designing multi-user mimo for energy efficiency

Designing Multi-User MIMO for Energy Efficiency

Emil Björnson‡*

Joint work with: Luca Sanguinetti‡§, Jakob Hoydis†, and Mérouane Debbah‡

‡Alcatel-Lucent Chair on Flexible Radio, Supélec, France*Dept. Signal Processing, KTH Royal Institute of Technology, Sweden

§Dip. Ingegneria dell’Informazione, University of Pisa, Pisa, Italy†Bell Laboratories, Alcatel-Lucent, Stuttgart, Germany

2013-11-07 Greentouch Open Forum, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec & KTH) 1

When is Massive MIMO the Answer?

http://www.greentouch.org/index.php?page=home

Outline

• Presentation is based onE. Björnson, L. Sanguinetti, J. Hoydis, M. Debbah, “Designing Multi-User MIMO for Energy Efficiency: When is Massive MIMO the Answer?,” Submitted IEEE WCNC 2014 Preprint available on arXiv: http://arxiv.org/abs/1310.3843.

• Main Question- How should a single-cell downlink system be designed to

maximize energy efficiency?- Optimization variables: Number of base station

antennasNumber of active user

equipmentsData rate guaranteed per user

• Conclusions- Result depends strongly on physical layer precoding scheme- Unconventionally many users and antennas can be optimal!2013-11-07 Greentouch Open Forum, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec & KTH) 2


Introduction

What are the Expectations?

• Tons of Plenary Talks and Overview Articles- Fulfilling dream of ubiquitous wireless connectivity

• Expectation: Many Metrics Should Be Improved in 5G- Higher user data rates - Higher area throughput- Great scalability in number of connected devices- Higher reliability and lower latency- Better coverage with more uniform user rates- Improved energy efficiency

• These are Conflicting Metrics!- Impossible to maximize all metrics simultaneously- Our goal: High energy efficiency (EE) with uniform user rates


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 carrier

• Multi-Antenna Precoding- Spatially directed signals- Signal improved by array gain- Adaptive control of interference- Serve multiple users in parallel


Space-division multiple access(SDMA)

Multi-User MIMO System (2)

• Cell: Area with user location and pathloss distribution• Scheduling: Pick users randomly, with random location


Clean-Slate Design

Select and to maximize EE!

Some UEDistribution

How to Measure Energy Efficiency?

• Energy Efficiency in bits/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 reasonable power consumption model



System Model:Average Sum Throughput

Time-Division Duplex (TDD) Protocol

• Coherence Period: [channel uses]


Assumption:Perfect estimation

Average Sum Throughput

• System Model- Precoding vector of User : - Channel vector of User :

• Random User Selection, - Channel variances Independent random variables,

• Achievable Rate of User :


Cost of estimation

Average over channels and user selectionSignal-to-interference+noise ratio

(SINR)

𝐡1❑ 𝐡2

❑

Impact of Precoding

• What Determines User Rates?- Precoding (vector directions and power allocations)- “Optimal” precoding: Extensive computations – Not efficient

• Notation- Matrix form: , - Total radiated power: )

• Heuristic Closed-Form Precoding- Maximum ratio transmission (MRT): - Zero-forcing (ZF) precoding: - Regularized ZF (RZF) precoding:


Maximizesignal

Minimizeinterference

Balance signal and interference

Uniform User Performance

• Assumption: Uniform user performance- Same rate at every user: - Scaling parameter can be optimized

• Consequence:- We use ZF in analysis and other precoding for simulation


Lemma 1

Consider ZF precoding and the user rates above, the average radiated power is

where depends on UE distribution, propagation, etc.


System Model:Power Consumption

Reasonable Power Consumption Model

• What Consumes Power?- Examples will motivate our model

• Transmit Power: - = Average radiated transmit power- = Efficiency of power amplifier at BS

• Transceiver Chains: - = Circuit power / BS antenna (converters, mixers, filters)- = Power of common oscillator at BS- = Circuit power / UE (oscillator, converters, mixer, filters)

• Coding and Decoding: - = Power for coding at BS / user- = Power for decoding at each user


Reasonable Power Consumption Model (2)

• Computational Efficiency: operations per Joule

• Uplink Channel Estimation: - Only once per coherence period- channel components per user, processed separately

• Precoding: - Only once per coherence period- Depends on precoding:

• Architectural Costs: - Control signaling, backhaul infrastructure,

load-independent processing, etc.


Reasonable Power Consumption Model (3)

• Summary- General model of power consumption: for some parameters and


Energy Efficiency for ZF

User rate: Radiated power:

Design parameters: , , and


Optimize System Parametersfor Energy Efficiency

Preliminaries

• Our Goal:- Optimize number of antennas - Optimize the (normalized) transmit power - Optimize number of active UEs

• Definition- Lambert function, , solves equation - The function is increasing and satisfies - behaves almost as linear:


Optimal Number of BS Antennas

• Find that maximizes EE with ZF precoding:

• Observations- Increases sublinearly with power but linearly at high - Increases with circuit power coefficients independent of - Decreases with circuit power coefficients multiplied with


Theorem 1 (Optimal )

Optimal Transmit Power

• Find that maximizes EE with ZF precoding:𝜌

• Observations- Increases power with number of antennas as - Opposite to recent claim: Power should decrease as - Intuition: Higher circuit power Use more transmit power


Theorem 2 (Optimal 𝜌)

Optimal Number of Active UEs

• Find that maximizes EE with ZF precoding:

where and are fixed.

• Observations- Decreases with circuit power coefficients multiplied with or - Increases with the static hardware power - Increases with the propagation parameter


Theorem 3 (Optimal )

Solution is a root to a quartic polynomial:Closed-form but very large expressions


Numerical Illustrations

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


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 Theorem 2: Transmit power increases with - Figure shows EE-maximizing power for different

• Intuition: More Circuit Power Use More Transmit Power2013-11-07 Greentouch Open Forum, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec & KTH) 27

Essentiallylinear

growth


Conclusions

Conclusions

• What if a Single-Cell System Designed for High EE?- Need: Reasonable throughput model- Need: Reasonable power consumption model

• Contributions- General power consumption model- Closed-form results for ZF: Optimal number of antennas

Optimal number of active 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, but huge difference in throughput!


2013-11-07 30Greentouch Open Forum, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec & KTH)

Thank You for Listening!

Questions?

Main reference:E. Björnson, L. Sanguinetti, J. Hoydis, M. Debbah, “Designing Multi-User MIMO for Energy Efficiency:

When is Massive MIMO the Answer?”

Submitted IEEE WCNC 2014Preprint available on arXiv: http://arxiv.org/abs/1310.3843

http://www.greentouch.org/index.php?page=home

designing multi-user mimo for energy efficiency

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