Buddy Routing: A Routing Paradigm for NanoNets Based on Physical Layer Network Coding
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Buddy Routing: A Routing Paradigm for NanoNets Based on
Physical Layer Network Coding
Ruiting Zhou+, Zongpeng Li+, Chuan Wu*, Carey Williamson+
+University of Calgary*University of Hong Kong
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Outline
• 1. Introduction to NanoNets• 2. Enabling Buddy Routing • 3. Theoretical Analysis• 4. Buddy Routing: Unicast• 5. Buddy Routing: Multicast• 6. Conclusion
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• 1. Introduction to NanoNets NanoNets Collaborative Data Forwarding
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1.1 NanoNets • NanoNets: Networks of
nanomachines at extremely small dimensions -- on the order of nanometers or micrometers.
• Basic computing and communication feasible on nanonodes.• Large network size and node density, low cost and available power.
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1.2 Collaborative Data Forwarding
• Group nanonodes into collaborating pairs: overcome power constraint, enhance the communication range and rate.
• Enabled by physical layer network coding (PNC).
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1.2 Collaborative Data Forwarding
• Enabled by Amplify&Forward (A&F).• Intra-pair transmission: transmit an
amplified version of the received analog signal:
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• 2. Enabling Buddy Routing PNC vs A&F: Multi-hop
Transmission PNC vs A&F: One-hop BER
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2.1 PNC vs A&F: Multi-hop Transmission
• PNC: Except at the source pair, no need for half-packet sharing.
• A&F: Intra-pair sharing of a half-packet is required at each hop, an extra step of transmission.
• lower end-to-end data throughput.
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2.2 PNC vs A&F: One-hop BER• BER of PNC is almost the same as, but
slightly worse than, that of A&F, under the same SNR at the receiver side.
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• 3. Theoretical Analysis System Model and Parameters Capacity and Power
Consumption
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3.1 System Model and Parameters
• MAC layer protocol: TDMA• Two types of time slots:
Long time slot : long-hop data transmission happen simultaneously every three hops
Short time slot: all the intra-pair short hops transmit simultaneously
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3.2 Capacity and Power Consumption
• We analyzed the end-to-end routing capacity of a BR route at very high SNR with and without noise considered.
• The extra power consumption overhead caused by BR is below 20%.
• The capacity is increased by a factor of 2.
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• 4. Buddy Routing: Unicast BR Unicast Routing Algorithm Simulation
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4.1 BR Unicast Algorithm
• Step 1: Pair-to-pair greedy geographic unicast routing. Forwarding process: it looks for a
next-hop pair between the two co-axial circles of radius and , which is closest to the destination.
If the last pair is too close to the destination, replaced by a new pair such that the distance between the new pair and the destination is just larger than .
and are the minimum and maximum allowed distances between two neighbor buddy pairs.
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• Radius of red circle is the maximum distance between a pair of buddy nodes.
• When Pair 70 looks for the next hop towards the destination, it can only search the area in the blue ring.
4.1 BR Unicast Algorithm
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• Step 2: Iterative MAC layer optimization. Adjust time slot: so that the
capacity in each time slot is equal. Inter-pair power optimization:
adjust the long hop transmission power — achieve equal capacity at bottleneck link & 2 neighbor links.
Intra-pair power optimization: adjust the short hop transmission power —achieve equal capacity at bottleneck pair & 2 neighbor pairs.
4.1 BR Unicast Algorithm
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4.1 BR Unicast Algorithm
• End-to-end throughput is doubled after the iterative power/MAC optimization
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4.2 Simulation• BR Unicast, end-to-end throughput comparison,
different x-axis.• Throughput of BR after optimization is almost
twice that of point-to-point routing
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• 5. Buddy Routing: Multicast The Multicast BR Gadget BR Multicast Tree
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• Multicast: has branches in the transmission topology.
• Replicate a data packet from an upstream node pair to two pairs.
5.1 The Multicast BR Gadget
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5.2 BR Multicast Tree• One-to-four multicast
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6. Conclusion• We proposed a new PNC-based
routing paradigm, Buddy Routing, for NanoNets.
• BR has a potential to break through the nodal power limit in NanoNets.
• It can substantially improve the unicast and multicast throughput, as verified by our theoretical analysis and simulation results 22/ 23
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• Thanks!
• Questions?
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