Designing Multi-User MIMO for Energy Efficiency

Report
Designing Multi-User MIMO
for Energy Efficiency
When is Massive MIMO the Answer?
Emil Björnson‡*,
Luca Sanguinetti‡§, Jakob Hoydis†, and Mérouane Debbah‡
‡Alcatel-Lucent
*Dept.
Chair on Flexible Radio, Supélec, France
Signal Processing, KTH, and Linköping University, Linköping, Sweden
§Dip.
Ingegneria dell’Informazione, University of Pisa, Pisa, Italy
†Bell
Laboratories, Alcatel-Lucent, Stuttgart, Germany
Best Paper Award
2014-04-07
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Introduction: Multi-User MIMO System
• Multi-User Multiple-Input Multiple-Output (MIMO)
-
One base station (BS) with array of  antennas
 single-antenna user equipments (UEs)
Downlink: Transmission from BS to UEs
Share a flat-fading subcarrier
• Multi-Antenna Precoding
-
Spatially directed signals
Signal improved by array gain
Adaptive control of interference
Serve multiple users in parallel
K users, M antennas
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WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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What if We Design for Energy Efficiency?
• Cell: Area with user location and pathloss distribution
• Pick  users randomly and serve with rate 
Some UE
Distribution
Clean-Slate
Design
Select (, , )
to maximize EE!
2014-04-07
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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How to Measure Energy Efficiency?
• Energy Efficiency (EE) in bit/Joule
 =
bit
channel use
Joule
Power Consumption
channel use
Average Sum Throughput
• Conventional Academic Approaches
- Maximize throughput with fixed power
- Minimize transmit power for fixed throughput
• New Problem: Balance throughput and power consumption
- Crucial: Account for overhead signaling
- Crucial: Use detailed power consumption model
2014-04-07
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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System Model
2014-04-07
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Average Sum Throughput
1
• System Model
2
- Precoding vector of User : v
- Channel vector of User : h ~ (, λ )
• Random User Selection
- Channel variances λ from some distribution λ ()
• Achievable Rate of User :
- TDD mode, perfect channel estimation (coherence time )
Average over channels
and user locations
Signal-to-interference+noise ratio (SINR)
Cost of estimation
2014-04-07
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Average Sum Throughput (2)
• How to Select Precoding?
- The same rate  =  for all users
- “Optimal” precoding: Extensive computations – Not efficient
• Notation
- Matrix form:  = [1 , … ,  ],  = [1 , … ,  ]
- Power allocation: 1 , … , 
Maximize
signal
Minimize
interference
• Heuristic Closed-Form Precoding
- Maximum ratio transmission (MRT): v =
- Zero-forcing (ZF) precoding:  =    
- Regularized ZF (RZF) precoding:
 h
−1 diag( , … ,  )
1

 = ( 2  +
Balance signal and interference
2014-04-07
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Detailed Power Consumption Model
• Many Things that Consume Power
- Radiated transmit power tr( )
- Baseband processing (e.g., precoding)
- Active circuits (e.g., converters, mixers, filters)
• Generic Power Consumption
E{tr  )
+ 0,0 + 0,1  + 1,0  + 1,1  + 2,0  2 + 3,0  3 + 2,1  2
η
Power amplifier
(η is efficiency)
Circuit power per
transceiver chain
Cost of channel estimation
and precoding computation
Fixed power
(control signals,
Coding/decoding
load-independ. processing, data streams
backhaul infrastructure)
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Nonlinear
function of  and 
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Problem Formulation
• Define power parameter 
- Rate per user:   =  = 1 −


log 2 1 +   − 
Lemma 1 (Average radiated power with ZF)
E{tr  ) = λ
where λ = E
Simple
expression
ZF in analysis
Other precoding
in simulations
2014-04-07
2
λ
depends on UE distribution, propagation, etc.
Maximize Energy Efficiency for ZF
 =
Average Sum Throughput
=
1
Power Consumption

 1 −  log 2 1 +   − 
η λ +
3  
=0 ,0
+
2  
=0 ,1
Maximize with respect to , , and 
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Overview of Analytic Results
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WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Analytic Results and Observations
• Optimization Results
- EE is quasi-concave function of (, , )
- Closed-form optimal , , or  when other two are fixed
Antennas 
Reveals how
variables are
connected
Users 
Transmit
power λ
Large Cell
More antennas,
users, power
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Increases with
Decreases with
Power , coverage area λ , and
-independent circuit power
-related circuit power
Fixed circuit power 0,0 and
coverage area λ
-related circuit power
Circuit power, coverage area λ ,
antennas , and users 
-
More Circuit
Power
Use more
transmit power
Limits of , 
More Antennas
Circuit power that
scales with ,
Use more
transmit power
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Numerical Examples
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WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Simulation Scenario
• Main Characteristics
- Circular cell with radius 250 m
- Uniform user distribution with 35 m minimum distance
- Uncorrelated Rayleigh fading, typical 3GPP pathloss model
• Realistic Modeling Parameters
- See the paper for details!
2014-04-07
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Optimal System Design: ZF Precoding
Optimum
 = 165
 = 85
ߩ = 4.6
User rates:
as 256-QAM
Massive
MIMO!
Very many
antennas,
/ ≈ 2
2014-04-07
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Optimal System Design: MRT
Optimum
=4
=1
ߩ = 12.7
User rates:
as 64-QAM
Single-user
transmission!
Only exploit
precoding gain
2014-04-07
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Why This Huge Difference?
• Interference is the Limiting Factor
- ZF: Suppress interference actively
- MRT: Only indirect suppression by making  ≫ 
Only 2x
difference
in EE
100x
difference
in throughput
• More results: RZF≈ZF, same trends under imperfect CSI
2014-04-07
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Energy Efficient to Use More Power?
• Recall: Transmit power increases with 
- Figure shows EE-maximizing power for different 
Almost
linear
growth
- Different from recent 1/ scaling laws
- Power per antennas decreases, but only logarithmically
2014-04-07
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Conclusions
2014-04-07
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Conclusions
• What if a Single-Cell System Designed for High EE?
• Contributions
- General power consumption model
- Closed-form results for ZF: Optimal number of antennas
Optimal number of UEs
Optimal transmit power
- Observations: More circuit power  Use more transmit power
• Numerical Example
- ZF/RZF precoding: Massive MIMO system is optimal
- MRT precoding: Single-user transmission is optimal
- Small difference in EE, huge difference in throughput!
2014-04-07
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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Thank You for Listening!
Questions?
More details and multi-cell results:
E. Björnson, L. Sanguinetti, J. Hoydis, M. Debbah,
“Optimal Design of Energy-Efficient Multi-User MIMO Systems:
Is Massive MIMO the Answer?,”
Submitted to IEEE Trans. Wireless Communications, Mar. 2014
Matlab code available for download!
Best Paper Award
2014-04-07
WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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