Networking with massive MU-MIMO Lin Zhong

Guiding Principles Spectrum is scarce Hardware is cheap, and getting cheaper 2

Antennas 3

4 Data 1 Omni-directional base station Poor spatial reuse; poor power efficiency; high inter-cell interference

5 Data 1 Data 2 Data 3 Sectored base station Better spatial reuse; better power efficiency; high inter-cell interference

6 Data 2 Data 1 Data 3 Data 5 Single-user beamforming base station Better spatial reuse; best power efficiency; reduced inter-cell interference

7 Data 2 Data 1 Data 6 Data 3 Data 4 Data 5 Multi-user MIMO base station M: # of BS antennas K: # of clients (K ≤ M) Best spatial reuse; best power efficiency; reduced inter-cell interference

Key benefits of MU-MIMO High spectral efficiency High energy efficiency Low inter-cell interference Orthogonal to Small Cell solutions –Centralized vs. distributed antennas 8

Why massive? More antennas  Higher spectral efficiency More antennas  Higher energy efficiency Simple baseband technique becomes effective 9 T.L. Marzetta. Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans. on Wireless Comm., 2010.

Background: Beamforming 10

Background: Beamforming 11 = Constructive Interference

Background: Beamforming 12 = Constructive Interference = Destructive Interference

Background: Beamforming 13 = Destructive Interference = Constructive Interference

Background: Beamforming 14 ?

Due to environment and terminal mobility estimation has to occur quickly and periodically BS The CSI is then calculated at the terminal and sent back to the BS A pilot is sent from each BS antenna Background: Channel Estimation Align the phases at the receiver to ensure constructive interference For uplink, send a pilot from the terminal then calculate CSI at BS Path Effects (Walls) Uplink?

Background: Multi-user MIMO 16 BS M: # of BS antennas K: # of clients K ≤ M

Multi-user MIMO: Precoding 17 BS (M x 1 matrix) (Kx1 matrix) M: # of BS antennas K: # of clients K ≤ M

Linear Precoding 18 BS (M x 1 matrix) (Kx1 matrix) M: # of BS antennas K: # of clients K ≤ M

Background: Zeroforcing Beamforming 19 Data 1 Null

Background: Zeroforcing Beamforming 20 Data 2 Null

Background: Zeroforcing Beamforming 21 Data 2 Data 1 Data 6 Data 3 Data 4 Data 5

Background: Conjugate Beamforming 22 Data 1

With more antennas 23 Data 1

With even more antennas 24 Data 1

Data 3 Data 5 Conjugate Multi-user Beamforming Data 1 Data 6 Data 2 Data 4 Conjugate approaches Zeroforcing as M/K  ∞

Conjugate vs. Zeroforcing Trivial computation Suboptimal capacity Scalable Nontrivial computation Close to capacity achieving Not scalable 26

Recap 1)Estimate channels 2)Calculate weights 3)Apply linear precoding 27

Scalability Challenges 1)Estimate channels –M+K pilots, then MK feedback 2)Calculate weights –O(MK 2 ), non-parallelizable, centralized data 3)Apply linear precoding –O(MK), then O(M) data transport 28

Argos’ Solutions 1)Estimate channels –New reciprocal calibration method 2)Calculate weights –Novel distributed beamforming method 3)Apply linear precoding –Carefully designed scalable architecture O(MK) → O(K) O(MK 2 ) → O(K) O(MK) → O(K) C. Shepard et al. Argos: Practical many-antenna base stations. ACM MobiCom, 2012.

Solution: Argos Architecture 30 Central Controller Argos Hub Module … … … Data Backhaul Module Radio …

Argos Implementation 31 Central Controller Argos Hub Module … Central Controller (PC with MATLAB) Sync Pulse Ethernet Clock Distribution Argos Hub Argos Interconnect Argos Interconnect WARP Module FPGA Power PC FPGA Fabric Hardware Model Peripherals and Other I/O Clock Board Daughter Cards Radio 4 Radio 3 Radio 2 Radio 1 WARP Module FPGA Power PC FPGA Fabric Hardware Model Peripherals and Other I/O Clock Board Daughter Cards Radio 4 Radio 3 Radio 2 Radio 1 16 … … … Ethernet WARP Module FPGA Power PC FPGA Fabric Hardware Model Peripherals and Other I/O Clock Board Daughter Cards Radio 4 Radio 3 Radio 2 Radio 1 Argos Interconnect Argos Interconnect

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33 WARP Module s Central Controller Argos Hub Clock Distribution Ethernet Switch Sync Distribution Argos Interconnects

Experimental Setup Time Division Duplex (TDD) –Uplink and Downlink use the same band Downlink 34 Listen to pilot Calculate BF weights Send data

Conjugate vs. Zeroforcing 35

Without considering computation 36 Listen to pilot Calculate BF weights Send data

Linear gains as # of BS antennas increases Capacity vs. M, with K = 15 37

Linear gains as # of users increases Capacity vs. K, with M = 64 38

Considering computation 39 Listen to pilot Calculate BF weights Send data

40 Zeroforcing with various hardware configurations M = 64 K = 15

Conclusion First many-antenna beamforming platform –Demonstration of manyfold capacity increase Devised novel techniques and architecture –Unlimited Scalability Simplistic conjugate beamforming works Need adaptive solutions 41

Ongoing work A network of massive MU-MIMO base stations 42 Inter-cell interference management Pilot contamination Client grouping & scheduling

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44 ~$2,000 per antenna

Acknowledgments 45

More BS antennas + MU-MIMO  Higher efficiency & lower interference Data 2 Data 1 Data 6 Data 3 Data 4 Data 5

Data 10 Data 12 Data 2 Data 8 Data 6 Data 4 Data 5 Data 9 Data 1 Data 11 Data 3 Data 7 More BS antennas + MU-MIMO  Higher efficiency & lower interference