On the fundamental limit to the use of cognitive radio in underwater acoustic sensor networks
- 20 Downloads
Cognitive communication is an effective solution to the spectrum scarcity issues in wireless networks. The underwater sensor networks are prone to large propagation delays which result in the fundamental limitation on introducing cognitive aspects in underwater scenario. This letter explores the fundamental limitation of using cognitive communication in large propagation delay underwater networks. This work proposes a method to find the optimal position of the secondary user, to minimize the interference to primary users, in an underwater cognitive acoustic network. The proposed method also considers the effect of channel randomness which is modeled using the log-normal shadowing model. The method can also be used to select and schedule the secondary user transmissions, from a set of secondary users, such that the interruption time to the primary users is minimized.
KeywordsUnderwater cognitive acoustic networks Secondary user placement Large propagation delay Multiple primary user scenario Log-normal shadowing
Compliance with ethical standards
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
- 4.Jin, L., Huang, D. D., Zou, L., & Zhang, A. Y. J. (2012). Cognitive acoustics: A way to extend the lifetime of underwater acoustic sensor networks. Cognitive Communications: Distributed Artificial Intelligence (DAI), Regulatory Policy and Economics, Implementation, 39(5), 106–112.Google Scholar
- 9.Yang, W. B., & Yang, T. C. (2006). Characterization and modeling of underwater acoustic communications channels for frequency-shift-keying signals. In OCEANS 2006 (pp. 1–6).Google Scholar
- 10.Christhu, R. M., & Sukumaran, R. (2016). Stochastic network calculus for various fading techniques in underwater wireless communication. International Journal of Applied Mathematics and Statistics, 54(3), 100–112.Google Scholar
- 12.Fredricks, A., Colosi, J. A., Lynch, J. F., Gawarkiewicz, G., Chiu, C. S., & Abbot, P. (2005). Analysis of multipath scintillations from long range acoustic transmissions on the New England continental slope and shelf. The Journal of the Acoustical Society of America, 117(3), 1038–1057.CrossRefGoogle Scholar
- 13.Stojanovic, M. (2006). Underwater wireless communications: Current achievements and research challenges. IEEE Oceanic Engineering Society Newsletter, 41(2), 1–5.Google Scholar
- 14.Tomasi, B., Casari, P., Badia, L., & Zorzi, M. (2010). A study of incremental redundancy hybrid ARQ over Markov channel models derived from experimental data. In Proceedings of the fifth ACM international workshop on underwater networks (p. 4). London: ACM.Google Scholar
- 16.Rappaport, T. S. (1996). Wireless communications: Principles and practice (Vol. 2). New Jersey: Prentice Hall PTR.Google Scholar
- 18.Dennis, J. E., & Woods, D. J. (1987). Optimization on microcomputers: The Nelder–Mead simplex algorithm. New Computing Environments: Microcomputers in Large-Scale Computing, 11, 6–122.Google Scholar
- 19.Xie, P., Cui, J. H., & Lao, L. (2006). VBF: Vector-based forwarding protocol for underwater sensor networks. In International conference on research in networking (pp. 1216–1221). Berlin: Springer.Google Scholar