• Skip to main content
  • Skip to secondary menu
  • Skip to primary sidebar
  • Skip to footer
  • IEEE.org
  • IEEE Xplore
  • IEEE Standards
  • IEEE Spectrum
  • More Sites

IEEE Potentials Magazine

The magazine for high-tech innovators

  • Home
  • Theme
    • Features
    • Columns/Departments
  • About Us
  • Contact
  • Associated Links
    • Potentials at IEEE Students
    • Potentials Media Guide
  • Highlighted Articles
  • Call For Papers
  • Recent Issues
    • Nov/Dec 2025
    • Sept/Oct 2025
    • July/Aug 2025
    • May/June 2025
    • March/April 2025
    • Jan/Feb 2025

A framework for spectrum sharing in cognitive radio networks for military applications

September 1, 2021 by Bindu Bharti, Prabhat Thakur, and Ghanshyam Singh

Image of a US Army member using a radio.
©SHUTTERSTOCK.COM/PRESSLAB

In this article, the dynamic spectrum-accessing techniques—the overlay, underlay, and hybrid spectrum-sharing approaches-are exploited for the military communication. Furthermore, the importance of the energy efficiency of cognitive radio (CR) networks (CRNs) for military applications is discussed. We also emphasize security threats over the physical layer and their mitigation in CRNs. Finally, we explore why the cross-layer design of protocols is preferred for greater energy efficiency and security.

For more about this article see link below.
To access the PDF version of this article, member sign-in is required.

https://ieeexplore.ieee.org/document/9529345

Filed Under: Past Features Tagged With: Energy efficiency, Military communication, Physical layer, Protocols, Security, Weapons

Primary Sidebar

Current Issue

Get the entire issue now.

About the Magazine

IEEE Potentials Magazine is the publication dedicated to undergraduate and graduate students and young professionals. IEEE Potentials explores career strategies, the latest in research, and important technical developments. Through its articles, it also relates theories to practical applications, highlights technology’s global impact, and generates international forums that foster the sharing of diverse ideas about the profession.

POPULAR ARTICLE

Privacy-preserving distributed learning: Techniques, applications, and future challenges

The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.

Read More…

Search

Past Issues

Footer

IEEE Potentials Magazine is a member benefit for IEEE Student members.

The magazine is archived in IEEE Xplore, and articles from all issues are available for download.

Home | Sitemap | Contact & Support | Accessibility | Nondiscrimination Policy | IEEE Ethics Reporting | IEEE Privacy Policy | Terms

© Copyright 2025 IEEE - All rights reserved. A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.