Mobility Increases the Capacity of Ad-hoc Wireless Networks

1. Oct 21, 2004 CS 260 Mobility Increases the Capacity of Ad-hoc Wireless Networks Matthias Grossglauser, David Tse IEEE Infocom 2001(Best paper award) Presented By Som C. Neema

2. Introduction • Study a model of ad-hoc network with n nodes • Communicate in random source destination pairs • Examine per- session throughput for applications with loose delay constraints • Show that mobility increases throughput

3. Mobile Ad-hoc Network Model • Mobility model: Nodes move randomly and independently on a disk of unit area. • Channel model: path loss factor of r¡āat distance r, with ā > 2 (slow fading) • Communication model: a packet is successfully received if signal-to-interference ratio is greater than a prescribed threshold.

4. outline • Part1 : motivation and idea • Part2 : math, simulation and results • Discussion

5. Part1 : the motivation and the idea

6. Throughput in stationary ad-hoc networks • Piyush Gupta and P. R. Kumar. The Capacity of Wireless Networks. IEEE Transactions on Information Theory, 46(2):388–404, March 2000. • As the number of nodes per unit area n increases, the throughput per source destination pair decreases as • Notice the scalability problem Reason: • Interference => Long Range communication not feasible • Increase in Relay traffic (a typical route has Number of hops )

7. Using mobility to increase throughput • Why not just wait (hold the packet) until the destination is just one hop away i.e. direct communication • Problem • Delay increases • Probability of the above occurrance =1/n

9. Improving on the Idea • Let the source node distribute packets to other nodes • These other nodes relay the packet when they become next hop neighbors of the destination node.

11. Why this might work • Increases probability • S-D Throughput is high as each packet goes through only one relay node.

12. Pros and Cons Average long-term throughput per S-D pair can be kept constant even as the number of nodes per unit area n increases. Large end-end delay, hence not for all applications

15. Assumptions Made • Nodes move independently and randomly • Buffer size in nodes is ∞

17. Capacity of the network • The above discussion pertains to a single source-destination pair. • They show that every S-D pair can follow the same strategy simultaneously. • O(n) simultaneous nearest neighbor communication is possible, due to power law decay of the received power from a randomly located node.

18. Part2 : The Math, Simulations and Results

19. Understanding the math • Why skipping it makes sense

20. Still… Lets Try • The notations and other assumptions • n nodes in a circular region of unit area • Especially interested in asymptotic behavior as n increases • Location of ith user at time t is Xi(t) • At any time t, node i transmits data as rate R packets/sec • βis signal to noise ratio • Pi(t) is power level for the senders • λ(n) is the avg long term throughput /s-d pair

21. Fixed Nodes • Theorem 3.1 Throughput tends to zero as R/√n

22. Mobile node without relaying Lemma 3.2 Number of simultaneous long range communication is limited by interference Theorem 3.3 for Alpha = 2, for large n

23. Mobile Nodes with relaying • Theorem 3.4 The expected number E[Nt] of sender-receiver pairs is O(n) • Theorem 3.5 Throughput per S-D pair =O(1)

24. Sender-Centric vs. Receiver-Centric Approach • The authors choose a sender-centric approach It is the senders that select the closest receiver to send to. Probability of capture (SIR> B) for single receiver decreases with increasing sender density in the sender-centric approach. • But they say that Receiver Centric Policy is preferable in terms of signal to interference ratio for a single receiver. The signal from the selected sender is always the strongest and doesn’t depend on the sender density. Better when Ns>Nr.

25. Comparison Chart From presentation by Delbert Huang

26. Simulations

27. Results Normalized per node thoughput as a function of sender density for different values of ā

28. Interpretation from graph • There exists an optimal sender density that maximizes the throughput • For small ā , sender density should be small for max throughput • For large ā , sender density should be higher for max throughput

29. Discussion

30. Future directions • Try and exploit dependent motion of the mobile nodes  How to address the finiteness of the buffer space

31. References • Matthias Grossglauser (AT&T Labs - Research), David Tse (University of California at Berkeley), “Mobility Increases the Capacity of Ad-hoc Wireles Networks”, IEEE Infocom, April, 2001 • Multiuser Diversity in Wireless Networks: Smart Scheduling, Dumb Antennas and Epidemic Communication IMA Wireless Workshop http://www.eecs.berkeley.edu/~dtse/ima810.pdf • Presentation by Delbert Huang http://nesl.ee.ucla.edu/courses/ee206a/2001s/lectures/SP5_delbert.ppt

32. Thanks