Skip to main content
Log in

Distributed Conjugate Gradient Algorithm for Signal Reconstruction of MOSGFBs

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

In this paper, we investigate the signal reconstruction of M-channel oversampled graph filter banks (MOSGFBs) on sparse graphs. The analysis filter bank of MOSGFBs can be in polynomial or node-variant (NV) form. Given the analysis filter bank and sampling matrix, the synthesis procedure can be formulated into a quadratic optimization problem. To solve this problem, a novel distributed conjugate gradient (DCG) algorithm is proposed, in which the step size and increment involved in the conjugate gradient iteration are evaluated in a distributed manner. The local step sizes and increments are first calculated using data from neighboring nodes, and then patched to participate in the iteration. The DCG algorithm involves the exchange of information between neighboring nodes and executes the synthesis computation locally; therefore, the synthesis procedure has a low implementation complexity and is suitable for processing large-scale datasets. Numerical examples conducted on synthetic and real-world datasets demonstrate the effectiveness of the proposed algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Algorithm 1
Algorithm 2

Similar content being viewed by others

Availability of Data and Materials

The data used to support the findings of this study are available in [2, 9, 12, 13, 15, 16, 20].

References

  1. M. Crovella, E. Kolaczyk, Graph wavelets for spatial traffic analysis, in IEEE INFOCOM 2003. 22nd Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428) (2003), pp. 1848–1857. https://doi.org/10.1109/INFCOM.2003.1209207

  2. H. Feng, J. Jiang, H. Wang, F. Zhou, Design of time-vertex node-variant graph filters. Circuits Syst. Signal Process. 40(4), 2036–2049 (2021). https://doi.org/10.1007/S00034-020-01548-X

    Article  Google Scholar 

  3. K. Guo, Y. Hu, Z. Qian, H. Liu, K. Zhang, Y. Sun, J. Gao, B. Yin, Optimized graph convolution recurrent neural network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 22(2), 1138–1149 (2021). https://doi.org/10.1109/TITS.2019.2963722

    Article  Google Scholar 

  4. D.K. Hammond, P. Vandergheynst, R. Gribonval, Wavelets on graphs via spectral graph theory. Appl. Comput. Harmon. Anal. 30(2), 129–150 (2011). https://doi.org/10.1016/j.acha.2010.04.005

    Article  MathSciNet  Google Scholar 

  5. I. Jabłoński, Graph signal processing in applications to sensor networks, smart grids, and smart cities. IEEE Sens. J. 17(23), 7659–7666 (2017). https://doi.org/10.1109/JSEN.2017.2733767

    Article  ADS  Google Scholar 

  6. J. Jiang, C. Cheng, Q. Sun, Nonsubsampled graph filter banks: theory and distributed algorithms. IEEE Trans. Signal Process. 67(15), 3938–3953 (2019). https://doi.org/10.1109/TSP.2019.2922160

    Article  ADS  MathSciNet  Google Scholar 

  7. J. Jiang, H. Feng, D.B. Tay, S. Xu, Theory and design of joint time-vertex nonsubsampled filter banks. IEEE Trans. Signal Process. 69, 1968–1982 (2021). https://doi.org/10.1109/TSP.2021.3064984

    Article  ADS  MathSciNet  Google Scholar 

  8. J. Jiang, D.B. Tay, Decentralised signal processing on graphs via matrix inverse approximation. Signal Process. 165, 292–302 (2019). https://doi.org/10.1016/j.sigpro.2019.07.010

    Article  Google Scholar 

  9. J. Jiang, D.B. Tay, Q. Sun, S. Ouyang, Recovery of time-varying graph signals via distributed algorithms on regularized problems. IEEE Trans. Signal Inf. Process. Over Netw. 6, 540–555 (2020). https://doi.org/10.1109/TSIPN.2020.3010613

    Article  MathSciNet  Google Scholar 

  10. S. Li, Y. Jin, D.I. Shuman, Scalable \(m\)-channel critically sampled filter banks for graph signals. IEEE Trans. Signal Process. 67(15), 3954–3969 (2019). https://doi.org/10.1109/TSP.2019.2923142

    Article  ADS  MathSciNet  Google Scholar 

  11. M. Ma, S. Xu, J. Jiang, A distributed gradient descent method for node localization on large-scale wireless sensor network. IEEE J. Miniatur. Air Space Syst. 4(2), 114–121 (2023). https://doi.org/10.1109/JMASS.2023.3236765

    Article  Google Scholar 

  12. S.K. Narang, A. Ortega, Perfect reconstruction two-channel wavelet filter banks for graph structured data. IEEE Trans. Signal Process. 60(6), 2786–2799 (2012). https://doi.org/10.1109/TSP.2012.2188718

    Article  ADS  MathSciNet  Google Scholar 

  13. S.K. Narang, A. Ortega, Compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs. IEEE Trans. Signal Process. 61(19), 4673–4685 (2013). https://doi.org/10.1109/TSP.2013.2273197

    Article  ADS  MathSciNet  Google Scholar 

  14. A. Ortega, P. Frossard, J. Kovačević, J.M.F. Moura, P. Vandergheynst, Graph signal processing: overview, challenges, and applications. Proc. IEEE 106(5), 808–828 (2018). https://doi.org/10.1109/JPROC.2018.2820126

    Article  Google Scholar 

  15. N. Perraudin, J. Paratte, D. Shuman, L. Martin, V. Kalofolias, P. Vandergheynst, D.K. Hammond, GSPBOX: a toolbox for signal processing on graphs. arXiv e-prints (2014). arXiv:1408.5781

  16. K. Qiu, X. Mao, X. Shen, X. Wang, T. Li, Y. Gu, Time-varying graph signal reconstruction. IEEE J. Sel. Top. Signal Process. 11(6), 870–883 (2017). https://doi.org/10.1109/JSTSP.2017.2726969

    Article  ADS  Google Scholar 

  17. A. Sandryhaila, J.M.F. Moura, Discrete signal processing on graphs. IEEE Trans. Signal Process. 61(7), 1644–1656 (2013). https://doi.org/10.1109/TSP.2013.2238935

    Article  ADS  MathSciNet  Google Scholar 

  18. A. Sandryhaila, J.M.F. Moura, Big data analysis with signal processing on graphs: representation and processing of massive data sets with irregular structure. IEEE Signal Process. Mag. 31(5), 80–90 (2014). https://doi.org/10.1109/MSP.2014.2329213

    Article  ADS  Google Scholar 

  19. A. Sandryhaila, J.M.F. Moura, Discrete signal processing on graphs: frequency analysis. IEEE Trans. Signal Process. 62(12), 3042–3054 (2014). https://doi.org/10.1109/TSP.2014.2321121

    Article  MathSciNet  Google Scholar 

  20. Sea-level pressure (1948–2010) (2016). http://research.jisao.washington.edu/datasets/reanalysis

  21. S. Segarra, A.G. Marques, A. Ribeiro, Optimal graph-filter design and applications to distributed linear network operators. IEEE Trans. Signal Process. 65(15), 4117–4131 (2017). https://doi.org/10.1109/TSP.2017.2703660

    Article  ADS  MathSciNet  Google Scholar 

  22. Y. Tanaka, A. Sakiyama, \(M\)-channel oversampled graph filter banks. IEEE Trans. Signal Process. 62(14), 3578–3590 (2014). https://doi.org/10.1109/TSP.2014.2328983

    Article  MathSciNet  Google Scholar 

  23. D.B. Tay, J. Jiang, Time-varying graph signal denoising via median filters. IEEE Trans. Circuits Syst. II Express Br. 68(3), 1053–1057 (2021). https://doi.org/10.1109/TCSII.2020.3017800

    Article  Google Scholar 

  24. D.B.H. Tay, Y. Tanaka, A. Sakiyama, Near orthogonal oversampled graph filter banks. IEEE Signal Process. Lett. 23(2), 277–281 (2016). https://doi.org/10.1109/LSP.2016.2514490

    Article  Google Scholar 

  25. W. Wang, K. Ramchandran, Random multiresolution representations for arbitrary sensor network graphs, in 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings (ICASSP), p. IV (2006). https://doi.org/10.1109/ICASSP.2006.1660930

  26. F. Zhou, J. Jiang, P. Shui, Optimization design of m-channel oversampled graph filter banks via successive convex approximation. Circuits Syst. Signal Process. 38(10), 1–12 (2019). https://doi.org/10.1007/s00034-019-01086-1

    Article  CAS  Google Scholar 

Download references

Funding

This work is supported in part by the National Natural Science Foundation of China (Grant No. 62171146), by the Guangxi Natural Science Foundation for Distinguished Young Scholar (Grant No. 2021GXNSFFA220004), by the Guangxi special fund project for innovation-driven development (Grant No. GuikeAA21077008) and by the Innovation Project of GUET Graduate Education (Grant No. 2022YCXS039).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mou Ma.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Ma, M. & Jiang, J. Distributed Conjugate Gradient Algorithm for Signal Reconstruction of MOSGFBs. Circuits Syst Signal Process 43, 1823–1838 (2024). https://doi.org/10.1007/s00034-023-02541-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-023-02541-w

Keywords

Navigation