advanced

EnablingTechnology

CollaborativeMIMO/

NetworkMIMO

LowerBackhauling

Latency

AdditionalStandardized

Measurements

LargerBandwidths

Beamforming/Spatial

ComponentICIC

Time/Freq.Dynamic

ICIC

DynamicSpectrum

Access

Coordinated multi-point transmission and reception

Candidate Technology for LTE Advanced

AcceleratedProcedures

(Access, HO)

SONManagement

Mobility enhancements

Support of wider bandwidth up to 100 MHz


KPIs used for assessments

Performance: Net cell throughput and cell-edge rate (5%)

Reference signal and signalling overhead to be subtracted(e.g., dedicated versus common RS)

CSI overhead (Quantized PMI, Channel magnitude and Phase) to be subtracted

Cost: Backhaul requirements (bit rate, latency) Computational complexity Preferred antenna configuration; No Sectors: 3,6,12; number of TX and

RX Synchronization and calibration requirements

Other aspects Sensitivity to practical impairments Scalability of the solution ( can it be extended over the network?) Central or distributed processing … not a KPI


Selection process for COMP methods

1 Fundamental phase Prioritize MIMO and COMP options, eliminate options as far as possible according to their complexity and performance. using basic considerations and fundamental calculations for performance assessment.

2 Real-world asessment phase First analysis to rank the options

Include real-world aspects like: amount of pilot and signalling overhead required Influence of real-world receivers: channel estimation errors .. Cost of opportunity : e.g. HARQ gain is reduced or HARQ not useable, constraints for backhauling and antenna deployments.

System simulations to focus on winning options Use NGMN compliant system simulation Use results for decisions and standardization inputs ( Work Item )

3 Standardisation phase. Include status in standardization Re-adjust models according to decisions already taken in 3GPP. Further assess solutions in comparison to other options in 3GPP.


COMP/ Network MIMO


spectra

l effi

cien

cy

Classification of COMP schemes / 3GPP for LTE

High gains- Backhaul

requirements!

Downlink Uplink

Joint processingcoherent combining

Joint processingnon-coherent

combining

Coordinated scheduling

(per sector or per beam)

Joint processingcoherent

superposition(Radio samples or

softbits)


High potential gains

-pilot overhead- Backhaul

requirements

Collaborative MIMO :

Lower potential gains than coherent

Efficient interference reduction

back

hau

l req

uire

men

ts (b

an

dw

idth

, late

nm

cy)


Network MIMO: Potential performance gains under ideal conditions

Uplink:Users to Bases

Downlink:Bases to Users

(1,1) (2,2) (4,4) (1,1) (2,2) (4,4)

Thr

ough

put (

bps/

Hz/

base

)

5

0

10

15

20

25

30

Conventional: SU MIMO, no coordination

Network MIMO

(Base antennas, terminal antennas)

Factorof 5

[KFV06] [V07]

Factorof 3

Hexagonal cellular network with coordination up to 4 rings of cells.

Uplink: MMSE detection among coordinating bases

Downlink: DPC transmission among coordinating bases

Eliminate 10% of users with lowest SINR; maximize minimum rate supported by remaining users. Equal rate criteria for a

given channel realization. Ideal assumptions: perfect

channel knowledge, synchronization, etc.


DL Network MIMO (coherent case)– What is needed to make it real?

Multiple TX sites increase delay

spread - long CP costs 20%

of resources

traffic

CSI

backhaul

CSI:Amount of DL channels

to be estimated explodes for increasing number of coordinated

cells.

CSI: data to be compressed and

transferred with low delay and a minimum of

uplink resources.

Requires large number of DL pilots (orthogonal

in code, time and/or freq)

LTE R8:17% DL resources for 4

orthogonal pilotsSynchronization between eNB required

Strong increase of backhaul traffic

Large gains for large coordinated areas (e.g. 4 rings/ 182 sectors)

Requires dedicated pilots per stream


Enabler for DL Network MIMO Robust multi-cell channel estimation* (HHI)

cell planning

• Common pilots are scrambled with cell-specific sequence in time domain (low mobility).

• Sequence design exploits partical correlation (e.g. Hadamard, DFT).• Closer cells are identified by sequences which can be orthogonalized already

using shorter correlation window, distant cells by sequences which need a larger window.

* Volker Jungnickel et al, “” Multi-Cell Channel Estimation using Virtual Pilots”, in IEEE 67th Vehicular Technology Conference, VTC2008-Spring, Singapore, May 2008.


Enabler for DL Network MIMO Calibration of distant antennas

Basic principle: Distribute optically RF carrier to neighboring sites Required phase jitter: 50 ps Jitter due to polarization mode dispersion: 2 ps for 20 km link

Case 2 : (C)WDM multiplex for digital & clock Central Unit

Digital E/Otransmitter

Reference oscillator

Analog E/Otransmitter

Digital baseband

Remote Radio Head

D/Adigital in

RFmodulation

WDM linkclock+radio data

System is static for ~ 1s


Summary on DL Network MIMO

Situation today Highest theoretical gains among all DL COMP methods

For Downlink FDD: Theoretical gains of the method are consumed by impairments

e.g. Uplink is consumed by Channel state signalling Pilots consume large part of Downlink

We need further progress on enablers Synchronization and antenna calibration Principles are established Pilots First proposals Efficient channel feedback ideas for single sector, to be extended

We think that coherent COMP will not be available in the next 3 years!


Coordinated scheduling – simple approach

Coordination area around every cell is defined a coordination area

with the cell in the center a cell coordinates only with cells within its coordination area

Establish data basis for interference estimation correlation matrix describing every channel between every BS antenna and

UE antenna within a given coordination area around each cell– requires knowledge of inter-cell channels

Coordinated uplink sounding– BS measures all inter-cell channels

within its coordination area

Optimize scheduling & beamforming/Tx powerper UE within coordination area

1. select UEs with lowest mutual interferenceto be scheduled on same resource and

2. balance their SINR/throughput by adapting Tx power/beam pattern/direction

ca1 cb1

BSa BSb

ca2 cb2

UE1

cell 1

cell 2

UE2


Distributed computation by prioritization of cells cell includes results from higher priority cells within its

coordination area as constraints to own optimization(B includes 3xA, C includes 3xB & 3xA)

– prevents conflicting results inthe overlap of coordination areas

– fairness among cells achieved bypermuting priority assignments over time

Optimize for a number of sub-frames, not for single allocations in order to reduce communicationload

Our View Stable algorithm This approach with scheduling coordination has

been pursued for WiMAX 802.16m

mult

iple

su

b-f

ram

es

schedule

dacc

ord

ing t

o c

om

pu

tati

on

3A 2B 1C

2C 3B 4A

4B 5A 3C

0A 4C 5B

5C 1A 0B

1B 0C 2A

AB

CA

B

CA

B

CB

CA

B

CA

B

CA

AB

CA

B

CA

B

C

1-tier coordination

tim

e

0

1

2

3

4

5

Coordinated scheduling – simple approach


0 0.5 1 1.5 2 2.5 30

100

200

300

400

500

600

700

800

900

1000

spectral efficiency [bit/s/Hz]

5-pe

rcen

tile

thro

ughp

ut [

kbps

]

1x2 SIMO

2x2 SU-MIMO (TxDiv + PARC)

4x2 Grid-of-fixed-beams4x2 SDMA (GoFB)

4x2 SDMA (GoFB) + intra-site Coop

• 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

Coordinated scheduling Combination with SDMA using 4x2 Grid-of-fixed beams

+ Intra-Node B BF co-ordination

+ Intra-Node B BF co-ordination

+ Inter-Node BCo-ordination

+ Inter-Node BCo-ordination

SDMA w/o BF co-ordination

SDMA w/o BF co-ordination


COMP in Uplink / Macro diversity

Simpler situation than DL: single point of transmissionForwarding of radio samples Forwarding of softbits ( soft combining )Forwarding of data (selection combining)

Inter-site

COMP

Reception in 2 sectors

Single sector


UL COMP Reception over 2 sectors (result from Uni Stuttgart)

LMMSE LMMSE-SIC0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Ave

rage

Spe

ctra

l Eff

icie

ncy

[bit/

s/H

z/se

ctor

]

Joint detection onlyJoint detection & link adaptationNo cooperation

Results: Joint Link Adaptation (II)

11

March 4, 2009

Case II: Proportional-Fair scheduler, with HARQ (baseline)

Spectral Efficiency

+19%+26%+17%+21%

LMMSE LMMSE-SIC0

50

100

150

200

250

300

350

400

5 %

Cel

l Edg

e T

hrou

ghpu

t [k

bps]

Joint detection onlyJoint detection & link adaptationNo cooperation

Cell-Edge Throughput

+21%+22% +24%+30%

As before, additional gains relatively small compared to gains due to joint detectionalone and gains generally significantly smaller than for (dumb) RR scheduling


COMP in Uplink - (Distributed RRH case)

Simulation results for 1x4 antenna configuration (QPSK 1/3, near-uncorrelated antennas)

Up to 4.5 dB SNR gain for frequency-selective channel

Normalized Throughput

0

10

20

30

40

50

60

70

80

90

100

-8,0 -3,0 2,0 7,0

actual avg. SNR [dB]

Th

rou

gh

pu

t (k

bp

s)

AWGN 4Rx

AWGN 2 Rx

PedB3 4 Rx

PedB3 2 Rx

Throughput gainespecially on cell edge

Clear improvement of Uplink throughput for UEs at cell edge

Improved fairness Candidate for LTE advanced

Remote

Radio Head

Smart NodeBCentral Unit

SM fibre, 1,25 Gb/s

Remote

Radio Head

Block Error Rate (1st transmission, with 95% confidence interval)

0,1%

1,0%

10,0%

100,0%

-8,0 -3,0 2,0 7,0

actual avg. SNR [dB]

BL

ER

Princple(Berlin set-up)


Additional BW4

5 Mbps (control traffic, in & out)Latency: 10ms for decoupled (lower gains!))< 1 ms*/arbitrary (scheduling and coordination coupled)

Additional BW2

30 Mbps (CSI, in)

45 Mbps (user data, out)3

Latency < 3 ms*

spectra

l effi

cien

cy

Classification of COMP schemes and backhaul requirements

Downlink Uplink

Joint processingcoherent combining

Joint processingnon-coherent

combining


Joint processingcoherent

superposition


back

hau

l req

uire

men

ts (b

an

dw

idth

)

§ calculation assumptions: 10 MHz system BW, 4 antennas per cell, 20 UEs per cell+ time and frequency domain IQ samples (in this order)* latency is site to site one way, includes eNB processing time!

X2 Backhaul§

Additional BW incoming2

coherent: 3.7 or 2 Gbps (IQ samples+)non-coherent: 250 Mbps (softbits) 30 Mbps (user data)

Latency < 3 ms*

X2 Backhaul§

Additional BW at coordinator1

8- 14 Gbps (samples or CSI, in)11 or 6 Gbps (IQ

samples+, out)Latency < 1 ms*

1 3 sites with 3 cells each, coordinator co-located at one site2 2 slave cells3 30 % of UEs in CoMP mode4 7 cell coordination area, 3-step coordination cycle

same as downlink

High gains- pilot overhead (-TDD only)

- backhaul requirements!

High gains- backhaul requirements

Efficientinterference reduction

low latency helps!

Non-coherent combining:lower gains than coherent

Moderate backhaul


Enabling technologies - Efficient feedback

- pre-coding schemes- IRC and SIC receivers


Example for Enablers: Feedback compression

Challenge for FDD systems: Transfer channel information from the UE to the Base Station

MIMO channel state information (CSI) Frequency-selective channel quality information (CQI)

with strongly limited feedback signalling bandwidth Approach: Source coding for CSI/CQI

BS MS

MIMO channel

Channel estimation

Downlink data transmission and pilots

Uplink feedback of compressed channel state information

Feedback compression


Common pilots

CTFCE IDF

T CIRE{ }t,ant PDP Finger

sdelays τf,

powers pf

Quantized long-term feedback

Quantized short-term feedback

Reduction on fingers

Uplink CSI feedback signalling

Collection at Rx and synthesis to CIRDFT

CTF

Principle: based on time/frequency domain

transformations separating of short-term and long-term

feedback (using a RAKE-receiver-like “finger”-approach)

Focus on CSI compression: Complex-valued coefficients per Tx-Rx antenna channel

LTE-A T-Docs

“OFDM channel feedback compression based on a RAKE approach”

Example for Enablers: Feedback compression


Efficient feedback concepts:The ‘best /worst companion’ concept

UEs measure channel and report best beam index (preferred rank 1 PMI) for their serving cell

UEs further measure channels from a set of dominant interfering cells

UEs report best-companion (BCI) or worst-companion PMIs (WCI) for the set of interfering cells

UEs report CQI for the case that the best-companion PMIs are not used and for the case that the best companion PMIs are used (Delta-CQI).

FDD DL Reference symbols

PMI, CQI + BCI/WCI, Delta CQI

for BS 2, BS3

User 1

Backhaul connection

BS 1

BS 2

BS 3 Codebook

… … w 2 2 w 1 1

Weight vector

Index

… … w 2 2 w 1 1

Weight vector

Index

Additional low-rate-feedback enabling COMP beam coordination based on coordinated scheduling


MU-MIMO with and without Best Companion signaling(multi-link simulation result)

0%

20%

40%

60%

80%

100%

120%

140%

Urban macro Urban micro

min. Beam dist.

Best Comp.

Relative gain in total average sector throughput Simulation assumptions (see appendix A.5)Using a subset of the LTE-codebookDL FDD [email protected] MHz4 Tx linear array, 2 RxMU-MIMO pairing of 2 usersIntra-cell interference fully modeledInter-cell interference taken from Geometry of 19*3 sector system with 500m ISD and statistically modeledSCME channel

-40 -30 -20 -10 0 10 20 30 400

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1subband SINR cdf

SINR dB

cum

ulat

ive

prob

abili

ty

BCI

no BCI

-40 -30 -20 -10 0 10 20 30 400

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1subband SINR cdf

SINR dB

cum

ulat

ive

prob

abili

ty

BCI

no BCI

SINR per subband at decoder input

Urban macro Urban micro

Conclusion:With very small additional feedback signaling overhead (0.8 kbit/s), throughput gains in the order of 20%


Optimized pre-coding Closed Loop MIMO, optimized codebooks

Comparison of different Antenna Systems and Pre-coding Matrices,500m ISD

0

100

200

300

400

500

600

1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0Spectral Efficiency [bit/s/Hz/sector]

Ce

ll B

ord

er

Th

rou

gh

pu

t [k

bit

/s]

500 1x1 Single Antenna TX

500 1x2 Single Antenna TX

500 2x2 CL TX Div & PSRC (36.211)

500 4x2 CL TX Div & PSRC (36.211)

500 4x2 Directional CL TX Div & PARC, 4 Beams, 4 Wts

500 4x2 Directional CL TX Div & PARC, 16 Beam, 8 Wts

1x1

1x2

2x2

4x2

optimized codebook

36.211 codebook

Gains with optimum codebook

We go for downloadable codebooks in 3GPP:pre-coding will be adaptable per cell. Low impact on

UE.


Summary :

We aim at a smooth introduction of LTE-A into the field System should adapt to available spectrum System should extend on LTE (antenna) deployment System should allow to select optimum COMP scheme

depending on available backhauling capabilities(bandwidth and latency )

Depending on existing antenna sites and configurations


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LTE Advanced Description