Full-Dimension MIMO: Status and Challenges in Design and - - PowerPoint PPT Presentation

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Full-Dimension MIMO: Status and Challenges in Design and - - PowerPoint PPT Presentation

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Full-Dimension MIMO: Status and Challenges in Design and Implementation Gary Xu, Yang Li, Young-Han Nam and Taeyoung Kim and Ji-Yun Seol Charlie Zhang DMC R&D Center, Samsung Electronics Co., Ltd. Samsung Research America (Dallas) May

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SLIDE 1

Full-Dimension MIMO: Status and Challenges in Design and Implementation

Gary Xu, Yang Li, Young-Han Nam and Charlie Zhang Samsung Research America (Dallas) Taeyoung Kim and Ji-Yun Seol DMC R&D Center, Samsung Electronics Co., Ltd.

May 27, 2014

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SLIDE 2

Outline

Current Status of FD-MIMO 1 Challenges of FD-MIMO 2

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SLIDE 3

Background of Full-Dimension MIMO

  • Theory Behind: Massive MIMO*

– Spatial resolution increases as number of eNB antennas – Narrow beam transmission with little MU interference

*Marzetta, “Non-cooperative cellular wireless with unlimited numbers of base station antennas,” IEEE TWireless Nov. 2010

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  • Active Antenna Array (AAA)

– 2D vs. 1D AAA

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SLIDE 4

Full-Dimension MIMO (FD-MIMO)

4 Elevation beamforming Azimuth beamforming

FD-MIMO simultaneously supports elevation & azimuth beamforming and > 10 UEs MU-MIMO

FD-MIMO eNB Rel-10 FD-MIMO 32-64 Tx FD-MIMO 100 Tx Capacity 3-5x 10x

MU-MIMO with 10s of UEs 2-dimentional AAA & ~100 antennas 3D-spatial channel model

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SLIDE 5

λ=12cm @ 2.5GHz

  • Eg. 1: 8x8 array with full digital

beamforming across 64 elements

λ/2 λ/2

0.5m 0.5m

λ/2 λ/2 2λ

0.5m 1m

FD-MIMO 2D AAS Form Factor Examples

Eg.2: 8x8 array with full digital beamforming across 64 elements

  • Eg. 1: 8x4 array with each element

4 antennas with analog beamforming

Urban Macro Urban Micro

0.25m

λ/2 λ/2

0.25m

Eg.3: 8x8 array with cross-pol. Digital beamforming 64 elements.

FD-MIMO antenna panel form factor is well within practical range

Eg.4: 1x8 array. Digital elevation beamforming

λ/2

Small Cell

0.5m

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SLIDE 6

Industry Status and 3GPP roadmap

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2014 2015 2016 2013

Start 3D channel model study item (SI) Complete 3D channel model (SI) Complete channel& baseline calibration

Expected start of Elevation Beamforming (EB)/FD-MIMO SI

EB/FD-MIMO SI Completion Start EB/FD-MIMO work item (WI) WID Complete (Dec 2016)

1st FD-MIMO Prototype: 32 antenna LTE base-station

PoC for Small Cell 3GPP development PoC for Macro Cell

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SLIDE 7

3-Dimension (3D) Channel Model

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In SCM, channel is a composite response of cluster/subclusters to Tx/Rx antennas:

  • Number of cluster/subclusters
  • Delay of clusters
  • Power of clusters
  • Phases (due to e.g. reflection)
  • Angle of Departure/Arrival (AoD/AoA)

Note: AoA/AoD critically determines channel correlations

Spatial channel model (SCM) 2D model assumes all clusters zero elevation angles and cannot describe elevation differences. 3D model captures elevation angles and thus clusters can be distinguished in elevation domain

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SLIDE 8

5 10 15 20 25 30 35 40 45 50 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Channel condition number (dB) cdf Urban Macro Urban Micro

Statistics in 3GPP 3D Channel Model

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70 75 80 85 90 95 100 105 110 115 120 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 cdf Elevation angle of depature (degree) Urban Macro Urban Micro

  • Elevation angle (w.r.t. zenith) has a range of 30-deg for UMa and 50-deg for UMi
  • 80% channels have condition number > 5dB

Note: see “3GPP TR 36.873” for more details of 3D channel model and UE distribution. 2 Rx antennas (+ pol.)

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SLIDE 9

System-Level Simulator (SLS) Evaluation

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Simulation Setup:

  • 3D ITU, UMa
  • 57 sectors with K=10 UEs per sector
  • Center frequency 2GHz, bandwidth 10MHz
  • UE speed 3km/h or 30km/h, uniformly

distributed

  • 40 drops, 4s per drop
  • UE: 2 Rx, 1Tx

Overhead: 20% Ideal SRS estimation 4 ms scheduling delay Normalized by # of DL subframes Baseline: SU-MIMO with rank1

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SLIDE 10

0.04 0.08 0.12 0.16 0.2

4x2 32x2: 2 UE 32x2: 4 UE

0.045 0.053 0.17 0.1 0.2 0.3

4x2 32x2: 2 UE 32x2: 4 UE

0.082 0.163 0.298

SLS Simulation Results (Up to 4 UE MU-MIMO)

2 4 6 8 10

4x2 32x2: 2 UE 32x2: 4 UE

2.24 5.09 8.04

127% 58% 99% 83%

1 2 3 4 5 6 7

4x2 32x2: 2 UE 32x2: 4 UE

1.95 3.73 6.26

91% 68% 18% 220%

3 km/h 30 km/h

Average cell throughput (bps/Hz) Cell-edge throughput (bps/Hz) Average cell throughput (bps/Hz) Cell-edge throughput (bps/Hz)

10 4Tx 32Tx

32Tx

4Tx 32Tx 32Tx 4Tx 32Tx 32Tx 4Tx 32Tx 32Tx

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SLIDE 11

Outline

Current Status of FD-MIMO 1 Challenges of FD-MIMO 2

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SLIDE 12

FD-MIMO Framework in LTE/LTE-A

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SLIDE 13

Antenna Virtualization & CQI Prediction

Cell-wide beamforming by antenna virtualization UE-specific beamforming

Issue: (1) How to generate wide beam from a large array (2) CQI (channel quality indicator) mismatch

  • Wide-beam (ant. virtualization) for control signal (coverage)
  • Narrow-beam (precoding) for data signal
  • UE CQI* is measured based on wide-beam

*CQI is a UE feedback value and is essential for eNB to decide transmission scheme, code rate, modulation for each UE. 13

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SLIDE 14
  • 180
  • 120
  • 60

60 120 180

  • 180
  • 120
  • 60

60 120 180 270 300 240 330 210 180 30 150 60 120 90 2 4 6 8 10 12

Antenna Virtualization & CQI Prediction (2)

Mean of error: 0.0941

  • Var. of error: 0.0424

𝜍𝑞𝑠𝑓𝑒𝑗𝑑𝑢 = |𝒊𝑙𝒙𝑙|2 |𝒊𝑙𝒙0|2 𝜍𝑛𝑓𝑏𝑡𝑣𝑠𝑓𝑒

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Synthesized antenna virtual pattern (32 ant.) CQI prediction

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SLIDE 15

FD-MIMO in TDD: Antenna Calibration

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PA LNA

eNB transceiver 1

PA LNA

Ant 1

PA LNA

Ant M

𝑢1 𝑠

1

𝑢2 𝑠2 𝑢𝑁 𝑠𝑁

eNB transceiver 2 eNB transceiver M

… …

Ant 2

H

Reciprocal

Uplink sounding Downlink transmission

𝑢1 =•••= 𝑢𝑁, 𝑠1 =•••= 𝑠M 𝑢1 − 𝑠1 =•••= 𝑢 𝑁 − 𝑠 M Joint Tx/Rx calibration Independent Tx/Rx calibration

Challenges:

  • Inherent error in calibration circuit
  • Complexity grows with antennas
  • Prefer independent Tx/Rx calibration

Calibration requirement:

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SLIDE 16

Front-haul Complexity

Possible Solutions:

  • Front-haul (CPRI) compression
  • New baseband architectures

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CPRI

Number of sectors 3 System bandwidth (MHz) 20 Sampling rate (Msps) 30.72 Bit width per I/Q-branch 16 Number of TX antennas (paths) 32 CPRI throughput (Gbps)

~96

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SLIDE 17

FD-MIMO in FDD: Exploit Uplink Correlation

CSI acquired by training & feedback in FDD LTE/LTE-A Pilot & feedback bits proportional to # of Tx antennas Issue: CSI (channel state information) acquisition Possible to use uplink channel for downlink precoding?

Uplink Downlink

Uplink & downlink channels are correlated FD-MIMO can measure uplink better

*Sana Salous and Hulya Gokalp, “Medium- and Large-Scale Characterization of UMTS-Allocated Frequency Division Duplex Channels”, IEEE TVT.

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SLIDE 18

FD-MIMO in FDD: Exploit Uplink Correlation (2)

  • Duplex distance: 45 MHz. Channel condition: NLOS.

Downlink: 2300 MHz; uplink: 2250MHz

100 200 300 400 500 600 700 800 900 1000 5 10 15 20 25 30 35 40 45 50 Block Index Angle between Downlink and Uplink Beams WCS Band

𝐐𝐬 𝐁𝐨𝐡𝐦𝐟 < 𝟒𝟏𝟏 = 𝟘𝟘. 𝟕%

𝐃𝐩𝐬𝐬𝐟𝐦𝐛𝐮𝐣𝐩𝐨 𝐃𝐩𝐟𝐠𝐠𝐣𝐝𝐣𝐟𝐨𝐮 = 0. 0.995

PMI*: Highly correlated CQI: Highly correlated

Angle between eigenvector of downlink & uplink

*PMI (Precoding Matrix Indicator): quantized channel direction. 18

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SLIDE 19

Other Challenges in FD-MIMO

  • How to reduce overhead by exploiting channel

correlation in azimuth and elevation domain?

  • Possible to combine with uplink measurement to

provide better accuracy? Feedback and codebook design in FDD

  • How to accurately estimate a large number of channels?
  • How to reduce channel estimation complexity?

Uplink sounding in TDD

  • How to optimally schedule ~10 MU-MIMO UEs without

exponentially increasing complexity? Scheduling & precoding complexity

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SLIDE 20

Summary

20 Elevation beamforming Azimuth beamforming FD-MIMO simultaneously supports elevation & azimuth beamforming and > 10 UEs MU-MIMO FD-MIMO eNB

  • Full-dimension MIMO is a promising technology to

significantly improve cellular capacity (by x3-5)

  • Challenges ahead include system design and

implementation

  • “The Next Big Thing is Here” in wireless industry
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SLIDE 21

THANK YOU!

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