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Fine-grained Spectrum Adaptation in WiFi Networks Sangki Yun, Daehyeok Kim and Lili Qiu University of Texas at Austin 1 ACM MOBICOM 2013, Miami, USA.

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Presentation on theme: "Fine-grained Spectrum Adaptation in WiFi Networks Sangki Yun, Daehyeok Kim and Lili Qiu University of Texas at Austin 1 ACM MOBICOM 2013, Miami, USA."— Presentation transcript:

1 Fine-grained Spectrum Adaptation in WiFi Networks Sangki Yun, Daehyeok Kim and Lili Qiu University of Texas at Austin 1 ACM MOBICOM 2013, Miami, USA

2 Current trend in WiFi Wireless applications increasing throughput demand Channel width is increasing Benefit of wide channel: higher throughput a/b/g 20MHz n 40MHz ac 160MHz Is wide channel always better?

3 Disadvantage of wideband channel High framing overhead High energy consumption Lower spectrum efficiency due to frequency diversity 3 data ACK channel access preamble SIFS wide channel data ACK channel access preamble SIFS wide channel transmission idle period transmission idle period

4 Lessons Static spectrum access (wide or narrow spectrum exclusively) is insufficient Need dynamic spectrum access to get the best of both worlds 4

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 5 5MHz 10MHz 20MHz time Spectrum efficiency Energy efficiency

6 Challenges Enable per-frame spectrum adaptation Sender and receiver agree on the spectrum Dynamically allocate spectrum efficiently 6

7 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 ac – RTS/CTS for dynamic bandwidth management – Not fine grained (minimum channel width 20MHz) 7

8 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 8

9 Transmitter design 9 PHY encoder upsampler RF LPF... CF shift mixer 20MHz bandwidth OFDM signal Reduces bandwidth Interpolation & remove images Center frequency shifting PHY encoder upsampler LPF CF shift

10 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 frequency 20MHz LPF frequency 20MHz Narrowband signal 10

11 Center frequency shifting is performed and the signals in different spectrum are added Sending signals together 11 20Hz Narrowband signal adding another narrowband signal 20Hz Shifted signal Center frequency shifting 20Hz Mixed signal Deliver to RF RF 20Hz

12 Receiver design 12 RF... Spectrum detector down- sampler LPF PHY decoder CF shift down- sampler LPF PHY decoder CF shift

13 Receiver design 13 / 35 RF... down- sampler LPF PHY decoder CF shift down- sampler LPF PHY decoder CF shift Spectrum detector is key component Spectrum detector

14 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 14

15 Spectrum detection using STF It is ideal to detect spectrum using existing 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 15

16 Characteristic of STF Time domain: 10 repetitions of 16 signals Frequency domain: 12 spikes out of 64 subcarriers with 4 subcarrier intervals 16 / 35 t1t1 t2t2 t3t3 t4t4 t5t5 t6t6 t7t7 t8t8 t9t9 t 10 We exploit the subcarrier interval for the spectrum detection!

17 Spectrum detector design (Cont.) 17 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

18 Spectrum detection using STF 20MHz transmitter to 20MHz receiver 18 20MHz receiver 20MHz transmitter 20MHz STF in the frequency domain at the 20MHz receiver

19 Spectrum detection using STF 10MHz transmitter to 20MHz receiver 19 20MHz receiver 10MHz transmitter 20MHz STF in the frequency domain at the 20MHz receiver Two subcarriers of 10MHz transmitter is merged into one subcarrier of 20MHz receiver

20 Spectrum detection using STF 5MHz transmitter to 20MHz receiver 20 20MHz receiver 20MHz STF in the frequency domain at the 20MHz receiver 5MHz transmitter

21 Spectrum detection using STF The subcarrier interval difference let us easily identify the spectrum 21 20MHz receiver 20MHz STF in the frequency domain at the 20MHz receiver 20MHz receiver 20MHz transmitter 20MHz

22 Spectrum detector design (Cont.) 22 5MHz 10MHz 5MHz 10MHz Transform spectrum detection into pattern matching.

23 Spectrum detector design Optimal Euclidean distance based spectrum detection Binary detection 23 RF- frontend preamble detection FFT- 64 spectrum detection Received signal sampled in 20MHz rate Cross-correlation check Magnitude of 64 subcarriers Maximum likelihood pattern matching

24 Spectrum Allocation 24 AP Controller client buffer AP

25 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 25

26 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 26

27 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 27

28 Spectrum detection accuracy 28

29 Spectrum detection delay Median detection delay 4.2 us < detection delay budget 29

30 Throughput evaluation – no interference 30 FSA improves throughput by exploiting frequency diversity

31 Throughput evaluation – interference 31 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 32

33 Q & A 33 Thank you!

34 Comparison with WiFi-NC Simulation in fading channel width RMS of delay spread = 100 ns 34 WiFi NC incurs lower SNR due to sharp filtering

35 Discussion Detection accuracy Antenna gain control Bi-directional traffic 35


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