Understanding Radio Irregularity in Wireless Networks Torsten Mütze, ETH Zürich joint work with...

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Understanding Radio Irregularity in Wireless Networks Torsten Mütze, ETH Zürich joint work with Patrick Stüdi, Fabian Kuhn and Gustavo Alonso
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Page 1: Understanding Radio Irregularity in Wireless Networks Torsten Mütze, ETH Zürich joint work with Patrick Stüdi, Fabian Kuhn and Gustavo Alonso.

Understanding Radio Irregularity in Wireless Networks

Torsten Mütze, ETH Zürichjoint work with Patrick Stüdi, Fabian Kuhn and Gustavo Alonso

Page 2: Understanding Radio Irregularity in Wireless Networks Torsten Mütze, ETH Zürich joint work with Patrick Stüdi, Fabian Kuhn and Gustavo Alonso.

Outline

Motivation Network Model Connectivity Interference

Page 3: Understanding Radio Irregularity in Wireless Networks Torsten Mütze, ETH Zürich joint work with Patrick Stüdi, Fabian Kuhn and Gustavo Alonso.

Motivation

Ideal:- circular transmission rangePath Loss Model (deterministic)

Connectivity?Capacity?

More realistic:- obstacles in the transmission path- non-isotropic antennasLog-normal Shadowing Model (randomized)

Page 4: Understanding Radio Irregularity in Wireless Networks Torsten Mütze, ETH Zürich joint work with Patrick Stüdi, Fabian Kuhn and Gustavo Alonso.

50 100 150 200

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Network Model (1)

Page 5: Understanding Radio Irregularity in Wireless Networks Torsten Mütze, ETH Zürich joint work with Patrick Stüdi, Fabian Kuhn and Gustavo Alonso.

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Network Model (2)Path Loss Model

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Log-normal Shadowing Model

[Hekmat, Mieghem 06; Bettstetter, Hartmann 05; Miorandi, Altman 05; Orriss, Barton 03]

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Radio irregularity is controlled througha single parameter, the shadowing deviation

Page 6: Understanding Radio Irregularity in Wireless Networks Torsten Mütze, ETH Zürich joint work with Patrick Stüdi, Fabian Kuhn and Gustavo Alonso.

Connectivity (1)Connectivity: probability of the network graph to be connected

Biased Analysis: Connectivity increase is caused by a higher expected

node degree (=enlarged radio transmission range)How to do a fair comparison between different levels of radio irregularity?

Expected node degree

[Bettstetter, Hartmann 05]

Page 7: Understanding Radio Irregularity in Wireless Networks Torsten Mütze, ETH Zürich joint work with Patrick Stüdi, Fabian Kuhn and Gustavo Alonso.

Connectivity (2)Normalization!When increasing , vary thetransmission power p0 as afunction of such that theexpected node degree remainsconstant

[Jonasson 01], [Roy, Tanemura 02]

Why?Edge length distribution

Longer connections help

Surprise: Connectivity increases withirregularity, even under constant nodedegree

Page 8: Understanding Radio Irregularity in Wireless Networks Torsten Mütze, ETH Zürich joint work with Patrick Stüdi, Fabian Kuhn and Gustavo Alonso.

Interference limits the throughput capacity of a network [Stüdi, Alonso 06]

Interference (1)

Interferers: smallest set of nodes that must not transmit concurrently to the communication from b to a

signal

interference + noisethreshold

Signal-to-interference-plus-noise ratio model (SINR)

ab

pab

I interferers

non-interferersI

Page 9: Understanding Radio Irregularity in Wireless Networks Torsten Mütze, ETH Zürich joint work with Patrick Stüdi, Fabian Kuhn and Gustavo Alonso.

Expected number of interferers for fixed pab

Biased Analysis: Interference increase is caused by a higher cumulated noiseNormalization! Keep the expected cumulated noise constant when varyingExpected number of interferers for fixed pab

Interference (2)Expected cumulated noise

a

Why?Power density function

More nodes with small/largetransmission power (=more non-interferers)

Surprise: Interference decreases with irregularity (under constant expected cumulated noise)

Page 10: Understanding Radio Irregularity in Wireless Networks Torsten Mütze, ETH Zürich joint work with Patrick Stüdi, Fabian Kuhn and Gustavo Alonso.

Summary Studied impact of log-normal shadowing on

connectivity and interference First unbiased analysis: fair comparison between

different levels of radio irregularity Beneficial impact of log-normal shadowing on

both connectivity and interference (improved connectivity, reduced interference)

Existing bounds on connectivity and capacity derived in a circular transmission range model are lower instead of upper bounds

Page 11: Understanding Radio Irregularity in Wireless Networks Torsten Mütze, ETH Zürich joint work with Patrick Stüdi, Fabian Kuhn and Gustavo Alonso.

Thank you!