Abstract
Multirate filter banks can be implemented efficiently using fast-convolution (FC) processing. The main advantage of the FC filter banks (FC-FB) compared with the conventional polyphase implementations is their increased flexibility, that is, the number of channels, their bandwidths, and the center frequencies can be independently selected. In this paper, an approach to optimize the FC-FBs is proposed. First, a subband representation of the FC-FB is derived. Then, the optimization problems are formulated with the aid of the subband model. Finally, these problems are conveniently solved with the aid of a general nonlinear optimization algorithm. Several examples are included to demonstrate the proposed overall design scheme as well as to illustrate the efficiency and the flexibility of the resulting FC-FB.
Similar content being viewed by others
Notes
This contribution extends the work in [21] by generalizing the optimization method for the wider class of problems and by providing detailed descriptions of the overall FC-FB design scheme.
References
Baylon, D.M., & Lim, J.S. (1990). Transform/subband analysis and synthesis of signals. Tech. Rep. 559, Research Laboratory of Electronics, Massachusetts Institute of Technology.
Bogucka, H., Alexander, A.W.M., Pagadarai, S., & Kliks, A. (2011). Spectrally agile multicarrier waveforms for opportunistic wireless access. IEEE Communications Magazine, 49(6), 108–115.
Borgerding, M. (2006). Turning overlap-save into a multiband mixing, downsampling filter bank. IEEE Signal Processing Magazine, 23(2), 158–162.
Boucheret, M.L., Mortensen, I., & Favaro, H. (1999). Fast convolution filter banks for satellite payloads with on-board processing. IEEE Journal on Selected Areas in Communications, 17(2), 238–248.
Coleman, T., Branch, M.A., & Grace, A. (1999). Optimization Toolbox User’s Guide. The MathWorks, Inc. Version 2.
Crochiere, R.E., & Rabiner, L.R. (1983). Multirate digital signal processing. Englewood Cliffs: Prentice-Hall.
Eghbali, A., Johansson, H., & Löwenborg, P. (2011). Reconfigurable nonuniform transmultiplexers using uniform modulated filter banks. IEEE Transactions on Circuits and Systems I, 58(3), 539–547.
Farhang-Boroujeny, B., & Kempter, R. (2008). Multicarrier communication techniques for spectrum sensing and communicationin cognitive radios. IEEE Communications Magazine, 46(4), 80– 85.
Mirabbasi, S., & Martin, K. (2002). Design of prototype filter for near-perfect-reconstruction overlapped complex-modulated transmultiplexers. In Proceedings IEEE International Symposium Circuits Systems (pp. 821–824). Scottsdale, Arizona, USA.
Pucker, L. (2003). Channelization techniques for software defined radio. In Proceedings Software Defined Radio Technical Conference (SDR’03). Orlando, Florida, USA.
Quatieri, T.F. (2001). Discrete-time speech signal processing: Principles and practice: Prentice Hall.
Rabiner, L.R., & Gold, B. (1975). Theory and application of digital signal processing, (pp. 63–67). Englewood Cliffs: Prentice-Hall.
Renfors, M., & Harris, F. (2011). Highly adjustable multirate digital filters based on fast convolution. In Proceedings European Conference Circuit Theory Design (pp. 9–12). Linköping, Sweden.
Renfors, M., Yli-Kaakinen, J., & Harris, F. (2014). Analysis and design of efficient and flexible fast-convolution based multirate filter banks. IEEE Transactions Signal Processing, 62(15), 3768–3783.
Shao, K., Alhava, J., Yli-Kaakinen, J., & Renfors,M. (2015). Fastconvolution implementation of filter bank multicarrier waveform processing. To be presented in: IEEE International Symposium Circuits Systems. Lisbon, Portugal.
Sorensen, H.V., & Burrus, C.S. (1993). Fast DFT and convolution algorithms. InMitra, S.K., & Kaiser, J.F. (Eds.) Handbook for digital signal processing (pp. 491–610). New York: John Wiley and Sons, Inc.
Sorensen, H.V., Heideman, M.T., & Burrus, C.S. (1986). On computing the split-radix FFT. IEEE Transactions on Acoustics Speech Signal Processing, 34(1), 152–156.
Viholainen, A., Alhava, J., & Renfors, M. Efficient implementation of complex modulated filter banks using cosine and sine modulated filter banks. EURASIP Journal on Applied Signal Processing. doi: 10.1155/ASP/2006/58564
Viholainen, A., Ihalainen, T., Stitz, T.H., Renfors, M., & Bellanger, M.G. (2009). Prototype filter design for filter bank based multicarrier transmission. In Proceedings European Signal Processing Conference (EUSIPCO) (pp. 1359–1363). Glasgow, Scotland.
Yli-Kaakinen, J., & Renfors, M. (2013). Fast-convolution filter bank approach for non-contiguous spectrum use. In Proceedings Future Network and Mobile Summit. Lisbon, Portugal.
Yli-Kaakinen, J., & Renfors, M. (2014). Optimization of flexible filter banks based on fast-convolution. In Proceedings International Conference Acoustics, Speech, Signal Process (pp. 8342–8345). Florence, Italy.
Zhang, C., & Wang, Z. (2000). A fast frequency domain filter bank realization algorithm. In Proceedings International Conference Signal Processing (Vol. 1 pp. 130–132). Beijing, China.
Acknowledgments
The authors acknowledge the financial support by the European Union FP7-ICT project EMPhAtiC (http://www.ict-emphatic.eu) under grant agreement no. 318362.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yli-Kaakinen, J., Renfors, M. Optimization of Flexible Filter Banks Based on Fast Convolution. J Sign Process Syst 85, 101–111 (2016). https://doi.org/10.1007/s11265-015-1004-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11265-015-1004-6