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 mode
Com
puting
LCD
LCD
lighting
Keyboard lighting
Speaker
Discoverable
Paging
Connected
Transmission
Connected
Transmission
Connected
Transmission
1
10
100
1000
10000
Pow
er (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
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
BasebandNetwork interface
Network protocol stack Hardware implementation
Physical
7
Power consumption (Contd.)
Baseband processorAntenna
interface
LNA
Low-noise amplifier
PA
Power amplifier
Intermediate Frequency (IF) signal processing
Local Oscillator (LO)
Physical Layer
IF/B
aseb
and
Conv
ersio
n
MAC Layer & above
>60% non-display power consumed in RF
RF technologies improve much slower than IC
8
Power consumption (Contd.)
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 processorAntenna
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/B
aseb
and
Conv
ersio
n
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 processorAntenna
interface
LNA
Low-noise amplifier
PA
Power amplifier
Intermediate Frequency (IF) signal processing
Local Oscillator (LO)
Physical Layer
IF/B
aseb
and
Conv
ersio
n
MAC Layer & above
Radio Deep Sleep Wake-up latency on the order of micro seconds
14
Power-saving modes (Contd.)
Baseband processorAntenna
interface
LNA
Low-noise amplifier
PA
Power amplifier
Intermediate Frequency (IF) signal processing
Local Oscillator (LO)
Physical Layer
IF/B
aseb
and
Conv
ersio
n
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
Wireless energy cost
• Connection– Establishment– Maintenance
• Transfer data– Transmit vs. receive
19
Wasteful wireless communication
21
TimeMicro power management
SpaceDirectional communication
SpectrumEfficiency-driven cognitive radio
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)
Dat
a S
ize
(Byt
e)
0
20
40
60
80
100
Time Energy
Idle
inte
rval
s in
busy
tim
e (%
)
User1 User2 User3 User4
Observed from an 802.11g user
25
1E+02 1E+03 1E+04 1E+05 1E+06 1E+07Throughout (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)
Rad
io A
ctiv
ity
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
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.
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?
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
100
101
102
1030
0.1
0.2
0.3
0.4
Rotational speed( /s)
100ms1s10s
10-4
10-3
10-2
10-1
100
101
102
1030
0.1
0.2
0.3
0.4
Rotational speed( /s)
100ms1s10s
10-4
10-3
10-2
10-1
100
101
102
1030
0.1
0.2
0.3
0.4
Rotational speed( /s)
100ms1s10s
38
Directional channel still reciprocal
42
0 60 120 180 240 300 360
-60
-50
-40
-30
-20NLOS ind. / 5dBi antenna
Direction( )
RSS
(dB
m)
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)
Erro
r(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)
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
Gai
n(dB
)
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
Gai
n(dB
)
Upper bound Symbol-based Packet-based
Two 5dBi antennas enough
52
5dBi 8dBi0
1
2
3
4
5
6
Antenna Gain
Gai
n(dB
)
Upper bound Symbol-based Packet-based
0 60 120 180 240 300 360
-60
-50
-40
-30
-20NLOS ind. / 5dBi antenna
Direction( )
RSS
(dB
m)
Dir-ClientDir-APOmni-ClientOmni-AP
0 60 120 180 240 300 360
-60
-50
-40
-30
-20NLOS ind. / 8dBi antenna
Direction( )
RSS
(dB
m)
Dir-ClientDir-APOmni-ClientOmni-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
Thro
ughp
ut(M
bps)
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)
Goo
dput
(Mbp
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)
%
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’07.Journal version with new results to appear in IEEE TMC
P(WiFi|Context)
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