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Secondary distribution systems provide electrical energy at the utilization voltage levels from distribution transformers to customer meters. Typical secondary voltages in the United States include 120/240 V for residential use, 208Y/120 V for residential and commercial use, and 480Y/277 V for industrial and high-rise commercial use.
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Related Experiment Video

Updated: Mar 22, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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Dynamic channel allocation for secondary users in cognitive radio network.

Sanjeetha Gowthaman1, P T V Bhuvaneswari2,3, Parameswaran Ramesh2

  • 1Department of Electronics Engineering, Madras Institute of Technology, Anna University, Chennai, India. gsanjeetha@gmail.com.

Scientific Reports
|March 21, 2026
PubMed
Summary

This study introduces a fuzzy logic model for Cognitive Radio Networks (CRNs) to improve spectrum utilization. The proposed system enhances throughput and reduces delays for Secondary Users (SUs) by intelligently assigning channels.

Keywords:
CRNChannel AllocationFuzzyIoT and SINR

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Area of Science:

  • Wireless Communication
  • Network Engineering
  • Artificial Intelligence

Background:

  • Cognitive Radio Networks (CRNs) address spectral resource shortages by enabling Secondary Users (SUs) to opportunistically access underutilized frequency bands.
  • Ensuring the integrity of Primary Users (PUs) is crucial for effective spectrum sharing in CRNs.
  • Traditional CRN paradigms face challenges in dynamic spectrum management.

Purpose of the Study:

  • To develop an independent channel assignment model for CRNs using Mamdani fuzzy logic.
  • To optimize spectrum utilization and network performance by coordinating channel selection based on network conditions.
  • To enhance the adaptive responsiveness of CRNs to dynamic network environments.

Main Methods:

  • Implementation of a 27-rule Mamdani fuzzy logic decision model for channel assignment.
  • Utilizing an energy-detection approach for spectrum sensing to identify idle channels.
  • Employing a distributed decision-making framework at individual SU units for adaptive channel selection.

Main Results:

  • The fuzzy-based allocation system demonstrated significant improvements in network throughput.
  • A notable reduction in service delay and packet drop rate was observed.
  • Increased spectrum utilization was achieved compared to traditional CRN approaches.

Conclusions:

  • Fuzzy logic-based channel assignment effectively enhances CRN performance.
  • The proposed distributed system offers adaptive spectrum management capabilities.
  • Cognitive intelligence combined with fuzzy decision-making is vital for future IoT communications.