Fine-grained Spectrum Adaptation in WiFi Networks

1. Fine-grained Spectrum Adaptation in WiFi Networks Sangki Yun, Daehyeok Kim and Lili Qiu University of Texas at Austin 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 802.11a/b/g 20MHz 802.11n 40MHz 802.11ac 160MHz Is wide channel always better?

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

4. 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 adaptationto communicates with different clients • Preferred channel may change over time -> further increase the need for per frame adaptation 20MHz 5MHz time Spectrum efficiency Energy efficiency 10MHz 20MHz

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

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 802.11ac • RTS/CTS for dynamic bandwidth management • Not fine grained (minimum channel width 20MHz)

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

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

10. Generating narrowband signals • Convert 5 or 10MHz signal based on 20MHz signal through upsampling and low pass filtering LPF upsampling frequency frequency 20MHz 20MHz frequency 20MHz Narrowband signal 20MHz signal Upsampling generates images outside tx band

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

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

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

14. 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

15. 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

16. Characteristic of 802.11 STF • Time domain: 10 repetitions of 16 signals • Frequency domain: 12 spikes out of 64 subcarriers with 4 subcarrier intervals t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 We exploit the subcarrier interval for the spectrum detection!

17. Spectrum detector design (Cont.) • Depending on the transmitter spectrum width, the received STF has various subcarrier intervals 20MHz Subcarrier interval: 4 10MHz Subcarrier interval: 2 5MHz Subcarrier interval: 1

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

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

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

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

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

23. Spectrum detector design Cross-correlationcheck Maximum likelihood pattern matching • Optimal Euclidean distance based spectrum detection • Binary detection RF-frontend 802.11 preamble detection FFT-64 spectrum detection Received signal sampled in 20MHz rate Magnitude of64 subcarriers .

24. Spectrum Allocation Controller buffer AP AP AP client client client client

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

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

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

28. Spectrum detection accuracy

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

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

31. Throughput evaluation – interference 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

33. Q & A Thank you!

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

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