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

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¡ ā at 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 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

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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.

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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

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

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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 i th user at time t is X i (t) At any time t, node i transmits data as rate R packets/sec β is signal to noise ratio P i (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[N t ] 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 Presentation by Delbert Huang bert.ppt

32 Thanks