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Selection Metrics for Multi-hop Cooperative Relaying

Selection Metrics for Multi-hop Cooperative Relaying. Jonghyun Kim and Stephan Bohacek Electrical and Computer Engineering University of Delaware. Contents. Introduction Diversity Goal of Cooperative Relaying Brief look at how to overcome challenge Dynamic programming

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Selection Metrics for Multi-hop Cooperative Relaying

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  1. Selection Metrics for Multi-hop Cooperative Relaying Jonghyun Kim and Stephan Bohacek Electrical and Computer Engineering University of Delaware

  2. Contents • Introduction • Diversity • Goal of Cooperative Relaying • Brief look at how to overcome challenge • Dynamic programming • Simulation environment • Selection Metrics • Differences between Selection Metrics • Conclusion and Future/current Work

  3. Introduction One possible path destination source

  4. Introduction Another possible path destination source

  5. Introduction Many possible path destination source • Not all paths are the same • The “best” path will vary over time

  6. Diversity • Link quality and hence path quality can be modeled as a stochastic process • If there are many alternative paths, there will be some very good path • The best path changes over time

  7. Goal of cooperative relaying • Take advantage of diversity • (Don’t get stuck with a bad path • Switch to a good (best) path)

  8. Challenge • How to find and use the best path with minimal overhead Potential benefits • The focus of this talk

  9. Brief look at how to overcome the challenge relay-set (1) relay-set (2) (1,1) (2,1) destination source (2,2) (1,2) Nodes within relay-set (2) have decoded data from source

  10. Brief look at how to overcome the challenge RTS relay-set (1) relay-set (2) (1,1) (2,1) destination source (2,2) (1,2) • Nodes within relay-set (2) simultaneously broadcast RTS with a different • CDMA code

  11. Brief look at how to overcome the challenge RTS relay-set (1) relay-set (2) R(2,1),(1,1) (1,1) (2,1) destination source R(2,2),(1,1) R(2,1),(1,2) (2,2) (1,2) R(2,2),(1,2) • Nodes within relay-set (1) receive RTSs and make channel gain • measurements • - R(n,i),(n-1,j) : channel gain from node (n,i) to (n-1,j)

  12. Brief look at how to overcome the challenge CTS relay-set (1) relay-set (2) R(2,1),(1,1) R(2,2),(1,1) J(1,1) (1,1) (2,1) destination source (2,2) (1,2) R(2,1),(1,2) R(2,2),(1,2) J(1,2) • Nodes within relay-set (1) broadcast CTS • CTS contains channel gain measurements and J • J encapsulates downstream channel information (to be discussed later)

  13. Brief look at how to overcome the challenge CTS relay-set (1) relay-set (2) (1,1) (2,1) destination R(2,1),(1,1) R(2,1),(1,2) R(2,2),(1,1) R(2,2),(1,2) J(1,1) J(1,2) source (2,2) (1,2) R(2,1),(1,1) R(2,1),(1,2) R(2,2),(1,1) R(2,2),(1,2) J(1,1) J(1,2) Channel matrix • All nodes within relay-set (2) have the same information

  14. Brief look at how to overcome the challenge DATA relay-set (1) relay-set (2) (1,1) (2,1) destination source (2,2) (1,2) • Based on this information, the nodes within relay-set (2) all select the • same node to transmit the data • If node (2,1) is selected, it broadcasts the data

  15. Brief look at how to overcome the challenge DATA relay-set (1) relay-set (2) (1,1) (2,1) destination source (2,2) (1,2) • The process repeats • Best-select protocol (BSP)

  16. Dynamic programming J(n,i) is the “cost” from the ith node in the nth relay-set to destination • Various meanings of J • Probability of packet delivery • Minimum channel gain through the path • Minimum bit-rate through the path • End-to-end delay • End-to-end power • End-to-end energy J(n,i) = f (R(n,1),(n-1,1) , R(n,1),(n-1,2) , …. , R(n,i),(n-1,j) , J(n-1,1) , J(n-1,2) , … , J(n-1,j)) Channel gains Js from the downstream relay-set Stage costs Costs to go

  17. Simulation environment - Idealized urban BSP # of nodes Mobility Channel gains Area Tool used : 64, 128 : UDel mobility simulator (realistic tool) : UDel channel simulator (realistic tool) : Paddington area of London : Matlab - Implemented urban BSP # of nodes Mobility Channel gains Area Tool used : 64, 128 : UDel mobility simulator : UDel channel simulator : Paddington area of London : QualNet

  18. UDel mobility simulation • Current Simulator • US Dept. of Labor Statistics time-use study • When people arrive at work • When they go home • What other activities are performed during breaks • Business research studies • How long nodes spend in offices • How long nodes spend in meetings • Agent model • How nodes get from one location to another • Platooning and passing

  19. UDel channel simulation Propagation during a two minute walk • Signal strength is found with beam-tracing (like ray tracing) • Reflection (20 cm concrete walls) • Transmission through walls • Uniform theory of diffraction • Indoors uses the Attenuation Factor model • No fast-fading • No delay spread • No antenna considerations

  20. Selection Metrics Maximizing Delivery Prob. ( J = Delivery Prob.) The best J in relay-set (n) : Data sending node : node (n,k) • X • f(V) : transmission power which is fixed in this metric : prob. of successful transmission : an order of the nodes in the (n-1)-th relay-set such that

  21. Selection Metrics 1 0.8 0.6 0.4 0.2 0 2 4 6 8 10 Maximizing Delivery Prob. ( J = Delivery Prob.) idealized urban improvement in error prob. (ratio) Sparse Dense min relay-set size • This plot show the error prob. (i.e., 1- J(n,i) ) • X-axis : minimum relay-set size along the path from source to destination • Y-axis : Avg( (1-J(n,1) )BSP/(1-J(n,1) )Least-hop ) J(n,1) is source’s J • Comparison stops once the least-hop path fails

  22. Selection Metrics Maximizing Minimum Channel Gain ( J = Channel Gain ) The best J in relay-set (n) : Data sending node : node (n,k) • The link with the smallest channel gain can be thought of as • the bottleneck of the path. • The objective is to select the path with the best bottleneck

  23. Selection Metrics Maximizing Minimum Channel Gain ( J = Channel Gain ) idealized urban implemented urban 30 30 Sparse Dense 25 25 20 20 improvement in channel gain (dB) 15 15 10 10 5 5 0 0 2 4 6 8 10 2 4 6 8 10 min relay-set size • Y-axis : Avg( (min channel gain)BSP - (min channel gain)Least-hop )

  24. Selection Metrics Maximizing Throughput ( J = Bit-rate ) The best J in relay-set (n) : Data sending node : node (n,k) • Bit-rate : 1Mbps, 2Mbps, 4Mbps, 6Mbps, 8Mbps, 10Mbps,12Mbps • The objective is to select the path with the best bottleneck in terms of bit-rate

  25. Selection Metrics 15 10 5 0 2 4 6 8 10 Maximizing Throughput ( J = Bit-rate ) idealized urban implemented urban 15 Sparse Dense improvement in throughput (ratio) 10 5 0 2 4 6 8 10 min relay-set size • Y-axis : Avg( (min bit-rate)BSP / (min bit-rate)Least-hop ) • Least-hop approach uses the fixed bit-rate

  26. Selection Metrics Minimizing End-to-End Delay ( J = Delay ) The best J in relay-set (n) : Data sending node : node (n,k) • Delay to next relay-set (if the transmission is successful) • Delay from next relay-set to destination (depends on which node was able to decode) • If no node in the next relay-set succeeds in decoding, then a large delay T is incurred due to transport • layer retransmission

  27. Selection Metrics Minimizing End-to-End Delay ( J = Delay ) idealized urban implemented urban 15 15 Sparse Dense 10 10 improvement in delay (ratio) 5 5 0 0 2 4 6 8 10 2 4 6 8 10 min relay-set size • Y-axis : Avg( (end-to-end delay)Least-hop / (end-to-end delay)BSP )

  28. Selection Metrics Minimizing Total Power ( J = Power ) The best J in relay-set (n) : Data sending node : node (n,k) • CH* : per link channel gain constraint • If a node transmits a data with power X (dBm)= CH* - R(n,I),(n-1,j) , then channel • gain constraint will be met • E.g.) CH* = -86 dBm, R (n,I),(n-1,j) = -60dBm • X(dBm) = -86 – (-60) = -26

  29. Selection Metrics Minimizing Total Power ( J = Power ) idealized urban implemented urban 4 4 10 10 Sparse Dense 3 3 10 10 improvement in power (ratio) 2 2 10 10 1 1 10 10 0 0 10 10 2 4 6 8 10 2 4 6 8 10 min relay-set size • Y-axis : Avg( (end-to-end power)Least-hop / (end-to-end power)BSP ) • Least-hop approach uses the fixed transmission power

  30. Selection Metrics Minimizing Total Energy ( J = Energy ) The best J in relay-set (n) : Data sending node : node (n,k) • Energy to next relay-set • Energy from next relay-set to destination • M represents the energy required to retransmit the packet due to transport layer retransmission • Best node will transmit a data with power X and bit-rate B

  31. Selection Metrics Minimizing Total Energy ( J = Energy ) idealized urban implemented urban 3 3 10 10 Sparse Dense 2 2 10 10 improvement in energy (ratio) 1 1 10 10 0 0 10 10 2 4 6 8 10 2 4 6 8 10 min relay-set size • Y-axis : Avg( (end-to-end energy)Least-hop / (end-to-end energy)BSP ) • Least-hop approach uses the fixed transmission power and bit-rate

  32. Differences between Selection Metrics 1 Max Delivery Prob. vs. Max-Min Channel Gain 0.8 Min Delay vs. Max Throughput Min Total Power vs. Min Energy 0.6 fraction of relays shared 0.4 0.2 0 2 4 6 8 10 mean size of relay-set • On average about 40% of the paths are shared when mean size of relay-set is 2 • The bigger mean size of relay-set, the more the paths are disjoint • While metrics all use the channel gain, different meanings of metrics lead • to difference in the paths selected

  33. Conclusion and Future Work Conclusion • Diversity allows BSP to achieve significant improvement in various metrics • Recall that in physical layers such as 802.11 received power varies over a range of 5-6 orders of magnitude (-36 dBm to -96 dBm). That is, a good link may be 100,000 ~ 1,000,000 times better than a bad link. • In communication theory, the link is given, regardless of whether the link is bad or good. • In networking, we do not have to use the bad links; we can pick links that are perhaps 100,000 ~1,000,000 times better Future/current Work • Reduce overhead of RTS/CTS control packets • Investigate optimum size of relay-set • Better method of joining, leaving relay-set and detecting route failures

  34. Webpage of our group : http://www.eecis.udel.edu/~bohacek/UDelModels/index.html

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