Abstract
In this study, we investigate a dynamic wireless sensor network based on single-input multiple-output (SIMO) channels and attempt to estimate the transmitted signal blindly. The distributed blind equalization over networks follows the rule of combination among neighboring sensor nodes and it becomes more difficult for the different channel characteristics. In a dynamic context, the degree of noisy channel outputs, on the other hand, has a significant impact on performance. Eigenvalue-spread (EVS) based rule of combination is investigated in which the weights are inferred via surrounding channel outputs rather than just the node degrees. We consider a dynamic wireless sensor network to validate the robustness of the EVS based rule of combination. Through mean square error (MSE) performance, the simulation result demonstrates the robustness of the EVS-based rule of combination in comparison to conventional rule of combination.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pottie, G., Kaiser, W.J.: Wireless integrated network sensors. Commun. ACM 43(5), 51–58 (2000)
Estrin, D., Girod, L., Pottie, G., Srivastava, M.: Intrumenting the world with wireless sensor networks, In: Proc. IEEE International Conference Acoustitcs, Speech, Signal Process. (ICASSP), pp. 2033–2036, May (2001)
Yu, C., Xie, L.: On recursive blind equalization in sensor networks. In: IEEE Trans. on Signal Process. 63(3), 662–672 (2015)
Liu, Y., Cai, Y.: Distributed blind equalization in networked systems. In: Proceedings of the IEEE International Conference Acoustics, Speech, Signal Process, (ICASSP), pp. 4296–4300 (2017)
Abed-Meraim, K., Qiu, W., Hua, Y.: Blind system identification. Proc. IEEE 85(8), 1310–1322 (1997)
Sayed, A.H.: Diffusion adaptation over networks. In: Academic Press Library in Signal Process., Elsevier, vol. 3, pp. 323–453 (2014)
Parvin, M.N., Shimamura, T.: New rule of combination over Wireless Sensor Networks. In: IEEE 2nd International Conference on Information Communication and Signal Process, (ICICSP) (2019)
Parvin, M.N., Shimamura, T.: Eigenvalue-spread-based rule of combination for distributed blind equalization in networked system. J. Signal Process. 23(6), 243–256 (2019)
Liu, Y., Li, L.: Distributed blind estimation over sensor networks. IEEE Access 5, 18343–18355 (2017)
Haykin, S.: Adaptive Filter Theory Prentice-Hall (1996)
Makhoul, J.: Linear prediction: a tutorial review. Proc. IEEE 63(4), 561–580 (1975)
Welch, P.D.: The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. In: IEEE Trans. Audio and Electroacoust., vol. AU-15, no. 2, pp. 70–73 (1967)
Itoh, K., Shimamura, T., Suzuki, J.: Prefiltering for blind equalization. Electron. Commun. Jpn., part 3, 78(9) 1–11 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Parvin, M.N., Rahman, M.S., Ahmed, M.T., Rahman, M. (2023). Robustness of Eigenvalue-Spread Based Rule of Combination in Dynamic Networked System with Link Failures. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 491. Springer, Cham. https://doi.org/10.1007/978-3-031-34622-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-34622-4_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34621-7
Online ISBN: 978-3-031-34622-4
eBook Packages: Computer ScienceComputer Science (R0)