Suboptimal Approach to Distributed Detection in Cognitive Radio Networks

Authors

  • Shweta Kumari Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram image/svg+xml
  • Sirimilla Praveena Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram image/svg+xml

DOI:

https://doi.org/10.26636/jtit.2021.144720

Keywords:

cognitive radio, distributed detection, energy detector, myopic policy, spectrum sensing

Abstract

In this paper a dynamic spectrum access (DSA) concept is explored for mitigating the paucity of spectral bandwidth in cognitive radio (CR) for opportunistic, dynamic access of the spectrum without any interference. Dynamic spectrum access schemes are proposed for a distributed cognitive radio network consisting of one secondary user (SU) and many primary users (PUs). The SU has to make decisions for accessing PU channels within discrete time slots. The design of sensing and access strategies that govern channel choice in each slot for near-optimal throughput performance of the SU may be formulated as a partially observable Markov decision process (POMDP). Furthermore, it is considered that the SU incurs a cost whenever it switches to a different channel. The switching cost is expressed in terms of delay, packet loss and packet overhead. In this work, the SU access policy based on a myopic approach is proposed and evaluated.

Downloads

Download data is not yet available.

References

[1] K. Patil, R. Prasad, and K. Skouby, „A survey of worldwide spectrum occupancy measurement campaigns for cognitive radio", in Proc. Int. Conf. on Devices and Commun. ICDeCom IEEE, Mesra, India, 2011, pp. 1-5.
View in Google Scholar

[2] E. Axell, G. Leus, E. G. Larsson, and H. V. Poor, „Spectrum sensing for cognitive radio: State-of-the-art and recent advances", IEEE Signal Process. Mag., vol. 29, no. 3, pp. 101-116, 2012.
View in Google Scholar

[3] R. Umar and A. U. Sheikh, „A comparative study of spectrum awareness techniques for cognitive radio oriented wireless networks", Physical Commun., vol. 9, pp. 148-170, 2013.
View in Google Scholar

[4] A. A. Khan, M. H. Rehmani, and M. Reisslein, „Cognitive radio for smart grids: Survey of architectures, spectrum sensing mechanisms, and networking protocols", IEEE Commun. Surveys & Tutorials, vol. 18, no. 1, pp. 860-898, 2016.
View in Google Scholar

[5] M. Riahi Manesh, N. Kaabouch, H. Reyes, and W.-C. Hu, „A Bayesian approach to estimate and model SINR in wireless networks", Int. J. of Commun. Systems, vol. 30, no. 9, pp. 1-11, 2016.
View in Google Scholar

[6] J. A. Bazerque and G. B. Giannakis, „Distributed spectrum sensing for cognitive radio networks by exploiting sparsity", IEEE Transactions on Signal Process., vol. 58, no. 3, pp. 1847-1862, 2010.
View in Google Scholar

[7] G. Ganesan and Y. Li, „Cooperative spectrum sensing in cognitive radio networks", in Proc. 1st IEEE Int. Symp. on New Frontiers in Dynamic Spectrum Access Networks. DySPAN 2005, Baltimore, USA, 2005, pp. 137-143.
View in Google Scholar

[8] T. Weiss, J. Hillenbrand, A. Krohn, and F. K. Jondral, „Efficient signaling of spectral resources in spectrum pooling systems", In Proc. 10th Symp. on Commun. and Vehicular Technol. SCVT, 2003 [Online]. Available: https://www.teco.edu/~krohn/efficient signaling.pdf
View in Google Scholar

[9] A. Wald, Probability and Mathematical Statistics. Wiley, 1947 (ISBN: 9780471918066).
View in Google Scholar

[10] Y. Xin, H. Zhang, and S. Rangarajan, „SSCT: A simple sequential spectrum sensing scheme for cognitive radio", in Proc. IEEE Global Telecommun. Conf. GLOBECOM, Honolulu, USA, 2009, pp. 1-6.
View in Google Scholar

[11] D. V. Djonin, Q. Zhao, and V. Krishnamurthy, „Optimality and complexity of opportunistic spectrum access: a truncated Markov decision process formulation", in Proc. of IEEE Int. Conf. on Commun. ICC, 2007, pp. 5787-5792.
View in Google Scholar

[12] A. Fanous, Y. E. Sagduyu, and A. Ephremides, „Reliable spectrum sensing and opportunistic access in network-coded communications", IEEE J. on Selected Areas in Commun., vol. 32, no. 3, 2013, pp. 400-410.
View in Google Scholar

Downloads

Submitted

2023-05-12

Published

2021-03-30

Issue

Section

ARTICLES FROM THIS ISSUE

How to Cite

[1]
S. Kumari and S. Praveena, “Suboptimal Approach to Distributed Detection in Cognitive Radio Networks”, JTIT, vol. 83, no. 1, pp. 32–36, Mar. 2021, doi: 10.26636/jtit.2021.144720.