1 Downlink link adaptation scheme for IEEE m Document Number: C80216m-08/742 Date Submitted: Source: Shanshan Zheng ) Intel Yuval ) Intel Hongming Intel Yang-seok Choi Intel Corporation Call for contributions on project m SDD: Link Adaptation Scheme Session #56: Denver, USA Base Contribution: IEEE C80216m-08/742 Purpose: For discussion and adoption by IEEE m group Notice: This document does not represent the agreed views of the IEEE Working Group or any of its subgroups. It represents only the views of the participants listed in the Source(s) field above. It is offered as a basis for discussion. It is not binding on the contributor(s), who reserve(s) the right to add, amend or withdraw material contained herein. Release: The contributor grants a free, irrevocable license to the IEEE to incorporate material contained in this contribution, and any modifications thereof, in the creation of an IEEE Standards publication; to copyright in the IEEEs name any IEEE Standards publication even though it may include portions of this contribution; and at the IEEEs sole discretion to permit others to reproduce in whole or in part the resulting IEEE Standards publication. The contributor also acknowledges and accepts that this contribution may be made public by IEEE Patent Policy: The contributor is familiar with the IEEE-SA Patent Policy and Procedures: and. Further information is located at and.

2 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 AMC permutation

3 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

4 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

5 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

6 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 (CQI MCS) 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 Channel Measurement MCS Design receive Slow table Update CQI ACK/NACK Traffic BS MS Appendix B - Effect of different metrics with outer loop Appendix C - Outer loop abstraction

7 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

8 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)Appendix D Post processing CINR CDF of different MIMO scheme

9 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 m not defined yet We assume m is likely to support rate matching and/or continuous code rates therefore MCS scale doesnt strictly exist as in e Additional MCS granularity can be added in order to improve link adaptation performance (Appendix E Different actual transmission MCS comparison)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

10 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

11 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 and allow reduction of map overhead by adapting map of unicast IE-s MCS to user conditions –However doesnt 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

12 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 MCS 11.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. Channel Measurement MCS Design receive Slow table Update CQI ACK/NACK Traffic BS MS

13 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.

14 Appendices and backup

15 Appendix A - Effects on link adaptation known to MS and BS The following factors affect MCS selection: FactorBest known by Comments Current channel and interference condition MS Channel dynamicsMSBS can estimate from CQI dynamics Interference dynamicsBSRelated to other BS scheduling/precoding algorithms Precoding/boostingBS Resource / power / interference budget BSE.g. in light load BS decides to use more resource with lower power MS receiver performance in various scenarios MSDifference between different MS depends on scenario and specific channel instance (interference / MIMO etc)

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

17 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)

18 Appendix C - Outer loop abstraction Its 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) Channel Measurement Metric v.s. MCS Table instantaneous Packet loss & Throughput Offline table optimization

19 Appendix D - Post processing CINR CDF of different MIMO scheme

20 Improvement SE Gain11 MCS levels16 MCS levels Distributed SU STBC/SM03.98% Localized SU STBC/SM02.83% Localized Predefined MU03.06% Localized Channel Aware MU03.29% SLS is followed EMD Ideal channel estimation 5ms CQI feedback delay Improvement SE Gain11 MCS levelsImprovement Gain Localized SU STBC/SM07.41% Localized Predefined MU04.62% Without CQI feedback delay Appendix E – Different actual transmission MCS comparison