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Downlink link adaptation scheme for IEEE 802.16m

Document Number: C80216m-08/742Date Submitted: 2008-07-07Source: Shanshan Zheng (shanshan.zheng@intel.com ) Intel Corporation Yuval Lomnitz(yuval.lomnitz@intel.com ) Intel Corporation Hongming Zheng(hongming.zheng@intel.com) Intel Corporation Yang-seok Choi (yang-seok.choi@intel.com) Intel CorporationVenue: Call for contributions on project 802.16m SDD: Link Adaptation Scheme Session #56: Denver, USABase Contribution: IEEE C80216m-08/742Purpose: For discussion and adoption by IEEE 802.16m group Notice:

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Introduction

• Link Adaptation is the process of setting transmission parameters according to link state information

• In this contribution, we deal with adaptive modulation and coding selection (AMCS *) in the downlink which is based on channel quality information (CQI)

• Adaptive Modulation and Coding Selection (AMCS)– Key points

– Background

– AMCS procedure

– LA combined with HARQ

• Appendices

*: The term AMCS is used to distinguish this from 802.16 AMC permutation

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Summary of key points

• We recommend BS controlled link adaptation• CQI feedback : virtual MCS

– Represent achievable spectral efficiency of link

– Actual transmitted MCS is a subset of virtual MCS

– 802.16e has 11 MCS levels of actual transmission. The granularity of virtual MCS can be higher for example 16 levels

• Separate mechanism required for submap adaptation

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Background (1) - Concept

• What is MCS– Code rate + modulation (Modulation and coding scheme)

– Tightly coupled with Rank & MIMO mode selection

• AMCS metric (CQI) - attempts to capture the quality of the channel by a single number

• CQI usage– MCS selection

– User selection

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Background (2) – BS/MS controlled

Approaches to link adaptation• MS controlled: MS responsible for selecting MCS for future frames, BS

generally follows recommendation– MS performs adaptation to mobility– Pros: best knowledge of interference & channel information, mobility/dynamics,

and especially of specific modem performance– Cons: MS cannot optimize H-ARQ, only optimizes for specific packet length &

PER. Recommendation is good only till certain time limit. MS is unaware of BS (serving and interfering) behavior which affects the choice

• BS controlled: MS reports a metric (e.g. CINR), BS selects MCS– BS performs adaptation of the measurement to the CQI delay + mobility– Pros: BS has system info (scheduling tradeoffs, QoS, delay) to optimize MCS for

each packet length, delay from CQI and H-ARQ parameters, BS knows system interference behavior; BS adapts MCS to selected precoding/MIMO mode etc

– Cons: CINR metric may not accurately capture different receiver performance at MS; Only coarse adaptation to specific modem performance

Appendix A - Effects on link adaptation known to MS and BS

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Background (3) - Outer loop

• Preliminary knowledge, such as mobility, channel model, and BS specific knowledge is used to generate table mapping from CQI to transmit MCS.

• In fast link adaptation process, BS uses the table to map CQI feedback to MCS decision

• BS tracks the ACKs to measure actual packet error and updates the table. This slow ‘learning’ process is the outer loop.

• The target of BS is to refine table (CQIMCS) that optimize performance (e.g. optimize throughput with given PER for given delay and packet size)

• The outer loop adds robustness to unexpected/unmodeled behavior (e.g. of an interfering BS)

• The outer loop enables to implicitly adapt to CQI delay, estimation errors, interference dynamics etc, etc without explicitly modeling these effects

• Observation: With outer loop, specific metric has small effect on performance

ChannelMeasurement

MCS Design

receive

Slow table Update

CQI

ACK/NACK

TrafficBS

MS

Appendix B - Effect of different metrics with outer loop

Appendix C - Outer loop abstraction

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AMCS proposal

• Target: get the gain from both MS controlled and BS controlled approaches• BS controlled

– MS feeds back CQI– MS is not responsible for mobility/interference dynamics/delay– BS decides actual transmitted modulation and coding scheme

• MS-dependant metric, representing specific MS capabilities– Aid user selection – Faster convergence of outer loop

• We propose to use Virtual MCS metric

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Virtual MCS• Based on channel condition, MS reports its preferred MCS that would have been

used (in given conditions: distributed/localized, precoding, etc), in the measured frame

• This MCS is the MS estimate which MCS would achieve maximum throughput with the maximum FEC block size

• This metric is a practical embodiment of “mutual information” – how much information this channel can carry

• E.g. based on measuring SINR/MI and fitting to modem LLS performance• Advantages compared to PCINR/other metrics

– Represents specific MS conditions for faster outer loop convergence & can be directly used for user selection

– Externally testable for conformance tests

– Not dependent on receiver structure (As opposed to post processing CINR which is based on receiver detection mode (MRC/MMSE/MLD). MLD receiver doesn't have post processing SINR)

– Unified metric for different MIMO modes (As opposed to post processing CINR which has different effective range. See Appendix D Post processing CINR CDF of different MIMO scheme)

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MCS granularity

• Currently EMD defines 11 types of MCS can be used as actual transmission QPSK 1/2 Rep 2, 4, 6, QPSK (1/2; 3/4), 16QAM (1/2; 3/4), 64QAM (1/2; 2/3; 3/4; 5/6)

• Coding scheme of 802.16m not defined yet

• We assume 802.16m is likely to support rate matching and/or continuous code rates therefore MCS scale doesn’t strictly exist as in 802.16e

• Additional MCS granularity can be added in order to improve link adaptation performance (Appendix E Different actual transmission MCS comparison)

• Virtual MCS as CQI for feedback– Granularity of virtual MCS can be higher than the granularity of actual transmitted

MCS– For example, 16 types of MCS can be selected as virtual MCS QPSK 1/2 Rep 2,4,6, QPSK(1/2; 3/5; 2/3; 3/4)

16QAM (1/2; 3/5; 2/3; 3/4; 4/5), 64QAM (3/5; 2/3;3/4; 5/6)

– The higher virtual MCS granularity is for differentiating user quality and achieving more user scheduling gain

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Link adaptation with HARQ

• Several H-ARQ modes can be considered– Fixed MCS, fixed transmission size (regularly assumed)

• In this case LA problem is to choose initial MCS• When MS reports the MCS BS should use higher MCS for H-ARQ (in order to

increase initial PER and gain from cases where channel is better than expected)

– Fixed MCS, fixed set of transmission sizes• E.g. second transmission is always ½ of first (see **)• Similar to first option in terms of link adaptation

– Fully adaptive H-ARQ• BS selects MCS and/or duration of each retransmission• BS can utilize MS CQI reports in order to:

– Learn about future: channel changes during H-ARQ process

– Learn about past: information accumulated in MS receiver from past transmissions

• Requires further study• We believe most of gain can be obtained using burst-duration adaptation and

SE/MCS related CQI

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Miscellaneous issues

• CQI feedback overhead– Localized permutation : CQI/sub-band/user or best-M CQI/user

– Distributed permutation : CQI/user

• Map adaptation– Telescopic maps / submaps are already supported in 802.16 and allow

reduction of map overhead by adapting map of unicast IE-s MCS to user conditions

– However 802.16 doesn’t have suitable link adaptation mechanism

– We propose for 16m to add a slow message-based link adaptation (similar to subheader / REP-RSP) for the maps

– The MS will report preferred map-MCS based on long term PER for each of the map portions

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Proposed Text for SDD

11.x Link adaptation

11.x.1 Downlink Adaptive modulation and coding selection All MS should estimate individual channel quality and feed back CQI to BS. Based on CQI feedback, BS should decide actual transmitted MCS11.x.1.1 Generic AMCS Architecture

The generic AMCS architecture is shown in figure xxx

11.x.1.2 BS controlled

Preliminary knowledge, such as mobility, channel model, and BS specific knowledge is used to generate table of mapping from CQI to transmitted MCS. BS tracks the ACKs to measure actual packet error and updates the table.

ChannelMeasurement

MCS Design

receive

Slow table Update

CQI

ACK/NACK

Traffic

BS

MS

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Proposed Text for SDD (Cont’d)

11.x.1.3 MS-dependant metric Based on channel condition, MS reports its preferred MCS that would have been used (in given conditions: distributed/localized, precoding, etc) in the measured frame. This MCS as virtual MCS is the MS estimate which MCS would achieve maximum throughput with the maximum FEC block size.

11.x.2 Link adaptation with HARQ Several H-ARQ modes can be considered, such as fixed MCS and transmission size (regularly assumed), fixed MCS and set of transmission sizes, fully adaptive H-ARQ.

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Appendices and backup

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Appendix A - Effects on link adaptation known to MS and BS

• The following factors affect MCS selection:

Factor Best known by

Comments

Current channel and interference condition

MS

Channel dynamics MS BS can estimate from CQI dynamics

Interference dynamics BS Related to other BS scheduling/precoding algorithms

Precoding/boosting BS

Resource / power / interference budget

BS E.g. in light load BS decides to use more resource with lower power

MS receiver performance in various scenarios

MS Difference between different MS depends on scenario and specific channel instance (interference / MIMO etc)

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n

k nk

k nkNant

nnn

n

avE

h

NI

SCINR 2

,

2

,

1)(

0)(

)(

)15(

Appendix B - Effect of different metrics with outer loop (1)

k kkk

k kPP

vhhE

hSNR

21*

2

-4 -2 0 2 4 610

-3

10-2

10-1

Sum of SNR (sum(S/N)) over antennas [dB]

PE

R

[2.09] PER per channel v.s. Sum of SNR (sum(S/N)) over antennas

- matched receiver - mismatched receiver

-2 -1 0 1 2 3 4 510

-3

10-2

10-1

Post Processing Physical SNR (mismatched, receiver independent) [dB]

PE

R

[2.08] PER per channel v.s. Post Processing Physical SNR

- matched receiver - mismatched receiver

• These plots show two metrics ability to forecast PER– Pre-processing CINR– Post processing CINR

• Each point is PER in one channel instance (over random data & noise instances)• We can see in this comparison post-processing performs much better

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Appendix B - Effect of different metrics with outer loop (2)

• Following plot shows performance (w/o H-ARQ) in interference scenario

• SIMO, ITU-Ped-B 3Km/h• Perfect outer loop

operation• Single strong interferer• Post-CINR and Pre-CINR

(eq 15 and 15a from CINR RPD)

• Although post and pre-CINR have very different accuracy in forecasting PER (see prev slide), there is a very small difference in link adaptation performance

• With H-ARQ the difference is further decreased

• Conclusion: with outer loop, link adaptation dependence on specific metric is small (as long as there is sufficient correlation with decoding quality)

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Appendix C - Outer loop abstraction• It’s possible to abstract the outer loop by considering very long convergence

times that can be used for optimal adjustment• In simulation, the optimal table values for given metric, delay, transmission

mode, etc can be computed• There is a statistical relation between the CQI and the PER of the packet

which is function of the MCS, channel model, velocity, CQI delay, retransmit delay, measurement error, etc.

• Possible criteria for optimization:– Maximize throughput under PER constraint– “Throughput” and “PER” can be average or in 99% confidence– We optimize average equal-bandwidth PER under average PER constraint

(closed-form solution)

ChannelMeasurement

Metric v.s.MCSTable

instantaneousPacket loss & Throughput

Offline table

optimization

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Appendix D - Post processing CINR CDF of different MIMO scheme

-20 -15 -10 -5 0 5 10 15 20 25 300

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Average post-SINR per RB(dB)

CD

F

Different MIMO Mode Post-SINR CDF at 4 Lamda

SVD based CL R2 MCW 2nd stream

SVD based CL R2 MCW 1st stream

SVD based CL R2 SCW

SVD based CL R1

SM SCW AMC

Long termPUSC R1(144x22)

STBC AMC

STBC PUSC(144x22)

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Improvement SE Gain 11 MCS levels 16 MCS levels

Distributed SU STBC/SM 0 3.98%

Localized SU STBC/SM 0 2.83%

Localized Predefined MU 0 3.06%

Localized Channel Aware MU 0 3.29%

• SLS is followed EMD • Ideal channel estimation

5ms CQI feedback delay

Improvement SE Gain 11 MCS levels Improvement Gain

Localized SU STBC/SM 0 7.41%

Localized Predefined MU 0 4.62%

Without CQI feedback delay

Appendix E – Different actual transmission MCS comparison