NYU Wireless
Sundeep Rangan, NYU-PolyJoint work with Felipe Gomez-Cuba,
Ted Rappaport, Elza Erkip
March 11, 2015Rutgers Colloquium
Millimeter Wave Wireless Networks:Potentials and Challenges
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NYU Wireless
Outline
Millimeter Wave: A New Frontier for Cellular
Can it Work? Measurements in NYC
NYU WIRELESS
Relaying Revisited
Other Projects and Future Work
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NYU Wireless
MmWave: The New Frontier for Cellular Massive increase in bandwidth Near term opportunities in LMDS and E-Bands Up to 200x total over long-time
Spatial degrees of freedom from large antenna arrays
From Khan, Pi “Millimeter Wave Mobile Broadband: Unleashing 3-300 GHz spectrum,” 2011
Commercial 64 antenna element array
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Millimeter Wave Before Cellular
Jagadis Bose at Royal Institution, London 1898Demonstration of 60 GHz transmission
60 GHz Wireless LAN 802.11adPhoto from WiLocity
mmWave backhaul Image from http://mobilebackhaul.blog.com/
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Key Challenges for Mobile Cellular
All transmissions are directional:
Friis’ Law: 𝑃𝑃𝑟𝑟𝑃𝑃𝑡𝑡
= 𝐺𝐺𝑡𝑡𝐺𝐺𝑟𝑟𝜆𝜆4𝜋𝜋𝑟𝑟
2⇒ Path loss ∝ 𝜆𝜆−2
Can be overcome with beamforming: 𝐺𝐺𝑡𝑡 ,𝐺𝐺𝑟𝑟 ∝ 𝜆𝜆−2
But requires directional search, tracking to support mobility
Shadowing Mortar, brick, concrete > 150 dB Human body: Up to 35 dB NLOS propagation relies on reflections and scattering
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Millimeter Wave Cellular Vision
Small cells Directional transmissions Relaying / mesh topology
http://www.miwaves.eu/Uday Mudoi, Electronic Design, 2012
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NYU Wireless
Outline
Millimeter Wave: A New Frontier for Cellular
Can it Work? Measurements in NYC
NYU WIRELESS
Relaying Revisited
Other Projects and Future Work
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NYU Wireless
NYC 28 and 73 GHz Measurements Focus on urban canyon
environment Likely initial use case Mostly NLOS “Worst-case” setting
Measurements mimic microcell type deployment: Rooftops 2-5 stories to street-level
Distances up to 200m All images here from Rappaport’s measurements:
Azar et al, “28 GHz Propagation Measurements for OutdoorCellular Communications Using Steerable BeamAntennas in New York City,” ICC 2013
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Isotropic Path Loss Models
Standard linear path loss model𝑃𝑃𝑃𝑃 𝑑𝑑 = 𝛼𝛼 + 𝛽𝛽 log𝑑𝑑 + 𝜉𝜉
Measures total power Aggregate across all directions
Separate LOS and NLOS models
Akdeniz, Liu, Samimi, Sun, Rangan, Rappaport, Erkip JSAC 2014
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Isotropic Path Loss Comparison
Isotropic NLOS path loss measured in NYC ~ 20 - 25 dB worse than
3GPP urban micro model for fc=2.5 GHz
But beamforming will offset this loss.
Bottom line:mmW has no effective increase in path loss
20 dB loss
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Hybrid LOS-NLOS-Outage Model mmW signals susceptible to
severe shadowing. Not incorporated in standard
3GPP models New three state link model:
LOS-NLOS-outage Form derived from random
shape theory (Bai, Vaze, Heath 13)
Outages significant only at d>150m Will help small cell by
reducing interference
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Angular Spread Measured powers at different
TX-RX angular pairs. Avg. of 2 clusters of paths
detected More likely with time
resolution
Typical beamwidth in each cluster: ~10 deg in AoA RX ~ 7 deg in AoATX
Typical 2-3 spatial DoFs
RX power at different angles
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Simulations: SNR Distribution
Simulation assumptions: 200m ISD 3-sector hex BS 20 / 30 dBm DL / UL power 8x8 antenna at BS 4x4 (28 GHz), 8x8 (73 GHz) at UE
A new regime: High SNR on many links Much better than current
macro-cellular Interference is non dominant
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Comparison to Current LTE Initial results show significant gain over LTE
Further gains with spatial mux, subband scheduling and wider bandwidths
System antenna
Duplex BW
fc (GHz)
Antenna Cell throughput (Mbps/cell)
Cell edge rate(Mbps/user, 5%)
DL UL DL UL
mmW 1 GHz TDD
28 4x4 UE8x8 eNB
1514 1468 28.5 19.9
73 8x8 UE8x8 eNB
1435 1465 24.8 19.8
Current LTE
20+20MHz FDD
2.5 (2x2 DL,2x4 UL)
53.8 47.2 1.80 1.94
~ 25x gain ~ 10x gain10 UEs per cell, ISD=200m, hex cell layoutLTE capacity estimates from 36.814
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NYU Wireless
Outline
Millimeter Wave: A New Frontier for Cellular
Can it Work? Measurements in NYC
NYU WIRELESS
Relaying Revisited
Other Projects and Future Work
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NYU Wireless
NYU WIRELESS
• Exciting new center Ted Rappaport, founder Faculty across ECE, Courant and Med school
• Pioneering in mmWave for cellular• First to demonstrate feasibility
Measurements in NYC
• Research includes: Cellular design, capacity studies, prototyping Biological impact, medical applications
• Bringing technology to reality
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NYU Wireless
NYU WIRELESS is Leading Industry
Industrial Affiliates
14 industrial affiliates Vendors, carriers & test
Leading contributions to industry Channel modeling groups FCC Notice of Inquiry
Host: Brooklyn 5G Summit New entrants:
SiBeam: Leader in mmWave RF Ics StraightPath: Nationwide holder of
38 GHz spectrum
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NYU Wireless
Outline
Millimeter Wave: A New Frontier for Cellular
Can it Work? Measurements in NYC
NYU WIRELESS
Relaying Revisited
Other Projects and Future Work
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Multihop Relaying for mmWave
Significant work in multi-hop transmissions for cellular
Gains have been minimal Why? Current cellular systems are
bandwidth-limited
Millimeter wave may be different Overcome outage via macrodiversity Many degrees of freedom
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Consider Two Possible Protocols
Direct transmissions to mobiles
Similar to current cellular
ISH: Infrastructure single hop IMH: Infrastructure multi hop
Multi-hop transmissions using UEs
Rarely used today
[Gomez-Cuba, Rangan, Erkip, Gonzalez-Castano, ISIT 2014]
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Scaling Laws Analysis
Param Scaling
Bandwidth 𝑊𝑊 = 𝑊𝑊0𝑛𝑛𝜓𝜓
Num BS antenans
ℓ = ℓ0𝑛𝑛𝛾𝛾
Area 𝐴𝐴 = 𝐴𝐴0𝑛𝑛𝜈𝜈
Num BS 𝑚𝑚 = 𝑚𝑚0𝑛𝑛𝛽𝛽
Randomly drop 𝑛𝑛 mobiles. Area, bandwidth, number of antennas
scale with 𝑛𝑛 Estimate scaling on 𝑅𝑅 𝑛𝑛
lim𝑛𝑛→∞
1n
log𝑅𝑅 𝑛𝑛 Estimate rate to a constant factor. Assume EGoS
Base stations have multiple antennas Mobiles have single antenna
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Protocols for Downlink
Infrastructure Single Hop BSs transmit using to UEs directly using MU-MIMO Frequency reuse one. Treat out-of-cell interference as noise
Infrastructure Multi Hop Cell is divided into subcells Traffic is routed from BS to mobiles via multi-hop MU-MIMO on first hop from BS. Partial frequency reuse and time-division for half-duplex
constraint and interference Sub-cell size is optimized
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Achievable Rates: IMH vs. ISH
Param Scaling
Bandwidth 𝑊𝑊 = 𝑊𝑊0𝑛𝑛𝜓𝜓
Num BS antenans
ℓ = ℓ0𝑛𝑛𝛾𝛾
Area 𝐴𝐴 = 𝐴𝐴0𝑛𝑛𝜈𝜈
Num BS 𝑚𝑚 = 𝑚𝑚0𝑛𝑛𝛽𝛽
Path loss 𝐶𝐶𝑑𝑑−𝛼𝛼
Degs of freedom per BS (bandwidth * antenna)
Fixed DoFs(current cellular)
Gain with widebands andlarge num mobiles / BS.
Rat
e p
er u
ser
Increasing DoFs(e.g. mmW)
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Regimes
Interference vs power limit regime: When DoFs per BS is large, rate scaling ceases to increase There is a limit to very large DoFs
Multi-hop transmissions can better exploit high DoFs Shortens range ⇒ reduces power limit But, requires large scaling on number of mobiles / base station
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What is the Right Protocol for 5G?
Technology Scaling Protocol Rate per user
No action BSs density fixedDoF per BS is fixed
ISH or IMH Decreases with mobiles per cell
Densification Increase BS per mobileDoF per BS is fixed
ISH or IMH Improves due to increased BS density
Massive MIMOor mmWave
BS per mobile fixedIncrease DoF per BS
ISH Increases until the SNR to furthest mobile hits threshold
IMH Increases until the SNR to first mobile hits threshold
Area constant. Increase mobile density
IMH is not needed for densification But, IMH is required for Massive MIMO or mmWave
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Link Layer Capacity Assume point-to-point MIMO link has capacity scaling:
𝑅𝑅 = �Θ(𝑊𝑊min ℓ𝑟𝑟 , ℓ𝑡𝑡 ) 𝑊𝑊 ≤ 𝑊𝑊∗
Θ ℓ𝑟𝑟min(ℓ𝑟𝑟,ℓ𝑡𝑡)
𝑃𝑃𝑁𝑁𝐼𝐼
𝑊𝑊 ≥ 𝑊𝑊∗
Critical bandwidth
𝑊𝑊∗ = Θℓ𝑟𝑟𝑃𝑃
𝑊𝑊𝑁𝑁𝐼𝐼 min(ℓ𝑟𝑟 , ℓ𝑡𝑡)
Applies to coherent and non-coherent channel For non-coherent, number of channel parameters can grow linearly
with bandwidth Assumes CSIT at transmitter Channel estimation and feedback overhead is a constant factor
Bandwidth limited
Power limited
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Estimating the Network Capacity
Assume path loss model 𝑃𝑃𝑟𝑟 = 𝐶𝐶𝑃𝑃𝑡𝑡𝑑𝑑−𝛼𝛼 , 𝛼𝛼 > 2 Assume MU-MIMO can use full spatial DoFs Treat interference as noise Use worst case distances For IMH, use partial frequency reuse to separate neighbors in
adjacent sub-cells (most a constant capacity loss)
For IMH, optimize subcell size / number of hops Ensure every sub-cell has at least one mobile
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Can We Do Better than IMH?
IMH is capacity achieving. Use a cut-set argument
Hierarchical cooperation not necessary Difference with
infrastructure vs ad hoc
IRH: Infra relay hop Use if IMH is not practical Multihop from fixed relays
not mobiles
Fixed DoFs(current cellular) Increasing DoFs
(e.g. mmW)
Rat
e p
er u
ser
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Making Multihop Work
Many issues
Network discovery
Beamforming synchronization & tracking
Directional isolation
Dynamic duplexing
Interference-to-noise
Qualcomm FlashLinQframe structure
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Significant Potential GainsNumRNs
Capacity Cell edge
DL UL DL UL
0 2090 1890 10.0 3.40
2 2370 2280 28.2 5.99
4 2440 2330 238.6 244.5
10 UEs per cell, 4x4 antenna array,single stream
Garcia-Rois, Gomez-Cuba, Akdeniz, Gonzalez-Castano, Burguillo-Rial, Rangan, Lorenzo, IEEE TWC, in revision
Significant gains possible Esp. cell edge Cell capacity limited by single stream
Dynamic duplexing Multi-hop optimal scheduling
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NYU Wireless
Outline
Millimeter Wave: A New Frontier for Cellular
Can it Work? Measurements in NYC
NYU WIRELESS
Relaying Revisited
Other Projects and Future Work
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NYU Wireless
Channel Measurements Outage Current measurements: Outage from a single base station
Next steps: Joint probability multiple base stations Changes over time, distance Combine with ray tracing?
Needed to assess: Macro-diversity, handover
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Channel MeasurementsDeployment Models
Current measurements: Rooftop antenna Traditional microcell Wide coverage, but NLOS
Next set of measurements: “Street furniture” Lower range, but greater LOS paths More likely deployment model
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Low-Power Beamforming Architectures
A/D present major power bottleneck Wide bandwidths, large number of antennas Current solutions use analog BF via phase shifters Hybrid BF
Many research questions Low-rate fully digital architectures? Synchronization, channel estimation? Implications for multiple access, cell search?
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Directional Cell Search and Adaptation
Synchronization may be key limiting factor
Rapid changes from blockage Obstructions by buildings, hands, …
Other challenges: Demands for very low latency Multi-flow connectivity Must discover many elements
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Spatial Channel Estimation
Estimating directions of arrival is essential for: Tracking, synchronization, …
Analog BF, can “look” in only direction at a time Spatial covariance via non-negative matrix completion
min𝑄𝑄>0
�𝑖𝑖𝑝𝑝 𝑧𝑧𝑖𝑖 𝑤𝑤𝑖𝑖∗𝑄𝑄𝑤𝑤𝑖𝑖 + 𝜆𝜆𝜆𝜆𝜆𝜆 𝑄𝑄 , 𝑄𝑄 = 𝐸𝐸 𝐻𝐻𝐻𝐻∗
Beamforming with a 8x8 arrayMultipath NLOS channelNYC measurements
[Amir-Eliasi, Rangan, Rappaport 2015]
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Cell Search with Low-Rate Digital Architectures Significant gains for low-rate digital architectures Enable searching in multiple directions Also beneficial for control messaging / multiple access
Detection in initial cell searchDigital BF vs. analog BF
Barati, Hosseini, Rangan, SPAWC 201412 dB gain
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Other MAC Layer Work
How to design LTE for mmW?
Key concerns Modifications for directionality Intermittency in links
Areas of focus for next year: Cell search, synchronization Dynamic duplexing Multi-flow, carrier aggregation
Qualcomm FlashLinQframe structure
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Adaptive and MultiPath TCP
Rapid changes in rate: Cells intermittently blocked.
Current TCP is slow to adapt
New research directions New stochastic TCP mechanisms Multi-path congestion control
Tingting Lu, Subramanian, Panwar
Gateway
UE
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Heterogeneous Networks
Many aspects of heterogeneity 4G and 5G Indoor / outdoor Third party ISP
vs Cellular operator
Many questions: New spectrum license models? Load balancing? Pricing?
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Prototyping
This year: Demonstrated mmW
LTE-like signal
Next steps: Integrated NI platform
with ns3 for current LTE Demonstrated at EuCNC,
June 2014 Will modify for mmW
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A Big QuestionWhat is the killer app for mmWave? What applications can drive huge amounts of data? Many applications for human interaction are limited. Video < 20 Mbps. Much lower on mobile devices.
Will data be driven by machine to machine? Many users bursty vs. few users continuous?
What will drive very low latency (e.g. ~1ms)? What will be the network delays? What is the partition of mobile vs. cloud? Where will data be located?
What about power, form factor, cost?
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Summary
MmWave offers tremendous potential A once in a generation technological advance
But, many new challenges New regime where degrees of freedom are plentiful Dominant challenge is synchronization & intermittency Capacity tied closely with front-end capabilities
Cellular will be re-designed Many opportunities for research, commercialization & theory
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Thanks
Faculty: Ted Rappaport, Elza Erkip, Shiv Panwar, Pei Liu Michele Zorzi (U Padova)
Postdoc: Marco Mezzavilla Students: Felipe Gomez Cuba (U Vigo) Mustafa Riza Akdeniz, Parisa Amir Eliasi, Russell Ford,
Yuanpeng Liu, George McCartney, Oner Orhan, Matthew Samimi, Shu Sun
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References
Khan, Pi, “Millimeter-wave Mobile Broadband (MMB): Unleashing 3-300GHz Spectrum,” Feb 2011, http://www.ieee-wcnc.org/2011/tut/t1.pdf
Rappaport et al. "Millimeter wave mobile communications for 5G cellular: It will work!." Access, IEEE 1 (2013): 335-349.
Rangan, Rappaport, Erkip, “Millimeter Wave Cellular Systems: Potentials and Challenges”, Proc. IEEE, April 2014
Akdeniz, Liu, Rangan, Rappaport, Erkip, “Millimeter Wave Channel Modeling and Cellular Capacity Evaluation”, JSAC 2014
Gomez, Rangan, Erkip, “Scaling Laws for Infrastructure Single and MultihopWireless Networks in Wideband Regimes”, ISIT 2014, http://arxiv.org/abs/1404.7022
Barati, Hosseini, Rangan, et al, “Directional Cell Search for Millimeter Wave Cellular Systems” http://arxiv.org/abs/1404.5068
Eliasi, Rangan, and Rappaport. "Low-Rank Spatial Channel Estimation for Millimeter Wave Cellular Systems." http://arxiv.org/abs/1410.4831
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