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Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

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Dynamic channel selection in wireless communications via a multi-armed bandit algorithm using laser chaos time

Shungo Takeuchi¹, Mikio Hasegawa², Kazutaka Kanno³

¹Department of Electrical Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo, 125-8585, Japan. s-takeuchi@haselab.ee.kagu.tus.ac.jp.

Scientific Reports

|February 2, 2020

Summary

This study demonstrates using laser chaos time series for dynamic channel selection in wireless local area networks (WLANs). This ultrafast approach improves communication quality by efficiently balancing channel exploration and exploitation.

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Area of Science:

Wireless communication
Chaos theory
Machine learning

Background:

Dynamic channel selection is crucial for optimizing wireless communication in fluctuating electromagnetic environments.
Multi-armed bandit (MAB) algorithms offer a solution to balance exploring new channels and exploiting known high-quality channels.
Semiconductor laser chaos time series have shown potential for ultrafast MAB problem solutions.

Purpose of the Study:

To experimentally demonstrate a MAB algorithm utilizing laser chaos time series for dynamic channel selection in a WLAN.
To validate the effectiveness of ultrafast chaotic sequences in real-world wireless applications.
To numerically analyze the adaptation mechanism of a simplified MAB algorithm.

Main Methods:

Implementation of a MAB algorithm with laser chaos time series in an IEEE802.11a-based, four-channel WLAN.
Experimental demonstration of autonomous and adaptive dynamic channel selection.
Numerical examination of the MAB algorithm's adaptation mechanism.

Main Results:

Successful autonomous and adaptive dynamic channel selection was achieved in the experimental WLAN setup.
The results confirm the utility of pre-arranged laser chaos time series for practical wireless communication.
A simplified MAB algorithm's adaptation mechanism was numerically investigated.

Conclusions:

Ultrafast chaotic laser sequences are effective for dynamic channel selection in WLANs.
This research represents a foundational step towards employing chaotic lasers in advanced wireless networks.
The study highlights the potential of chaos-based MAB algorithms for future high-performance wireless communication.