Designing Wireless Broadband Access for Energy Efficiency

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1 Designing Wireless Broadband Access for Energy Efficiency Are Small Cells the Only Answer? Emil Björnson 1, Luca Sanguinetti 2,3, Marios Kountouris 3,4 1 Linköping University, Linköping, Sweden 2 University of Pisa, Pisa, Italy 3 CentraleSupélec, Gif-sur-Yvette, France 4 Huawei Technologies, Paris, France

Emil Björnson 1, Luca Sanguinetti 2,3, Marios Kountouris 3,4 1 Linköping

2 2 INTRODUCTION

3 Energy Efficiency Benefit-Cost Analysis of Networks Cost: Power Consumption [Watt = Joule/s] Network Benefit: Sum Rate [bit/s] Definition: Energy Efficiency (EE): Average Sum Rate bit/s/km 9 EE [bit/joule] = Power Consumption Joule/s/km 9 Future networks: 1000x more data à 1000x higher EE How to Improve Energy Efficiency? One approach: Reduce radiated power Achieved by smaller cells 3 Is Smaller Cells the Only Answer? 1. Formulate EE maximization mathematically 2. Optimize cell density and other parameters what do we get?

1000x more data à 1000x higher EE How to Improve Energy Efficiency?

4 4 PROBLEM FORMULATION

5 System Model and Average Rate Random Network Deployment Access points (AP) positions as Poisson point process (PPP) M antennas per AP, K users per cell Pathloss: ω DE (distance [km]) DJ Scenario: Downlink Broadband Access Perfect channel knowledge Zero- forcing precoding Transmit power per user: ρ Hardware distortion at users: ε 9 5 Proposition 1: Lower Bound on Average Rate R = B O log Bandwidth (1 ε 9 )(M K) 2K α 2 + ε 9 M K + M ωσ 9 πλ J/9 ρ

Access Perfect channel knowledge Zero- forcing precoding Transmit power per user: ρ Hardware distortion at users:

6 Generic Power Consumption Model Many Components Consume Power Radiated transmit power Baseband signal processing (e.g., precoding) Active circuits (e.g., converters, mixers, filters) Area Energy Consumption [Joule/s/km 9 ]: Nonlinear increasing function of M and K 6 AEC = λ Kρ η + C Z + D Z M + C E K + D E MK Power amplifier (η is efficiency) Circuit power per transceiver chain Fixed power (backhaul, load- ind. processing) Cost of digital signal processing (e.g., precoding) Many coefficients: η, C ], D^ for i = 0,1

(e.g., precoding) Active circuits (e.g., converters, mixers, filters) Area Energy Consumption [Joule/s/km 9 ]: Nonlinear

7 Problem Formulation maximize ρ, λ, M, K Energy Efficiency Optimization subject to R/B = γ λkr λ Kρ η + C Z + D Z M + C E K + D E MK Optimization variables: ρ = transmit power, λ = AP density, M = antennas per AP, K = users per AP Spectral efficiency (SE) constraint γ needed to not get overly low rates 7

variables: ρ = transmit power, λ = AP density, M = antennas per AP, K =

8 ANALYTICAL AND NUMERICAL RESULTS 8

9 Optimality of Small Cells Theorem 1: Optimal AP Density The EE increases with λ. EE maximized as λ or at some upper value λ ghi Saturation Property Higher density λ à Less transmit power à Eventually negligible Simulations show saturation at λ meters between APs: Saturation appears in practice! 9

EE maximized as λ or at some upper value λ ghi Saturation Property Higher

10 Optimization of Remaining Variables Theorem 2: Optimal Transmit Power Constraint satisfied if ρ = l m no onl m p l qrl s(t/luo) vw t/l xdyd lm no onl m p l lz tnl Removes ρ from EE optimization problem (Only M and K remain) Theorem 3: Optimal Number of Antennas (fixed K) EE maximized by M = K + 9y(9m DE) (JD9)(ED9 m { l ) + 9m DE y } l ~(J/9E) ED9 m { t/l ( ƒ o y) Theorem 4: Optimal Number of Users (fixed M/K = β) 10 EE maximized by K = l m no onl m p l qrl s(t/luo) vw t/l + ƒ o ( DED lm no onl m p l l tnl ) o Tradeoffs and connections established formulas! Iterate between these till convergence: Find real- valued global solution

l ) + 9m DE y } l ~(J/9E) ED9 m { l @ t/l ( ƒ o y) Theorem 4: Optimal Number of Users (fixed M/K = β) 10 EE maximized by K = l m no onl m p l qrl s(t/luo) vw

11 Simulation Parameters Simulation Parameter Symbol Value Pathloss exponent α 3.76 Pathloss over noise at 1 km ω/σ 9 33 dbm Amplifier efficiency η 0.39 Level of hardware impairments ε 0.05 Bandwidth B 20 MHz Static power C Z 10 W Circuit power per active user C E 0.1 W Circuit power per AP antenna D Z 1 W Signal processing coefficient D E 3.12 mw 11

12 Impact of Number of Antennas and Users Simulation AP density λ = 10 Š SE constraint: γ = 3 Observations Optimal: M = 193, K = 21 M K : Called Massive MIMO Alternating optimization finds real- valued solution 12

13 Is it Ridiculous with 200 Antennas? Dimensionality: Half- wavelength Antenna Spacing Example: 3.7 GHz Spacing: 4 cm Array = Flat- screen TV 160 dual-polarized antennas, LuMaMi testbed, Lund University Why Massive MIMO, Not Only Small Cells? Small cells improve SNR, but not SINR Massive MIMO improves SINRs by precoding Circuit power costs are shared between users 13

7 GHz Spacing: 4 cm Array = Flat- screen TV 160 dual-polarized antennas, LuMaMi testbed,

14 Impact of User Density Simulation text Fixed user density μ users/km 2 EE maximization with: Kλ = μ Range: μ = 10 9 (rural) to μ = 10 Ž (mall) Energy Efficiency: Low User Density Add more cells with K 1 Most important to reduce pathloss AP Density: High User Density Small cells with Massive MIMO Saturation for μ 100 Covers most practical scenarios: EE independent of user load! 14

cells with K 1 Most important to reduce pathloss AP Density: High User Density Small cells with

15 15 SUMMARY

16 Summary Designing Networks for Energy Efficiency Optimize: AP density, transmit power, and antennas/users per cell Analytical optimization: EE maximizing network deployment was found! Solution: Small cells with Massive MIMO capability Intuition: Small cells à Negligible transmit power Massive MIMO à Less interference, share costs over users Further Results: Take channel estimation and imperfect channel knowledge into account 1. E. Björnson, L. Sanguinetti, M. Kountouris, Deploying Dense Networks for Maximal Energy Efficiency: Small Cells Meet Massive MIMO, Submitted to IEEE JSAC. ( 2. E. Björnson, L. Sanguinetti, M. Kountouris, Energy-Efficient Future Wireless Networks: A Marriage between Massive MIMO and Small Cells, Proceedings of IEEE SPAWC, July ( 16

and imperfect channel knowledge into account 1. E. Björnson, L. Sanguinetti, M.

17 QUESTIONS? Visit Emil Björnson online:

CARLETON UNIVERSITY Department of Systems and Computer Engineering. SYSC4700 Telecommunications Engineering Winter 2014. Term Exam 13 February 2014

CARLETON UNIVERSITY Department of Systems and Computer Engineering SYSC4700 Telecommunications Engineering Winter 2014 Term Exam 13 February 2014 Duration: 75 minutes Instructions: 1. Closed-book exam