A Survey on Cognitive Radio Ad-Hoc Network Architecture

Year : 2024 | Volume :14 | Issue : 03 | Page : –
By

Sanskruti Rane,

Shraddha Magdum,

Shreya Shirsat,

Ajay Pawar,

  1. Student, Department of Electronics and Telecommunication, Smt. Kashibai Navale College of Engineering (SKNCOE), SPPU, Pune, Maharashtra, India
  2. Assistant Professor, Department of Electronics and Telecommunication, Smt. Kashibai Navale College of Engineering (SKNCOE), SPPU, Pune, Maharashtra, India
  3. Student, Department of Electronics and Telecommunication, Smt. Kashibai Navale College of Engineering (SKNCOE), SPPU, Pune, Maharashtra, India
  4. Student, Department of Electronics and Telecommunication, Smt. Kashibai Navale College of Engineering (SKNCOE), SPPU, Pune, Maharashtra, India

Abstract

Cognitive Radio Ad-Hoc Networks (CRAHNs) represent an innovative paradigm in wireless communication, leveraging the dynamic spectrum access capabilities of cognitive radios (CRs) to enhance network performance and spectrum efficiency. The architecture of CRAHNs integrates cognitive radio capabilities with ad hoc networking principles, enabling devices to autonomously and intelligently manage spectrum resources in a decentralized manner. This abstract outline the key components and functionalities of CRAHN architecture, highlighting its potential to enhance spectrum efficiency and network performance. CRAHNs consist of cognitive radio nodes equipped with the ability to sense their electromagnetic environment, identify unused spectrum bands (spectrum holes), and adapt their transmission parameters accordingly. The architecture is designed to operate in a highly dynamic environment, where spectrum availability can change rapidly due to primary user activity. In CRAHNs, nodes equipped with CRs autonomously identify unused spectrum bands, known as spectrum holes or white spaces, and adjust their transmission parameters to use these frequencies without interfering with licensed users. This approach addresses the spectrum scarcity problem by optimizing the use of available bandwidth, thereby improving network throughput and connectivity. CRAHNs face several challenges, including reliable spectrum sensing, efficient spectrum management, security issues, and ensuring seamless communication in highly dynamic environments. Addressing these challenges requires innovative approaches in spectrum sensing techniques, robust routing protocols, and enhanced security mechanisms. Ad hoc networks represent a vital field of research and development in wireless communication. These networks are self-configuring, where nodes dynamically establish and maintain network connectivity without a fixed infrastructure. Nodes in these networks act both as end systems and routers, forwarding data for other nodes, which makes the network highly dynamic and flexible. The decentralized nature of ad-hoc networks, combined with the flexibility of cognitive radios, enables CRAHNs to support a wide range of applications, from emergency response and military operations to rural communications and smart grid technologies. The ability of CRAHNs to dynamically adapt and efficiently utilize spectrum resources makes them a promising solution for future wireless networks.

Keywords: CRN, Network Performance, Radio spectrum band, self – configuring, Smart grid

[This article belongs to Journal of Communication Engineering & Systems (joces)]

How to cite this article:
Sanskruti Rane, Shraddha Magdum, Shreya Shirsat, Ajay Pawar. A Survey on Cognitive Radio Ad-Hoc Network Architecture. Journal of Communication Engineering & Systems. 2024; 14(03):-.
How to cite this URL:
Sanskruti Rane, Shraddha Magdum, Shreya Shirsat, Ajay Pawar. A Survey on Cognitive Radio Ad-Hoc Network Architecture. Journal of Communication Engineering & Systems. 2024; 14(03):-. Available from: https://journals.stmjournals.com/joces/article=2024/view=171870



Fetching IP address…

References

  1. Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6, 13–18.
  2. FC Commission. Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies. FCC Report, ET Docket 03. 2003 Dec;322. Available from https://www.fcc.gov/document/facilitating-opportunities-flexible-efficient-and-reliable-spectrum-1
  3. Akyildiz, I. F., Lee, W. Y., Vuran, M. C., & Mohanty, S. (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50, 2127–2159.
  4. Chen, K.-C., Peng, Y.-J., Prasad, N., Liang, Y.-C., & Sun, S. (2008). Cognitive radio network architecture: Part I—general structure. Presented at the Proceedings of the 2nd international conference on Ubiquitous information management and communication, Suwon, Korea.
  5. Krishnamurthy S, Thoppian M, Venkatesan S, Prakash R. Control channel based MAC-layer configuration, routing and situation awareness for cognitive radio networks. InMILCOM 2005-2005 IEEE Military Communications Conference 2005 Oct 17 (pp. 455-460). IEEE.
  6. Zhao Y, Mao S, Neel JO, Reed JH. Performance evaluation of cognitive radios: Metrics, utility functions, and methodology. Proceedings of the IEEE. 2009 Apr 7;97(4):642-59.
  7. Wang B, Liu KR. Advances in cognitive radio networks: A survey. IEEE Journal of selected topics in signal processing. 2010 Nov 18;5(1):5-23.
  8. Liang YC, Chen KC, Li GY, Mahonen P. Cognitive radio networking and communications: An overview. IEEE transactions on vehicular technology. 2011 Jun 6;60(7):3386-407.
  9. Zhao Y, Morales L, Gaeddert J, Bae KK, Um JS, Reed JH. Applying radio environment maps to cognitive wireless regional area networks. In2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks 2007 Apr 17 (pp. 115-118). IEEE.
  10. Yucek T, Arslan H. A survey of spectrum sensing algorithms for cognitive radio applications. IEEE communications surveys & tutorials. 2009 Mar 4;11(1):116-30.
  11. Akyildiz IF, Lee WY, Vuran MC, Mohanty S. A survey on spectrum management in cognitive radio networks. IEEE Communications magazine. 2008 Apr 3;46(4):40-8.
  12. Htike Z, Hong CS. Neighbor discovery for cognitive radio ad hoc networks. InProceedings of the 7th international conference on ubiquitous information management and communication 2013 Jan 17 (pp. 1-6).
  13. Asterjadhi A, Baldo N, Zorzi M. A cluster formation protocol for cognitive radio ad hoc networks. In2010 European wireless conference (EW) 2010 Apr 12 (pp. 955-961). IEEE.
  14. Pešović UM, Mohorko JJ, Benkič K, Čučej ŽF. Single-hop vs. Multi-hop–Energy efficiency analysis in wireless sensor networks. In18th telecommunications forum, TELFOR 2010 Nov.
Regular Issue Subscription Review Article
Volume 14
Issue 03
Received July 3, 2024
Accepted September 6, 2024
Published September 12, 2024
Views: 34

Check Our other Platform for Workshops in the field of AI, Biotechnology & Nanotechnology.
Learn more
Check Out Platform for Webinars in the field of AI, Biotech. & Nanotech.
Learn more