Designing Multi-User MIMO for Energy Efficiency

1. Designing Multi-User MIMO for Energy Efficiency When is Massive MIMO the Answer? Emil Björnson‡*, Luca Sanguinetti‡§, Jakob Hoydis†, and MérouaneDebbah‡ ‡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 Best Paper Award WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

2. 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 usersin parallel K users, M antennas WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

3. 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! WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

4. 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 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

5. System Model WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

6. 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 ) • Average over channels and user locations • Signal-to-interference+noise ratio (SINR) • Cost of estimation WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

7. 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: • Maximize • signal • Minimizeinterference • Balance signal and interference WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

8. 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 • Circuit power pertransceiver chain • Cost of channel estimationand precoding computation • Power amplifier( is efficiency) • Fixed power(control signals, load-independ. processing,backhaul infrastructure) • Coding/decodingdata streams Nonlinearfunction of and WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

9. Problem Formulation • Define power parameter • Rate per user: Lemma 1 (Average radiated power with ZF) • where depends on UE distribution, propagation, etc. Simple expressionZF in analysis Other precoding in simulations Maximize Energy Efficiency for ZF • Maximize with respect to , , and WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

10. Overview of Analytic Results WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

11. Analytic Results and Observations • Optimization Results • EE is quasi-concave function of • Closed-form optimal , , or when other two are fixed • Reveals howvariables are connected Limits of ,Circuit power that scales with , • More Circuit Power Use more transmit power • More AntennasUse more transmit power Large CellMore antennas, users, power WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

12. Numerical Examples WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

13. 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! WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

14. Optimal System Design: ZF Precoding Optimum User rates: as 256-QAM Massive MIMO! Very many antennas, WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

15. Optimal System Design: MRT Optimum User rates: as 64-QAM Single-user transmission! Only exploitprecoding gain WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

16. 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 • 100x • difference • in throughput • Only 2x • difference • in EE WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

17. 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 • Almost • linear • growth WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

18. Conclusions WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

19. 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! WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)

20. 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 WCNC 2014, Designing Multi-User MIMO for Energy Efficiency, E. Björnson (Supélec, KTH, Linköping)