Fine-grained Spectrum Adaptation in WiFi Networks Sangki Yun, Daehyeok Kim and Lili Qiu University...
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Transcript of Fine-grained Spectrum Adaptation in WiFi Networks Sangki Yun, Daehyeok Kim and Lili Qiu University...
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Fine-grained Spectrum Adaptation in WiFi Networks
Sangki Yun, Daehyeok Kim and Lili QiuUniversity of Texas at Austin
ACM MOBICOM 2013, Miami, USA
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Current trend in WiFi
• Wireless applications increasing throughput demand
• Channel width is increasing
• Benefit of wide channel: higher throughput
802.11a/b/g 20MHz
802.11n 40MHz
802.11ac 160MHz
Is wide channel always better?
30
5
10
15
20
25
20MHz channel
SNR
(dB)
0
5
10
15
20
25
20MHz channel
SNR
(dB)
Disadvantage of wideband channel
• High framing overhead• High energy consumption• Lower spectrum efficiency due to frequency
diversity data ACK
channel access preamble SIFS
wide channel data
ACK
channel access preamble SIFS
wide channel
transmissionidle period transmission
idle period
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Lessons
• Static spectrum access (wide or narrow spectrum exclusively) is insufficient
• Need dynamic spectrum access to get the best of both worlds
5
Ideal case: per-frame adaptation
• Clients select channel based on their preference • AP needs per-frame spectrum adaptation to communicates with
different clients • Preferred channel may change over time -> further increase the
need for per frame adaptation
5MHz
10MHz
20MHz
20MH
z
time
Spectrum efficiencyEnergy efficiency
6
Challenges
• Enable per-frame spectrum adaptation
• Sender and receiver agree on the spectrum
• Dynamically allocate spectrum efficiently
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Related work
• Dynamic spectrum access (WiMAX, LTE, FICA)– Requires tight synchronization among clients– Significant signaling overhead
• Spectrum adaptation (SampleWidth, FLUID)– Focus on spectrum allocation and ignore spectrum agreement– Slow to adjust the channel width
• WiFi-NC– Channel width is fixed to 5MHz– Requires longer CP to reduce guard bandwidth
• IEEE 802.11ac– RTS/CTS for dynamic bandwidth management– Not fine grained (minimum channel width 20MHz)
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FSA: Fine-grained spectrum adaptation
• Per-frame spectrum access– Change spectrum per-frame – Communicate with multiple nodes on different
subbands using one radio• In-band spectrum detection using existing
preamble
• Efficient spectrum allocation
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Transmitter design
PHY encoder upsampler
RF
LPF
. . .
. . .
. . .
CF shift
mixer
20MHz bandwidth OFDM signal
Reduces bandwidth
Interpolation &remove images
Center frequencyshifting
PHY encoder upsampler LPF CF
shift
Generating narrowband signals
• Convert 5 or 10MHz signal based on 20MHz signal through upsampling and low pass filtering
upsampling
20MHz frequency
20MHz signal Upsampling generates images outside tx band
frequency20MHz
LPF
frequency20MHzNarrowband signal
10
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• Center frequency shifting is performed and the signals in different spectrum are added
Sending signals together
20Hz
Narrowband signal
𝑠10 [𝑛]
adding another narrowband signal
20Hz
Shifted signal
𝑠10𝑓𝑠 [𝑛 ]
Center frequency shifting
𝑠10𝑓𝑠 [𝑛 ]=𝑠10 [𝑛 ]𝑒 𝑗 2𝜋∆
20Hz
Mixed signal
𝑠[𝑛]Deliver to RF
RF20Hz
𝑠 [𝑛 ]=𝑠10𝑓𝑠 [𝑛 ]+𝑠5𝑓𝑠 [𝑛 ]
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Receiver design
RF . . .
. . .
. . . Spectrum
detector
down-samplerLPF
PHYdecoder
CF shift
down-samplerLPF
PHYdecoder
CF shift
Receiver design
13 / 35
RF . . .
. . .
. . .
down-samplerLPF
PHYdecoder
CF shift
down-samplerLPF
PHYdecoder
CF shift
Spectrum detector is key component
Spectrum detector
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Spectrum detector
• Goal: Receiver identifies the spectrum used by the transmitter
• Possible solutions–Use control channel or frame• Too much overhead• Target for attack• Control channel may not be always available
further increase overhead
–Design special preamble [Eugene,12]• Deployment issue
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Spectrum detection using STF
• It is ideal to detect spectrum using existing 802.11 frame detection preamble (STF)
• One solution: Spectral and Temporal analysis of the detection preamble (STD)– Power spectral density to detect the total spectrum width– Temporal analysis to identify exact spectrum allocation– Costly and inaccurate especially in noisy channel
• Our approach– Exploit special characteristics of STF for spectrum
detection
Characteristic of 802.11 STF
• Time domain: 10 repetitions of 16 signals
• Frequency domain: 12 spikes out of 64 subcarriers with 4 subcarrier intervals
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t1 t2 t3 t4 t5 t6 t7 t8 t9 t10
We exploit the subcarrier interval for the spectrum detection!
Spectrum detector design (Cont.)
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20MHz
5MHz
• Depending on the transmitter spectrum width, the received STF has various subcarrier intervals
10MHz Subcarrier interval: 2
Subcarrier interval: 4
Subcarrier interval: 1
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Spectrum detection using STF
• 20MHz transmitter to 20MHz receiver
20MHz receiver20MHz
transmitter
20MHz
STF in the frequency domain at the 20MHz receiver
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Spectrum detection using STF
• 10MHz transmitter to 20MHz receiver
20MHz receiver10MHz
transmitter
20MHz
STF in the frequency domain at the 20MHz receiver
Two subcarriers of 10MHz transmitter is merged into one
subcarrier of 20MHz receiver
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Spectrum detection using STF
• 5MHz transmitter to 20MHz receiver
20MHz receiver
20MHz
STF in the frequency domain at the 20MHz receiver
5MHz transmitter
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Spectrum detection using STF
• The subcarrier interval difference let us easily identify the spectrum
20MHz receiver
20MHz
STF in the frequency domain at the 20MHz receiver
20MHz receiver20MHz
transmitter
20MHz
22
Spectrum detector design (Cont.)
5MHz
10MHz
10MHz 5MHz 5MHz
10MHz 10MHz
Transform spectrum detection into pattern matching.
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Spectrum detector design
• Optimal Euclidean distance based spectrum detection
• Binary detection
RF-frontend
802.11 preamble detection
FFT-64
spectrum detection
Received signal sampled in 20MHz rate
Cross-correlationcheck
Magnitude of64 subcarriers
Maximum likelihoodpattern matching
.
�̂�=argmin𝑖𝐗𝑖⊕𝐘
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Spectrum Allocation
AP
Controller
client client client client
buffer
AP AP
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Spectrum Allocation (Cont.)
• Input– Destinations of buffered frames– CSI between APs and clients– Conflict graph
• Goal: Minimize finish time – Avoid interference– Harness frequency diversity
• Knobs– Spectrum– Schedule– AP used for transmission
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Spectrum allocation (Cont.)
• Break a frame into mini-frames• Break the entire spectrum into mini-channels• Greedily assign a mini-frame to a mini-channel that
minimizes the overall finish time while avoiding interference
• Find a swapping with an assigned mini-frame that leads to the largest improvement, go to step 3
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Evaluation methodology
• Implemented testbed in Sora– 2.4GHz– 20MHz maximum bandwidth
• Evaluates detection accuracy and latency, spectrum allocation performance in testbed
• Trace based simulation for spectrum allocation in large-scale network
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Spectrum detection accuracy
20 - 15 15 - 10 10 - 5 5 - 00.0
0.2
0.4
0.6
0.8
1.0Delivery rate
Detection rate - STD
Detection rate - FSA (binary)
Detection rate - FSA (ED)
SNR range (dB)
Prob
abili
ty
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Spectrum detection delay
Median detection delay 4.2 us < detection delay budget
0 19 38 57 76 95 1141331521711902092280
0.2
0.4
0.6
0.8
1
STD
FSA
Detection delay (µs)
CDF
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Throughput evaluation – no interference
1 2 3 4 5 6 7 8 9 100
5
10
15
20FSAFixed
Thro
ughp
ut (M
bps)
FSA improves throughput by exploiting frequency diversity
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Throughput evaluation – interference
1 2 3 4 5 6 7 8 9 100
5
10
15FSAFixed
Thro
ughp
ut (M
bps)
With narrowband interference, the gain grows larger
32
Summary
• FSA – a step towards enabling dynamic spectrum access– Flexible baseband design– Fast and accurate channel detection method– Spectrum adaptation
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Q & A
Thank you!
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Comparison with WiFi-NC
Simulation in fading channel width RMS of delay spread = 100 ns
10 15 20 25 300
20
40
60WiFi-NCFSA
SNR
Thro
ughp
ut (M
bps)
WiFi NC incurs lower SNR due to sharp filtering
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Discussion
• Detection accuracy• Antenna gain control• Bi-directional traffic