Power Consumption by Wireless Communication Lin Zhong ELEC518, Spring 2011.

Power Consumption by Wireless Communication

Lin ZhongELEC518, Spring 2011

2

Power consumption (SMT5600)

Lighting: Keyboard; 72.937037227937; 3%

Lighting: Display I; 147.835647317401; 5%

Lighting: Display II; 61.2835089189649; 2%

LCD; 12.8726439856928; 0%

Speaker; 45; 2%

Bluetooth; 440; 16%

GPRS; 1600; 58%

Compute; 370; 13%

Cellular network; 17; 1%

Flight mode: Sleep; 3; 0%

3

Power consumption (T-Mobile)

IDLE

-Flight m

ode

Com

puting

LCD

LCD

lighting

Keyboard lighting

Speaker

Discoverable

Paging

Connected

Transm

ission

Connected

Transm

ission

Connected

Transm

ission

1

10

100

1000

10000

Po

wer

(m

W)

Bluetooth Wi-Fi Cellular

4

Power consumption (Contd.)

• Theoretical limits– Receiving energy per bit > N * 10-0.159

• N: Noise spectral power level• Wideband communication

Distance: d

Propagation constant: a (1.81-5.22)

PRXPTX∝ PRX*da

5


• What increases power consumption– Government regulation (FCC)

• Available spectrum band (Higher band, higher power)• Limited bandwidth• Limited transmission power

– Noise and reliability– Higher capacity

• Multiple access (CDMA, TDMA etc.)– Security– Addressability (TCP/IP)– More……

6

Wireless system architecture

Application

Transport

Network

Data link

Host computer

RF front ends

BasebandNetwork interface

Network protocol stack Hardware implementation

Physical

7


Baseband processor

Antenna interface

LNA

Low-noise amplifier

PA

Power amplifier

Intermediate Frequency (IF) signal processing

Local Oscillator (LO)

Physical Layer

IF/B

aseb

and

Conv

ersi

on

MAC Layer & above

>60% non-display power consumed in RF

RF technologies improve much slower than IC

8


67%

18%

8%

5%

1%

PAFSMixerLNABaseband

Source: Li et al, 2004

Components Power (mW)

Power amplifier (PA)

246

Frequency synthesizer (VCO/FS)

67.5

Mixer 30.3

LNA 20

Baseband processing

5

Low-noise amplifier (LNA)

• Bandwidth (same as the signal)• Gain (~20dB)• Linearity (IP3)• Noise figure (1dB)• Power consumption

10

Circuit power optimization

• Major power consumers

Baseband processor

Antenna interface

LNA

Low-noise amplifier

High duty cycle

PA

Power amplifier

High power consumption



Almost always on

Physical Layer

IF/B

aseb

and

Conv

ersi

on

MAC Layer & above

Huge dynamic range 105

11

Circuit power optimization (Contd.)

• Reduce supply voltage– Negatively impact amplifier linearity

• Higher integration– CMOS RF– SoC and SiP integration

• Power-saving modes

12

Circuit power optimization (Contd.)

• Power-saving modes– Complete power off

• (Circuit wake-up latency + network association latency) on the order of seconds

– Different power-saving modes• Less power saving but short wake-up latency

13

Power-saving modes

Baseband processor

Antenna interface

LNA

Low-noise amplifier

PA

Power amplifier



Physical Layer

IF/B

aseb

and

Conv

ersi

on

MAC Layer & above

Radio Deep Sleep Wake-up latency on the order of micro seconds

14

Power-saving modes (Contd.)

Baseband processor

Antenna interface

LNA

Low-noise amplifier

PA

Power amplifier



Physical Layer

IF/B

aseb

and

Conv

ersi

on

MAC Layer & above

Sleep Mode Wake-up latency on the order of milliseconds

Low-rate clock with saved network association information

15

Network power optimization

• Use power-saving modes– Example: 802.11 wireless LAN (WiFi)

• Infrastructure mode: Access points and mobile nodes

– Example: Cellular networks

16

802.11 infrastructure mode• Mobile node sniffs based on a “Listen Interval”

– Listen Interval is multiple of the “beacon period”• Beacon period: typically 100ms

• During a Listen Interval– Access point

• buffers data for mobile node• sends out a traffic indication map (TIM), announcing buffered

data, every beacon period– Mobile node stays in power-saving mode

• After a Listen Interval– Mobile node checks TIM to see whether it gets buffered

data– If so, send “PS-Poll” asking for data

17

Buffering/sniffing in 802.11

Gast, 802.11 Wireless Network: The Definitive Guide

802.15.1/Bluetooth uses similar power-saving protocols: Hold and Sniff modes

Cellular networks

• Discontinuous transmission (DTX)• Discontinuous reception (DRX)

Wireless energy cost

• Connection– Establishment– Maintenance

• Transfer data– Transmit vs. receive

19

Energy per bit transfer

Oppermann et al., IEEE Comm. Mag. 200420

Wasteful wireless communication

21

TimeMicro power management

SpaceDirectional communication

SpectrumEfficiency-driven cognitive radio

Space waste

• Omni transmission huge power by power amplifier (PA)

22

Time waste

• Network Bandwidth Under-Utilization– Modest data rate required by applications

• IE ~ 1Mbps, MSN video call ~ 3Mbps– Bandwidth limit of wired link

• 6Mbps DSL at home

23230 0.2 0.4 0.6 0.8 1

0

200

400

600

800

1000

1200

1400

Time (s)

Da

ta S

ize

(Byt

e)

0

20

40

60

80

100

Time Energy

Idle i

nterva

ls in b

usy ti

me (%

)

User1 User2 User3 User4

Spectrum waste

24

Observed from an 802.11g user

25

1E+02 1E+03 1E+04 1E+05 1E+06 1E+07

Throughout (bps)

Energy per bitDistribution of observed 802.11g throughput

Temporal waste

26

0 0.2 0.4 0.6 0.8 10

1

Time(s)

Ra

dio

Activity

90% of time & 80% of energy spent in idle listeningFour 802.11g laptop users, one week

Fundamental problem with CSMA

• CSMA: Carrier Sense Multiple Access– Clients compete for air time

• Incoming packets are unpredictable

27

Fundamental problem with CSMA

28

Micro power management (µPM)

• Sleep during idle listening• Wake up in time to catch retransmission• Monitor the traffic not to abuse it

• ~30% power reduction• No observed quality degradation

29J. Liu and L. Zhong, "Micro power management of active 802.11 interfaces," in Proc. MobiSys’08.

Directional waste

Ongoing project with Ashutosh Sabharwal

Directional waste

Two ways to realize directionality

• Passive directional antennas– Low cost– fixed beam patterns

• Digital beamforming– Flexible beam patterns– High cost

32Phased-array antenna system from Fidelity Comtech

Desclos, Mahe, Reed, 2001

Challenge I: Rotation!!!

33

Solution: Don’t get rid of the omni directional antennasUse multiple directional antennas

But can we select the right antenna in time?

Challenge II: Multipath fading

34

Challenge III

• Can we do it without changing the infrastructure?

35

Characterizing smartphone rotation

• How much do they rotate?• How fast do they rotate?

• 11 HTC G1 users, each one week• Log accelerometer and compass readings

– 100Hz when wireless in use36

Device orientation described by three Euler angles

• θ and φ based on tri-axis accelerometer • ψ based on tri-axis compass and θ and φ

37

Rotation is not that much

• <120° per second

10-4

10-3

10-2

10-1

100

101

102

103

0

0.1

0.2

0.3

0.4

Rotational speed( /s)

PD

F

100ms1s10s

10-4

10-3

10-2

10-1

100

101

102

103

0

0.1

0.2

0.3

0.4


PD

F

100ms1s10s

10-4

10-3

10-2

10-1

100

101

102

103

0

0.1

0.2

0.3

0.4


PD

F

100ms1s10s

38

Directionality indoor

39

5 dBi

8 dBi

8dBi antenna 5dBi antenna

Measurement setup

• RSSI measured at both ends

41

Data packets

ACK packets

Directional channel still reciprocal

42

0 60 120 180 240 300 360

-60

-50

-40

-30

-20

NLOS ind. / 5dBi antenna

Direction( )

RS

S(d

Bm

)

Dir-ClientDir-APOmni-ClientOmni-AP

Directional beats omni close to half of the time

[0,0.1) [0.1,1) [1,10) [10,inf)0

5

10

15

20

25

30

tota

l tim

e(%

)

superiority intervals(s)

5dBi

43

Field collected rotation traces replayed

RSS is predictable (to about 100ms)

44

10ms 100ms 1s 10s

0.01

1

100

Prediction Intervals(s)

Err

or(

dB

)

5dBi

Zero order First order

Multi-directional antenna design (MiDAS)

• One RF chain, one omni antenna, multiple directional antennas

• Directional ant. only used for data transmit and ACK Reception– Standard compliance– Tradeoff between risk and benefit

45

Omni-directional antenna

Antenna switch

. . .

Directional antennas

Transceiver

Antenna selection

RSSI

Packet-based antenna selection

• Assess an antenna by receiving a packet with it– Leveraging channel reciprocity

• Continuously assess the selected antenna• Find out the best antenna by assessing them one

by one– Potential risk of missing packets

• Stay with omni antenna when RSS changes rapidly

• No change in 802.11 network infrastructure

46

Symbol-based antenna selection

• Assess all antennas through a series of PHY symbols– Similar to MIMO antenna selection

• Needs help from PHY layer

47

Antenna training packet

SEL

Regular packet

ACK

Trace based evaluation

• Rotation traces replayed on the motor• RSSI traces collected for all antennas• Algorithms evaluated on traces offline

0 5 10 15 20-60

-55

-50

-45

RS

S(d

B)

time(second)

Dir1 Dir

3

Dir 3

Omni

48

An early prototype

49

Controllable motor

3 directional antennas1 omni antenna

WARP

Laptop

Finalist of MobiCom’08 Best Student Demo

The busier the traffic, the better

10ms 100ms 1s 10s0

1

2

3

4

5

6

Average Packet Interval

Ga

in(d

B)

Upper bound Symbol-based Packet-based

50

Two 5dBi antennas enough

51

three two-opp two-adj one0

1

2

3

4

5

6

Antenna Configuration

Ga

in(d

B)


Two 5dBi antennas enough

52

5dBi 8dBi0

1

2

3

4

5

6

Antenna Gain

Ga

in(d

B)


0 60 120 180 240 300 360

-60

-50

-40

-30

-20


Direction( )

RS

S(d

Bm

)


0 60 120 180 240 300 360

-60

-50

-40

-30

-20


Direction( )

RS

S(d

Bm

)


Real-time experiments: 3dB gain

• Packet-based antenna selection• Three 5dBi antennas• Continuous traffic (1400 byte packets)• Field collected rotation trace

NLOS ind. LOS ind.-75

-60

-45

Environment

Av

g. R

SS

(dB

)

Omni Multi antenna

53

Throughput improvement

54

NLOS ind. LOS ind.0

1

2

3

4

Environment

Th

rou

gh

pu

t(M

bp

s)

Omni Multi antenna

SNR vs. transmission rate (802.11a)

55

(D. Qiao, S. Choi, and K. Shin, 2002)

0 10 20 300

5

10

15

20

25

30

35

SNR (dB)

Go

od

pu

t (M

bp

s)

6Mbps9Mbps 12Mbps 18Mbps 24Mbps 36Mbps48Mbps54Mbps

MiDAS+rate adaptation+power control

• Recall that RSS is quite predictable up to 100ms

56

0 5 10 15 20 25 30 35 400

50

100

150

200

Goodput Gain-Upper boundGoodput Gain-MiDASTX power reduction-Upper boundTX power reduction-MiDAS

Omni SNR(dB)

%

Protocol waste

Cellular network WLAN (Wi-Fi)

Connection

Transmission efficiency

Availability

58

How to combine the strength of both Wi-Fi and Cellular network?

Estimate Wi-Fi network condition WITHOUT powering on Wi-Fi interface

Use context to predict WiFi availability

• Visible cellular network towers• Motion• Time of the day, day of the week

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Context Wi-Fi Conditions

Statistical learning

Ahmad Rahmati and Lin Zhong, "Context for Wireless: Context-sensitive energy-efficient wireless data transfer," in Proc. MobiSys’07.Journal version with new results to appear in IEEE TMC

P(WiFi|Context)

Cellular network offers clues

We don’t move that much

62

moving (1, 5] (5, 10] (10, 30] (30, 60] (60, 120] (120, inf)0%

10%

20%

30%

40%

50%

Length of motionless period (minute)

Shoehorned smartphone with accelerometer

Data collected from 2 smartphone users 2006

Our life is repetitive

63

0 1 2 3 40.5

0.6

0.7

0.8

0.9

1

Time (days)

Prob

abili

ty o

f sam

e W

i-Fi

avai

labi

lity

(nor

mal

ized

auto

corr

elet

aion

)

Data collected from 11 smartphone users

WiFi availability is HIGHLY predictable

64

• Application– Mobile EKG monitoring– 35% battery life improvement (12 to 17 hours)

0 120 240 360 480 6000.5

0.6

0.7

0.8

0.9

1

Time (minutes)

Pred

ictio

n ac

cura

cy o

f Wi-F

i av

aila

bilit

y

Power Consumption by Wireless Communication Lin Zhong ELEC518, Spring 2011.

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