Abstract
In this paper, we study the influence of noisy links on the effectiveness of cooperation in incremental LMS adaptive network (ILMS). The analysis reveals the important fact that under noisy communication, cooperation among nodes may not necessarily result in better performance. More precisely, we first define the concept of cooperation gain and compute it for the ILMS algorithm with ideal and noisy links. We show that the ILMS algorithm with ideal links outperforms the non-cooperative scheme for all values of step-size (cooperation gain is always bigger than 1). On the other hand, in the presence of noisy links, cooperation gain is not always bigger than 1 and based on the channel and data statistics, for some values of step-size, non-cooperative scheme outperforms the ILMS algorithm. We presented simulation results to clarify the discussions.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Lopes, C.G., Sayed, A.H.: Incremental adaptive strategies over distributed networks. IEEE Transactions on Signal Processing 55(8), 4064–4077 (2007)
Sayed, A.H., Lopes, C.G.: Distributed recursive least-squares strategies over adaptive networks. In: Proc. Asilomar Conference on Signals, Systems and Computers, pp. 233–237 (2006)
Li, L., Chambers, J.A., Lopes, C.G., Sayed, A.H.: Distributed estimation over an adaptive incremental network based on the affine projection algorithm. IEEE Transactions on Signal Processing 58(1), 151–164 (2010)
Ram, S.S., Nedic, A., Veeravalli, V.V.: Stochastic incremental gradient descent for estimation in sensor networks. In: Proc. Asilomar Conference on Signals, Systems and Computers, pp. 582–586 (2007)
Lopes, C.G., Sayed, A.H.: Randomized incremental protocols over adaptive networks. In: Proc. IEEE ICASSP, Dallas, TX, pp. 3514–3517 (2010)
Cattivelli, F., Sayed, A.H.: Analysis of spatial and incremental LMS processing for distributed estimation. IEEE Transactions on Signal Processing 59(4), 1465–1480 (2011)
Lopes, C.G., Sayed, A.H.: Diffusion least-mean squares over adaptive networks: Formulation and performance analysis. IEEE Trans. on Signal Processing 56(7), 3122–3136 (2008)
Cattivelli, F.S., Lopes, C.G., Sayed, A.H.: Diffusion recursive least-squares for distributed estimation over adaptive networks. IEEE Trans. on Signal Processing 56(5), 1865–1877 (2008)
Cattivelli, F.S., Sayed, A.H.: Diffusion LMS strategies for distributed estimation. IEEE Transactions on Signal Processing 58(3), 1035–1048 (2010)
Stankovic, S.S., Stankovic, M.S., Stipanovic, D.M.: Decentralized parameter estimation by consensus based stochastic approximation. In: Proc. IEEE Conference on Decision and Control, New Orleans, pp. 1535–1540 (2007)
Shin, Y.-J., Sayed, A.H., Shen, X.: Adaptive models for gene networks. PLoS ONEÂ 7(2), e31657 (2012), doi:10.1371/journal.pone.0031657
Cattivelli, F.S., Sayed, A.H.: Multilevel diffusion adaptive networks. In: Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), Taipei, Taiwan (2009)
Schizas, I.D., Mateos, G., Giannakis, G.B.: Distributed LMS for consensus-based in-network adaptive processing. IEEE Transactions on Signal Processing 57(6), 2365–2382 (2009)
Khalili, A., Tinati, M.A., Rastegarnia, A.: Performance analysis of distributed incremental LMS algorithm with noisy links. Inter. Journal of distributed sensor networks 2011, 1–10 (2011)
Khalili, A., Tinati, M.A., Rastegarnia, A.: Steady-state analysis of incremental LMS adaptive networks with noisy links. IEEE Trans. Signal Processing 59(5), 2416–2421 (2012)
Khalili, A., Tinati, M.A., Rastegarnia, A.: Analysis of incremental RLS adaptive networks with noisy links. IEICE Electron. Express 8(9), 623–628 (2011)
Khalili, A., Tinati, M.A., Rastegarnia, A., Chambers, J.A.: Steady-state analysis of diffusion LMS adaptive networks with noisy links. IEEE Trans. Signal Processing 60(2), 974–979 (2012)
Khalili, A., Tinati, M.A., Rastegarnia, A., Chambers, J.A.: Transient analysis of diffusion least-mean squares adaptive networks with noisy channels. Wiley Int. Journal of Adaptive Control and Signal Processing 26(2), 171–180 (2012)
Sayed, A.H.: Fundamentals of Adaptive Filtering. John Wiley and Sons, New York (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Khalili, A., Bazzi, W.M., Rastegarnia, A. (2014). Cooperation Gain in Incremental LMS Adaptive Networks with Noisy Links. In: Das, V.V., Elkafrawy, P. (eds) Signal Processing and Information Technology. SPIT 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-319-11629-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-11629-7_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11628-0
Online ISBN: 978-3-319-11629-7
eBook Packages: Computer ScienceComputer Science (R0)