Skip to main content
SpringerLink
Log in

Under-Sampling of PPM-UWB Communication Signals Based on CS and AIC

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

Abstract

Pulse position modulation ultra-wideband (PPM-UWB) is a new technology developed for high-speed wireless communication. Nonetheless, the sampling of PPM-UWB communication signal is limited by its ultra-wide bandwidth. According to the compressed sensing (CS) theory, the original signal can theoretically be under-sampled by a projection matrix with fewer rows than columns. However, the multiplication of matrix and signal demands that the received signal should already be sampled completely. The random matrix cannot be realized using hardware, and the process of random under-sampling is also uncontrollable. To address these problems, we propose a practical under-sampling method for the PPM-UWB communication signal based on CS theory and analog-to-information conversion (AIC) technology. Random matrix is replaced with AIC in the CS measuring projection stage, and the entire structure of AIC can be constructed using hardware. Whether or not the system matrix of AIC can satisfy the restricted isometry property is verified with the Johnson–Lindenstrauss lemma. Finally, a detection platform is constructed for the PPM-UWB communication signal based on CS and AIC, and it is considered for implementation. Although the proposed method cannot guarantee the precise reconstruction of a target vector, it can still be applied to under-sample the PPM-UWB communication signal practically. An analysis of performance results demonstrates the validity and applicability of the proposed method.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. S. Niranjayan, N.C. Beaulieu, Novel adaptive nonlinear receivers for UWB multiple access communications. IEEE Trans. Wirel. Commun. 12(5), 2014–2023 (2013)

    Article  Google Scholar 

  2. P. X. Li, H. W. Chen, M. H. Chen, S. Z. Xie, Beyond 2.5Gb/s photonic generation and wireless transmission of different pulse modulation formats for a high speed impulse radio UWB over fiber system, Optical Fiber Communication and the National Fiber Optic Engineers Conference (OFC/NFOEC), (2011), pp. 1–3

  3. H.L. Xiong, W.S. Zhang, Z.F. Du, B. He, D.F. Yuan, Front-end narrowband interference mitigation for DS-UWB receiver. IEEE Trans. Wirel. Commun. 12(9), 4328–4337 (2013)

    Article  Google Scholar 

  4. P. Kalansuriya, N.C. Karmakar, E. Viterbo, On the detection of frequency-spectra-based chipless RFID using UWB impulsed interrogation. IEEE Trans. Microw. Theory Tech. 60(12), 4187–4197 (2012)

    Article  Google Scholar 

  5. IEEE draft standard for local and metropolitan area networks part 15.4: low rate wireless personal area networks (LR-WPANs) amendment: physical layer (PHY) specifications for low data rate wireless smart metering utility networks, (2011), pp. 1–212

  6. D.L. Donoho, J. Tanner, Exponential bounds implying construction of compressed sensing matrices, error-correcting codes, and neighborly polytopes by random sampling. IEEE Trans. Inf. Theory 56(4), 2002–2016 (2010)

    Article  MathSciNet  Google Scholar 

  7. Y.C. Eldar, Compressed sensing of analog signals in shift-invariant spaces. IEEE Trans. Signal Process. 57(8), 2986–2997 (2009)

    Article  MathSciNet  Google Scholar 

  8. J.L. Paredes, G.R. Arce, Z.M. Wang, Ultra-wideband compressed sensing: channel estimation. IEEE J. Sel. Topics Signal Process. 1(3), 383–395 (2007)

    Article  Google Scholar 

  9. J.A. Tropp, M.B. Wakin, M.F. Duarte, D. Baron, R. Baraniuk, Random filters for compressive sampling and reconstruction. IEEE Int. Conf. Acoust. Speech Signal Process. Toulouse Fr. 3, 872–875 (2006)

    Google Scholar 

  10. J. N. Laska, S. Kirolos, M. F. Duarte, T. Ragheb, R. Baraniuk, Y. Massoud, Theory and implementation of an analog-to-information converter using random demodulation, in IEEE International Symposium on Circuits and Systems, (2007), pp. 1959–1962

  11. W.D. Wang, J.A. Yang, H.B. Yin, S.H. Wang, Reconstruction method for pulse position modulation-ultra wideband communication signal based on compressed sensing. IET Commun. 8(5), 707–713 (2014)

    Article  Google Scholar 

  12. P. Koiran, A. Zouzias, Hidden cliques and the certification of the restricted isometry property. IEEE Trans. Inf. Theory 60(8), 4999–5006 (2014)

    Article  MathSciNet  Google Scholar 

  13. R. Baraniuk, M. Davenport, R. DeVore, M. Wakin, A simple proof of the restricted isometry property for random matrices. Constr. Approx. 28(3), 253–263 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  14. H. Shao, N.C. Beaulieu, An analytical method for calculating the bit error rate performance of rake reception in UWB multipath fading channels. IEEE Trans. Commun. 58(4), 1112–1120 (2010)

    Article  Google Scholar 

  15. N. Michelusi, U. Mitra, A.F. Molisch, M. Zorzi, UWB sparse/diffuse channels, part I: channel models and bayesian estimators. IEEE Trans. Signal Process. 60(10), 5307–5319 (2012)

    Article  MathSciNet  Google Scholar 

  16. A.B. Ramirez, G.R. Arce, D. Otero, J.L. Paredes, B.M. Sadler, Reconstruction of sparse signals from \(\ell _1 \) dimensionality-reduced cauchy random projections. IEEE Trans. Signal Process. 60(11), 5725–5737 (2012)

    Article  MathSciNet  Google Scholar 

  17. S.S. Chen, D.L. Donoho, M.A. Saunders, Atomic decomposition by basis pursuit. Soc. Ind. Appl. Mathe. Rev. 43(1), 129–159 (2001)

    MATH  MathSciNet  Google Scholar 

  18. P.Y. Chen, I.W. Selesnick, Group-sparse signal denoising: non-convex regularization, convex optimization. IEEE Trans. Signal Process. 62(13), 3464–3478 (2014)

    Article  MathSciNet  Google Scholar 

  19. M.A.T. Figueiredo, R.D. Nowak, S.J. Wright, Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J. Sel. Topics Signal Process. 1(4), 586–598 (2007)

    Article  Google Scholar 

  20. E.T. Liu, V.N. Temlyakov, The orthogonal super greedy algorithm and applications in compressed sensing. IEEE Trans. Inf. Theory 58(4), 2040–2047 (2012)

    Article  MathSciNet  Google Scholar 

  21. D.H. Smith, F.H. Hunt, S. Perkins, Exploiting spatial separations in CDMA systems with correlation constrained sets of Hadamard matrices. IEEE Trans. Inf. Theory 56(11), 5757–5761 (2010)

    Article  MathSciNet  Google Scholar 

  22. X.H. Tang, U. Parampalli, On the noncyclic property of Sylvester Hadamard matrices. IEEE Trans. Inf. Theory 56(9), 4653–4658 (2010)

    Article  MathSciNet  Google Scholar 

  23. H. Mamaghanian, N. Khaled, D. Atienza, P. Vandergheynst, Design and exploration of low-power analog to information conversion based on compressed sensing. IEEE J. Emerg. Sel. Topics Circuits Syst. 2(3), 493–501 (2012)

    Article  Google Scholar 

  24. S. Kirolos, J. Laska, M. Wakin, M. Duarte, D. Baron, T. Ragheb, Y. Massoud, R. Baraniuk, Analog-to-information conversion via random demodulation, in Proceedings of the IEEE Dallas Circuits and Systems Workshop, (2006), pp. 71–74

  25. A.S. Bandeira, E. Dobriban, D.G. Mixon, W.F. Sawin, Certifying the restricted isometry property is hard. IEEE Trans. Inf. Theory 59(6), 3448–3450 (2013)

    Article  MathSciNet  Google Scholar 

  26. A. F. Molisch, K. Balakrishnan, D. Cassioli, C. C. Chong, S. Emami, A. Fort, J. Karedal, J. Kunisch, H. Schantz, U. Schuster, K. Siwiak, IEEE 802.15.4a channel model-final report, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.318.1348&rep=rep1&type=pdf, (2004)

  27. T. Blumensath, M.E. Davies, Gradient pursuits. IEEE Trans. Signal Process. 56(6), 2370–2382 (2008)

    Article  MathSciNet  Google Scholar 

  28. G. Adamiuk, T. Zwick, W. Wiesbeck, UWB antennas for communication systems. Proc. IEEE 100(7), 2308–2321 (2012)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Anhui Provincial Natural Science Foundation under Grant Nos. 1308085QF99 and 1208085MF94, the National Science Foundation of China under Grant Nos. 61272333 and 61171170.

Author information

Authors and Affiliations

  1. Electronic Engineering Institute, Key Laboratory of Electronic Restriction Anhui Province, Hefei, China

    Weidong Wang, Jun-an Yang & Hui Liu

  2. Northen Institute of Electric Equipment of China, Beijing, China

    Shafei Wang

Authors
  1. Weidong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shafei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun-an Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weidong Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, W., Wang, S., Yang, Ja. et al. Under-Sampling of PPM-UWB Communication Signals Based on CS and AIC. Circuits Syst Signal Process 34, 3595–3609 (2015). https://doi.org/10.1007/s00034-015-0026-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-015-0026-4

Keywords

Search

Navigation