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Mobility Increases the Capacity of Ad-hoc Wireless NetworksMatthias Grossglauser, David Tse

IEEE Infocom 2001 (Best paper award)

Oct 21, 2004

Som C. Neema

CS 260

Presented By

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Introduction

Study a model of ad-hoc network with n nodes Communicate in random source destination pairsExamine per- session throughput for applications with loose delay constraintsShow that mobility increases throughput

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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.

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outline

Part1 : motivation and ideaPart2 : math, simulation and resultsDiscussion

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Part1 : the motivation and the idea

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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 )

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Using mobility to increase throughput

Why not just wait (hold the packet) until the destination is just one hop away i.e. direct communication

ProblemDelay increasesProbability of the above occurrance =1/n

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Improving on the Idea

Let the source node distribute packets to other nodesThese other nodes relay the packet when they become next hop neighbors of the destination node.

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Why this might work

Increases probability

S-D Throughput is high as each packet goes through only one relay node.

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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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Nodes move independently and randomly

Buffer size in nodes is ∞

Assumptions Made

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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.

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Part2 : The Math, Simulations and Results

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Understanding the math• Why skipping it makes sense

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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 β is signal to noise ratio Pi(t) is power level for the senders λ(n) is the avg long term throughput /s-d

pair

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Fixed Nodes

Theorem 3.1

Throughput tends to zero as R/√n

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Mobile node without relayingLemma 3.2 Number of simultaneous long range communication is limited by interference

Theorem 3.3 for Alpha = 2, for large n

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Mobile Nodes with relaying

Theorem 3.4The expected number E[Nt] of sender-receiver

pairs is O(n)

Theorem 3.5

Throughput per S-D pair =O(1)

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Sender-Centric vs. Receiver-Centric Approach

The authors choose a sender-centric approachIt 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.

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Comparison ChartResults: “Long-Term” Throughput per S-D pair

Fixed node case + Relaying

Mobile Nodes + No Relaying

Mobile Nodes + Relaying

Upper Bound

, constant

Decay order per S-D pair

O( ) O( ) 0(1), constant, no decay

Expected number of feasible send – receiver pairs

Not applicable O(n) O(n)

Probability that sender sees receiver

Not applicable O(1/n) O(n)*O(1/n)=O(1)

Per S-D throughput as n gets large

ZERO ZERO O(1)

Total system throughput as n gets large

ZERO ZERO O(n)

From presentation by Delbert Huang

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Simulations

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Results

Normalized per node thoughput as a function of sender density for different

values of ā

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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

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Discussion

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Future directions

Try and exploit dependent motion of the mobile nodes

How to address the finiteness of the buffer space

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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, 2001Multiuser 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 Huanghttp://nesl.ee.ucla.edu/courses/ee206a/2001s/lectures/SP5_delbert.ppt

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Thanks