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) 1 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 2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 2 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) 3 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) 4 System Model 2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 5 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) 6 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) 7 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) 2014-04-07 Nonlinear function of and WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 8 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) 9 Overview of Analytic Results 2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 10 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 2014-04-07 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) 11 Numerical Examples 2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 12 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 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) 14 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) 15 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) 16 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) 17 Conclusions 2014-04-07 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping) 18 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) 19 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) 20