1. Combating Cross-Technology Interference Shyamnath Gollakota Fadel Adib Dina Katabi Srinivasan Seshan

2. ISM Band Is Increasingly Crowded Most problems are from cross-technology high-power interferers Responsible for more than 50% of the customer complaints Lead to complete loss of connectivity Microwave Ovens Cordless Phones Baby Monitors Multiple independent studies [Cisco, Ofcom, Jupiter, Farpoint]

3. Experimental Setup Two Netgear 802.11n devices Baby monitors, cordless phones and microwave ovens WiFi devices about 20 feet away from each other Move interferer 1-90 feet away from WiFi receiver WiFi tx WiFi rx 20 feet

4. Effect of High-Power Interferers on WiFi 1 foot 90 feet Line of sight Non- Line of sight Interferer Location #

5. Without Interferers With Microwave With baby Monitor With Cordless Phone Effect of High-Power Interferers on WiFi Interferer Location # Line of sight Non- Line of sight 1 foot 90 feet

6. Traditional Solutions to Cross Technology Interference Don’t Work Avoid interferer frequencies  Much wider bandwidth than WiFi  Interferer can occupy multiple WiFi channels

7. Traditional Solutions to Cross Technology Interference Don’t Work Avoid interferer frequencies  Much wider bandwidth than WiFi  Interferer can occupy multiple WiFi channels Treat interferer as noise and use lower rate  High power interferers (e.g., 8-100X WiFi power)  Can’t get even lowest WiFi rate How can we deal with such high-power interference?

8. Technology Independent Multiple Output (TIMO) First WiFi receiver that decodes in presence of high-power cross-technology interferers Is agnostic to the interferer’s technology Implemented and evaluated with baby monitors, microwave ovens and cordless phones  Convert no-connectivity scenarios to operational networks

9. Idea: Try to leverage MIMO AP Client Today, streams are of the same technology

10. Idea: Try to leverage MIMO AP Client If MIMO can work across diverse technologies

11. Idea: Try to leverage MIMO AP Client Challenge: Current MIMO doesn’t work with diverse technologies If MIMO can work across diverse technologies

12. MIMO Primer AP Client If channels are known, AP can solve equations to decode the two streams, S1 and S2 How do current APs estimate the channels? Client sends a known preamble on the two antennas AP correlates with known preamble to estimate channels Doesn’t work across technologies How do current APs estimate the channels? Client sends a known preamble on the two antennas AP correlates with known preamble to estimate channels Doesn’t work across technologies

13. Say, Interferer is One of the Streams AP Client But, AP doesn’t know interferer technology / preamble  Can’t compute interferer channels, h 3 and h 4 But, AP doesn’t know interferer technology / preamble  Can’t compute interferer channels, h 3 and h 4

14. Scenario 2 Interference Channel Scenario 1 Interference Channel Fundamental Limitation of Channel Estimation Can’t distinguish between the two scenario  Impossible to exactly estimate interferer channels Can’t distinguish between the two scenario  Impossible to exactly estimate interferer channels

15. How Does TIMO Work? AP is not interested in decoding baby monitor AP Client Reduce the number of unknowns to three

16. How Does TIMO Work? AP Client AP is not interested in decoding baby monitor Reduce the number of unknowns to three

17. How Does TIMO Work? AP Client AP is not interested in decoding baby monitor Reduce the number of unknowns to three β is the interferer channel ratio

18. How Does TIMO Work? AP Client AP is not interested in decoding baby monitor Reduce the number of unknowns to three β is the interferer channel ratio Focus on channel ratio instead of channels

19. Getting Around the Fundamental Limitation Unlike channels, the channel ratio is not ambiguous Scenario 2 Interference Channel Scenario 1 Interference Channel The scaling factor, c, introduces ambiguity into channels

20. If β Can be Computed, AP Can Decode WiFi Client AP Client AP can solve the two equations to decode the WiFi client

21. Question: How do we compute β? Answer: Send known symbol WiFi client sends known symbol at beginning of its packet

22. Question: How do we compute β? Answer: Send known symbol Known WiFi client sends known symbol at beginning of its packet Solve equations to get β Once β is known, it can be used to decode subsequent symbols

23. Use β to decode subsequent symbols But, what if interferer is concentrated in time Time Known symbol Question: How do we compute β? Answer: Send known symbol

24. Known symbol Time Known symbol Question: How do we compute β? Answer: Send known symbol But, what if interferer is concentrated in time We have a solution to compute β without known symbols

25. Intuition: Exploit the WiFi Symbol Structure Real Imaginary BPSK – ‘1’ bit sent as +1 and ‘0’ bit sent as -1 +1

26. Intuition: Exploit the WiFi Symbol Structure Real Imaginary BPSK – ‘1’ bit sent as +1 and ‘0’ bit sent as -1 If no interference, received symbols are close to expected symbols +1

27. Intuition: Exploit the WiFi Symbol Structure Real Imaginary +1 BPSK – ‘1’ bit sent as +1 and ‘0’ bit sent as -1 If no interference, received symbols are close to expected symbols If interference, received symbols are far from expected symbols Correct estimate  Average error is small Error correct

28. Intuition: Exploit the WiFi Symbol Structure Real Imaginary +1 BPSK – ‘1’ bit sent as +1 and ‘0’ bit sent as -1 If no interference, received symbols are close to expected symbols If interference, received symbols are far from expected symbols Bad estimate  Average error is big Error correct guess1

29. Intuition: Exploit the WiFi Symbol Structure Real Imaginary +1 BPSK – ‘1’ bit sent as +1 and ‘0’ bit sent as -1 If no interference, received symbols are close to expected symbols If interference, received symbols are far from expected symbols Better Estimate  Average error reduce Error Design gradient descent style algorithm to iteratively converge to actual channel ratio Paper described algorithm that works across modulations Design gradient descent style algorithm to iteratively converge to actual channel ratio Paper described algorithm that works across modulations correct guess1 guess2

30. Performance

31. Implement using USRP2s WiFi modulations and coding rates OFDM over 10 MHz Bits rates between 3-27 Mbps No carrier sense Implementation

32. Testbed Place USRP prototype for 802.11 at blue locations Change the location of interferer over red locations Rx Tx

33. Throughput Performance with Baby Monitor Interferer Location # Line of sight Non- Line of sight WiFi 1 foot 90 feet

34. Interferer Location # Line of sight Non- Line of sight 60 feet away Throughput Performance with Baby Monitor USRP WiFi WiFi 1 foot 90 feet Despite disabling carrier sense, complete loss of connectivity in more than half the location

35. USRP WiFi with TIMO Interferer Location # 1 foot 90 feet Line of sight Non- Line of sight Throughput Performance with Baby Monitor Without interference WiFi USRP WiFi

36. Throughput Performance Interferer Location # Cordless Phones w/o TIMO with TIMO Interferer Location # w/o TIMO with TIMO Microwave Ovens TIMO transforms scenarios with a complete loss of connectivity to operational networks

37. Decoding Interference [IC, SAM, Beamforming, …] Cognitive Communication [Samplewidth, Jello, Swift, …] Related Work - Don’t work with cross-technology interference - Don’t operate on the same frequency First system to decode in the presence of cross-technology interference on same band

38. Conclusions First WiFi receiver that decodes in presence of high-power cross-technology interferers Enable MIMO to work across technologies Implemented and evaluated with baby monitors, microwave ovens and cordless phones  Convert no-connectivity scenarios to operational networks