Networking Devices
over White Spaces
Ranveer Chandra
Collaborators:
Thomas Moscibroda, Rohan Murty, Victor Bahl, Srihari Narlanka
Wi-Fi’s Success Story
• Wi-Fi is extremely popular (billion $$ business)
– Enterprise/campus LANs, Home networks, Hotspots
• Why is Wi-Fi successful
– Wireless connectivity: no wires, increased reach
– Broadband speeds: 54 Mbps (11a/g), 200 Mbps (11n)
– Free: operates in unlicensed bands, in contrast to
cellular
Problems with Wi-Fi
• Poor performance:
– Contention with Wi-Fi devices
– Interference from other devices in 2.4 GHz, such
as Bluetooth, Zigbee, microwave ovens, …
• Low range:
– Can only get to a few 100 meters in 2.4 GHz
– Range decreases with transmission rate
Overcoming Wi-Fi’s Problems
• Poor performance:
– Fix Wi-Fi protocol – several research efforts (11n,
MIMO, interference cancellation, …)
– Obtain new spectrum?
• Low range:
– Operate at lower frequencies?
Higher Frequency
Analog TV  Digital TV
USA (2009)
Japan (2011)
Canada (2011)
Broadcast TV
UK (2012)
China (2015)
….
Wi-Fi (ISM)
….
…..
5
What are White Spaces?
Wireless Mic
TV
0 54-88 170-216 470
700
MHz•50 TV Channels
-60
ISM (Wi-Fi)
2400 2500
5180
7000
MHz
5300
“White spaces”
•Each channel is 6 MHz wide
dbm
•FCC Regulations*
TV Stations in America
•Sense TV-100
stations and Mics
Frequency 700 MHz
470 MHz
•Portable devices
on channels
21 - 51
White Spaces are Unoccupied TV Channels
6
Why should we care
about White Spaces?
7
The Promise of White Spaces
Wireless Mic
TV
0 54-90 174-216 470
MHz
ISM (Wi-Fi)
2400 2500
700
Up to 3x of 802.11g
More
Spectrum
Longer
Range
}
5180
7000
MHz
5300
Potential Applications
Rural wireless broadband
City-wide mesh
at least 3 - 4x of Wi-Fi
……..
……..
8
Goal: Deploy Wireless Network
Base Station
(BS)
Good throughput for all nodes
Avoid interfering with incumbents
9
Why not reuse Wi-Fi
based solutions, as is?
10
Fraction of Spectrum Segments
White Spaces Spectrum Availability
0.8
Urban
0.7
Differences from ISM(Wi-Fi)
0.6
Suburban
0.5
Rural
Fragmentation
Variable channel widths
0.4
0.3
0.2
1 20.13 4 5
0
1
1 2 3 4 5
2
3
4
5
6
# Contiguous Channels
>6
Each TV Channel is 6 MHz wide
Spectrum
is Fragmented
 Use
multiple channels for more bandwidth
11
White Spaces Spectrum Availability
Differences from ISM(Wi-Fi)
Fragmentation
Variable channel widths
Spatial Variation
Cannot assume same
channel free everywhere
1 2 3 4 5
1 2 3 4 5
TV
Tower
Location impacts spectrum availability  Spectrum exhibits spatial variation
12
White Spaces Spectrum Availability
Differences from ISM(Wi-Fi)
Fragmentation
Variable channel widths
Spatial Variation
Cannot assume same
channel free everywhere
1 2 3 4 5
1 2 3 4 5
Temporal Variation
Same Channel will
not always be free
Any connection can be
disrupted any time
Incumbents appear/disappear over time  Must reconfigure after disconnection
13
Cognitive (Smart) Radios
Frequency
Signal Strength
Signal Strength
1. Dynamically identify currently unused portions of spectrum
2. Configure radio to operate in available spectrum band
 take smart decisions how to share the spectrum
Frequency
Networking Challenges
The KNOWS Project (Cogntive Radio Networking)
How should nodes connect?
Which spectrum-band should two
cognitive radios use for transmission?
1. Frequency…?
2. Channel Width…?
3. Duration…?
How should they discover
one another?
Need analysis tools to
reason about capacity &
overall spectrum
utilization
Which protocols should we use?
MSR KNOWS Program
Prototypes
• Version 1: Ad hoc networking in white spaces
– Capable of sensing TV signals, limited hardware functionality, analysis of
design through simulations
• Version 2: Infrastructure based networking (WhiteFi)
– Capable of sensing TV signals & microphones, deployed in lab
• Version 3: Campus-wide backbone network (WhiteFi +
Geolocation)
– Deployed on campus, and provide coverage in MS Shuttles
Version 2: WhiteFi System
Prototype Hardware Platform
Base Stations and Clients
Algorithms and Implementation
Discovery
Spectrum Assignment
Handling Disconnections
Evaluation
Deployment of prototype nodes
Simulations
17
Hardware Design
• Send high data rate signals in TV bands
– Wi-Fi card + UHF translator
• Operate in vacant TV bands
– Detect TV transmissions using a scanner
• Avoid hidden terminal problem
– Detect TV transmission much below decode threshold
• Signal should fit in TV band (6 MHz)
– Modify Wi-Fi driver to generate 5 MHz signals
• Utilize fragments of different widths
– Modify Wi-Fi driver to generate 5-10-20-40 MHz signals
KNOWS Platform: Salient Features
• Can dynamically adjust channel-width and
center-frequency.
• Low time overhead for switching
 can change at fine-grained time-scale
Transceiver can tune
to contiguous spectrum
bands only!
Frequency
Changing Channel Widths
Scheme 1: Turn off certain subcarriers ~ OFDMA
10
20 MHz
Issues: Guard band? Pilot tones? Modulation scheme?
Changing Channel Widths
Scheme 2: reduce subcarrier spacing and width!
 Increase symbol interval
10
20 MHz
Properties: same # of subcarriers, same modulation
Adaptive Channel-Width
• Why is this a good thing…?
1. Fragmentation
5Mhz
20Mhz
Frequency
 White spaces may have different sizes
 Make use of narrow white spaces if necessary
2. Opportunistic, load-aware channel allocation
 Few nodes: Give them wider bands!
 Many nodes: Partition the spectrum in narrower bands
KNOWS White Spaces Platform
Windows PC
TV/MIC
detection
Scanner (SDR)
FFT
Net
Stack
FPGA
UHF RX
Daughterboard
Whitespace Radio
Connection Manager
Atheros Device Driver
Wi-Fi
Card
UHF
Translator
Variable Channel
Width Support
25
WhiteFi System Challenges
Fragmentation
Spatial
Variation
Temporal
Variation
Impact
Discovery
Spectrum
Assignment
Disconnection
26
Discovering a Base Station
Discovery Problem
1 2 3 Goal
4 5
Quickly find channels 1BS 2
is using
3 4 5
Discovery Time = (B x W)
Fragmentation
 the
Try different
center
channel
and widths
How
does
new
client
discover
BS and
Clients
must
use
same
channels
Can
we
optimize
this
discovery
time?
channels used by the BS?
27
Whitespaces Platform: Adding SIFT
PC
TV/MIC
detection
Net
Stack
Scanner (SDR)
FFT
Temporal Analysis
(SIFT)
FPGA
UHF RX
Daughterboard
Whitespace Radios
Connection Manager
Atheros Device Driver
Wi-Fi
Card
UHF
Translator
SIFT: Signal Interpretation before Fourier Transform
28
SIFT, by example
10
5 MHz
MHz
SIFT
SIFT
Does not decode packets
Pattern match in time domain
Amplitude
ADC
BeaconData
Beacon
ACK
SIFS
Time
29
BS Discovery: Optimizing with SIFT
1 2 3 4 5
1 2 3 4 5
Amplitude
18 MHz
Matched against 18 MHz packet signature
Time
SIFT enables faster discovery algorithms
30
BS Discovery: Optimizing with SIFT
Linear SIFT (L-SIFT)
1 2 3 4 5
Jump SIFT (J-SIFT)
1 2 3 4 5 6 7 8
31
Discovery: Comparison to Baseline
Baseline =(B x W)
L-SIFT = (B/W)
J-SIFT = (B/W)
1
Discovery Time Ratio
(compared to baseline)
0.9
0.8
Linear-SIFT
Jump-SIFT
0.7
2X reduction
0.6
0.5
0.4
0.3
0.2
0.1
0
0
30
60
90
120
White Space - Contiguous Width (MHz)
150
180
32
WhiteFi System Challenges
Fragmentation
Spatial
Variation
Temporal
Variation
Impact
Discovery
Spectrum
Assignment
Disconnection
33
Channel Assignment in Wi-Fi
1
6
11
1
6
11
Fixed Width Channels  Optimize which channel to use
34
Spectrum Assignment in WhiteFi
Spectrum Assignment Problem
Goal
Maximize Throughput
Include
Spectrum at clients
1 2 3 4 5
Assign
1 2 3 4 5
Center Channel
&
Width
Fragmentation  Optimize for both, center channel and width
Spatial Variation  BS must use channel iff free at client
35
Accounting for Spatial Variation
1 2 3 4 5
1 2 3 4 5

1 2 3 4 5
1 2 3 4 5

1 2 3 4 5
1 2 3 4 5
=
1 2 3 4 5
36
Intuition
Intuition
Use widest possible channel
BS
But
Limited by most busy channel
1 2 3 4 5
 Carrier Sense Across All Channels
 All channels must be free
ρBS(2 and 3 are free) = ρBS(2 is free) x ρBS(3 is free)
Tradeoff between wider channel widths
and opportunity to transmit on each channel
37
Throughput (Mbps)
Multi Channel Airtime Metric (MCham)
3.5
3
2.5
2
1.5
1
0.5
0
20 Mhz
5 MHz
10 MHz
W
BS
MChamn (F, W) =
n (c)

5
Mhz c(30F ,W )
0
10
20
40
Background1traffic
4 delay
5 (ms)
2 - 3Packet
50
Pick (F, W) that maximizes
20 Mhz
10 MHz
(N5 MHz
* MChamBS + ΣnMChamn)
2
1
ρn(c)
=(2)Approx.
opportunity
node n will
ρ
(2)

ρ

Free
Air
Time
on
Channel
2
BS
1.5
BS
ρBS(2) = Max (Free Air
Time
onContention
channel
2, 1/Contention)
get
to
transmit
on
channel
c
1
MCham-value
2.5
0.5
0
0
10
20
30
40
Background traffic - Packet delay (ms)
50
38
WhiteFi Prototype Performance
Throughput (Mbps)
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
WhiteFi
0
25
50
75
100
125
OPT
150
175
200
225
250
Seconds
39
WhiteFi System Challenges
Fragmentation
Spatial
Variation
Temporal
Variation
Impact
Discovery
Spectrum
Assignment
Disconnection
40
MSR KNOWS Program
Prototypes
• Version 1: Ad hoc networking in white spaces
– Capable of sensing TV signals, limited hardware functionality, analysis of
design through simulations
• Version 2: Infrastructure based networking (WhiteFi)
– Capable of sensing TV signals & microphones, deployed in lab
• Version 3: Campus-wide backbone network (WhiteFi +
Geolocation)
– Deployed on campus, and provide coverage in MS Shuttles
Geo-location Service
Shuttle Deployment
World’s first urban white space network!
Goal: Provide free Wi-Fi Corpnet access in MS shuttles
• Use white spaces as backhaul, Wi-Fi inside shuttle
• Obtained FCC Experimental license for MS Campus
• Deployed antenna on rooftop, radio in building & shuttle
• Protect TVs and mics using geo-location service & sensing
Some Results
Demo
Summary & On-going Work
• White Spaces enable new networking scenarios
• KNOWS project researched networking problems:
–
–
–
–
Spectrum assignment: MCham
Spectrum efficiency: variable channel widths
Network discovery: using SIFT
Network Agility: Ability to handle disconnections
• Ongoing work:
– MIC sensing, mesh networks, co-existence among
white space networks, …
45
Questions
SIGCOMM 2008 Talk
A Case for
Adapting Channel Width
in Wireless Networks
Ranveer Chandra, Ratul Mahajan,
Thomas Moscibroda, Victor Bahl
Microsoft Research
Ramya Raghavendra
University of California, Santa Barbara
Adaptation in Wireless Networks
• Existing knobs:
– Transmit rate/Modulation: auto rate algorithms
• Adapt how tightly bits are packed in spectrum
– Transmit power: TPC algorithms
• Adapt tx power for connectivity, spectrum reuse
–…
• This paper:
– Channel Width: how & why?
49
Channelization in IEEE 802.11
802.11 uses 20 MHz wide channels
70 MHz
2427 MHz
2402 MHz
2452 MHz
2412 MHz
1
2
3
11
6
2407 MHz
20 MHz
50
2472 MHz
Why Adapt Channel Widths?
One Scenario
More spectrum 
+ more capacity (Shannon’s)
– higher idle power consumption (coming up)
Challenge:
40
20
MHz determine app demand
5 MHz
Dynamically
& adapt channel width
For throughput
intensive
apps, for
go wider
for best
data rate
When idle,
go narrow
least power
consumption
51
Our Contributions
• Demonstrate feasibility of dynamic channel
width adaptation on off-the-shelf hardware
• Characterize properties of channel widths
– Throughput, range, energy consumption
• SampleWidth to dynamically select best
channel width
52
Implementing Variable Widths
Antenna
Typical Wireless Card
Baseband/MAC
RF Component
(coding/decoding,
timing, encryption)
(PLLs, upconverters
Power Amplifiers)
REF CLOCK
Modify driver to programmatically tune clock frequency
Channel width proportional to clock frequency
53
Variable Channel Widths in OFDM
In 802.11: 48 data subcarriers, 4 pilots
Pilot tone
Data
Subcarriers
20 MHz
Subcarrier Spacing: 0.3125 MHz
At 20 MHz:
Guard Interval: 0.8 s
Symbol Period = 1/0.3125 s + GI = 4 s
54
Variable Channel Widths in OFDM
To reduce width to 10 MHz, halve the clock frequency
Pilot tone
Data
Subcarriers
20
10 MHz
Subcarrier Spacing: 0.3125/2 MHz
At 10 MHz:
Guard Interval: 0.8*2 s
Symbol Period = (1/0.3125 s + GI)*2 = 8 s
55
Our Implementation
• Using Atheros cards on Windows
– Implemented 5, 10, 20, 40 MHz
– MAC parameters scale with clock
• e.g. SIFS: 20 s at 20 MHz, 40 s at 10 MHz
– We keep 802.11 slot time constant for interop
56
Properties of Channel Widths
Impact on:
• Throughput
• Transmission Range
• Battery Power
57
Experimental Setup
• Conducted (clean) experiment
– Using attenuator & CMU emulator
• Indoor experiments at MSR & UCSB
• Outdoor experiments in large park
58
Throughput
• Throughput increases with channel width
UDP Throughput (in Mbps)
– (Shannon’s) Capacity = Bandwidth * log (1 + SNR)
– In practice, protocol overheads come into play
• Twice bandwidth has less than double throughput
5 MHz
10 MHz
20 MHz
40 MHz
108 Mbps@40 MHz
54 Mbps@20 MHz
27 Mbps@10 MHz
13.5 Mbps@5MHz
Modulation
59
Actual Data Rate:
Transmission Range
• Reducing channel width increases range
Loss Rate
– Narrow channel widths have same signal energy but lesser noise
~ 3 dB
 better SNR
60
5MHz
10MHz
20MHz
40MHz
Attenuation (dB)
Impact of Guard Interval
Loss Rate (%)
• Reducing width increases guard interval
 more resilience to delay spread (more range)
5MHz
10MHz
20MHz
40MHz
Delay Spread (in ns)
61
Need for Width Adaptation
With auto rate:
40 MHz
20 MHz
10 MHz
5 MHz
There is no single best channel width!
62
Energy Consumption
• Lower channel widths consume less power
– Similar to CPU clock scaling
Send
Idle
Receive
5MHz
1.92
1.00
1.01
10MHz
1.98
1.11
1.13
20MHz
2.05
1.25
1.27
40MHz
2.17
1.41
1.49
• When idle, lowest channel width is best
• During send/receive, best energy/bit width depends on
distance
63
Recap: Channel Width Properties
• When nodes are near, higher channel widths have
more throughput
• Lower channel widths have more range
– Better SNR, resilience to delay spread
• Lower channel widths consume less power
Lower widths increase range while consuming less power!
64
Application: Song Sharing
Zune Social over Wi-Fi
1. Zunes advertise (periodically beacon) their song list
2. Interested Zunes download songs from peers
Issues: throughput, power!
Our Solution: Adapt channel width based on traffic
(SampleWidth)
65
SampleWidth for Throughput
Goal: Use minimum width that satisfies demand
• Algorithm:
– Start at minimum width – best energy, range
– When interface queue is full, probe higher width
• During song transfer
– Periodically probe adjacent (higher/lower) widths
– Return to minimum width when no traffic
66
SampleWidth Evaluation
• SampleWidth adapts to best throughput width
67
Reducing
Power Consumption
Start 20 MB
5MHz
10MHz
20MHz
40MHz
Energy (Joule)
file transfer
@ 25 sec
Seconds
68
Total Energy (Joules)
SampleWidth for Energy
69
~ 25%
savings
Application Scenarios
1. Throughput/energy-aware song sharing
2. Load aware spectrum allocation in WLANs
3. Improved capacity in 802.11
4. Cognitive (DSA-based) networking
70
Summary
• Channel width can be adapted
– On off-the-shelf hardware
– To improve application performance
– To design better, more efficient networks
• Future work
– Explore other channel width strategies
• e.g. modifying number of subcarriers
– Communication across channel widths
• Nodes on different widths cannot communicate
– Build larger systems using adaptive channel widths
71
Questions?
http://research.microsoft.com/netres/projects/spawn/
72
73