Beamforming on mobile devices: A first study Hang Yu, Lin Zhong , - - PowerPoint PPT Presentation

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Beamforming on mobile devices: A first study Hang Yu, Lin Zhong , - - PowerPoint PPT Presentation

Aug 20, 2022 128 likes •574 views

Beamforming on mobile devices: A first study Hang Yu, Lin Zhong , Ashutosh Sabharwal, David Kao http://www.recg.org Two invariants for wireless Spectrum is scarce Hardware is cheap and getting cheaper 2 Passive directional antennas 3.2

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SLIDE 1

Beamforming on mobile devices: A first study

Hang Yu, Lin Zhong, Ashutosh Sabharwal, David Kao

http://www.recg.org

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SLIDE 2

Two invariants for wireless

  • Spectrum is scarce
  • Hardware is cheap and getting cheaper

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SLIDE 3
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SLIDE 4

Passive directional antennas

3.2 cm 3.2 cm

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Ardalan Amiri Sani, Lin Zhong, and Ashutosh Sabharwal, "Directional antenna diversity for mobile devices: characterizations and solutions," in Proc. ACM MobiCom, September 2010.

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SLIDE 5

Findings: ~3 dB gain

  • Multifold throughput increase at network edge
  • ~50% TX power reduction at network center

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Can we go beyond 3 dB?

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SLIDE 7

Beamforming?

  • Studied in the past for use on cellular base station, 802.11 access

points, vehicles, and even wireless sensor nodes, e.g., MobiSteer (MobiSys’07), R2D2 (MobiSys’09), DIRC (SIGCOMM’09)

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SLIDE 8

Beamforming primer

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SLIDE 9

Beamforming primer

Fixed transmission power

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SLIDE 10

Beamforming primer

Fixed transmission power

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SLIDE 11

Beamforming primer

Fixed transmission power

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SLIDE 12

Beamforming primer

Fixed transmission power

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SLIDE 13

Is beamforming practical?

  • Beamforming

– Antenna array – Narrow beam – Power hungry

  • Mobile devices

– Small form factor – Rotate and move – Battery powered

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SLIDE 14

Form factor?

0.3-0.4 λ : 4.5-6 cm at 2 GHz

0.1 0.2 0.3 0.4 0.5 1 2 3 4 5 6 7 Antenna spacing (wavelength) Peak beamforming gain (dB) 4 antennas 3 antennas 2 antennas

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SLIDE 15

Form factor!

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0.3-0.4 λ (4.5-6 cm at 2 GHz)

18 cm 24 cm 6 c cm 12 cm

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Rotation?

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Infrastructure Node Client Node

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N=2 N=4 3 6 Beamforming gain (dB) Indoor Max Static 90d/s 180d/s

CSI estimation every 100 ms

Rotation?

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SLIDE 18

N=2 N=4 3 6 Beamforming gain (dB) Indoor Max Static 90d/s 180d/s

CSI estimation every 10 ms

Rotation!

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SLIDE 19

Power? (uplink only)

19 Frequency Synthesizer Baseband Signal DAC Filter Mixer Filter PA1 Baseband Signal DAC Filter Mixer Filter PAN

N

PPA =PTX / η PCircuit PShared

P = Pshared + N∙PCircuit + PTX / η

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SLIDE 20

Tradeoff No. 1

P=Pshared + 1∙PCircuit + PTX / η

Fixed receiver SNR

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SLIDE 21

Tradeoff No. 1

P=Pshared + 2∙PCircuit + PTX

TX / η

Fixed receiver SNR

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Tradeoff No. 1

P=Pshared + 3∙PCircuit + PTX

TX / η

Fixed receiver SNR

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SLIDE 23

Tradeoff No. 1

P=Pshared + 4∙PCircuit + PTX

TX / η

Fixed receiver SNR

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SLIDE 24

Tradeoff No. 1

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𝑂𝑝𝑞𝑢 = 𝑏 ∙ 𝑄𝑃/𝑄𝐷𝑗𝑠𝑑𝑣𝑗𝑢 − 𝑐 ∙ 𝑄𝑃

  • Optimal number of antennas for efficiency
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Hardware is cheap & getting cheaper

2002 2004 2006 2008 2010 200 400 600 800 1000 1200 Year Transmitter Power Consumption (mW) SISO 2x2 MIMO

Sources: IEEE Int. Solid-State Circuits Conferences (ISSCC) and IEEE Journal of Solid-State Circuits (JSSC)

P = Pshared + N∙PCircuit + PTX / η

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SLIDE 26

Power!

  • Beamforming with state-of-the-art multi-RF

chain realization is already more efficient!

  • Tradeoff No. 1 is increasingly profitable!
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Beyond a single link

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What the carrier wants: Use all your antennas!

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What you want:

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𝑂𝑝𝑞𝑢 = 𝑏 ∙ 𝑄𝑃/𝑄𝐷𝑗𝑠𝑑𝑣𝑗𝑢 − 𝑐 ∙ 𝑄𝑃

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Tradeoff No. 2

  • Network capacity vs. client efficiency

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How can clients figure out its N without talking to each other?

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BeamAdapt

  • Distributed algorithm to minimize TX

power under uplink capacity constraints

– No explicit inter-client cooperation – Iterative – Guaranteed to converge – Converge in a few iterations in practice – Converge to a good solution in practice

  • Can be built on top of uplink power

control in cellular networks

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WARPLab-based prototype

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Laptop with MATLAB Ethernet Router Infrastructure Node 1 Infrastructure Node 2 Client Node 1 Client Node 2 Uplink (Wireless) Uplink (Wireless)

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Received SNR stable

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5 10

  • 10

10 20 SINR (dB) Time (s) Client Node 2 5 10 1 2 3 4 Beamforming size Time (s)

Link SNR constraint: 5 dB

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SLIDE 35

Power close to optimal

I: Indoor O: Outdoor S: Stationary M: Mobile / Rotational

I/S I/M O/S O/M 500 1000 1500 2000 Power consumption (mW) 5dB BeamAdapt Genie-aided

Link SNR constraint: 5 dB

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SLIDE 36

UMTS; Client movement: 0-70 mph; Client rotation: 0-120 °/s

4km 4km

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SLIDE 37

CBR traffic

Power reduced

N=1 N=2 N=4 N=8 200 400 600 800 1000 Client Power Consumption (mW) Beamforming/Omni BeamAdapt

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SLIDE 38

CBR traffic

Network throughput maintained

N=1 N=2 N=4 N=8 0.5 1 1.5 2 x 10

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Network Throughput (b/s) Beamforming/Omni BeamAdapt

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Conclusions

  • Beamforming is feasible for mobile devices
  • Lower-power uplink for mobile devices
  • Distributed optimization feasible
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Looking forward

  • Benefits of beamforming orthogonal to
  • ther spectrum efficiency technologies

such as network MIMO

  • Network capacity implications
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Treating interference as noise

Strong interference regime: Far from optimal from information theoretic perspective

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Treating interference as noise

Weak interference regime: Existing architecture yields close to optimal capacity

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http://www.recg.org