u niversity of m assachusetts, a mherst department of computer science leveraging interleaved signal...
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UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Leveraging Interleaved Signal Edges for Concurrent Backscatter
by Pan Hu, Pengyu Zhang, Deepak Ganesan
University of Massachusetts Amherst
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science
Can we enable concurrent backscatter?
2
Backscatter has tremendous potential as wireless backhaul for IoT and wearables.
Concurrent backscatter canGreatly reduce co-ordination overhead
Enable simpler hardware design for tags
Backscatter reader
Sensor
Fast bit rate
Slow control msg MAC
Processing
Msg
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science 3
Decode bits based on I-Q clustersIdeal 4QAM clusters: 00,01,10,11
Approach 1: QAM-like clustering
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science 4
Phase and Amplitude form QAM-like clustersUsing classification for decoding [Angerer 2010]
Works well for few nodes!
Approach 1: QAM-like clustering
UNIVERSITY OF MASSACHUSETTS AMHERST • 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
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Approach 2: Belief-propagation decoding
Linear combination of channel coefficients and TX signal [Buzz Sigcomm12]
Received signal : known
UNIVERSITY OF MASSACHUSETTS AMHERST • 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.
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science 8
Approach 2: Belief-propagation decoding
Linear combination of channel coefficients and TX signal [Buzz Sigcomm12]
TX signal : unknown
UNIVERSITY OF MASSACHUSETTS AMHERST • 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!
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science 10
Approach 2: Belief-propagation decoding
Channel coefficient is not always stableCan be affected by object movement
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science 11
Approach 2: Belief-propagation decoding
Channel coefficient is not always stableCan be affected by object movement, tag rotation
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science 12
Approach 2: Belief-propagation decoding
Channel coefficient is not always stableCan be affected by object movement, tag rotation and cross-tag coupling
UNIVERSITY OF MASSACHUSETTS AMHERST • 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
UNIVERSITY OF MASSACHUSETTS AMHERST • 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)
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science 15
Design of BST Backscatter Spike Train
But, edge amplitude/direction depends on who else is concurrently transmitting!
UNIVERSITY OF MASSACHUSETTS AMHERST • 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
UNIVERSITY OF MASSACHUSETTS AMHERST • 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
UNIVERSITY OF MASSACHUSETTS AMHERST • 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 .
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science
Preliminary Result
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Throughput comparison of BST with TDMA and BUZZ
Up to10x improvement over TDMA & Buzz!
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science
Conclusion
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Concurrent transmission for backscatter WITHOUT scheduling or encoding.
- Key idea: leverage interleaved signal edges to decode collided signals!
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science
Thanks
21
Thank you!
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science 22
Backup Slides
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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
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science 24
Deleted Slides
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science 33
Multiple node transmitting, multiple edges
Design of BST Backscatter Spike Train
Backscatter reader
Sensor
Fast bit rate
Slow control msg MAC
Processing
Msg