1. U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science Leveraging Interleaved Signal Edges for Concurrent Backscatter by Pan Hu, Pengyu Zhang, Deepak Ganesan University of Massachusetts Amherst

2. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science Can we enable concurrent backscatter? 2 Backscatter has tremendous potential as wireless backhaul for IoT and wearables. Concurrent backscatter can Greatly reduce co-ordination overhead Enable simpler hardware design for tags Backscatter reader Sensor Fast bit rate Slow control msg MAC Processing Msg

3. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 3 Decode bits based on I-Q clusters Ideal 4QAM clusters: 00,01,10,11 Approach 1: QAM-like clustering

4. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 4 Phase and Amplitude form QAM-like clusters Using classification for decoding [Angerer 2010] Works well for few nodes! Approach 1: QAM-like clustering

5. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 5 Number of clusters grows exponentially (2^N) 64 dense clusters for 6 nodes Not scalable! Approach 1: QAM-like clustering

6. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 6 Approach 2: Belief-propagation decoding Linear combination of channel coefficients and TX signal [Buzz Sigcomm12] Received signal : known

7. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 7 Approach 2: Belief-propagation decoding Linear combination of channel coefficients and TX signal [Buzz Sigcomm12] Channel coefficients can be estimated.

8. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 8 Approach 2: Belief-propagation decoding Linear combination of channel coefficients and TX signal [Buzz Sigcomm12] TX signal : unknown

9. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 9 Approach 2: Belief-propagation decoding Linear combination of channel coefficients and TX signal [Buzz Sigcomm12] Channel coefficient is NOT constant!

10. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 10 Approach 2: Belief-propagation decoding Channel coefficient is not always stable Can be affected by object movement

11. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 11 Approach 2: Belief-propagation decoding Channel coefficient is not always stable Can be affected by object movement, tag rotation

12. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 12 Approach 2: Belief-propagation decoding Channel coefficient is not always stable Can be affected by object movement, tag rotation and cross- tag coupling

13. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science Design of BST Backscatter Spike Train 13 Key argument: We can separate concurrent transmissions in the time domain by detecting signal edges corresponding to different nodes. Node 1 TX signal Node 2 TX signal Collided signal

14. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science Design of BST Backscatter Spike Train 14 Why is this approach feasible in backscatter? Edges are sharp: wide spectrum allocated for RFID backscatter (902MHz to 928MHz in US) Edges are detectable: reader sampling rate >> tag bit rate (50MHz vs 100kbps)

15. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 15 Design of BST Backscatter Spike Train But, edge amplitude/direction depends on who else is concurrently transmitting!

16. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 16 Robust Vector Based Edge Detection - Uses both I and Q channel information to robustly detect edge. Design of BST Backscatter Spike Train

17. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 17 More design details can be found in paper: Handing edge collision - Efficient back off and bit rate adaptation Stop error diffusion - Current bit depend current symbol and previous bit Design of BST Backscatter Spike Train

18. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science Implementation 18 USRP N210 + 16 UMass Moo Nodes Node: 100kbits/s TX speed; USRP: 50MHz Sampling; Separated antennas for TX/RX.

19. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science Preliminary Result 19 Throughput comparison of BST with TDMA and BUZZ Up to10x improvement over TDMA & Buzz!

20. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science Conclusion 20 Concurrent transmission for backscatter WITHOUT scheduling or encoding. - Key idea: leverage interleaved signal edges to decode collided signals!

21. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science Thanks 21 Thank you!

22. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 22 Backup Slides

23. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 23 Handling Edge Collisions - Collision probability can be high - Either re-start or reduce bit rate *Sampling Frequency: 25MHz Tag transmitting Rate: 100kbps Design of BST Backscatter Spike Train

24. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 24 Deleted Slides

25. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science Related Works 25 Multiplexing for Backscatter nodes TDMA (Time Division Multiple Access) - Limited bandwidth - Control message overhead - Buffer required CDMA (Code Division Multiple Access) - Increased bit per joule - Computational complexity

26. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science Motivation 26 Architecture and Protocol Support for Micro- powered Heterogeneous Sensors Different data rate / power consumption Tens of sensors

27. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science Motivation 27 Traditional Sensor Architecture TDMA/FDMA CDMA Analog/Digital Data Acquisit ion Sensing MAC PHY Carrier wave generation Modulation

28. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science Motivation 28 Backscatter

29. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science Motivation 29 Backscatter Sensor Architecture TDMA/FDMA CDMA Analog/Digital Data Acquisit ion Sensing MAC PHY Simple Modulation

30. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science Motivation 30 “Dumb” Sensor Architecture Analog/Digital Data Acquisit ion Sensing PHY Simple Modulation

31. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science Motivation 31 “Dumb” Sensing Architecture MAC is too expensive for me! I can’t coordinate with other nodes! Just transmit. I can handle the situation.

32. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science Challenges 32 “Dumb” Sensing Challenges Sensor Side - Minimize energy/hardware cost - No buffer/MAC Reader Side - High aggregate bandwidth - Support for heterogeneous sensors

33. U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 33 Multiple node transmitting, multiple edges Design of BST Backscatter Spike Train

34. Backscatter reader Sensor Fast bit rate Slow control msg MAC Processing Msg