Power Consumption by Wireless

workablejeansMobile - Wireless

Nov 21, 2013 (3 years and 7 months ago)

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Power Consumption by Wireless
Communication

Lin Zhong

ELEC518, Spring 2011

2

Power consumption (SMT5600)

Lighting: Keyboard,
73, 3%

Lighting: Display I,
148, 5%

Lighting: Display II,
61, 2%

LCD, 13, 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)

1
10
100
1000
10000
IDLE-Flight mode
Computing
LCD
LCD lighting
Keyboard lighting
Speaker
Discoverable
Paging
Connected
Transmission
Connected
Transmission
Connected
Transmission
Power (mW)

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)

P
RX

P
TX


P
RX
*d
a

5

Power consumption (Contd.)


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

Baseband

Network interface

Network protocol stack

Hardware implementation

Physical

7

Power consumption (Contd.)

Baseband
processor

Antenna
interface


LNA

Low
-
noise amplifier

PA

Power amplifier

Intermediate
Frequency (IF)
signal processing

Local Oscillator
(LO)

Physical Layer

IF/Baseband
Conversion

MAC Layer &
above

>60% non
-
display power consumed in RF

RF technologies improve much slower than IC

8

Power consumption (Contd.)

67%

18%

8%

6%

1%

PA
FS
Mixer
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

Intermediate
Frequency (IF)
signal processing

Local Oscillator
(LO)

Almost always on

Physical Layer

IF/Baseband
Conversion

MAC Layer &
above

Huge dynamic
range 10
5

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

Intermediate
Frequency (IF)
signal processing

Local Oscillator
(LO)

Physical Layer

IF/Baseband
Conversion

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

Intermediate
Frequency (IF)
signal processing

Local Oscillator
(LO)

Physical Layer

IF/Baseband
Conversion

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.
2004

20

Wasteful wireless communication

21

Time

Micro power management

Space

Directional communication

Spectrum

Efficiency
-
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





23

23

0
0.2
0.4
0.6
0.8
1
0
200
400
600
800
1000
1200
1400
Time (s)
Data Size (Byte)
0
20
40
60
80
100
Time
Energy
Idle intervals in busy time (%)
User1
User2
User3
User4
Spectrum waste

24

Observed from an 802.11g user

25

1.E+02
1.E+03
1.E+04
1.E+05
1.E+06
1.E+07
Throughout (bps)

Energy per bit

Distribution of observed 802.11g throughput

Temporal waste

26




0
0.2
0.4
0.6
0.8
1
0
1
Time(s)
Radio Activity
90% of time & 80% of energy spent in idle listening

Four 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


29

J. 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

32

Phased
-
array antenna system from Fidelity
Comtech

Desclos
,
Mahe
, Reed, 2001

Challenge I: Rotation!!!

33


Solution:


Don’t get rid of the
omni

directional antennas


Use 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 use

36

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
10
0
10
1
10
2
10
3
0
0.1
0.2
0.3
0.4

Rotational speed(

/s)
PDF


100ms
1s
10s
10
-4
10
-3
10
-2
10
-1
10
0
10
1
10
2
10
3
0
0.1
0.2
0.3
0.4

Rotational speed(

/s)
PDF


100ms
1s
10s
10
-4
10
-3
10
-2
10
-1
10
0
10
1
10
2
10
3
0
0.1
0.2
0.3
0.4

Rotational speed(

/s)
PDF


100ms
1s
10s
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(

)
RSS(dBm)


Dir-Client
Dir-AP
Omni-Client
Omni-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
total time(%)
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)
Error(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
RSS(dB)
time(second)
Dir
1
Dir
3
Dir 3
Omni
48

An early prototype

49


Controllable motor
3 directional antennas
1
omni
antenna
WARP
Laptop
Finalist of MobiCom’08
Best Student Demo

The busier the traffic, the better

10ms
100ms
1s
10s
0
1
2
3
4
5
6
Average Packet Interval
Gain(dB)


Upper bound
Symbol-based
Packet-based
50

Two

5dBi antennas enough

51

three
two-opp
two-adj
one
0
1
2
3
4
5
6
Antenna Configuration
Gain(dB)


Upper bound
Symbol-based
Packet-based
Two
5dBi

antennas enough

52

5dBi
8dBi
0
1
2
3
4
5
6
Antenna Gain
Gain(dB)


Upper bound
Symbol-based
Packet-based
0
60
120
180
240
300
360
-60
-50
-40
-30
-20
NLOS ind. / 5dBi antenna
Direction(

)
RSS(dBm)


Dir-Client
Dir-AP
Omni-Client
Omni-AP
0
60
120
180
240
300
360
-60
-50
-40
-30
-20
NLOS ind. / 8dBi antenna
Direction(

)
RSS(dBm)


Dir-Client
Dir-AP
Omni-Client
Omni-AP
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
Avg. RSS(dB)


Omni
Multi antenna
53

Throughput improvement

54

NLOS ind.
LOS ind.
0
1
2
3
4
Environment
Throughput(Mbps)


Omni
Multi antenna
SNR vs. transmission rate (802.11a)

55

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

0
10
20
30
0
5
10
15
20
25
30
35
SNR (dB)
Goodput (Mbps)


6Mbps
9Mbps
12Mbps
18Mbps
24Mbps
36Mbps
48Mbps
54Mbps
MiDAS+rate

adaptation+power

control


Recall that RSS is quite predictable up to 100ms

56

0
50
100
150
200
0
10
20
30
40
%

Omni SNR(dB)

Goodput Gain-Upper bound
Goodput Gain-MiDAS
TX power reduction-Upper bound
TX power reduction-MiDAS
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


59

Context

Wi
-
Fi
Conditions

Statistical learning

Ahmad Rahmati and Lin Zhong, "Context for Wireless: Context
-
sensitive energy
-
efficient wireless data transfer,"


in
Proc. MobiSys’0
7.

Journal version with new results to appear in IEEE TMC

P
(
WiFi|Context
)

Cellular network offers clues

Cellular network offers clues

We don’t move that much

62

0%
10%
20%
30%
40%
50%
moving
(1, 5]
(5, 10]
(10, 30]
(30, 60]
(60, 120]
(120, inf)
Length of motionless period (minute)

Shoehorned smartphone with
accelerometer

Data collected from 2
smartphone

users 2006

Our life is repetitive

63

0.5
0.6
0.7
0.8
0.9
1
0
1
2
3
4
Probability of same Wi
-
Fi availability
(normalized autocorreletaion)


Time (days)

Data collected from 11 smartphone users

WiFi

availability is HIGHLY predictable

64


Application


Mobile EKG monitoring


35% battery life improvement (12 to 17 hours)

0.5
0.6
0.7
0.8
0.9
1
0
120
240
360
480
600
Prediction accuracy of Wi
-
Fi
availability

Time (minutes)