LEARNING-BASED ADAPTIVE TRANSMISSION FOR LIMITED FEEDBACK MULTIUSER MIMO-OFDM

Alberto Rico-Alvariño and Robert W. Heath Jr.

Outline •  Introduction

•  System model

•  Link adaptation •  Precoding •  Interference estimation •  MCS selection •  User and mode selection

•  Results

•  Conclusions

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Introduction to Link Adaptation •  Select transmit parameters depending on channel •  Ex: select modulation and coding scheme (MCS)

Transmitter Receiver

time

Rate Power Beamforming …

gain

Channel

Explicit feedback

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MU-MIMO-OFDM in IEEE 802.11ac

Link adaptation

SU/MU? User selection? Mode selection? Beamforming?

MCS?

Limited feedback Sounding

Sounding and channel estimation Channel quantization and limited feedback Parameter selection Multiuser MIMO transmission

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Prior Work on MCS Selection •  Single Carrier •  Adapt MCS to meet outage constraint [GolChu97], [ChuGol01] •  Look-up table for every SNR range

•  MIMO - OFDM •  Average SNR is not effective for MCS selection [LamRohZir02] •  Link performance metrics – effective SNR [BruAstSal05], [LamRey05] •  Machine learning techniques [DanCarHea10] - outperform effective SNR

•  Multiuser MIMO – OFDM •  Multicast communication with limited feedback [YunCarHea11] •  Perfect CSIT: Joint scheduling and link adaptation [EssRieFer11] [EssRieFer12] •  RVQ limited feedback, 1 stream per RX, effect. SNR [CheWanPenCao10]

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No comprehensive solution for MU-MIMO-OFDM

Contributions • Develop link adaptation for MU-MIMO-OFDM •  Uses joint scheduling and link adaptation •  Accommodates practical limited feedback techniques •  Allows multiple data streams per user

•  Built using elements of machine learning •  Flexible and robust •  Works with practical coding and interleaving strategies

•  Based on the IEEE 802.11ac standard

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System Model

…

… …

…

…

…

…

Transmitter Receiver 1

Receiver U

Channel

antennas

antennas

antennas

streams

streams

Linear precoders

Combiner Equalizer

… …

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Coding and interleaving applied to each stream

System Model •  Signal after equalization:

•  Representation with equivalent channel:

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System Model •  Post-processing SNR for user u and streami on carrier n

•  is the covariance matrix of the interference plus noise

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Key Assumptions •  Block fading •  Channel is constant during one link adaptation period •  Good model for the indoor WLAN scenario

•  Fixed frame length (for FER calculation)

•  Perfect CSI at the receiver •  Journal version explores the minor impact of estimation error

•  Perfect instantaneous SNR information at the transmitter

• Quantized beamforming based on Givens rotations c/f 802.11ac

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Problem Statement

Large number of variables FER prediction from SNR values Post processing SNR is unknown due to limited feedback

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Proposed Solution Step 1: User and mode selection.

Greedy algorithm

Step 2: Precoding and equalization from limited feedback information. Block diaganolization precoding. Step 3: SINR estimation from precoders and limited feedback

information. Analytical approximation exploiting feedback structure. Note: Only found in the journal version submitted to IEEE Trans Wireless

Step 4: FER prediction using SINR information. Machine learning classifier.

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Step 1: User and Mode Selection • Greedy algorithm •  Add layers one by one until the rate does not increase

Add temporary layer to user 1 Add temporary layer to user U

Compute sum throughput Compute sum throughput

…

Rate increment? Exit

Add layer to user j

Max # of layers? Yes

Yes

No

No

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Step 2: Precoding •  Precoders and combiners chosen to use Block Diagonalization

Received signal after combining

Remove interuser interference terms

Problem: Unknown interference estimation due to limited feedback

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Step 4: MCS Selection • Machine learning based classifier for MCS selection

•  Leverages history of •  Per-subcarrier SNR (based on channel state) •  Resulting frame error (based on CRC check)

(1) Reduce dimensionality of the feature space

(2) Use classifier to determine if FER constraint is met

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Dimensionality Reduction •  Step by step •  Calculate SNR values (including interference estimation) •  Order SNR values •  Decimate

•  Subset selection: equi-spaced SNR values •  Some improvement can be obtained by optimizing the subset selection

3.5 6.2 5.9 4 8.1 2.6 5 8 6 2.5

2.5 2.6 3.5 4 5 5.9 6 6.2 8.1 8.6

2.5 4 6 8.6

SNR vector

Ordered SNR

Feature vector

2.5 2.6 3.5 4 5 5.9 6 6.2 8.1 8.6

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subcarriers

•  From a set of training samples , obtain the MCS of the feature vector

• One classifier for each MCS and number of layers. Binary output •  “-1”: •  “+1”:

•  Each classifier is a support vector machine

Classification

Optimization

Class of training sample Inner product in Hilbert space (kernel)

Subset of ordered SNR

Subset of ordered SNR current channel

Boolean output

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Link Adaptation Results •  4-antenna access point (AP)

•  Three 2-antenna stations (STA)

•  20MHz channel

•  Frame length: 128 bytes

•  Training set: 6000 samples (single user communication)

•  Target FER: 0.1

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Adaptation Results: Throughtput

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Gain from interference estimation

Gain from higher feedback rate

Proposed in EUSIPCO paper

Found in journal version

Adaptation Results: FER

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SINR is overestimated: MCS mismatch

FER constraint is met

Proposed in EUSIPCO paper

Found in journal version

Conclusions •  Link adaptation in MU-MIMO-OFDM requires many tricks

•  Proposed an approach for link adaptation •  Greedy user and spatial mode selection •  Precoding based on limited feedback •  Coding and modulation selection using data-driven machine learning

•  Further work estimates the interference from limited feedback •  Dramatically improves rates •  More information in journal version

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References •  [GolChu97] A. Goldsmith and S.-G. Chua, “Variable-rate variable-power MQAM for fading channels,” IEEE

Trans. Commun., vol. 45, pp. 1218 –1230, Oct. 1997. •  [ChuGol01] S. T. Chung and A. Goldsmith, “Degrees of freedom in adaptive modulation: a unified view,” IEEE

Trans. Commun., vol. 49, pp. 1561–1571, Sept. 2001 •  [LamRohZir02] M. Lampe, H. Rohling, and W. Zirwas, “Misunderstandings about link adaptation for frequency

selective fading channels,” in Proc. IEEE PIMRC, vol. 2, (Lisboa, Portugal), pp. 710 – 714 vol.2, Sept. 2002. •  [BruAstSal05] K. Brueninghaus, D. Astely, T. Salzer, S. Visuri, A. Alexiou, S. Karger, and G.-A. Seraji, “Link

performance models for system level simulations of broadband radio access systems,” in Proc. IEEE PIMRC, vol. 4, (Berlin, Germany), pp. 2306–2311 Vol. 4, Sept. 2005.

•  [LamRey05] M. Lamarca and F. Rey, “Indicators for PER prediction in wireless systems: A comparative study,” in Proc. IEEE VTC Spring, vol. 2, (Stockholm, Sweden), pp. 792–796 Vol. 2, May 2005.

•  [DanCarHea10] R. Daniels, C. Caramanis, and R. W. Heath Jr., “Adaptation in convolutionally coded MIMO-OFDM wireless systems through supervised learning and SNR ordering,” IEEE Trans. Veh. Technol., vol. 59, pp. 114– 126, Jan. 2010.

•  [YunCarHea11] S. Yun, C. Caramanis, and R. W. Heath Jr., “Distributed link adaptation for multicast traffic in MIMO-OFDM systems,” Physical Communication, vol. 4, no. 4, pp. 286–295, 2011.

•  [EssRieFer11] M. Esslaoui, F. Riera-Palou, and G. Femenias, “Fast link adaptation for opportunistic multiuser MIMO-OFDM wireless networks,” in Proc. ISWCS, (Aachen, Germany), pp. 372 –376, Nov. 2011.

•  [EssRieFer12] M. Esslaoui, F. Riera-Palou, and G. Femenias, “A fair MU-MIMO scheme for IEEE 802.11ac,” in Proc. ISWCS, (Paris, France), pp. 1049 –1053, Aug. 2012.

•  [CheWanPenCao10] Z. Chen, W. Wang, M. Peng, and F. Cao, “Limited feedback scheme based on zero-forcing precoding for multiuser MIMO-OFDM downlink systems,” in Proc. ICST WICON, (Singapore), pp. 1 –5, Mar. 2010.

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LEARNING-BASED ADAPTIVE TRANSMISSION FOR LIMITED FEEDBACK MULTIUSER MIMO-OFDM

Alberto Rico-Alvariño and Robert W. Heath Jr

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BACKUP SLIDES

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Feedback in IEEE 802.11ac •  SVD decomposition:

SNR information (we assume perfect knowledge at AP)

Preferred beamformers (we assume limited feedback CSI at the AP using Givens decomposition)

Unknown at the AP. Receive combiner contains some rows of this matrix

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•  Estimate residual interuser interference with limited feedback.

• Approximate the covariance matrix of interference

• High rate quantization theory •  Quantization error: uniform in quantization bin

Step 3: Interference Estimation

Some columns of Unknown

Closed form approximation of the

interference covariance matrix

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• Givens decomposition

• Quantization of angles with and bits.

•  The AP reconstructs the beamformer

Quantization and Reconstruction Complex phase information

Rotation angles

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Step 4: MCS Selection

16 QAM ¾, one layer

Carrier 1 Carrier 2 Carrier 3 Carrier 4

10 13 12.5 6 Yes

10 6 16 9 No

9 8 12 5 No

8 15 10 10 Yes

8.5 12 15 10 Yes

11 13 8 7 ????

History: previously observed SNR samples. 4 Carrier system

One table for each MCS and

number of layers

Current channel: use history to determine feasibility

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Step 4: MCS Selection •  Practical systems will have a large dimensionality •  52 carriers for 20MHz, single layer

Increased memory

Need to store more values

Increased complexity

Vectors and matrices are larger

Curse of dimensionality

Need more training samples

Solution: Dimensionality reduction

Intuition: FER is invariant to SNR permutations

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MCS Evolution Highest MCS frequently selected

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Number of Users Aggressive multiuser transmission

Moderate multiuser transmission

The link adaptation algorithm adapts to both the channel state and feedback rate

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Step 3: Interference Estimation

Very accurate for All quantization values

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MCS Selection • Comparison with average SNR classifier (FER < 10%)

Classification error rate (in %)

Misses 0.45 out of 100 Misses 9.4 out of 100 SVM corrects 95% of the errors of Av. SNR

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