1. Argos Clayton W. Shepard Hang Yu, Narendra Anand, Li Erran Li, Thomas Marzetta, Richard Yang, Lin Zhong Practical Many-Antenna Base Stations

2. Motivation Spectrum is scarce Hardware is cheap 2

3. MU-MIMO Theory More antennas = more capacity –Multiplexing –Power –Orthogonality 3

4. 4 Data 2 Data 1 Data 6 Data 3 Data 4 Data 5

5. Why not? Nothing scales with the number of antennas –CSI Acquisition –Computation –Data Transportation 5

6. Background: Beamforming 6 = Constructive Interference = Destructive Interference ?

7. 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 7 + + = 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) Tx RxRx RxRx RxRx Uplink? Tx RxRx Measured channels are not reciprocal due to differences in the Tx and Rx hardware!

8. Background: Multi-User Beamforming 8 Data 1

9. Background: Multi-User Beamforming 9 Data 2

10. Background: Null Steering 10 Data 1 Null

11. Background: Null Steering 11 Data 2 Null

12. Background: Multi-User Beamforming 12 Data 2 Data 1 Data 6 Data 3 Data 4 Data 5

13. Background: Scaling Up 13 Data 1

14. Background: Scaling Up 14 Data 1

15. Background: Scaling Up 15 Data 1

16. Data 3 Data 5 Background: Scaling Up 16 Data 1 Data 6 Data 2 Data 4

17. Background: Linear Precoding Calculate beamweights –Every antenna has a beamweight for each terminal Multiply symbols by weight, then add together: 17

18. Recap 1)Acquire CSI 2)Calculate Weights 3)Apply Linear Precoding 18

19. Scalability Challenges 1)Acquire CSI –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 19

20. Argos’ Solutions 1)Acquire CSI –New reciprocal calibration method 2)Calculate Weights –Novel distributed beamforming method 3)Apply Linear Precoding –Carefully designed scalable architecture 20 O(MK) → O(K) O(MK 2 ) → O(K)* O(MK) → O(K) *

21. Solutions Reciprocal Calibration Distributed Beamforming Scalable Architecture 21

22. Channel Reciprocity Pilot transmission source? –Basestation –Terminal Base station pilot transmission –Requires feedback –M pilots (M ≥ K) Terminal pilot transmission –No feedback –K pilots 22

23. Channel Reciprocity 23 Tx C Rx C Tx A Rx A Tx B Rx B Channel Estimation Transmission Tx/Rx Chain differences require calibration Can we do this without terminal involvement? Tx A +Rx C -Tx C - Rx A Tx B +Rx C -Tx C - Rx B Tx A +C+Rx C -Tx C - C-Rx A

24. Key Idea 24 = = Any constant phase shift results in same beampattern!

25. Channel Reciprocity 25 Tx C Rx C Tx A Rx A Tx B Rx B Channel Estimation Transmission Tx A +Rx C -Tx C - Rx A Tx B +Rx C -Tx C - Rx B Tx A -Rx A Tx B -Rx B

26. Find phase difference between A and B Tx from A: Phase offset = Tx A -Rx A Tx from B: (Tx B -Rx B ) Internal Reciprocal Calibration 26 Tx A Rx A Tx B Rx B Tx A +C+Rx B Tx B +C+Rx A Tx A +Rx B - Tx B - Rx A add phase difference + (Tx A +Rx B - Tx B - Rx A ) = Tx A - Rx A

27. Solutions Reciprocal Calibration Distributed Beamforming Scalable Architecture 27

28. Problems with Existing Methods Central data dependency Transport latency causes capacity loss Can not scale –Becomes exorbitantly expensive then infeasible 28

29. Conjugate Beamforming Requires global power scaling by constant: Where, e.g.: This creates a central data dependency 29

30. BS Good Channel Conjugate Beamforming Power 30 Okay Channel Bad Channel

31. BS High Power Conjugate Beamforming Power 31 Normal Power Low Power

32. Distributed Conjugate Beamforming Scale power at each antenna: Maximizes utilization of every radio –More appropriate for real-world deployments Quickly approaches optimal as K increases –Channels are independent and uncorrelated 32

33. BS High Power Distributed Conjugate Beamforming 33 High Power

34. Solutions Reciprocal Calibration Distributed Beamforming Scalable Architecture 34

35. ? ? Scalable –Support thousands of BS antennas Cost-effective –Cost scales linearly with # of antennas 35 Architectural Design Goals … Data

36. Linear Precoding 36 … M K K K K … … … …

37. Scalable Linear Precoding 37 … M K K K K … … … …

38. Scalable Linear Precoding 38 … M K K K K Common Databus! … … … …

39. MUBF Linear Precoding: Uplink 39 … M K … K K K … … …

40. Scalable Linear Precoding 40 … … … … … M K Constant Bandwidth! K K K

41. Partition Ramifications CSI and weights are computed and applied locally at each BS radio –No overhead for additional BS radios No central data dependency –No latency from data transport –Constant data rate common bus (no switching!) Unlimited scalability! 41

42. How do we design it? Daisy-chain (series) –Unreliable –Large end to end latency Token-ring | Interconnected –Not amenable to linear precoding –Variable Latency –Routing overhead 42 … … … Flat structure –Unscalable –Expensive, with large fixed cost

43. Solution: Argos Architecture 43 Central Controller Argos Hub Module … … … Data Backhaul Module Radio …

44. Argos Implementation 44 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

45. 45

46. 46 WARP Module s Central Controller Argos Hub Clock Distribution Ethernet Switch Sync Distribution Argos Interconnects

47. System Performance 47

48. Linear Gains as # BS Ant. Increases Capacity vs. M, with K = 15 48

49. Linear Gains as # of Users Increases Capacity vs. K, with M = 64 49

50. Scaling # of Users with 16 BS Ant. Capacity vs. K, with M = 16 50

51. Zero-forcing is not always better! Capacity vs. K, with M = 16 | Low Power 51

52. Calibration is stable for hours! 52

53. Conclusion First many-antenna beamforming platform –Real-world demonstration of manyfold capacity increase Devised novel techniques and architecture –Unlimited Scalability 53 http://recg.rice.e du http://argos.rice.edu