designing multi-user mimo for energy efficiency

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


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


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



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 )


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:


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


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:


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


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


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


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!


Optimal System Design: ZF Precoding


Optimum

User rates:as 256-QAM

Massive MIMO!

Very many antennas,

Optimal System Design: MRT


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


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


Almostlinear

growth


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 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

designing multi-user mimo for energy efficiency

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