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Clustering Transmission Opportunity Length (CTOL) Model over Cognitive Radio Network.

Mas Haslinda Mohamad^1,2, Aduwati Sali³, Fazirulhisyam Hashim⁴

¹Research Centre of Excellence for Wireless and Photonics Network (WiPNET), Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia. mashaslinda@utem.edu.my.

Sensors (Basel, Switzerland)

|December 15, 2018

Summary

This study optimized secondary user (SU) throughput in wireless networks by modeling primary user (PU) activity. The proposed CTOL model significantly improved SU performance compared to existing methods.

Keywords:

WLAN cognitive radio opportunistic access primary user secondary user transmission opportunity length

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Area of Science:

Wireless Communications
Spectrum Sensing
Performance Analysis

Background:

Secondary users (SUs) aim to utilize spectrum opportunistically without interfering with primary users (PUs).
Understanding primary user (PU) activity patterns is crucial for maximizing secondary user (SU) throughput.
Existing models for PU activity often lack realism, impacting SU performance predictions.

Purpose of the Study:

To investigate secondary user (SU) throughput performance under realistic primary user (PU) activity.
To propose an optimized sensing and frame duration strategy for SUs to maximize throughput.
To evaluate the proposed model against static and dynamic PU activity models.

Main Methods:

Modeled primary user (PU) activity patterns from experimental wireless local area network (WLAN) data.
Developed a clustered transmission opportunity length (CTOL) model to categorize PU activity into large and small durations.
Analyzed and compared SU throughput and collision probability using the CTOL model against static and dynamic PU models.

Main Results:

The CTOL model demonstrated a significant improvement in SU throughput, outperforming static and dynamic PU models by 45% and 12.2%, respectively.
SU throughput and collision probability were found to be sensitive to the minimum contention window and maximum back-off stage parameters.
Increased contention windows negatively impacted SU throughput, even with abundant transmission opportunities.

Conclusions:

The proposed CTOL model offers a more effective approach for enhancing SU throughput in dynamic spectrum environments.
Optimizing sensing and frame durations, alongside careful parameter selection (contention window, back-off stage), is vital for efficient spectrum sharing.
Realistic modeling of PU activity is essential for accurate performance prediction and optimization in cognitive radio networks.