Many-antenna base stations are interesting systems Lin Zhong http://recg.org
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• How we got started • Why many-antenna base station • What we have learned • What we are doing now 3
How we started Why a mobile system guy got interested in massive MIMO 4
Wireless consumes a lot of power 1800 1615 1600 1400 Power (mW) HTC Wizard 1200 October 2005 1000 900 800 725 704 600 400 315 221 180 142 142 200 97 92 93 90 88 80 32 25 5 9 3 2 0 Power profile !=Energy profile 5
First insight • Wi-Fi more efficient than cellular – MobiSys’07 6
Why is Wi-Fi more efficient? P TX = a*D 2 D 7
Horribly wasteful 8
Directional transmission! 9
Passive directional antenna to save energy (MobiCom’10) • No power overhead • Fixed bean patterns 10
Beamforming to save energy (MobiCom’11) • Extra transceivers • Steerable beams 11
Power by multi-antenna systems (uplink) P Circuit P PA =P TX / η DAC Filter Mixer Filter PA 1 Baseband Signal Frequency N Synthesizer P Shared DAC Filter Filter PA N Baseband Signal Mixer P = P shared + N·P Circuit + P TX / η 12
Circuit vs. radiation power tradeoff P= P shared + 1 ·P Circuit + P TX / η Fixed receiver SNR
Circuit vs. radiation power tradeoff P= P shared + 2 ·P Circuit + P TX / η Fixed receiver SNR
Circuit vs. radiation power tradeoff P= P shared + 3 ·P Circuit + P TX / η Fixed receiver SNR
Circuit vs. radiation power tradeoff P= P shared + 4 ·P Circuit + P TX / η Fixed receiver SNR
Circuit vs. radiation power tradeoff • • Optimal number of antennas for efficiency 𝑂 ��� = 𝑏 ∙ 𝑄 � /𝑄 ������� − 𝑐 ∙ 𝑄 � 17
Hardware is cheap & getting cheaper P = P shared + N·P Circuit + P TX / η Transmitter Power Consumption (mW) 1200 SISO 2x2 MIMO 1000 800 600 400 200 0 2002 2004 2006 2008 2010 Year Sources: IEEE Int. Solid-State Circuits Conferences (ISSCC) and IEEE Journal of Solid-State Circuits (JSSC)
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)
Circuit vs. radiation power tradeoff is increasingly profitable • 𝑂 ��� = 𝑏 ∙ 𝑄 � /𝑄 ������� − 𝑐 ∙ 𝑄 � • The most energy-efficient way is to use all the antennas 20
Beyond a single link 21
What the carrier wants: Use all your antennas! 22
Guiding principles distilled • Spectrum is scarce • Hardware is cheap, and getting cheaper 23
You can’t really fit a lot of antennas in a mobile device L 24
Got a call from Erran Li, Bell Labs Spring 2011 25
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
Clay Shepard went to Bell Labs Summer 2011 27
Why many-antenna base station? 28
Omni-directional base station Data 1 Poor spatial reuse; poor power efficiency; high inter-cell interference 29
Sectored base station Data 1 Better spatial reuse; better power efficiency; high inter-cell interference 30
Single-user beamforming base station Data 1 Data 3 Better spatial reuse; best power efficiency; reduced inter-cell interference 31
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
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., 2010. 33
How multi-user MIMO works H M: # of BS antennas K: # of clients M ≥ K 34
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
Linear Precoding s = W ⋅ s ! s (Kx1 matrix) (M x 1 matrix) H M: # of BS antennas K: # of clients M ≥ K 36
Linear Precoding I: Zero-forcing Beamforming N u l l Data 1 Null Null 37
Zero-forcing Beamforming Data 2 N u l l Null 38
Zero-forcing Beamforming W = c ⋅ H * ( H T H * ) − 1 Data 2 Data 1 Data 5 39
Zero-forcing does not scale well W = c ⋅ H * ( H T H * ) − 1 Inversion of M X M matrix O(M*K 2 ) 40
Linear precoding II: Conjugate Beamforming Data 1 41
With more antennas Data 1 42
With even more antennas Data 1 43
Conjugate Multi-user Beamforming W = c ⋅ H * Data 2 Data 1 D a t a 5 Conjugate approaches Zeroforcing as M/K è ∞
Conjugate scales very well W = c ⋅ H * O(K) per antenna Marzetta’s key result: Conjugate approaches Zeroforcing as M/K è ∞ 45
Many-antenna vs. small cell Capital Expenditure (CAPEX) of Cell Site Fig. ¡3: ¡CAPEX ¡and ¡OPEX ¡Analysis ¡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 decrease ¡ the ¡ operators’ ¡ CAPEX ¡ and ¡ OPEX, ¡ but ¡ Fig. ¡4 ¡TCO ¡Analysis ¡of ¡Cell ¡Site ¡ � ’
Total Cost of Ownership (TCO) • Operating Expenditure (OPEX) • Operating & Maintenance (O&M) “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
If you’ve got a site, better use as many antennas as you can 48
After a summer at Bell Labs 10-antenna prototype in the anechoic chamber at Bell Labs 49
ArgosV1 (MobiCom’12) 50
Central Controller WARP Modules Argos Interconnects Sync Distribution Clock Distribution Argos Ethernet Hub Switch 51
What we have learned 52
Good news: Linear gains as # of users increases Capacity vs. K, with M = 64 53
Linear gains as # of BS antennas increases even as total P TX scaled with 1/M Capacity vs. M, with K = 15 54
Disappointment: Conjugate not approaching Zero-forcing up to 64 antennas Capacity vs. M, with K = 15 55
Disappointment: Conjugate not approaching Zero-forcing up to 64 antennas Capacity vs. M, with K = 4 30 Zero − forcing Conjugate 25 Total Capacity (bps/hz) Local Conj. SUBF 20 Single Ant. 15 10 5 0 20 30 40 50 60 56 Base Station Antennas
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
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
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
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
What happens in a single coherence period Listen to pilot Send data Time Receive data Calculate BF weights Send pilot Send data Time Receive data Within coherence time 61
Both theory and our experiments only consider …… Listen to pilot Send data Time Receive data Calculate BF weights Send pilot Send data Time Receive data 62
What if we factor all in? Listen to pilot Send data Time Receive data Calculate BF weights Send pilot Send data Time Receive data The base station can receive during calculation but the 63 opportunity is limited due to downlink/uplink asymmetry
What if we factor all in? Listen to pilot Send data Time Receive data Calculate BF weights • Client mobility – Channel coherence time • Number of clients – Time to listen to pilot • Computation hardware on base station – Time to calculate BF weights 64
M = 64 K = 15 Type Inv. Type Sym. S L Super Infiniband 40 Gbps 1 µ s FPGA Cluster 4x10GbE 40 Gbps 20 µ s 8xIntel i7 ⌅ High 2x10GbE 20 Gbps 20 µ s 4xIntel i7 ⌥ Mid 10GbE 10 Gbps 20 µ s 2xIntel i7 F Low GbE 1 Gbps 20 µ s Intel i7 N Zeroforcing with various hardware configurations 65
35 30 Achieved Capacity (bps/Hz) O(K) 25 O(MK 2 ) 20 15 10 Zero − Forcing 5 Conjugate 0 2 4 6 8 10 12 14 Number of Users Fixed coherence time of 30 ms with low-end hardware. 66
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
What we are working on 68
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