Many-antenna base stations are interesting systems. Lin Zhong

Transcription

1 Many-antenna base stations are interesting systems Lin Zhong

2 2

3 How we got started Why many-antenna base station What we have learned What we are doing now 3

4 How we started Why a mobile system guy got interested in massive MIMO 4

5 Wireless consumes a lot of power Power (mw) HTC Wizard October Power profile!=energy profile 5

6 First insight Wi-Fi more efficient than cellular MobiSys 07 6

7 Why is Wi-Fi more efficient? P TX = a*d 2 D 7

8 Horribly wasteful 8

9 Directional transmission! 9

10 Passive directional antenna to save energy (MobiCom 10) No power overhead Fixed bean patterns 10

11 Beamforming to save energy (MobiCom 11) Extra transceivers Steerable beams 11

12 Power by multi-antenna systems (uplink) P Circuit P PA =P TX / η Baseband Signal DAC Filter Mixer Filter PA 1 Frequency Synthesizer N P Shared Baseband Signal DAC Filter Mixer Filter PA N P = P shared + N P Circuit + P TX / η 12

13 Circuit vs. radiation power tradeoff P=P shared + 1 P Circuit + P TX / η Fixed receiver SNR

14 Circuit vs. radiation power tradeoff P=P shared + 2 P Circuit + P TX / η Fixed receiver SNR

15 Circuit vs. radiation power tradeoff P=P shared + 3 P Circuit + P TX / η Fixed receiver SNR

16 Circuit vs. radiation power tradeoff P=P shared + 4 P Circuit + P TX / η Fixed receiver SNR

17 Circuit vs. radiation power tradeoff Optimal number of antennas for efficiency N = a P /P b P

18 Hardware is cheap & getting cheaper P = P shared + N P Circuit + P TX / η Transmitter Power Consumption (mw) SISO 2x2 MIMO Year Sources: IEEE Int. Solid-State Circuits Conferences (ISSCC) and IEEE Journal of Solid-State Circuits (JSSC)

19 Hardware is cheap & getting cheaper P = P shared + N P Circuit + P TX / η Sources: IEEE Int. Solid-State Circuits Conferences (ISSCC) and IEEE Journal of Solid-State Circuits (JSSC)

20 Circuit vs. radiation power tradeoff is increasingly profitable N = a P /P b P The most energy-efficient way is to use all the antennas 20

21 Beyond a single link 21

22 What the carrier wants: Use all your antennas! 22

23 Guiding principles distilled Spectrum is scarce Hardware is cheap, and getting cheaper 23

24 You can t really fit a lot of antennas in a mobile device L 24

25 Got a call from Erran Li, Bell Labs Spring

26 3590 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 11, NOVEMBER 2010 Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas Thomas L. Marzetta 26

27 Clay Shepard went to Bell Labs Summer

28 Why many-antenna base station? 28

29 Omni-directional base station Data 1 Poor spatial reuse; poor power efficiency; high inter-cell interference 29

30 Sectored base station Data 1 Better spatial reuse; better power efficiency; high inter-cell interference 30

31 Single-user beamforming base station Data 1 Data 3 Better spatial reuse; best power efficiency; reduced inter-cell interference 31

32 Multi-user MIMO base station Data 2 Data 1 Data 5 M: # of BS antennas K: # of clients (K M) Best spatial reuse; best power efficiency; reduced inter-cell interference 32

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

34 How multi-user MIMO works H M: # of BS antennas K: # of clients M K 34

35 Multi-user MIMO: Precoding s s! = f (s, H) (Kx1 matrix) (M x 1 matrix) H M: # of BS antennas K: # of clients M K 35

36 Linear Precoding s (Kx1 matrix) s! = W s (M x 1 matrix) H M: # of BS antennas K: # of clients M K 36

37 Linear Precoding I: Zero-forcing Beamforming Null Data 1 Null Null 37

38 Zero-forcing Beamforming Data 2 Null Null 38

39 Zero-forcing Beamforming W = c H * (H T H * ) 1 Data 2 Data 1 Data 5 39

40 Zero-forcing does not scale well W = c H * (H T H * ) 1 Inversion of M X M matrix O(M*K 2 ) 40

41 Linear precoding II: Conjugate Beamforming Data 1 41

42 With more antennas Data 1 42

43 With even more antennas Data 1 43

44 Conjugate Multi-user Beamforming W = c H * Data 2 Data 1 Data 5 Conjugate approaches Zeroforcing as M/Kè

45 Conjugate scales very well W = c H * O(K) per antenna Marzetta s key result: Conjugate approaches Zeroforcing as M/Kè 45

46 Many-antenna vs. small cell Capital Expenditure (CAPEX) of Cell Site Major wireless equipment only 35% Just get the site to work: >50% China Mobile White Paper: C-RAN: The Road Towards Green RAN (Oct, 2011) 46

47 Total Cost of Ownership (TCO) Operating & Maintenance (O&M) Operating Expenditure (OPEX) The most effective way to reduce TCO is to decrease the number of sites. China Mobile White Paper: C-RAN: The Road Towards Green RAN (Oct, 2011) 47

48 If you ve got a site, better use as many antennas as you can 48

49 After a summer at Bell Labs 10-antenna prototype in the anechoic chamber at Bell Labs 49

50 ArgosV1 (MobiCom 12) 50

51 Central Controller WARP Modules Argos Interconnects Sync Distribution Argos Hub Clock Distribution Ethernet Switch51

52 What we have learned 52

53 Good news: Linear gains as # of users increases Capacity vs. K, with M = 64 53

54 Linear gains as # of BS antennas increases even as total P TX scaled with 1/M Capacity vs. M, with K = 15 54

55 Disappointment: Conjugate not approaching Zero-forcing up to 64 antennas Capacity vs. M, with K = 15 55

56 Disappointment: Conjugate not approaching Zero-forcing up to 64 antennas Capacity vs. M, with K = 4 Total Capacity (bps/hz) Zero forcing Conjugate Local Conj. SUBF Single Ant Base Station Antennas 56

57 The dirty secret of massive MIMO s s! = f (s, H) (Kx1 matrix) (M x 1 matrix) H M: # of BS antennas K: # of clients M K 57

58 The dirty secret of massive MIMO s s! = f (s, H) (Kx1 matrix) (M x 1 matrix) H M: # of BS antennas K: # of clients M K 58

59 Sounding-feedback does not scale s s! = f (s, H) (Kx1 matrix) (M x 1 matrix) M: # of BS antennas K: # of clients M K 59

60 One must use time-division duplex and client-sent pilot s s! = f (s, H) (Kx1 matrix) (M x 1 matrix) M: # of BS antennas K: # of clients M K 60

61 What happens in a single coherence period Listen to pilot Send data Calculate BF weights Receive data Time Send pilot Receive data Send data Time Within coherence time 61

62 Both theory and our experiments only consider Listen to pilot Send data Calculate BF weights Receive data Time Send pilot Receive data Send data Time 62

63 What if we factor all in? Listen to pilot Send data Calculate BF weights Receive data Time Send pilot Receive data Send data Time The base station can receive during calculation but the opportunity is limited due to downlink/uplink asymmetry 63

64 What if we factor all in? Listen to pilot Send data Calculate BF weights Receive data Time Client mobility Channel coherence time Number of clients Time to listen to pilot Computation hardware on base station Time to calculate BF weights 64

65 M = 64 K = 15 Type S L Inv. Type Sym. Super Infiniband 40 Gbps 1 µs FPGA Cluster 4x10GbE 40 Gbps 20 µs 8xIntel i7 High 2x10GbE 20 Gbps 20 µs 4xIntel i7 Mid 10GbE 10 Gbps 20 µs 2xIntel i7 F Low GbE 1Gbps 20 µs Intel i7 N Zeroforcing with various hardware configurations 65

66 Achieved Capacity (bps/hz) O(K) O(MK 2 ) Zero Forcing Conjugate Number of Users Fixed coherence time of 30 ms with low-end hardware. 66

67 What we have learned Computational resources matter significantly Simplistic Conjugate beamforming works Not in Marzetta s theoretical sense Need adaptive solutions # of clients; client mobility Precoding methods: Conjugate vs. Zero-forcing 67

68 What we are working on 68

69 Going for more antennas ArgosV2 (2013) 12 WARP V3 (48 antennas) per rack Polycarbonate, dado-style shelf Anti-static spray and thermal vent Battery-powered ArgosMobile 69

70 96-antenna configuration

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73 Ongoing Work: ArgosLab Software Framework for Rapid Prototyping Out-of-the-box Functionality Time/Frequency Synchronization Calibration CSI Collection Scheduled frame-based real-time Transmission

74 From Argos to ArgosNet 10 GbE ArgosBS 1 (Outdoor) Inter-cell interference management Pilot contamination Client grouping & scheduling Cloud RAN 10 Server GbE NetFPGA 10 GbE ArgosBS 4 (Indoor) NetFPGA Server 10 GbE NetFPGA Server 10 GbE ArgosCloud 10 GbE 10 GbE ArgosBS 2 (Outdoor) ArgosBS 3 (Outdoor) A network of massive MU-MIMO base stations 74

75 In summary 75

76 More BS antennas + MU-MIMOè Higher efficiency & lower interference Data 2 Data 1 Data 5

77 More BS antennas + MU-MIMOè Higher efficiency & lower interference Data 3 Data 1 Data 6 Data 12 Data 9 Data 10

78 Guiding Principles Spectrum is scarce Hardware is cheap, and getting cheaper 78

79 Acknowledgments 79