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2
Wireless Networks Interference Limited
Packet decoded successfully When interference substantially lower Else, collision
CollisionCollision
IEEE 802.11
3
Phy Layer Capture
Concurrent transmissions may not necessarily cause collision Possible to decode the frame with higher SINR As long as receiver not “locked” onto interference Known as PHY layer capture
What is locking onto a signal? What is locking onto a signal?
4
Implications of Capture
When stronger signal is of interest, AND Arrives within PLCP window
Concurrency feasible
PLCP
5
Implications of Capture
When stronger signal is of interest, AND Arrives within PLCP window
Concurrency feasible
However, PLCP duration small Probability of precise timing also small
millisecond20 us
6
Capture and MIM
Message in Message (MIM) Strong frame arrives after preamble of interfering frame Receiver locked onto interference by then, and decoding However, continues searching for another preamble Strong message can be extracted while in another message
7
Caveats
Recognizing arrival of new preamble
requires new preamble to be have higher SINR
Only then correlation shows a high value
8
SINR for MIM a function of relative arrival order and timingSINR for MIM a function of relative arrival order and timing
SINR Requirements [Lucent NIC]
4 dB
4 dB
10 dB
10 dB
9
Order Matters
Some signal-arrival orders will permit concurrency Productive
But the reverse order may cause collision Unproductive
Example …
10
MIM Aware Scheduling
AP1 must start first, followed by staggered
transmission from AP2 -- concurrency feasible
AP1 must start first, followed by staggered
transmission from AP2 -- concurrency feasible
10 dB5 dB
11
MIM Aware Scheduling
AP1 must start first, followed by staggered
transmission from AP2 -- concurrency feasible
AP1 must start first, followed by staggered
transmission from AP2 -- concurrency feasible
10 dB5 dB
In general, weaker transmission must start first,
stronger receiver suppresses it, and extracts own signal
In general, weaker transmission must start first,
stronger receiver suppresses it, and extracts own signal
12
MIM Aware Scheduling
AP1 must start first, followed by staggered
transmission from AP2 -- concurrency feasible
AP1 must start first, followed by staggered
transmission from AP2 -- concurrency feasible
10 dB5 dB
In general, weaker transmission must start first,
stronger receiver suppresses it, and extracts own signal
In general, weaker transmission must start first,
stronger receiver suppresses it, and extracts own signalObserve that 802.11 does not enforce this order,
hence will fail to exploit MIM capabilities
Observe that 802.11 does not enforce this order,
hence will fail to exploit MIM capabilities
13
Problem Definition:
Design an MIM-aware scheduling algorithm that reorders transmissions to augment concurrency
What is the bound on improvement?How to cope with time-vaying channel?How to sustain fairness and starvation?
14
Solution Space
Shuffle A centralized MIM-aware scheduling protocol for
Enterprise wireless LANs (EWLAN)
AP2AP2
ControllerController
AP1AP1 AP3AP3
15
Solution Space
Shuffle A centralized MIM-aware scheduling protocol for
Enterprise wireless LANs (EWLAN)
Why EWLAN?1. Becoming popular in single-admin environments
§ Offices, warehouses, libraries
2. Understand MIM for centralized systems, then goto distributed
§ Need to walk before running
AP2AP2
ControllerController
AP1AP1 AP3AP3
16
Shuffle: 3 Main Components
1. Measuring Interference Relation: Rehearsal Characterize interference map to identify MIM opportunity Cope with time-varying channel conditions
2. Packet Scheduler Use rehearsal outcome to schedule transmissions Scheduling = Reordering and staggering Protect from unfairness and starvation
3. Schedule Coordinator Execute MIM-aware schedule Cope with failures, retransmissions, and centralized bottleneck
17
Main Assumptions
Dominant download traffic Upload handled through periodic “upload windows”
Processing time and latencies Powerful controller, thin APs Wired backbone fast, but can become bottleneck
Additive Interference Total interference = sum of individual interferences
18
Feasibility First
What is the maximum improvement with Shuffle
in finite network scenarios?
Determine the optimal link selection, and their relative
order of initiation, to achieve this bound
Observe that graph coloring inapplicable
19
Analysis
Optimal MIM-aware link scheduling is NP-Hard Proof:
• Reduction from Independent Set selection
• MIM scheduling is special case
• Set SL (Signal Last SINR)=
• Relative order requires the optimal choice of links first.
• Hence, NP-Hard
20
Integer Linear Program
Use ILP to upper bound improvement For a large number of finite-sized topologies
23
Rehearsal
Central controller needs link conflict information Graph coloring notion of conflict not applicable Conflicts also change with time-varying channel
Basic idea: Controller orchestrates a rehearsal of transmissions Clients and APs record RSSI values as instructed times Recorded RSSI correlated at controller Inteference graph generated
24
Rehearsal
At network initialization APs and clients informed about time of transmissions Each AP transmits sequence of probes at base rate Clients transmit probes, piggybacks recorded RSSI values
At the end, APs forward gathered values to controller Controller derives interference map
• using additive interference assumption
25
Interference Map
Pairwise interferences mapped Controller populates table
i
j
Interference
from i to j
Interferer
Sniffer
. . .. . .
26
Rehearsal
Opportunistic rehearsal Continuous rehearsal expensive Utilize regular transmissions to piggyback overheard RSSI
Coping with Fading Convergence may take long with opportunistic Handling loss will require immediate conflict information Perform self-corrective rehearsal using data packets
• Schedule packets conservatively to also serve the rehearsal purpose
27
RehearsalRehearsal
MIM Scheduler
(Optimal NP-Hard)
MIM Scheduler
(Optimal NP-Hard)
i
j
Interference
from i to j
28
MIM-Aware Scheduler
Scheduler operation: Choose non-conflicting packets from queue Determine their relative starting order + stagger durations Dispatch batch to AP
Scheduler goal: Maximize batch size Protect from starvation Ensure high fairness
29
Greedy Heuristics
Basic greedy Fix a queue lookahead size for scheduling (say L) Controller takes in-order packets from FIFO queue Packet j scheduled if no conflict with pkts already scheduled
• Conflict is a function of SL and SF thresholds
If conflict, packet j postponed for next batch
No starvation, Good Fairness Every batch, a packet progresses in queue Head of the batch always transmitted
O(n2 )O(n2 )
30
Greedy Heuristics
Randomized Greedy Perform basic greedy on randomized subsets of queue Probability of choosing packets biased
• Earlier in the queue have higher probability Choose largest batch among all solutions
Least-Conflict Greedy Compute packet score = # of pair-wise conflicts
• Score higher if pkt must start earlier, lower else Sort packets based on score Perform basic greedy on this sorted order Incorporate aging for fairness/starvation
O(n2 logn)O(n2 logn)
O(n2)O(n2)
32
RehearsalRehearsal
MIM Scheduler
(Optimal NP-Hard)
MIM Scheduler
(Optimal NP-Hard)
Schedule Coordinator
(ReTx, Prefetch, Predict)
Schedule Coordinator
(ReTx, Prefetch, Predict)
< Batch of packets, Schedule >
33
Schedule Coordinator
Packets dispatched to APs Time synchronized between APs and contollers Pipeline Controller to AP, and AP to Client transmissions APs transmit at specified time
34
Schedule Coordinator
ACK Transmission Controller embed ACK schedule in Data Packet header Clients follow schedule (MIM-aware) AP forwards ACKs to controller (ACKs may have RSSI) When no ACK, AP forwards NACK Lost packets scheduled with highest priority
37
AP1
AP2
AP3
C1
C2
C3
C4
APs/Clients Stagger transmissions
C1
C3
C4
ACK
ACK
ACK
Data Staggering Order: AP2-AP3-AP1
ACK Staggering Order: C4-C3-C1
38
Coping with Fading Loss
Time varying channel Interference graph changes Subsequent MIM scheduling can cause further failures
Immediate corrective rehearsal Controller identifies links suspected of fading Schedules a packet batch only for these APs
• This is a partial rehearsal
• Packets are transmitted in serial order
APs and clients unaware, send Data and ACKs Controller updates interference map from ACK RSSIs
39
Pipelining Batches
Batch - ACK - Batch inefficient APs remain idle between batches (not negligible)
Controller sends 2 batches to AP AP sends batch 1 and receives ACKs Batch 2 started, ACKs forwarded to controller in parallel Controller processes ACKs and next batch in parallel Controller schedules batch 3, sends to APs AP finishes batch 2 Repeat …
44
Future Work
MIM aware routing Choose paths such that MIM is maximally activated
Distributed Shuffle We presented for EWLAN What about residential WLANs, organic emergence?
Can postambles be useful? As opposed to Preamble (Hari Balkrishnan, NSDI 08)
45
Conclusion
Necessary to pay attention to PHY layer capabilities Interference cancellation (its first steps) one example
MIM is ability to extract frame of interest Even under ongoing interference Provided some (relative order, SINR) conditions hold
Facilitating these conditions can enable MIM Rich performance gains feasible
MIM-aware link layer scheduling necessary
46
Conclusion
Shuffle - MIM scheduling for EWLANs EWLANs proliferating, also foundation for distributed case
NP-Hard problem Bounds characterized through linear programming
Greedy scheduling heuristics perform well Performance close to optimal
Evaluation on Linux testbed + Soekris boxes Consistent improvement, even under fading and losses
49
Successive Interference Cancellation (SIC)
State of the art allows only one reception The stronger one
SIC enables a receiver to receive both signals Stronger signal decoded and subtracted Residual signal decoded from the residue
50
SIC based WLANs
Existing schemes require SINR > Game of out-shouting each other
SIC offers payoff if transmitter Either out-shouts or whispers Fundamental changes for protocol design
52
Diving a Little More
Modulation 101 How do you transmit a bit sequence? Need to convert bits onto analog domain … convert back
Basic Idea:• Change the properties of a signal (amplitude, frequency, phase) to
reflect the bit that you are trying to convey
• Example: Shout loud for +1, whisper for 0
• Shouting loud is a symbol for +1 … whispering is a symbol for 0
53
Modulation 101
But humans can modulate their voice better So why not shout/whisper at different levels?
• Each level a symbol -- each symbol carries multiple bits
11
1001
00
54
Modulation 101
But humans can modulate their voice better So why not shout/whisper at different levels?
• Each level a symbol -- each symbol carries multiple bits
11
1001
00
So if recevier samples at the right time (during peak or trough) then it can get a value. Since it knows the ranges for each symbol, it knows what symbol was received … hence, what bit sequence.
So if recevier samples at the right time (during peak or trough) then it can get a value. Since it knows the ranges for each symbol, it knows what symbol was received … hence, what bit sequence.
55
With Actual Signals
More opportunity: Modulate a Sin(.) and a Cos(.) signal with different bits This is like 2D space
• Called constellation diagram
Send the sum of both as
a single symbol
Receiver gets sum, and can
extract both using a
coherent demodulator
57
16 QAM reception
Receiver gets a dot Computes nearest neighbor as the transmitted symbol
Hence, the bits are
now decoded
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
58
16 QAM reception
Receiver gets a dot Computes nearest neighbor as the transmitted symbol
Hence, the bits are
now decoded
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.Do you see why higherData rate increases the
Probability of error?
Because, separation betweenSymbols become smaller
Do you see why higherData rate increases the
Probability of error?
Because, separation betweenSymbols become smaller
59
Easier Said Than Done
Lots of issues: Rx is assumed to know the phase of transmitted signal
• So that Rx can sample at the right time
But difficult because signals getting reflected Also, Rx’s frequency needs to be exactly same as Tx
Recall PLCP It helps in straightening these out
60
So Then … How do You Do SIC?
Basic Idea: Let received combined signal be S’ …
• Stronger received signal be S1, weaker received signal be S2
Synchronize with the stronger signal• By detecting PLCP
Demodulate by treating the weaker as interference• Get the bits out
Now, model the stronger signal based on the bits (S1’)• To see how it would look without the interference (S1’ != S1)
Now subtract: i.e., S2’ = S’ - S1’ Demodulate S2’ to get the bits out
How
65
Modeling a Symbol from Bits
00
01
10
11
S’
S1’
-S1’
S2’ = S’ + (-S1’)Thus weaker signal is bit 11
66
All Modeling, Subtracting in Software
USRP (Universal Software Radio Peripheral) Connected to laptop for doing processing
This paper demonstrated offline SIC• All signals received, and procesing done after that
67
ZigZag Decoding: Combating Hidden Terminals in Wireless Networks
Shyamnath Gollakota and Dina Katabi
MIT CSAIL
SIGCOMM 2009
68
Hidden Terminal Problem
Leads to low utilization of bandwidth and unfairness in channel access
RTS/CTS induced too much overhead Collided packets may still be decodable!
Alice BobAPX
69
Basic idea of ZigZag Decoding
Chunk 1 from user A from 1st copy of collided packet can be decoded successfully Subtract from 2nd copy to decoded the Chunk 1 of user B
• Subtract from 1st copy of collided packet to decode Chunk 2 from user A– Subtract from 2nd copy of collided packet to decode Chunk 2 from user B
70
Wait! What about Shannon Capacity?
Requires retransmissions if collision occurs No overhead if no collision
R1
R2
TDMA
71
Other alternatives
CDMA Incompatible with WLAN Low efficiency in high SNR
Successive interference cancellation (SIC) Chunk == packet Decode the strong signal first, subtract from the sum and
then decode the weak signal No need for retransmissions Both transmitters need to transmit at a lower rate
73
Technical Barriers
How do I know packets collide Matching collision happened? (P1, P2) and (P1’, P2’) Frequency offset between transmitter and receiver Sampling offset Inter-symbol interference What if errors occur in chunks Acknowledgement? } subtraction is
non-trivial
74
Evaluation
14-node GNURadio testbed USRP with RFX2400 radio (2.4 GHz) BPSK Bit rate 500kbs 32-bit preamble 1500-byte payload, 32-bit CRC
Deficiency in GNURadio Cannot coordinate transmission and reception very closely CSMA, ACK
Transmitter Receiver
Software
80
Conclusion
ZigZag improves fairness & throughput Further research
Combination of analog network coding
82
Preliminary on communication
BPSK: 0 -> -1 1 -> 1
http://en.wikipedia.org/wiki/QPSK
driftfrequency todue ][][][
collision of presencein ][][][][
channel)invariant time(a ][][][
2 nwenHxny
nwnynyny
nwnHxny
fTj
BA
+=
++=+=
πδ
83
Collision Detection
Preamble Pseudo random number Correlation with moving
window thresholding
∑=
=ΔΓL
kB ksH
1
2|][|)('
84
Matching collision
Given (P1 + P2(Δ)) and (P1’, P2’(Δ’)), how to determine that P1 = P’ and P2 = P2’’ Determine offset first Correlation of P2(Δ) and P2’(Δ’)
85
Decode matching collision
Decode iteratively Re-encoding
Computing channel parameters• Channel gain estimated from
• Frequency offset and sampling error 1) coarse estimation from previously successful reception 2) iterative estimation
• Inter-symbol interference: take the inverse of linear filter (for removal of ISI)
∑=
=ΔΓL
kB ksH
1
2|][|)('
∑−=
+=L
LlISIl lixhix ][][
86
Decode matching collision (cont’d)
Re-encoding Account for sampling error
))((sin][][
][][ 2
inciyny
enxHny
AAAA
TfjAAA
A
−+=+
=
∑∞
∞−
μπμ
πδ
87
What about errors?
Will errors in decoding have a cascading effect? Error propagation dies out exponentially
• Error correction capability of modulation
Forward and backward decoding
89
Duke EWLAN Topology
Client, AP placement traces used to feed Qualnet Fading models from Qualnet
Only 4 topologies shown in graph
90
Increasing AP Density
Shuffle throughput higher in denser conditions Greater scope to “squeeze in” transmissions in space
92
Under Channel Fading
Corrective rehearsal effective to cope with fading We observed loss fraction of 12% under Ricean.
93
Ongoing Work
Integrating upload traffic Proposing upload windows Can be opportunistically used for download (ZMAC) Can be used to accommodate client joins, departure
Interference from external networks affect schedule Need to treat border APs separately
Interference cancellation may decode both signals More powerful than MIM, hence, new MAC necessary We are investigating possibilities through GNU radio
96
Mobile Phones = Powerful Sensors
Next Generation Mobile Phones Variety of embedded sensors
- Cameras, mic., accelerometer, health monitor, RFID reader
3 Billion active phones 2009 - phone sales will surpass computers Convergent device accepted technologically, socially
97
Vision
Envision each mobile phone as a virtual lens
Imagine an Information Telescope over 3 billion lenses
Enabling you to zoom in and perceive any part of the worldthrough the eyes and ears of these phones
And even querying them in real time, with automatic, social, or participatory replies
98
Micro-Blog
Virtual TelescopeVirtual Telescope
Internet, Cellular Networks
Internet, Cellular Networks Visualization ServiceVisualization Service
Web ServiceWeb Service
Sensors
Phones
People
Physical SpacePhysical Space
101
Post-Its in the Air
Information superimposed on virtual space Google maps, Microsoft SensorMap, etc.
Feasible to superimpose on physical space As if sticky notes floating in the air Downloadable into mobile phones
… Prototype for Duke campus
102
Micro-Blog [mobisys2008]
Project Website http://microblog.ee.duke.edu
Project live at http://152.3.193.194/microblog/dev7/microblog.php
104
Many Challenges
Energy-Aware Localization [mobisys08_poster]
GPS offers 7 hours battery, but hi accuracy Alternates tradeoff accuracy for energy
105
Many Challenges
Location Privacy Users do not want to reveal location Partial location important for contextual info.
Incentives No reason for user to participate Designing incentive schemes
Too much information entering the system Information distillation critical
… many many more
107
Analogy
Imagine a graphic equalizer How do you know what setting will play the song best?
If each song had a “known tune” preceding it• You could set the graphic equalizer based on the tune
• Then listen to the song well
• Analogous to “locking” on to the song
108
Similarly …
Payload in data frame preceded with PLCP PLCP like pilot signal Receiver uses for synchronization/correction with Tx
During synchronization, Rx susceptible to distraction Once synchronized, following bits can be well decoded
• However, if strong interference, then collision
BackBack
112
Optimal link schedule w, w/o MIM:shows potential gain with MIM-
awareness
Optimal link schedule w, w/o MIM:shows potential gain with MIM-
awareness
Integer Programming
113
Vision
Design a (software) information telescope to zoom into a any part of the world,
and view it through virtual lenses located there
Design a (software) information telescope to zoom into a any part of the world,
and view it through virtual lenses located there
Query the lenses in real timeQuery the lenses in real time
Incentivize participatory sensingEnable automatic sensing
Incentivize participatory sensingEnable automatic sensing
114
Our Research
PHY
MAC / Link
Network
Transport
Security
ApplicationIncentives
Channel fluctuations
Spatial Reuse
MobilityEnergy Savings
EavesdroppingLoss Discrimination
Privacy
Ubiquitous Services
Interference Mgmt.
What can be enabled(bottom up)
What can be enabled(bottom up)
What are the visions(top down)
What are the visions(top down)
115
Shuffle: 3 Main Components
1. Measuring interference relationship - Rehearsal- Controller orchestrates rehearsal- Each node measures interference map from all others- Result is a network-wide interference map
2. Scheduler determines links and order of transmission- From the interference map- Scheduling NP-Hard --> approximation algorithms- Packets scheduled in batches
3. Schedule manager executes schedule- Copes with failures, fading, mobility- Performs pre-fetching, speculation, prediction
116
Collection of wireless hosts Relay packets on behalf of each other Together form an arbitrary topology May be connected to wired infrastructure
2 reasons to prefer multihop Capacity and Power constraint
Wireless Multihop Networks
B
AC
D
120
Collection of wireless hosts Relay packets on behalf of each other Together form an arbitrary topology May be connected to wired infrastructure
2 reasons to prefer multihop Capacity and Power constraint
Wireless Multihop Networks
121
The Context
The edge of the internet becoming wireless 167,000 hotspots by 2008 end [GartnerSurvey06]
75 million user base Mesh network extensions to rural regions
Many Motivations to get unplugged Unrestricted mobility Significantly lower deployment/maintenance cost Ease of use
122
Proliferating Applications and Technologies
When combined in synergy …
Mesh NetworksMesh Networks Sensor NetworksSensor Networks
Social CommunitiesSocial Communities
Mobile NetworksMobile Networks
Ad Hoc NetworksAd Hoc Networks
RFID TrackingRFID Tracking
Personal Area NetworksPersonal Area NetworksHybrid NetworksHybrid Networks
Mobile BloggingMobile Blogging
Location ServicesLocation Services
Smart ClothesSmart Clothes
Information MappingInformation MappingGamingGaming
123
The Key Intuition
Ability to Decode = Ability to Cancel
In other words,
knowing the structure of interference, helps in coping with it
In other words,
Stronger, decipherable interference better than weak, undecipherable ones