Abstract
Diffusion adaptive networks are received attractive applications in various fields such as wireless communications. Selections of combination policies greatly influence the performance of diffusion adaptive networks. Many diffusion combination policies have been developed for the diffusion adaptive networks. However, these methods are focused either on steady-state mean square performance or on convergence speed. This paper proposes an effective combination policy, which is named as relative-deviation combination policy and uses the Euclidean norm of instantaneous deviation between the intermediate estimation vector of alone agent and the fused estimation weight to determine the combination weights of each neighbor. Computer simulations verify that the proposed combination policy outperforms the existing combination rules either in steady-state error or in convergence rate under various signal-to-noise ratio (SNR) environments.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Tu SY, Sayed AH. Mobile adaptive networks. IEEE J Sel Top Signal Process. 2011;5(4):649–64.
Cattivelli FS, Sayed AH. Modeling bird flight formations using diffusion adaptation. IEEE Trans Signal Process. 2011;59(5):2038–51.
Chen L, IEEE MS, Ho Y, Lee H, Wu H, Liu H. An open framework for participatory PM2. 5 monitoring in smart cities. IEEE Access. 2017;5:14441–54.
Nedic A, Ozdaglar A. Distributed subgradient methods for multi-agent optimization. IEEE Trans Automat Contr. 2009;54(1):48–61.
Kar S, Moura JMF. Distributed consensus algorithms in sensor networks with imperfect communication: link failures and channel noise. IEEE Trans Signal Process. 2009;57(1):355–69.
Srivastava K, Nedic A. Distributed asynchronous constrained stochastic optimization. IEEE J Sel Top Signal Process. 2011;5(4):772–90.
Rabbat MG, Nowak RD. Quantized incremental algorithms for distributed optimization. IEEE J Sel Areas Commun. 2005;23(4):798–808.
Lopes CG, Sayed AH. Incremental adaptive strategies over distributed networks. IEEE Trans Signal Process. 2007;55(8):4064–77.
Chen J, Richard C, Hero AO, Sayed AH. Diffusion LMS for multitask problems with overlapping hypothesis subspaces. In: IEEE international workshop on machine learning for signal processing; 2014. p. 1–6.
Sayed AH. Adaptive networks. Proc IEEE. 2014;102(4):460–97.
Sayed AH, Tu SY, Chen J, Zhao X, Towfic Z. Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior. IEEE Signal Process Mag. 2013;30:155–71.
Sayed AH. Diffusion adaptation over networks. E-Ref Sig Process. 2013;61:1419–33.
Chen J, Sayed AH. Diffusion adaptation strategies for distributed optimization and learning over networks. IEEE Trans Signal Process. 2012;60(8):4289–305.
Sayed AH. Adaptation, learning, and optimization over networks. Found Trends Mach Learn. 2014;7(4–5):1–501.
Tu S, Member S, Sayed AH. Diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks. IEEE Trans Signal Process. 2012;60(12):6217–34.
Blondel VD, Hendrickx JM, Olshevsky A, Tsitsiklis JN. Convergence in multiagent coordination, consensus, and flocking. In: Proceedings of the 44th IEEE conference on decision and control, and the European control conference; 2005. p. 2996–3000.
Xiao L, Boyd S. Fast linear iterations for distributed averaging. In: Pmedingr of the 42nd LEEK conference on UecMon and Conlrd; 2003. p. 65–78.
Scherber DS, Papadopoulos HC. Locally constructed algorithms for distributed computations in Ad-Hoc networks. In: Information processing in sensor networks (IPSN); 2004. p. 11–9.
Xiao L, Boyd S, Lall S. A scheme for robust distributed sensor fusion based on average consensus. In: Information processing in sensor networks; 2005. p. 63–70.
Cattivelli FS, Lopes CG, Sayed AH. Diffusion recursive least-squares for distributed estimation over adaptive networks. IEEE Trans Signal Process. 2008;56(5):1865–77.
Tu S, A. Sayed AH. Optimal combination rules for adaptation and learning over networks. In: IEEE international workshop on computational advances in multi-sensor adaptive processing (CAMSAP); 2011. p. 317–20.
Acknowledgements
This research was funded by State Grid Corporation Science and Technology Project (named “research on intelligent preprocessing and visual perception for transmission and transformation equipment”).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Fan, Q. et al. (2020). Novel Combination Policy for Diffusion Adaptive Networks. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_51
Download citation
DOI: https://doi.org/10.1007/978-981-13-6504-1_51
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6503-4
Online ISBN: 978-981-13-6504-1
eBook Packages: EngineeringEngineering (R0)