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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)
2
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
AMS-mmW MB-MIMO 2
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):
14
Performance-complexityoptimization
2D Arrays for 3D Beamforming
AMS-mmW MB-MIMO 15
k-th user channel:
BeamspaceTransformation Matrix:
(2D DFT)
(JB&AS’14)
2D steering vector:
9
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
16
1.1” x 6” ant. @ 80GHz
Cell coverage (200 m x 100m)
Sparse Beamspace Linear Precoding
AMS-mmW MB-MIMO
Lower-dimensional system
Beamspace precoder:
Multiuser channel:
17
Sparse set of dominant active beams(power thresholding)
Downlink system:
10
Sum Capacity: Sparse MMSE Precoding
AMS-mmW MB-MIMO 18
(MMSE precoder: Joham, Utschick, & Nossek 2005)(JB&AS ‘13, JB&AS ‘14)
Capacity:
MMSEPrecoder:
BeamspaceDownlink:
Performance vs Complexity: Perfect CSI
AMS-mmW MB-MIMO
Upperbound vs MMSE precoder performance
19
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
11
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
AMS-mmW MB-MIMO 21
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
12
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
AMS-mmW MB-MIMO 22
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
AMS-mmW MB-MIMO 23
AS and JB ICC 15
DLA 1
DLA 2
RF Bandwidth: 1GHzSymbol rate: 125MS/s4x4 CAP-MIMOReal-time comm + Channel meas.
13
P2MP Link: Spatio-Temporal Filtering & Coherent Detection
AMS-mmW MB-MIMO 24
Raw RX samples Spatial filtering Spatial+temporalfiltering
TX Frame RX Frame (MS2)
P2MP Channel Measurements
AMS-mmW MB-MIMO 25
MS1MS2
14
28 GHz CAP-MIMO Prototype
AMS-mmW MB-MIMO 26
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
AMS-mmW MB-MIMO 27
Last-mile connectivity (remote & urban)
Vehicular networks(sensing+comm)
M2M comm. (Gigabit)
Other Use Cases:
15
• 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
AMS-mmW MB-MIMO 28
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?
AMS-mmW MB-MIMO 29
16
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!