@inproceedings{00e686b7f8844d82af496a50deed9b65,
title = "The Power-Oja method for decentralized subspace estimation/tracking",
abstract = "This work proposes a decentralized and adaptive subspace estimation method, called the Power-Oja (P-Oja) method. Existing decentralized subspace tracking algorithms have slow convergence rate or are unable to adapt to time varying statistics. To resolve these issues, the P-Oja method is developed by combining the power method with Oja's learning rule. Our key innovation lies on the design of a modified objective function with enhanced spectral gap property. This allows the P-Oja method to track the principal subspace more quickly with a finite number of samples. Interestingly, the resulting method coincides with the conventional Oja's learning rule in some special cases. To enable decentralized signal processing, we further demonstrate that the proposed method can be implemented by using a gossip algorithm. Our simulation results show that the proposed P-Oja outperforms the conventional Oja's method in terms of estimation accuracy, and the power method in terms of tracking performance. The effect of the communication graph on the tracking performance is also studied.",
keywords = "Massive arrays, gossip algorithm, spectrum sensing, subspace estimation",
author = "Wu, {Sissi Xiaoxiao} and Wai, {Hoi To} and Anna Scaglione and Jacklin, {Neil A.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952812",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3524--3528",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
}