LEARNING-BASED ADAPTIVE TRANSMISSION FOR LIMITED FEEDBACK MULTIUSER MIMO-OFDM. Alberto Rico-Alvariño and Robert W. Heath Jr.

Transcription

1 LEARNING-BASED ADAPTIVE TRANSMISSION FOR LIMITED FEEDBACK MULTIUSER MIMO-OFDM Alberto Rico-Alvariño and Robert W. Heath Jr.

2 2 Outline Introduction System model Link adaptation Precoding Interference estimation MCS selection User and mode selection Results Conclusions

3 3 Introduction to Link Adaptation Select transmit parameters depending on channel Ex: select modulation and coding scheme (MCS) Transmitter Channel Receiver Rate Power Beamforming gain time Explicit feedback

4 4 MU-MIMO-OFDM in IEEE ac Channel Sounding Multiuser quantization Parameter and MIMO channel and selection transmission limited estimation feedback Link adaptation SU/MU? User selection? Mode selection? Beamforming? MCS? Limited Sounding feedback

5 5 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] No comprehensive solution for MU-MIMO-OFDM

6 6 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 ac standard

7 7 System Model Combiner Equalizer Channel Transmitter Receiver 1 streams antennas antennas Receiver U streams Linear precoders antennas Coding and interleaving applied to each stream

8 8 System Model Signal after equalization: Representation with equivalent channel:

9 9 System Model Post-processing SNR for user u and streami on carrier n is the covariance matrix of the interference plus noise

10 10 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 ac

11 11 Problem Statement Post processing FER Large prediction SNR number is unknown from of variables SNR due values to limited feedback

12 12 Proposed Solution Step 4: Step FER 1: prediction User and using mode SINR selection. information. Machine Greedy learning algorithm classifier. Step 2: Precoding and equalization from limited feedback information. Step 3: SINR estimation Block diaganolization from precoders precoding. and limited feedback information. Analytical approximation exploiting feedback structure. Note: Only found in the journal version submitted to IEEE Trans Wireless

13 13 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? Yes Add layer to user j No Exit No Max # of layers? Yes

14 14 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

15 15 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

16 16 Dimensionality Reduction Step by step Calculate SNR values (including interference estimation) Order SNR values Decimate subcarriers SNR vector Ordered SNR Feature vector Subset selection: equi-spaced SNR values Some improvement can be obtained by optimizing the subset selection

17 17 Classification From a set of training samples feature vector, obtain the MCS of the One classifier for each MCS and number of layers. Binary output -1 : +1 : Each classifier is a support vector machine Class of training sample Subset of ordered SNR Subset of ordered SNR current channel Inner product in Hilbert space (kernel) Boolean output Optimization

18 18 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

19 19 Adaptation Results: Throughtput Found in journal version Gain from higher feedback rate Gain from interference estimation Proposed in EUSIPCO paper

20 20 Adaptation Results: FER Proposed in EUSIPCO paper FER constraint is met Found in journal version SINR is overestimated: MCS mismatch

21 21 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

22 22 References [GolChu97] A. Goldsmith and S.-G. Chua, Variable-rate variable-power MQAM for fading channels, IEEE Trans. Commun., vol. 45, pp , Oct [ChuGol01] S. T. Chung and A. Goldsmith, Degrees of freedom in adaptive modulation: a unified view, IEEE Trans. Commun., vol. 49, pp , Sept [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 vol.2, Sept [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 Vol. 4, Sept [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 Vol. 2, May [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 , Jan [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 , [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 , Nov [EssRieFer12] M. Esslaoui, F. Riera-Palou, and G. Femenias, A fair MU-MIMO scheme for IEEE ac, in Proc. ISWCS, (Paris, France), pp , Aug [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

23 23 LEARNING-BASED ADAPTIVE TRANSMISSION FOR LIMITED FEEDBACK MULTIUSER MIMO-OFDM Alberto Rico-Alvariño and Robert W. Heath Jr

24 BACKUP SLIDES 24

25 25 Feedback in IEEE ac SVD decomposition: Unknown at the AP. Receive combiner contains some rows of this matrix SNR information (we assume perfect knowledge at AP) Preferred beamformers (we assume limited feedback CSI at the AP using Givens decomposition)

26 26 Step 3: Interference Estimation Estimate residual interuser interference with limited feedback. Some columns of Unknown Approximate the covariance matrix of interference High rate quantization theory Quantization error: uniform in quantization bin Closed form approximation of the interference covariance matrix

27 27 Quantization and Reconstruction Givens decomposition Complex phase information Quantization of angles with and bits. Rotation angles The AP reconstructs the beamformer

28 28 Step 4: MCS Selection History: previously observed SNR samples. 4 Carrier system One table for each MCS and number of layers 16 QAM ¾, one layer Carrier 1 Carrier 2 Carrier 3 Carrier Yes No No Yes Yes Current channel: use history to determine feasibility ????

29 29 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

30 30 MCS Evolution Highest MCS frequently selected

31 31 Number of Users Aggressive multiuser transmission Moderate multiuser transmission The link adaptation algorithm adapts to both the channel state and feedback rate

32 32 Step 3: Interference Estimation Very accurate for All quantization values

33 33 MCS Selection Comparison with average SNR classifier (FER < 10%) Misses 0.45 out of 100 Misses 9.4 out of 100 SVM corrects 95% of the errors of Av. SNR Classification error rate (in %)