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Wireless heterogeneous transmitter placement using multiobjective variable-length genetic algorithm.

Chuan-Kang Ting¹, Chung-Nan Lee, Hui-Chun Chang

¹Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 621, Taiwan. ckting@cs.ccu.edu.tw

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|April 14, 2009

Summary

This summary is machine-generated.

This study introduces a new genetic algorithm for optimizing wireless transmitter placement. The novel approach efficiently determines the optimal number, types, and locations of heterogeneous transmitters, improving coverage and reducing costs.

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Area of Science:

Computer Science
Electrical Engineering
Telecommunications

Background:

Wireless transmitter placement is a complex, NP-hard problem.
Network heterogeneity further complicates optimal placement strategies.
Existing methods often require pre-determining the number of transmitters.

Purpose of the Study:

To present a novel multiobjective variable-length genetic algorithm.
To simultaneously optimize the number, types, and positions of heterogeneous wireless transmitters.
To address coverage, cost, capacity, and overlap considerations.

Main Methods:

Development of a multiobjective variable-length genetic algorithm.
Simultaneous search for transmitter quantity, type, and placement.
Evaluation across six benchmark datasets.

Main Results:

The algorithm achieved optimal transmitter numbers in benchmarks.
Average network coverage exceeded 98% across tested scenarios.
Demonstrated efficient handling of heterogeneous transmitter networks.

Conclusions:

The proposed genetic algorithm effectively solves the wireless heterogeneous transmitter placement problem.
The method offers a robust solution for optimizing network deployment.
Results highlight the algorithm's capability in achieving high coverage and efficiency.