15-744: computer networking l-21 wireless broadcast
TRANSCRIPT
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15-744: Computer Networking
L-21 Wireless Broadcast
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Overview
• 802.11• Deployment patterns• Reaction to interference• Interference mitigation
• Mesh networks• Architecture• Measurements
• White space networks
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3
Hig
her
Fre
qu
ency
Wi-Fi (ISM)
Broadcast TV
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dbm
Frequency
-60
-100
“White spaces”
470 MHz 700 MHz
What are White Spaces?
4
0 MHz
7000 MHz
TVISM (Wi-
Fi)
700
470
2400
5180
2500
5300
are Unoccupied TV ChannelsWhite Spaces
54-90
170-216
Wireless Mic
TV Stations in America
•50 TV Channels
•Each channel is 6 MHz wide
•FCC Regulations*•Sense TV stations and Mics •Portable devices on channels 21 - 51
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The Promise of White Spaces
5
0 MHz
7000 MHz
TV ISM (Wi-Fi)
700
470
2400
5180
2500
5300
54-90
174-216
Wireless Mic
More Spectrum
Longer Range
Up to 3x of 802.11g
at least 3 - 4x of Wi-Fi
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White Spaces Spectrum AvailabilityDifferences from ISM(Wi-Fi)
6
FragmentationVariable channel widths
1 2 3 4 51 2 3 4 5
Each TV Channel is 6 MHz wide Use multiple channels for more bandwidthSpectrum is Fragmented
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White Spaces Spectrum Availability
Differences from ISM(Wi-Fi)
7
FragmentationVariable channel widths
1 2 3 4 5
Location impacts spectrum availability Spectrum exhibits spatial variation
Cannot assume same channel free everywhere
1 2 3 4 5
Spatial Variation
TVTower
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White Spaces Spectrum Availability
Differences from ISM(Wi-Fi)
8
FragmentationVariable channel widths
Incumbents appear/disappear over time Must reconfigure after disconnection
Spatial VariationCannot assume same channel free everywhere
1 2 3 4 5 1 2 3 4 5Temporal Variation
Same Channel will not always be free
Any connection can bedisrupted any time
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Channel Assignment in Wi-Fi
9
Fixed Width Channels Optimize which channel to use
1 6 11 1 6 11
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Spectrum Assignment in WhiteFi
10
1 2 3 4 5
Spatial Variation BS must use channel iff free at client
Fragmentation Optimize for both, center channel and width
1 2 3 4 5
Spectrum Assignment Problem
Goal Maximize Throughput
Include Spectrum at clients
AssignCenter Channel
Width&
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Accounting for Spatial Variation
11
1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
=1 2 3 4 5 1 2 3 4 51 2 3 4 51 2 3 4 5
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Intuition
12
BSUse widest possible channel
Intuition
1 3 4 52Limited by most busy channel
But
Carrier Sense Across All Channels
All channels must be freeρBS(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
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Discovering a Base Station
13
Can we optimize this discovery time?
1 2 3 4 5
Discovery Time = (B x W)
1 2 3 4 5
How does the new client discover channels used by the BS?
BS and Clients must use same channelsFragmentation Try different center channel and widths
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SIFT, by example
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ADC SIFT
Time
Am
plit
ude
10 MHz5 MHz
SIFT
Pattern match in time domain
Does not decode packets
Data ACK
SIFS
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Taking Advantage of Broadcast
• Opportunistic forwarding
• Network coding
• Assigned reading• 802.11 with Multiple Antennas for Dummies• XORs In The Air: Practical Wireless Network
Coding• ExOR: Opportunistic Multi-Hop Routing for
Wireless Networks
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Outline
• MIMO
• Opportunistic forwarding (ExOR)
• Network coding (COPE)
• Combining the two (MORE)
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How Do We IncreaseThroughput in Wireless?
• Wired world: pull more wires!
• Wireless world: use more antennas?
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MIMO Multiple In Multiple Out
• N x M subchannels• Fading on channels is largely independent
• Assuming antennas are separate ½ wavelength or more• Combines ideas from spatial and time diversity, e.g. 1 x N and
N x 1• Very effective if there is no direct line of sight
• Subchannels become more independent
N transmitantennas
M receiveantennas
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• No diversity:
i x pT x h x pR = o• Adding multi-path:
i x pT x h(t) x pR = o
T R
T R
Simple Channel Model
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Transmit and Receive Diversity Revisited
• Receive diversity:
i x H x PR = o• Transmit diversity:
i x PT x H = o
T R
T R
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MIMO How Does it Work?
• Coordinate the processing at the transmitter and receiver to overcome channel impairments• Maximize throughput or minimize interference
• Generalization of earlier techniques• Combines maximum ratio combining at transmitter
and receiver with sending of multiple data streams
T R
Channel Matrix
Precodingfrom Nx1
Combiningfrom 1xN
I x PT x H x PR = O
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A Math View
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• How do we pick PR ? Need “Inverse” of H: H-1
• Equivalent of nulling the interfering possible (zero forcing)• Only possible if the paths are completely independent
• Noise amplification is a concern if H is non-invertible – its determinant will be small• Minimum Mean Square Error detector balances two effects
Effect of transmission R = H * C + NM MxN N M
Decoding O = PR * R C = ID DxM M N N
Results O = PR * H * I + PR * N
Direct-Mapped NxM MIMO
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• How do we pick PR and PT ?
Effect of transmission R = H * C + NM MxN N M
Coding/decoding O = PR * R C = PT * ID DxM M N NxD D
Results O = PR * H * PT * I + PR * N
Precoded NxM MIMO
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MIMO Discussion
• Need channel matrix H: use training with known signal
• MIMO is used in 802.11n in the 2.4 GHz band• Can use two of the non-overlapping “WiFi channels
”• Raises lots of compatibility issues• Potential throughputs of 100 of Mbps
• Focus is on maximizing throughput between two nodes• Is this always the right goal?
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Outline
• MIMO
• Opportunistic forwarding (ExOR)
• Network coding (COPE)
• Combining the two (MORE)
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packet
packet
packet
Initial Approach: Traditional Routing
• Identify a route, forward over links• Abstract radio to look like a wired link
src
A B
dst
C
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Radios Aren’t Wires
• Every packet is broadcast• Reception is probabilistic
123456123 63 51 42345612 456 src
A B
dst
C
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packet
packetpacketpacketpacketpacket
Exploiting Probabilistic Broadcast
src
A B
dst
C
packetpacketpacket
• Decide who forwards after reception• Goal: only closest receiver should forward• Challenge: agree efficiently and avoid duplicate transmissions
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Why ExOR Might Increase Throughput
• Best traditional route over 50% hops: 3(1/0.5) = 6 tx• Throughput 1/# transmissions
• ExOR exploits lucky long receptions: 4 transmissions• Assumes probability falls off gradually with distance
src dstN1 N2 N3 N4
75%50%
N5
25%
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Why ExOR Might Increase Throughput
• Traditional routing: 1/0.25 + 1 = 5 tx
• ExOR: 1/(1 – (1 – 0.25)4) + 1 = 2.5 transmissions• Assumes independent losses
N1
src dst
N2
N3
N4
25%
25%
25%
25%
100%
100%
100%
100%
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ExOR Batching
• Challenge: finding the closest node to have rx’d • Send batches of packets for efficiency• Node closest to the dst sends first
• Other nodes listen, send remaining packets in turn
• Repeat schedule until dst has whole batch
src
N3
dst
N4
tx: 23
tx: 57 -23 24
tx: 8
tx: 100
rx: 23
rx: 57
rx: 88
rx: 0
rx: 0tx: 0
tx: 9
rx: 53
rx: 85
rx: 99
rx: 40
rx: 22
N1
N2
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Reliable Summaries
• Repeat summaries in every data packet
• Cumulative: what all previous nodes rx’d
• This is a gossip mechanism for summaries
src
N1
N2
N3
dst
N4
tx: {1, 6, 7 ... 91, 96, 99}
tx: {2, 4, 10 ... 97, 98}batch map: {1,2,6, ... 97, 98, 99}
batch map: {1, 6, 7 ... 91, 96, 99}
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Outline
• MIMO
• Opportunistic forwarding (ExOR)
• Network coding (COPE)
• Combining the two (MORE)
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Background
• Famous butterfly example:
• All links can send one message per unit of time
• Coding increases overall throughput
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Background
• Bob and Alice
Relay
Require 4 transmissions
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Background
• Bob and Alice
Relay
Require 3 transmissions
XOR
XORXOR
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Coding Gain
• Coding gain = 4/3
1 1+3
3
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Throughput Improvement
• UDP throughput improvement ~ a factor 2 > 4/3 coding gain
1 1+3
3
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Coding Gain: more examples
Without opportunistic listening, coding [+MAC] gain=2N/(1+N) 2.With opportunistic listening, coding gain + MAC gain ∞
3
5
1+2+3+4+5
2
4
1
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COPE (Coding Opportunistically)
• Overhear neighbors’ transmissions
• Store these packets in a Packet Pool for a short time
• Report the packet pool info. to neighbors
• Determine what packets to code based on the info.
• Send encoded packets
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Opportunistic Coding
B’s queue
Next hop
P1 A
P2 C
P3 C
P4 D
Coding Is it good?
P1+P2 Bad (only C can decode)
P1+P3 Better coding (Both A and C can decode)
P1+P3+P4
Best coding (A, C, D can decode)
B
A
C
D
P4 P3 P3 P1
P4 P3 P2 P1
P4 P1
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Packet Coding Algorithm
• When to send?• Option 1: delay packets till enough packets to code with• Option 2: never delaying packets -- when there’s a
transmission opportunity, send packet right away
• Which packets to use for XOR?• Prefer XOR-ing packets of similar lengths• Never code together packets headed to the same next
hop• Limit packet re-ordering• XORing a packet as long as all its nexthops can
decode it with a high enough probability
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Packet Decoding
• Where to decode?• Decode at each intermediate hop
• How to decode?• Upon receiving a packet encoded with n native
packets• find n-1 native packets from its queue• XOR these n-1 native packets with the received
packet to extract the new packet
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Summary of Results
• Improve UDP throughput by a factor of 3-4
• Improve TCP by• wo/ hidden terminal: up to 38% improvement• w/ hidden terminal and high loss: little improvement
• Improvement is largest when uplink to downlink has similar traffic
• Interesting follow-on work using analog coding
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Reasons for Lower Improvement in TCP
• COPE introduces packet re-ordering
• Router queue is small smaller coding opportunity• TCP congestion window does not sufficiently
open up due to wireless losses
• TCP doesn’t provide fair allocation across different flows
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Outline
• MIMO
• Opportunistic forwarding (ExOR)
• Network coding (COPE)
• Combining the two (MORE)
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• Best single path loss prob. 50% • In opp. routing [ExOR’05], any router that hears the
packet can forward it loss prob. 0.54 = 6%
Use Opportunistic Routing
Opportunistic routing promises large increase in throughput
Opportunistic routing promises large increase in throughput
src
R1
dst
R4
R2
R3
50%100%
50% 100%
100%
100%
50%
50%
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src
R1
dst
But
• Overlap in received packets Routers forward duplicates
R2
P1
P2
P10
P1
P2
P1
P2
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ExOR
• State-of-the-art opp. routing, ExOR imposes a global scheduler:
• Requires full coordination; every node must know who received what
• Only one node transmits at a time, others listen
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• Global coordination is too hard
• One transmitter You lost spatial reuse!
src
dst
Global Scheduling
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MORE (Sigcomm07)
• Opportunistic routing with no global scheduler and no coordination
• Uses random network coding
• Experiments show that randomness outperforms both current routing and ExOR
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src
R1
dst
Go Random
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R2
α P1+ ß P2
γ P1+ δ P2
Each router forwards random combinations of packets
Randomness prevents duplicates
No scheduler; No coordination
Simple and exploits spatial reuse
P1
P2
P1
P2
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src
dst1 dst2 dst3
P1P2P3P4
P1P2 P2
P3 P3P4
P3
P4
P1
P4
P1
P2
Random Coding Benefits Multicast
Without coding source retransmits all 4 packets
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src
dst1 dst2 dst3
P1P2P3P4
P1P2 P2
P3 P3P4
8 P1+5 P2+ P3+3 P4
7 P1+3 P2+6 P3+ P4
P3
P4
P1
P4
P1
P2
Random Coding Benefits Multicast
Without coding source retransmits all 4 packets
With random coding 2 packets are sufficient
Random combinations
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Summary
• Wireless behavior is not all bad
• Next lecture: Security: DDoS and Traceback
• Readings:• Practical Network Support for IP Traceback• Amplification Hell: Revisiting Network Protocols
for DDoS Abuse
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