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

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Presentation on theme: "Power Consumption by Wireless Communication Lin Zhong ELEC518, Spring 2011."— Presentation transcript:

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

2 2 Power consumption (SMT5600)

3 3 Power consumption (T-Mobile) Bluetooth Wi-Fi Cellular

4 4 Power consumption (Contd.) Theoretical limits – Receiving energy per bit > N * N: Noise spectral power level Wideband communication Distance: d Propagation constant: a ( ) P RX P TX P RX *d a

5 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 6 Wireless system architecture Application Transport Network Data link Host computer RF front ends Baseband Network interface Network protocol stackHardware implementation Physical

7 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 8 Power consumption (Contd.) Source: Li et al, 2004 ComponentsPower (mW) Power amplifier (PA) 246 Frequency synthesizer (VCO/FS) 67.5 Mixer30.3 LNA20 Baseband processing 5

9 Low-noise amplifier (LNA) Bandwidth (same as the signal) Gain (~20dB) Linearity (IP3) Noise figure (1dB) Power consumption

10 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 11 Circuit power optimization (Contd.) Reduce supply voltage – Negatively impact amplifier linearity Higher integration – CMOS RF – SoC and SiP integration Power-saving modes

12 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 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 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 15 Network power optimization Use power-saving modes – Example: wireless LAN (WiFi) Infrastructure mode: Access points and mobile nodes – Example: Cellular networks

16 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 17 Buffering/sniffing in Gast, Wireless Network: The Definitive Guide /Bluetooth uses similar power-saving protocols: Hold and Sniff modes

18 Cellular networks Discontinuous transmission (DTX) Discontinuous reception (DRX)

19 Wireless energy cost Connection – Establishment – Maintenance Transfer data – Transmit vs. receive 19

20 Energy per bit transfer Oppermann et al., IEEE Comm. Mag

21 Wasteful wireless communication 21 Time Micro power management Space Directional communication Spectrum Efficiency-driven cognitive radio

22 Space waste Omni transmission huge power by power amplifier (PA) 22

23 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

24 Spectrum waste 24

25 Observed from an g user 25 Energy per bit Distribution of observed g throughput

26 Temporal waste 26 90% of time & 80% of energy spent in idle listening Four g laptop users, one week

27 Fundamental problem with CSMA CSMA: Carrier Sense Multiple Access – Clients compete for air time Incoming packets are unpredictable 27

28 Fundamental problem with CSMA 28

29 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 interfaces," in Proc. MobiSys08.

30 Directional waste Ongoing project with Ashutosh Sabharwal

31 Directional waste

32 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

33 Challenge I: Rotation!!! 33 Solution: Dont get rid of the omni directional antennas Use multiple directional antennas But can we select the right antenna in time?

34 Challenge II: Multipath fading 34

35 Challenge III Can we do it without changing the infrastructure? 35

36 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

37 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

38 Rotation is not that much <120° per second 38

39 Directionality indoor 39 5 dBi 8 dBi

40 8dBi antenna 5dBi antenna

41 Measurement setup RSSI measured at both ends 41 Data packets ACK packets

42 Directional channel still reciprocal 42

43 Directional beats omni close to half of the time 43 Field collected rotation traces replayed

44 RSS is predictable (to about 100ms) 44

45 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

46 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 network infrastructure 46

47 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

48 Trace based evaluation Rotation traces replayed on the motor RSSI traces collected for all antennas Algorithms evaluated on traces offline 48

49 An early prototype 49 Finalist of MobiCom08 Best Student Demo

50 The busier the traffic, the better 50

51 Two 5dBi antennas enough 51

52 Two 5dBi antennas enough 52

53 Real-time experiments: 3dB gain Packet-based antenna selection Three 5dBi antennas Continuous traffic (1400 byte packets) Field collected rotation trace 53

54 Throughput improvement 54

55 SNR vs. transmission rate (802.11a) 55 (D. Qiao, S. Choi, and K. Shin, 2002)

56 MiDAS+rate adaptation+power control Recall that RSS is quite predictable up to 100ms 56

57 Protocol waste Cellular network WLAN (Wi-Fi) Connection Transmission efficiency Availability

58 58 How to combine the strength of both Wi-Fi and Cellular network? Estimate Wi-Fi network condition WITHOUT powering on Wi-Fi interface

59 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. MobiSys07. Journal version with new results to appear in IEEE TMC P(WiFi|Context)

60 Cellular network offers clues


62 We dont move that much 62 Shoehorned smartphone with accelerometer Data collected from 2 smartphone users 2006

63 Our life is repetitive 63 Data collected from 11 smartphone users

64 WiFi availability is HIGHLY predictable 64 Application – Mobile EKG monitoring – 35% battery life improvement (12 to 17 hours)

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