fine-grained spectrum adaptation in wifi networks

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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?

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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

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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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• 20MHz transmitter to 20MHz receiver

20MHz receiver20MHz

transmitter

20MHz

STF in the frequency domain at the 20MHz receiver

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20MHz receiver10MHz

transmitter

20MHz


Two subcarriers of 10MHz transmitter is merged into one

subcarrier of 20MHz receiver

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20MHz receiver

20MHz


5MHz transmitter

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• The subcarrier interval difference let us easily identify the spectrum

20MHz receiver

20MHz


20MHz receiver20MHz

transmitter

20MHz

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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

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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

fine-grained spectrum adaptation in wifi networks

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