Massive MIMO: It Really Works! · 2017-10-31 · Splitting the multi-antenna user into autonomous...
Transcript of Massive MIMO: It Really Works! · 2017-10-31 · Splitting the multi-antenna user into autonomous...
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Thomas L. Marzetta
NYU WIRELESS
New York University Tandon School of Engineering
October 26, 2017
Massive MIMO: It Really Works!
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The future: augmented reality everywhere
Throughputs: 100 – 1000x
Latency: 1/10 – 1/100x
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Timeless truths about wireless
Demand for wireless throughput, both mobile and fixed, will always increase: 10x, 100x, 1000x
The quantity of available electromagnetic spectrum will never increase◦ The best spectrum is below 5 GHz
◦ you can’t lay down more of this!
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Spectrum below 5 Ghz:the most valuable resource in the world!
FCC AWS-3 spectrum auction, January 2015◦ 65 MHz: 1695-1710 MHz, 1755-1780 MHz, 2155-2180 MHz
◦ $41.3 billion
◦ $630/Hz
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Outline
Taxonomy of MIMO
How to distinguish Massive MIMO from impostors
Numerical case studies
New research directions
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Taxonomy of MIMO
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Point-to-Point MIMORoy & Ottersten (1991); Paulraj & Kailath (1993); Foschini (1995); Raleigh & Cioffi (1998); Telatar (1999)
Brilliant invention
But not scalable◦ unfavorable propagation
◦ time required for training grows with system size
◦ disappointing multiplexing gains at cell edges
8x4 link, -3.0 dB SNR
# base station antennas 1 2 4 8
bits/second/Hz 1.51 1.83 2.06 2.19
In every wireless standard, but no further practical development possible
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Multi-User MIMOCaire & Shamai (2003); Viswanath & Tse (2003); Vishwanath, Jindal, & Goldsmith (2003)
Splitting the multi-antenna user into autonomous single-antenna users doesn’t decrease the sum-throughput!
Only single-antenna terminals required
Propagation is almost always favorable
But not scalable in its original form◦ dirty-paper coding/decoding needed
◦ both ends of link have to know channel state information (CSI)
Dual CSI requirement fundamentally unscalable
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Massive MIMOMarzetta (2006); Marzetta (2010)
Add many more base station antennas
CSI isn’t everything: it’s the only thing!◦ channel state information (CSI) only available to the base station
◦ use linear pre-coding/de-coding instead of dirty-paper
◦ users don’t do any signal processing
A practical Massive MIMO system can be much bigger than an orthodox-Shannon system
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Benefits of Massive MIMO
Area spectral efficiency (bits/sec/Hz/square-kilometer)
Scalability
Great service to all users via power control
Energy efficiency (bits/Joule)
Simplicity
A game-changer
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How to Distinguish Massive MIMO From Impostors
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More than just many antennas
Many physically small, low power, individually controlled antennas◦ channel orthogonality
◦ channel hardening
Create parallel flat virtual connections between base station and terminals◦ every terminal uses all time/frequency resources
Utilize measured channels rather than assumed channels
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Downlink data transmission: Maximum-Ratioantennas transmit the weighted message-bearing symbols to arrive in-phase at the intended user & out-of-phase elsewhere
The simplest possible pre-coding, but often very effective
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Uplink data transmission: Maximum-Ratiobase station weights and adds received signals for constructive reinforcement of the transmission from each user
Maximum-ratio permits decentralized signal processing
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TDD slot structure ensures timely CSI:M service-antennas, K users, unlimited M
Mobility limits the number of active users; FDD is a disaster!
TDD slot: training
FDD slot: training
K
2 KM
Up Data Down DataK Up Pilots
M Pilots Down Data
M CSI K Pilots Up Data
Down Link
Up Link
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Why so important to utilize measured propagation?
• Measured channels
• scalable
• gain grows linearly with number
of antennas
• irrespective of noisiness of
CSI
• no tightening of array
tolerance required
• Assumed channels
• not scalable
• gain eventually grows only
logarithmically
If channels are assumed, then not Massive MIMO!
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Scientific foundations of Massive MIMO
Using measured channels: Beamforming gain grows linearly with number of antennas, irrespective of the noisiness of the measurements
Frequency-independent power control: Based solely on long-scale (slow) fading; exceedingly effective
Pilot contamination: Ultimate limitation in non-cooperative multi-cell systems
No new mathematics, but a new philosophy!
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Experimental validation of Massive MIMO
Service
antennas
Terminals System spectral
efficiency
(b/s/Hz)
Bristol University /
Lund University
128 12 80 140
Bell Labs “FutureCell” 64 2 10 20 100
“Project ARIES”
96 24 71 100
Google 32 32 20
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Numerical Case Studies
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Mitigation of pilot contamination: Pilot re-use Factor 3, 4, 7re-use of pilot sequences causes coherent inter-cell interference
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The cost: extra training overhead
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Dense-urban/suburban cellular accessoptimum pilot re-use factor? maximum-ratio Or zero-forcing?
Dense Urban Suburban
Carrier frequency(GHz) 1.9 1.9
TDD spectral bandwidth (MHz) 20 20
Slot duration (ms) 2 1
User allowed mobility (km/h) 71 142
Uplink radiated power/user (mW) 200 200
Number of service antennas 64 256
Total downlink radiated power (W) 1 1
Active users/cell 18 18
Cell radius (km) .50 2.0
Power control Max/min Max/min
Pilot re-use factor 7 3
Pre-coding/de-coding Maximum-ratio Maximum-ratio
95% likely throughput/terminal Mb/s 4.5 down, 3.1 up 3.2 down, 1.1 up
Max-min power control: uniformly good service everywhere!
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Fixed wireless access: 3000 rural homes, each 20 Mbps down, 10 Mbps up
• 3000 homes randomly distributed over 11.3 km radius
Target down-link throughput: 20 Mbps for every home simultaneously
Target up-link throughput: 10 Mbps for every home simultaneously
10 W total downlink radiated power
1 W uplink radiated power per terminal
50 ms coherence time
800 MHz carrier frequency
20 MHz spectral bandwidth
How many antennas are needed?
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How many antennas are needed?
Zero-forcing: 3200 antennas (11m x 11m)
Maximum ratio: 8200 antennas (17m x 17m)
Total system throughput: 90 Gbs; 4500 b/s/Hz !!!
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New Research Directions
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Massive MIMO extensions
• Unlicensed spectrum operation
• mitigation of non-cooperative interference
Massive MIMO of Things: MMOT◦ huge numbers of things
◦ sporadic service
◦ short-duration messages
Limit behavior of Cell-Free Massive MIMO◦ continuum of access points (holographic MIMO)
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“a mathematical theory of communication”“a physical theory of communication”
is 10x beyond Massive MIMO possible?
Rigorously combine electromagnetic theory with communication theory
Re-examine old concepts◦ Super-directivity
◦ Resonant evanescent wave coupling
Meta-materials (negative dielectric constant) for antenna arrays
What is the minimum power that we have to draw from an antenna? Eb/N0 > ln 2: a purely mathematical construct
Concepts from near-field optical sub-wavelength imaging?
Multidisciplinary effort: wave propagation, electronics, mathematics, …
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Resonant evanescent wave couplingWITRICITY (MIT, 2007): 60 Watts, 2 meters, @ 10 MHz, 40% efficient
Wavelength 30 meters
Near-field dominated by evanescent waves
◦ Exponential decay
◦ Reactive power only
Tuned receiver coil alters boundary conditions, and pulls in power
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Wireless neurosensing: implantable intercranial transmitter
100 7.8 kHz neural channels: 3.2 – 3.8 GHz
Could MIMO handle 1000, 10000, … channels?
What are the ultimate limitations of near-field wireless communication?
Yin, Borton, Aceros, Patterson, & Nurmikko, IEEE Trans. Biomed. Circuits Syst., April 2013
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Massive sensor telemetryContinuous recording of signals from vast numbers of sensors
“Sensor networks” paradigm
◦ Impossible to collect all data wirelessly at one access point
◦ We couldn’t process so much data, even if we could collect it
◦ We have to pre-process and prune data
Massive MIMO changes the game!
◦ We can collect all of the data, intact
◦ Data governed by mathematical physics should be sampled at the Nyquist rate
◦ Big Data easier to process than Small Data (computer tomography, SAR, seismic exploration)
Potential applications of Massive Sensor Telemetry
◦ 3D exploration seismic surveys
◦ Monitoring of volcanoes
◦ Structural health monitoring
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MIMO in nonstandard mediaElectromagnetic propagation isn’t the only way
Still more hyperbolic MIMO
◦ Acoustic waves
◦ Elastic waves
Parabolic MIMO: heat equation
◦ Time scales as the square of distance
◦ Nanocommunications?
Elliptic MIMO: electrical conduction
◦ Updated version of Ground Telegraphy
◦ Lee Deforest, Arnold Sommerfeld, Richard Courant
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Conclusions
Future apps, such as Augmented Reality will require revolutionary developments at the physical layer
Massive MIMO is the only technology that can fully utilize the sub-5 GHz bands
Wireless communications will continue to be a vital research area, BUT future breakthroughs will result from multi-disciplinarycollaborations