WiOpt 2013
Life-Add: A novel WiFi design with battery
life, throughput and fairness improvement
Shengbo Chen*, Tarun Bansal*, Yin Sun*,
Prasun Sinha and Ness B. Shroff
Dept. ECE & CSE, The Ohio State University
Background

Battery life is a serious problem for most smartphone users
 WiFi, 4G LTE, GPS, Bluetooth, screen, CPU, ...

Web browsing via WiFi
 Test results in April 2013 by
 Battery life < 11 hours for most popular smartphones
iPhone 5
802.11n
HTC One
802.11ac
Samsung Galaxy S 4
802.11ac
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Existing Solutions to Prolong Lifetime

Mobile Charging Additional equipment
 Solar charger

portable battery
wireless charger
Reduce power when sensing
 Lower hardware clock-rate [E-MiLi, Mobicom 11]
 Broadcom SoC Solution
—802.11 ac
—Used in HTC One and Samsung Galaxy S 4
—Test: 7.8 hours by

Trade bandwidth/throughput for power reduction
—Cannot have both benefits
3
IEEE 802.11 Standard Evolution
WLAN
802.111997
2 Mbps,
DSSS, FHSS
802.11b
11 Mbps,
CCK, DSSS
802.11a
54 Mbps,
OFDM, 5 GHz
802.11g
54 Mbps,
OFDM, 2.4 GHz
802.11n
600 Mbps with
4x4 MIMO,
20/40 MHz BW,
2.4 or 5 GHz
802.11ac
256QAM
160MHz
802.11ad

802.11p
27 Mbps,10 MHz
BW, 5.9 GHz
Wireless Access
for Vehicular
Environment
802.11af
TVWS
TV White
Spaces
Wireless Gigabit, <6 GHz
Wireless Gigabit, 60 GHz
Physical layer
 Significant evolutions towards high throughput

MAC
 CSMA/CA and its enhancements
— QoS, security, frame aggregation, block ACK
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Can we do better?

Life-Add: An innovative MAC design
 Battery Lifetime
—Avoid unnecessary sensing
 Throughput
—Reduce collisions and starvations
 Fairness
—Near-far effect
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Contents

Background

Life-Add: An innovative MAC design

Simulation Results

Summary
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Life-Add: Smartphone energy model

Power source:
 Strong: Wall power, portable battery
 Weak: Solar charger

Other components
 4G LTE, CPU, screen, …

WiFi chip
 ON: Transmit/receive/sensing
—High power consumption
 OFF: Sleep
—Very low power consumption

Too much sensing means a significant waste of energy
 Sleep/wake (asynchronous)
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Life-Add: Sleep/Wake + Channel Contention

Uplink
Device 1
Device 2
ACK
AP
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Life-Add: Sleep/Wake + Channel Contention

Uplink
Device 1 wakes up earlier and senses the channel
Device 1
Device 2
ACK
AP
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Life-Add: Sleep/Wake + Channel Contention

Uplink
Device 1 transmits, Device 2 goes back to sleep
Data
Device 1
Device 2
ACK
AP
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Life-Add: Sleep/Wake + Channel Contention

Uplink
AP replies an ACK to Device 1. Cycle 1 completes.
Data
Device 1
Device 2
ACK
ACK
AP
Cycle 1
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Life-Add: Sleep/Wake + Channel Contention

Uplink
Devices 1 and 2 wake up at almost the same time
Data
Device 1
Device 2
ACK
ACK
AP
Cycle 1
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Life-Add: Sleep/Wake + Channel Contention

Uplink
A collision occurs, followed by a timeout. Cycle 2 completes.
Data
Data
Device 1
Data
Device 2
ACK
ACK
AP
Cycle 1
Cycle 2
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Life-Add: Sleep/Wake + Channel Contention

Uplink
Data
Data
Device 1
Data
Data
Device 2
ACK
ACK
AP
Cycle 1
Cycle 2
 A new renewal process model: each cycle is an i.i.d. period
—Requires 2 assumptions:
— Exponential distributed sleep period:
from last cycle)
Memoryless (independent
—Tdata + TACK≈ Tcollision + Ttimeout (only assumed in analysis, not in
simulations)
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Life-Add vs IEEE 802.11

Life-Add
Data
Data
Device 1
Data
Data
Device 2
ACK
ACK
AP
Cycle 2
Cycle 1

IEEE 802.11
Data
Data
Device 1
Data
Data
Device 2
ACK
ACK
AP
 Sleep backoff vs sensing backoff (save energy)
 Renewal process vs 2D Markov chain [Bianchi 2000] (simplify optimization) 15
Life-Add: Downlink
A short Ps-poll packet is used to contend for the channel
Ps-poll
Ps-poll
ACK
Device 1
ACK
Device 2
Ps-poll
Data
Data
AP
Beacon

Cycle 1
Cycle 2
Still a renewal process
 Uplink: sleep + data + overhead (ACK/collision/timeout)
 Downlink: sleep + data + overhead (ACK/Ps-poll/collision/timeout)
—Additional Ps-poll packet as part of overhead

Can be modeled together
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Life-Add: Single AP

Proportional-fair Utility Maximization
max
∑ log E{Throughput of Device i}
s.t.
E{Battery Life of Device i} ≥ Tmin,i
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Life-Add: Single AP

Proportional-fair Utility Maximization
max
∑ log E{Throughput of Device i}
s.t.
Pr{Device i’s RF is ON}≤ bi
 Maximal device-ON probability: bi
Variables: average sleep period 1/Ri
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Life-Add: Single AP

Proportional-fair Utility Maximization
max
∑ log E{Throughput of Device i}
s.t.
Pr{Device i’s RF is ON}≤ bi
 Maximal device-ON probability: bi
Variables: average sleep period 1/Ri
Non-convex
—Asynchronous network with collisions
—Channel access probabilities of the devices are coupled

We propose a solution: Life-Add
Theorem: Asymptotically optimal, as Tsensing /(Tdata + TACK)0
— E.g., 802.11b: Tsensing= 4us, Tdata + TACK=511us~1573us
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Problem formulation

where
,
is a scaling constant

is the transmission success probability

is the device-ON probability
 Proof idea: Problem structure, KKT necessary conditions
Upper and lower bounds converge to the same value
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Life-Add: Single AP

Pr{Device i’s RF is ON}≤ bi
Implementation procedure:
 Each device reports bi to the AP
 The AP computes
—If
,
—If
,
 Device n uses
 Use

and
,
and broadcast them to the devices
to compute
to generate the sleeping period
Low complexity, easy to implement
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Life-Add: Single AP

NS-3 simulation for a homogeneous scenario
 Red curve: simulated performance with no approximation
 Blue point: closed form solution of Life-Add
 Observation: Life-Add is near optimal
The renewal process model is reasonably accurate
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Life-Add: general multiple APs

Too complicated interference model
 Global optimization is very difficult

Near-far effect
 Device 1 can access the channel all the time
 Device 2 is in starvation

Hidden terminal problem
 Two devices cannot sense each other and cause collisions
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Life-Add: general multiple APs

Near-far effect
 Node collaboration
—Device 1 computes the two values of average sleep period
suggested by AP 1 and AP 2
—Device 1 chooses the longest average sleep period to reduce
collisions with Device 2, which is vulnerable
 To care for the vulnerable
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Life-Add: general multiple APs

Hidden terminal problem
 Increase average sleep period after a collision
 Reset average sleep period after a successful transmission
—Similar idea to 802.11 MAC
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Life-Add: general multiple APs

Implementation procedure:
 Each device reports bi to nearby APs
 Each AP computes and broadcasts
—If
,
—If
,
 Device n uses
and
to compute
 Choose to use the smallest
 Reduce
 Use
and
at collision, reset
suggested by nearby APs
value
after receiving ACK
to generate the sleeping period
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Life-Add: general multiple APs

NS-3 simulation results:
 Uplink: 4 APs, 30 smartphones, randomly located in a 500×500 m
field, UDP saturation
 bi = 1  no lifetime (power-ON prob.) constraints
 1/3 with battery, 1/3 with battery + solar panel, 1/3 to wall power
 Battery level: uniform distribution within 200~1000 mAh
 Lifetime and throughput benefits
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Life-Add: general multiple APs

NS-3 simulation results:
 Per-device performance:
 Battery life improvement for all 5 devices
 Significant throughput increase for the low-rate device
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Life-Add: general multiple APs

Average performance gain
 Battery Life:
—Sleep/Wake
 Throughput:
—Node collaboration (reduce collisions and starvations)
—Parameter optimization
 Fairness:
—Node collaboration (to care for the vulnerable)
—Proportional-fair utility
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Life-Add: general multiple APs

Coexisting with IEEE 802.11
 AP 1,2 and their users upgrade from IEEE 802.11 to Life-Add
 Battery life
— Longer if you use Life-Add
 Throughput
— Higher no matter you use Life-Add or not, due to less collisions
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Summary

A novel renewal process model for energy efficient WiFi design

Proportional-fair utility maximization problem
 Non-convex

Life-Add MAC design
 Near optimal for single AP cases
 Alleviate “near-far effect” and “hidden terminal problem” in general cases
 Easy to implement

Ns-3 simulations
 Battery life, throughput, and fairness improvement
 Coexists harmoniously with IEEE 802.11

Not just WiFi: Last-hop decentralized access
 Internet of Things, Military,…

US patent filed
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Tasks to do…

More simulations for joint uplink and downlink

Practical traffics
 Web browsing, video streaming, email, searching

Hardware testing
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