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Low Cost Sparse Multiband Signal Characterization Using Asynchronous Multi-Rate Sampling: Algorithms and Hardware

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Abstract

Characterizing the spectrum of sparse wideband signals of high-speed devices efficiently and precisely is critical in high-speed test instrumentation design. Recently proposed sub-Nyquist rate sampling systems have the potential to significantly reduce the cost and complexity of sparse spectrum characterization; however, due to imperfections and variations in hardware design, numerous implementation and calibration issues have risen and need to be solved for robust and stable signal acquisition. In this paper, we propose a low-cost and low-complexity hardware architecture and associated asynchronous multi-rate sub-Nyquist rate sampling based algorithms for sparse spectrum characterization. The proposed scheme can be implemented with a single ADC or with multiple ADCs as in multi-channel or band-interleaved sensing architectures. Compared to other sub-Nyquist rate sampling methods, the proposed hardware scheme can achieve wideband sparse spectrum characterization with minimum cost and calibration effort. A hardware prototype built using off-the-shelf components is used to demonstrate the feasibility of the proposed approach.

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References

  1. Babu DVS, Reddy PC (2012) Advancements of multi-rate signal processing for wireless communication networks: current state of the art. Glob J Comput Sci Technol 12(13-E).

  2. Bresler Y, Ping F (1996) Spectrum-blind minimum-rate sampling and reconstruction of 2-D multiband signals. Proc IEEE Int Conf Image Process 1:701–704

    Article  Google Scholar 

  3. Candès EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489–509

    Article  MATH  Google Scholar 

  4. Domınguez-Jiménez ME, González-Prelcic N (2012) Analysis and design of multi-rate synchronous sampling schemes for sparse multiband signals.

  5. Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306

    Article  MATH  MathSciNet  Google Scholar 

  6. Elbornsson J, Fredrik G, Eklund J-E (2005) Blind equalization of time errors in a time-interleaved ADC system. IEEE Trans Signal Process 53(4):1413–1424

    Article  MathSciNet  Google Scholar 

  7. Eldar YC, Oppenheim AV (2000) Filterbank reconstruction of bandlimited signals from nonuniform and generalized samples. IEEE Trans Signal Process 48(10):2864–2875

    Article  MathSciNet  Google Scholar 

  8. Feldster A, Shapira YP, Horowitz M, Rosenthal A, Zach S, Singer L (2009) Optical under-sampling and reconstruction of several bandwidth-limited signals. J Lightwave Technol 27(8):1027–1033

    Article  Google Scholar 

  9. Fleyer M, Horowitz M, Feldtser A, Smulakovsky V (2010) Multi-rate synchronous optical undersampling of several bandwidth-limited signals. Opt Express 18(16):16929–16945

    Article  Google Scholar 

  10. Fleyer M, Linden A, Horowitz M, Rosenthal A (2010) Multi-rate synchronous sampling of sparse multiband signals. IEEE Trans Signal Process 58(3):1144–1156

    Article  MathSciNet  Google Scholar 

  11. Johansson H, Lowenborg P (2002) Reconstruction of nonuniformly sampled bandlimited signals by means of digital fractional delay filters. IEEE Trans Signal Process 50(11):2757–2767

    Article  Google Scholar 

  12. Kirolos S et al. (2006) Analog-to-information conversion via random demodulation. 2006 I.E. Dallas/CAS Works Des Appl Integ Softw, IEEE.

  13. Laska J, Kirolos S, Massoud Y, Baraniuk R, Gilbert A, Iwen M, Strauss M (2006) Random sampling for analog-to-information conversion of wideband signals. 2006 I.E. Dallas/CAS Works Des Appl Integ Softw. IEEE. (pp. 119-122).

  14. Laska JN et al. (2007) Theory and implementation of an analog-to-information converter using random demodulation. 2007 I.E. Int Symp Circ Syst. 2007 I.E. ISCAS.

  15. Lu YM, Minh ND (2008) A theory for sampling signals from a union of subspaces. IEEE Trans Signal Process 56(6):2334–2345

    Article  MathSciNet  Google Scholar 

  16. Mishali M, Eldar YC (2009) Blind multiband signal reconstruction: compressed sensing for analog signals. IEEE Trans Signal Process 57(3):993–1009

    Article  MathSciNet  Google Scholar 

  17. Mishali M, Eldar YC (2010) From theory to practice: sub-Nyquist sampling of sparse wideband analog signals. IEEE J Sel Top Sign Process 4(2):375–391

    Article  Google Scholar 

  18. Mishali M, Eldar YC, Dounaevsky O, Shoshan E (2011) Xampling: analog to digital at sub-Nyquist rates. IET Circ Devices Syst 5(1):8–20

    Article  Google Scholar 

  19. Pfetsch S et al. (2008) On the feasibility of hardware implementation of sub-nyquist random-sampling based analog-to-information conversion. 2008 I.E. Int Symp Circ Syst. 2008 I.E. ISCAS.

  20. Ragheb T et al. (2008) A prototype hardware for random demodulation based compressive analog-to-digital conversion. 2008 I.E. 51st Midwest Symp Circ Syst. 2008 MWSCAS.

  21. Rosenthal A, Linden A, Horowitz M (2008) Multi-rate asynchronous sampling of sparse multiband signals. JOSA A 25(9):2320–2330

    Article  Google Scholar 

  22. Sun H, Nallanathan A, Jiang J, Wang CX (2013) Multi-rate sub-Nyquist spectrum sensing in cognitive radios. arXiv preprint arXiv:1302.1489.

  23. Tian Z, Giannakis GB (2007) Compressed sensing for wideband cognitive radios. IEEE Int Conf Acoust Speech Signal Process. 2007 I.E. ICASSP 4.

  24. Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655–4666

    Article  MATH  MathSciNet  Google Scholar 

  25. Tzou N et al. (2012) Low-cost wideband periodic signal reconstruction using incoherent undersampling and back-end cost optimization. 2012 I.E. International Test Conference (ITC), IEEE.

  26. Venkataramani R, Bresler Y (2001) Optimal sub-Nyquist nonuniform sampling and reconstruction for multiband signals. IEEE Trans Signal Process 49(10):2301–2313

    Article  Google Scholar 

  27. Wakin M, Becker S, Nakamura E, Grant M, Sovero E, Ching D, Yoo J, Romberg J, Emami-Neyestanak A, Candes E A (2012)A nonuniform sampler for wideband spectrally-sparse environments. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 2(3):516–529

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Acknowledgments

The authors would like to thank Hittite Microwave for supporting this research by supplying components for the hardware prototype.

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Correspondence to Nicholas Tzou.

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Responsible Editor: S. Sunter

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Tzou, N., Bhatta, D., Muldrey, B. et al. Low Cost Sparse Multiband Signal Characterization Using Asynchronous Multi-Rate Sampling: Algorithms and Hardware. J Electron Test 31, 85–98 (2015). https://doi.org/10.1007/s10836-015-5505-9

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  • DOI: https://doi.org/10.1007/s10836-015-5505-9

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