Abstract
Diffusion adaptive networks have 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 named as relative-deviation combination policy, which uses the Euclidean norm of instantaneous deviation between 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.
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References
Tu, S.Y., Sayed, A.H.: Mobile adaptive networks. IEEE J. Sel. Top. Signal Process. 5(4), 649–664 (2011)
Cattivelli, F.S., Sayed, A.H.: Modeling bird flight formations using diffusion adaptation. IEEE Trans. Signal Process. 59(5), 2038–2051 (2011)
Chen, L.J., Ho, Y.H., Lee, H.C., Wu, H.C., Liu, H.M.: An open framework for participatory PM2.5 monitoring in smart cities. IEEE Access 5, 14441–14454 (2017)
Nedic, A., Ozdaglar, A.: Distributed subgradient methods for multi-agent optimization. IEEE Trans. Automat. Contr. 54(1), 48–61 (2009)
Kar, S., Moura, J.M.F.: Distributed consensus algorithms in sensor networks with imperfect communication: link failures and channel noise. IEEE Trans. Signal Process. 57(1), 355–369 (2009)
Srivastava, K., Nedic, A.: Distributed asynchronous constrained stochastic optimization. IEEE J. Sel. Top. Signal Process. 5(4), 772–790 (2011)
Rabbat, M.G., Nowak, R.D.: Quantized incremental algorithms for distributed optimization. IEEE J. Sel. Areas Commun. 23(4), 798–808 (2005)
Lopes, C.G., Sayed, A.H.: Incremental adaptive strategies over distributed networks. IEEE Trans. Signal Process. 55(8), 4064–4077 (2007)
Chen, J., Richard, C., Hero, A.O., Sayed, A.H.: Diffusion LMS for multitask problems with overlapping hypothesis subspaces. In: IEEE International Workshop on Machine Learning for Signal Processing, pp. 1–6 (2014)
Sayed, A.H.: Adaptive networks. Proc. IEEE 102(4), 460–497 (2014)
Sayed, A.H., Tu, S.Y., 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. 30(May), 155–171 (2013)
Sayed, A.H.: Diffusion adaptation over networks. E-References Signal Process. 61, 1419–1433 (2013)
Chen, J., Sayed, A.H.: Diffusion adaptation strategies for distributed optimization and learning over networks. IEEE Trans. Signal Process. 60(8), 4289–4305 (2012)
Sayed, A.H.: Adaptation, learning, and optimization over networks. Found. Trends Mach. Learn. 7(4–5), 1–501 (2014)
Tu, S., Member, S., Sayed, A.H.: Diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks. IEEE Trans. Signal Process. 60(12), 6217–6234 (2012)
Blondel, V.D., Hendrickx, J.M., Olshevsky, A., Tsitsiklis, J.N.: Convergence in multiagent coordination, consensus, and flocking. In: Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, pp. 2996–3000 (2005)
Xiao,L., Boyd, S.: Fast linear iterations for distributed averaging. In: Pmedingr of the 42nd LEEK Conference on UecMon and Conlrd, pp. 65–78 (2003)
Scherber, D.S., Papadopoulos, H.C.: Locally constructed algorithms for distributed computations in ad-hoc networks. In: Information Processing in Sensor Networks (IPSN), pp. 11–19 (2004)
Xiao, L., Boyd, S., Lall, S.: A scheme for robust distributed sensor fusion based on average consensus. In: Information Processing in Sensor Networks, pp. 63–70 (2005)
Cattivelli, F.S., Lopes, C.G., Sayed, A.H.: Diffusion recursive least-squares for distributed estimation over adaptive networks. IEEE Trans. Signal Process. 56(5), 1865–1877 (2008)
Tu, S., Sayed, A.H.: Optimal combination rules for adaptation and learning over networks. In: IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 317–320 (2011)
Cattivelli, F.S., Sayed, A.H.: Diffusion LMS strategies for distributed estimation. IEEE Trans. Signal Process. 58(3), 1035–1048 (2010)
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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”).
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Fan, Q. et al. (2019). 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 515. Springer, Singapore. https://doi.org/10.1007/978-981-13-6264-4_51
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DOI: https://doi.org/10.1007/978-981-13-6264-4_51
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