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Multi-beam MIMO for Millimeter-Wave Wireless: Architectures, Prototypes, and 5G Use Cases

Akbar M. SayeedWireless Communications and Sensing Laboratory

Electrical and Computer EngineeringUniversity of Wisconsin-Madison

http://dune.ece.wisc.edu

IEEE WCNC'2016 Workshop on Millimeter Wave-Based Integrated Mobile Communications for 5G Networks

(mmW5G Workshop)

April 3, 2016

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Supported by the NSF and the Wisconsin Alumni Research Foundation

Explosive Growth in Wireless Traffic

AMS-mmW MB-MIMO 1

(2014 Cisco visual networking index)

(Qualcomm)

Current Industry Solution:Small Cells & Het Nets

Denser spatial reuse of spectrum

T. Kadous (Qualcomm)

J. Zhang (Samsung)

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New Frequencies: mm-Wave and cm-Wave

3kHz

300kHz

3MHz

30MHz

3GHz

30GHz

300MHz

300kHz

3MHz

30MHz

300MHz

3GHz

30GHz

300GHz

mmW - Short range: 60GHz

Longer range: 30-40GHz, 70/80/90GHz

Current cellular wireless: 300MHz - 5GHz

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cmW: 6-30GHz

mmW Wireless: 30-300 GHz A unique opportunity for addressing the wireless data challenge

– Large bandwidths (GHz)

– High spatial dimension: short wavelength (1-100mm)

Beamwidth: 2 deg@ 80GHz

35 deg@ 3GHz

45dBi

15dBi

80GHz

3 GHz

Highly directive narrow beams(low interference/higher security)

6” x 6” antenna @ 80GHz: 6400-element antenna array

Compact high-dimensional (massive) multi-antenna arrays

Large antenna gain

AMS-mmW MB-MIMO 3

3

Key Opportunities and Challenges

• Hardware complexity: spatial analog-digital interface

• Computational complexity: high-dimensional DSP

Electronic multi-beam steering & MIMO data multiplexing

Our Approach: Beamspace MIMO

Key Challenges:

Key Operational Functionality:

AMS-mmW MB-MIMO(AS&NB’10; JB,AS&NB ‘13)

4

Beamspace MIMO

Antenna space multiplexing

Beamspacemultiplexing

Discrete Fourier Transform (DFT)

n dimensional signal space

n-element array( spacing)

n orthogonal beams

Related: comm. modes in optics (Gabor ‘61, Miller ‘00, Friberg ‘07)

n spatial channels

(AS’02; AS&NB ’10; JB,AS&NB ‘13)

AMS-mmW MB-MIMO 5

Multiplexing data into multiple highly-directional (high-gain) beams

4

n-element (Phased) Antenna Array

Spatial angle

TX: steering vectoror

RX: response vector

AMS-mmW MB-MIMO

Spatial frequency:

n-dimensional spatial sinusoid

6

Orthogonal Spatial Beams

n orthogonal spatial beams

DFT spatial modulation matrix:

n = 40Spatial resolution/beamwidth:

(DFT)

Unitary

AMS-mmW MB-MIMO

(n-dimensional orthogonal basis)

7

5

Antenna vs Beamspace Representation

AMS-mmW MB-MIMO 8

TX RX

(AS’02)

(DFT)(DFT)

Multipath Channel

(correlated)

(decorrelated)

MIMO Channel Matrix Multipath Channel

mmW MIMO Channel: Beamspace Sparsity

Point-to-point LoS Link

Point-to-multipointmultiuser link

Communication occurs in a low-dimensional (p) subspaceof the high-dimensional (n) spatial signal space

How to optimally access the p active beams with the lowest – O(p) - transceiver complexity?

(DFT)(DFT)

TX Beam Dir.

RX

Beam

Dir

.

AMS-mmW MB-MIMO

• Directional, quasi-optical• Line-of-sight• Single-bounce multipath

9

Massive mmW Arrays

(AS&NB’10; Pi&Khan‘11; Rappaport et. al,‘13)

6

Continuous Aperture Phased (CAP) MIMO

Lens computes analog spatial DFT

Data multiplexing through

active beams

Performance vs Complexity Optimization

p digital data streams

n analog beams

O(p) transceivercomplexity Beam Selection

p << nactive beams

AMS-mmW MB-MIMO

Practical Hybrid Analog-Digital Beamspace MIMO Transceiver(patented)

(AS & NB‘10, ‘11; JB, AS, NB, ‘13) 10

Focal surface feed antennas:direct access to beamspace

CAP-MIMO vs Conventional MIMO: Spatial Analog-Digital Interface

CAP MIMO:Analog Multi-Beamforming

Conventional MIMO:Digital Beamforming

Beam Selectionp << n

active beams

O(p) transceiver complexityO(n) transceiver complexity

n: # of conventional MIMO array elements (1000-100,000)

p: # spatial channels/data streams (10-1000)AMS-mmW MB-MIMO 11

p data streams p data

streams

7

CAP-MIMO vs Phased-Array-Based Arch.

Multi-beam forming mechanismO(p) transceiver

complexity

Lens+

Beamspace Array+

mmW Beam Selector Network

Phase ShifterNetwork (np)

+Combiner Network

CAP-MIMO:

Phased-Array-Based:n phase shifters per data stream

AMS-mmW MB-MIMO (e.g., Samsung; Ayach et. al ‘12) 12

• Access Point Technology

• Backhaul

• Mobile Access

• Last Mile Connectivity

• Indoor Gigabit wireless (e.g., HDTV)

• Satellite links

• Vehicular communication networks; machine-to-machine communications

AMS-mmW MB-MIMO 13

5G Use Cases of MB mmW MIMO

Electronic multi-beam steering &MIMO data multiplexing

multi-Gbps speeds

millisecond latency

8

Dense Beamspace Multiplexing

x 2-200 increase in capacity due to beamspace multiplexing

x 10-100 increase in capacity due to extra bandwidth (~1-10GHz vs 100MHz)

200Gbps-200Tbps (per cell throughput)(20-200Gbps/user)

30dBAnt. gain

6” x 6” antenna

small-cell access point

Beamspace channel sparsity

AMS-mmW MB-MIMO(JB&AS ’13; JB&AS ’14)

Idealized upper bound (non-interfering K users):

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Performance-complexityoptimization

2D Arrays for 3D Beamforming

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k-th user channel:

BeamspaceTransformation Matrix:

(2D DFT)

(JB&AS’14)

2D steering vector:

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Small-Cell AP Design: 2D Beam Footprints

AMS-mmW MB-MIMO (JB&AS’14)

0.5” x 3” antenna @ 80GHz 2.3” x 12” antenna @ 80GHz

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1.1” x 6” ant. @ 80GHz

Cell coverage (200 m x 100m)

Sparse Beamspace Linear Precoding

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Lower-dimensional system

Beamspace precoder:

Multiuser channel:

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Sparse set of dominant active beams(power thresholding)

Downlink system:

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Sum Capacity: Sparse MMSE Precoding

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(MMSE precoder: Joham, Utschick, & Nossek 2005)(JB&AS ‘13, JB&AS ‘14)

Capacity:

MMSEPrecoder:

BeamspaceDownlink:

Performance vs Complexity: Perfect CSI

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Upperbound vs MMSE precoder performance

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p=16K=1600beams

2.3” x 12” ant. @ 80GHz

p=K=100beams

0.5” x 3” ant. @ 80GHz

p=4K=400beams

1.1” x 6” ant. @ 80GHz

p=K=100 (0.5” x 3”)

p=4K=400(1.1” x 6”)

p=16K=1600(2.3” x 12”)

x 3

x 10

4 beam mask/uservs

Full dimension

400 maxactive beams p=16K=1600

Full dim. vs low-dim.p=K, 2K, 4K

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Transmit SNR (dB)60 70 80 90 100

Cap

acit

y (

b/s

/Hz)

10-1

100

101

102

103

Full Dimensional Perfect CSIPerfect CSI, p=KNoisy BS, p=KNoisy CE, p=KNoisy BS+CE, p=K

Power-Complexity: with Beam Selection and Channel Estimation

Transmit SNR (dB)60 70 80 90 100

Cap

acit

y (

b/s

/Hz)

10-1

100

101

102

Full Dimensional Perfect CSIPerfect CSI, p=KNoisy BS, p=KNoisy CE, p=KNoisy BS+CE, p=K

Transmit SNR (dB)60 70 80 90 100

Cap

acit

y (

b/s

/Hz)

10-1

100

101

102

Full Dimensional Perfect CSIPerfect CSI, p=2KNoisy BS, p=2KNoisy CE, p=2KNoisy BS+CE, p=2K

K=10

K=50

p=K p=2K

(Path loss: 75-101 dB)

Transmit SNR (dB)60 70 80 90 100

Cap

acit

y (

b/s

/Hz)

10-1

100

101

102

103

Full Dimensional Perfect CSIPerfect CSI, p=2KNoisy BS, p=2KNoisy CE, p=2KNoisy BS+CE, p=2K

Full dim: ñ = 158

Transmit SNR (dB)60 70 80 90 100

Cap

acit

y (

b/s

/Hz/

use

r)

10-1

100

101

102

103

Full Dimensional Perfect CSIp=K Perfect CSIp=2K Perfect CSIFull Dimensional Noisy BS+CEp=K Noisy BS+CEp=2K Noisy BS+CE

Transmit SNR (dB)60 70 80 90 100

Cap

acit

y (

b/s

/Hz/

use

r)

10-1

100

101

102

103

Full Dimensional Perfect CSIp=K Perfect CSIp=2K Perfect CSIFull Dimensional Noisy BS+CEp=K Noisy BS+CEp=2K Noisy BS+CE

Full vs low-dim

Channel estimation (not Beam Sel.) is the limiting factorAMS-mmW MB-MIMO 20

Beam Squint: Multi-Beam Solution

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f/fc

-0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1

|Hb

,i(f

)|2/M

(d

B)

-18-15-12-9-6-30

i = io=25

i=24i=23i=26i=27

f/fc

-0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1

Σ|H

b,i(f

)|2/M

(d

B)

-18-15-12-9-6-30

3 beam5 beam

Phased array 3-beam CAP-MIMO 5-beam CAP-MIMO

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10GHz CAP-MIMO Prototype40cm x 40cm

Lens array

n=676, p=4 (channels), R=10ft

10 GHz LoS prototype theoretical performance:100 Gigabits/sec (1 GHz BW) at 20dB SNR

Compelling gains over state-of-the-art

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DLA 1DLA 2

(JB,AS,NB ’13; JB,PT,DV,AS ’14)

FPGA DAC IQ

VCO

BPF PA

FPGAADC

LNA BPF IQ

LO

P2P Link: Spatial Filtering + Differential Detection

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AS and JB ICC 15

DLA 1

DLA 2

RF Bandwidth: 1GHzSymbol rate: 125MS/s4x4 CAP-MIMOReal-time comm + Channel meas.

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P2MP Link: Spatio-Temporal Filtering & Coherent Detection

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Raw RX samples Spatial filtering Spatial+temporalfiltering

TX Frame RX Frame (MS2)

P2MP Channel Measurements

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MS1MS2

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28 GHz CAP-MIMO Prototype

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RF Bandwidth: 1 GHz Symbol rate: 370 MS/s

6” Lens Antenna with 16-feed Array

4 simultaneous beams/data streams

Powerful Altera Arria 10 FPGA board for backed DSP

AP – 4 MS bi-directional P2MP link

Real-time comm. + ch. Meas. + scaled-up testbed network

5G Outlook and Impact: Multi-scale mmW MIMO Networks

(Source: 3G.co.uk)

“small cell” Mobile Access

Network

Backhaulnetwork

Multi-GbpsSpeeds

Sub-millisecond latency

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Last-mile connectivity (remote & urban)

Vehicular networks(sensing+comm)

M2M comm. (Gigabit)

Other Use Cases:

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• Gen 2 prototype: 28 GHz, advanced functionality

• Channel Measurements: massive, beamspace, and multi-beam

• Lens Array and Beam Selector Network

• Spatial Analog-Digital Interface– Gigabit-rate DSP power hungry; more analog processing?

• Wideband High-Dimensional MIMO– New insights into the “beam-squint” problem model

– OFDM, SC, SC-OFDM? Short-Time Fourier Signaling

Ongoing Work

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Conclusion• Beamspace MIMO: Versatile theory & design framework for mmW

• CAP-MIMO: practical architecture – Performance-Complexity Optimization

• Compelling advantages over state-of-the-art– Capacity/SNR gains

– Operational functionality

– Electronic multi-beam steering & data multiplexing

• Timely applications (multi-Gigabits/s speeds)– Wireless backhaul networks: fixed point-to-multipoint links

– Smart 5G Basestations: dynamic beamspace multiplexing

– Last-mile connectivity, vehicular comm, M2M, satellite comm.

• Prototyping & technology development:– Multi-beam CAP-MIMO vs Phased arrays?

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Relevant Publications(http://dune.ece.wisc.edu)

• A. Sayeed, Deconstructing Multi-antenna Fading Channels, IEEE Trans. Signal Proc., Oct 2002

• A. Sayeed and N. Behdad, Continuous Aperture Phased MIMO: Basic Theory and Applications, Allerton Conference, Sep. 2010.

• J. Brady, N. Behdad, and A. Sayeed, Beamspace MIMO for Millimeter-Wave Communications: System Architecture, Modeling, Analysis, and Measurements, IEEE Trans. Antennas & Propagation, July 2013.

• G.-H Song, J. Brady, and A. Sayeed, Beamspace MIMO Transceivers for Low-Complexity and Near-Optimal Communication at mm-wave Frequencies, ICASSP 2013

• A. Sayeed and J. Brady, Beamspace MIMO for High-Dimensional Multiuser Communication at Millimeter-Wave Frequencies, IEEE Globecom, Dec. 2013.

• J. Brady and A. Sayeed, Beamspace MU-MIMO for High Density Small Cell Access at Millimeter-Wave Frequencies, IEEE SPAWC, June 2014.

• J. Brady, P. Thomas, D. Virgilio, A. Sayeed, Beamspace MIMO Prototype for Low-Complexity Gigabit/s Wireless Communication, IEEE SPAWC, June 2014.

• . Hogan and A. Sayeed, Beam Selection for Performance-Complexity Optimization in High-Dimensional MIMO Systems, 2016 Conference on Information Sciences and Systems (CISS), Princeton, NJ, March 2016.

• J. Brady and A. Sayeed, Differential Beamspace MIMO for High-Dimensional MultiuserCommunication, IEEE GlobalSIP, Orlando, December 2015.

• A. Sayeed and J. Brady, High Frequency Differential MIMO: Basic Theory and Transceiver Architectures,IEEE ICC, London, June 2015.

• J. Brady and A. Sayeed, Wideband Communication with High-Dimensional Arrays: New Results and Transceiver Architectures, IEEE ICC, Workshop on 5G and Beyond, London, June 2015.

• A. Sayeed and T. Sivanadyan, Wireless Communication and Sensing in Multipath Environments Using Multiantenna Transceivers, Handbook on Array Processing and Sensor Networks, S. Haykin & K.J.R. Liu Eds, 2010.

AMS-mmW MB-MIMO 30Thank You!