Selection Metrics for Multihop Cooperative Relaying Jonghyun Kim

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 Simulation environment Selection Metrics Differences between Selection Metrics Conclusion and Future/current Work

Introduction One possible path source destination

Introduction Another possible path source destination

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

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

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

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

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

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

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

Brief look at how to overcome the challenge relay-set (2) CTS relay-set (1) R(2, 1), (1, 1) R(2, 2), (1, 1) J(1, 1) (2, 1) (1, 1) (2, 2) (1, 2) source destination 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)

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

Brief look at how to overcome the challenge relay-set (2) DATA relay-set (1) (2, 1) (1, 1) (2, 2) (1, 2) source destination - 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

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

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 Stage costs Js from the downstream relay-set Costs to go

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

UDel mobility simulation • Current Simulator – US Dept. of Labor Statistics timeuse 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

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

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

Selection Metrics improvement in error prob. (ratio) Maximizing Delivery Prob. ( J = Delivery Prob. ) idealized urban 1 0. 8 0. 6 0. 4 Sparse Dense 0. 2 0 2 4 6 8 10 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

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

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

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

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

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

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

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 (d. Bm)= CH* - R(n, I), (n-1, j) , then channel gain constraint will be met E. g. ) CH* = -86 d. Bm, R (n, I), (n-1, j) = -60 d. Bm X(d. Bm) = -86 – (-60) = -26

Selection Metrics improvement in power (ratio) Minimizing Total Power ( J = Power ) idealized urban 4 10 implemented urban 4 10 Sparse Dense 3 10 2 2 10 1 10 0 10 10 10 0 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

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

Selection Metrics improvement in energy (ratio) Minimizing Total Energy ( J = Energy ) idealized urban 3 10 implemented urban 3 10 Sparse Dense 2 2 10 10 1 1 10 10 0 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

fraction of relays shared Differences between Selection Metrics 1 Max Delivery Prob. vs. Max-Min Channel Gain Min Delay vs. Max Throughput Min Total Power vs. Min Energy 0. 8 0. 6 0. 4 0. 2 0 2 4 6 8 mean size of relay-set 10 - 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

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 d. Bm to -96 d. Bm). That is, a good link may be 100, 000 ~ 1, 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 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

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