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 source destination One possible path

4. Introduction source destination Another possible path

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

6. Diversity Link quality and hence path quality can be modeled as a stochastic process 1.If there are many alternative paths, there will be some very good path 2.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 (2,1) (2,2) (1,1) (1,2) source destination relay-set (1) relay-set (2) Nodes within relay-set (2) have decoded data from source

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

11. Brief look at how to overcome the challenge (2,1) (2,2) (1,1) (1,2) source destination relay-set (1) relay-set (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) R (2,1),(1,1) R (2,2),(1,2) RTS R (2,2),(1,1) R (2,1),(1,2)

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

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

16. Dynamic programming - 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) is the “cost” from the i th node in the n th relay-set to destination 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) ) J s from the downstream relay-set Channel gains Costs to goStage costs

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. 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 Propagation during a two minute walk UDel channel simulation

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 Maximizing Delivery Prob. ( J = Delivery Prob.) min relay-set size improvement in error prob. (ratio) 2468 10 0 0.2 0.4 0.6 0.8 1 Sparse Dense idealized urban - 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 ) improvement in channel gain (dB) min relay-set size 246810 0 5 15 20 25 30 Sparse Dense idealized urbanimplemented urban 246810 0 5 15 20 25 30 - Y-axis : Avg( (min channel gain) BSP - (min channel gain ) Least-hop )

24. Selection Metrics Maximizing Throughput ( J = Bit-rate ) - 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 The best J in relay-set (n) : Data sending node : node (n,k)

25. Selection Metrics Maximizing Throughput ( J = Bit-rate ) min relay-set size improvement in throughput (ratio) 2468 10 0 5 15 Sparse Dense 246810 0 5 15 idealized urbanimplemented urban - 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) : : node (n,k)Data sending node - 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 ) min relay-set size improvement in delay (ratio) 246810 0 5 15 Sparse Dense 246810 0 5 15 idealized urbanimplemented urban - 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 ) min relay-set size improvement in power (ratio) 246810 0 1 2 3 4 2468 0 1 2 3 4 idealized urbanimplemented urban Sparse Dense - 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) : : node (n,k)Data sending node - 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 ) min relay-set size 246810 0 1 2 3 improvement in energy (ratio) Sparse Dense 246810 0 1 2 3 idealized urbanimplemented urban - 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 2468 10 0 0.2 0.4 0.6 0.8 1 fraction of relays shared mean size of relay-set Max Delivery Prob. vs. Max-Min Channel Gain Min Delay vs. Max Throughput Min Total Power vs. Min Energy - 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 Reduce overhead of RTS/CTS control packets Investigate optimum size of relay-set Better method of joining, leaving relay-set and detecting route failures 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 Conclusion

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