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Designing Multi-User MIMO for Energy Efficiency. When is Massive MIMO the Answer?. Emil Björnson ‡* , Luca Sanguinetti ‡§ , Jakob Hoydis † , and Mérouane Debbah ‡ ‡ Alcatel-Lucent Chair on Flexible Radio, Supélec , France - PowerPoint PPT Presentation
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Designing Multi-User MIMO for Energy Efficiency
Emil Björnson‡*, Luca Sanguinetti‡§, Jakob Hoydis†, and Mérouane Debbah‡
‡Alcatel-Lucent Chair on Flexible Radio, Supélec, France*Dept. Signal Processing, KTH, and Linköping University, Linköping, Sweden
§Dip. Ingegneria dell’Informazione, University of Pisa, Pisa, Italy†Bell Laboratories, Alcatel-Lucent, Stuttgart, Germany
2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 1
When is Massive MIMO the Answer?
Best Paper Award
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
2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 2
K users, M antennas
What if We Design for Energy Efficiency?
• Cell: Area with user location and pathloss distribution• Pick users randomly and serve with rate
2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 3
Clean-Slate Design
Select to maximize EE!
Some UEDistribution
How to Measure Energy Efficiency?
• Energy Efficiency (EE) in bit/Joule
• 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) 4
2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 5
System Model
Average Sum Throughput
• System Model- Precoding vector of User : - Channel vector of User :
• Random User Selection- Channel variances from some distribution
• Achievable Rate of User :- TDD mode, perfect channel estimation (coherence time )
2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 6
Cost of estimation
Average over channels and user locations
Signal-to-interference+noise ratio (SINR)
𝐡1❑ 𝐡2
❑
Average Sum Throughput (2)
• How to Select Precoding?- The same rate for all users- “Optimal” precoding: Extensive computations – Not efficient
• Notation- Matrix form: , - Power allocation:
• Heuristic Closed-Form Precoding- Maximum ratio transmission (MRT):
- Zero-forcing (ZF) precoding:
- Regularized ZF (RZF) precoding:
2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 7
Maximizesignal
Minimizeinterference
Balance signal and interference
Detailed Power Consumption Model
• Many Things that Consume Power- Radiated transmit power )- Baseband processing (e.g., precoding)- Active circuits (e.g., converters, mixers, filters)
• Generic Power Consumption
2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 8
Fixed power(control signals,
load-independ. processing,backhaul infrastructure)
Power amplifier( is efficiency)
Circuit power pertransceiver chain
Coding/decodingdata streams
Cost of channel estimationand precoding computation
Nonlinearfunction of and
Problem Formulation
• Define power parameter - Rate per user:
2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 9
Maximize Energy Efficiency for ZF
Maximize with respect to , , and
Lemma 1 (Average radiated power with ZF)
where depends on UE distribution, propagation, etc.
Simple expression
ZF in analysis
Other precoding in simulations
2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 10
Overview of Analytic Results
Analytic Results and Observations
• Optimization Results- EE is quasi-concave function of - Closed-form optimal , , or when other two are fixed
2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 11
Increases with Decreases with
Antennas Power , coverage area , and -independent circuit power
-related circuit power
Users Fixed circuit power and coverage area
-related circuit power
Transmit power
Circuit power, coverage area , antennas , and users
-
Large Cell
More antennas, users, power
More Circuit Power
Use more transmit power
More Antennas
Use more transmit power
Limits of ,
Circuit power that scales with ,
Reveals howvariables are connected
2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 12
Numerical Examples
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) 13
Optimal System Design: ZF Precoding
2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 14
Optimum
User rates:as 256-QAM
Massive MIMO!
Very many antennas,
Optimal System Design: MRT
2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 15
Optimum
User rates:as 64-QAM
Single-user transmission!
Only exploitprecoding gain
Why This Huge Difference?
• Interference is the Limiting Factor- ZF: Suppress interference actively- MRT: Only indirect suppression by making
• More results: RZFZF, same trends under imperfect CSI
2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 16
Only 2xdifference
in EE
100xdifference
in throughput
Energy Efficient to Use More Power?
• Recall: Transmit power increases with - Figure shows EE-maximizing power for different
- Different from recent 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) 17
Almostlinear
growth
2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 18
Conclusions
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 UEsOptimal 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) 19
2014-04-07 20WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)
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