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International Journal of Latest Research in Science and Technology

DOI:10.29111/ijlrst   ISRA Impact Factor:3.35,  Peer-reviewed, Open-access Journal

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UNDERSAMPLED INFORMATION RECOVERY IN OFDM ENVIRONMENT

Research Paper Open Access

International Journal of Latest Research in Science and Technology Vol.3 Issue 3, pp 40-46,Year 2014

UNDERSAMPLED INFORMATION RECOVERY IN OFDM ENVIRONMENT

Nikos Petrellis

Correspondence should be addressed to :

Received : 25 June 2014; Accepted : 28 June 2014 ; Published : 30 June 2014

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Article No. 10312
Abstract

An under-sampling technique that can be applied in an Orthogonal Frequency Division Multiplexing (OFDM) environment is presented in this paper. It allows the recovery of sparse input data, at the side of the receiver with fewer samples than the ones required by the Nyquist theorem. It is based on the fact that several samples can be replaced by others that have already been obtained at the side of the receiver if the input data are sparse and some properties of the Discrete Fourier Transform (DFT) are exploited. The Forward Error Correction (FEC) techniques employed can also assist in achieving a lower error in the recovery process. The proposed deterministic technique can be implemented with very low complexity hardware in contrast with Compressive Sampling techniques that require complicated optimization problems to be solved. The sampling of up to ¼ of the samples can be avoided at the side of the receiver reducing the size of the buffer memory used by the FFT, as well as the power consumption of the Analog Digital Converter at the receiver since a lower sampling rate is used at specific time intervals.

Key Words   
OFDM, Undersampling, DFT, Analog Digital Conversion

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References
  1. Carmi, A., Gurfil, P., Kanevsky, D., “Methods for Sparse Signal Recovery Using Kalman Filtering With Embedded Pseudo-Measurement Norms and Quasi-Norms,” IEEE Transactions on Signal Processing vol. 58(4), pp. 2405-2409, 2010.
  2. Massicotte, D., “A parallel VLSI architecture of Kalman-filter-based algorithms for signal reconstruction,” Elsevier Integration, The VLSI Journal, vol. 28(2), pp. 185-196, 1999.
  3. Vaswani, N., “Kalman filtered compressed sensing,” Proceedings of the IEEE Int. Conf. Image Processing (ICIP), pp. 893–896, San Diego (CA), 12-15 Oct 2008.
  4. Kanevsky, D., Carmi, A., Horesh, L., Gurfil, P., Ramabhadran, B., Sainath, T. N., “Kalman Filtering for Compressed Sensing,” Proceedings of the 13th Conference on Information Fusion (FUSION), Edimburgh, 26-29 July 2010.
  5. Candès, E.J., Wakin, M.B., “An Introduction To Compressive Sampling,” IEEE Signal Processing Magazine vol. 25(2), pp. 25-30, 2008.
  6. Mishali, M., Eldar, Y., “From Theory to Practice: Sub-Nyquist Sampling of the Sparse Wideband Analog Signals,” IEEE Journal of Selected Topics in Signal Processing, vol. 4(2), pp. 375-391, 2010.
  7. Mahalanomis, A., and Muise, R., “Object Specific Image Reconstruction using a Compressive Sensing Architecture for Application in Surveillance Systems,” IEEE Trans. On Aerospace and Electronic Systems, vol. 45(3), pp. 1167-1180, 2009.
  8. Qu, X., Guo, D., Cao, X., Cai, S., and Chen, Z. “Reconstruction of Self-Sparse 2D NMR Spectra from Undersampled Data in the Indirect Dimension,” MDPI Sensors, vol.11, pp. 8888-8909, 2011.
  9. Hardie, R.C., and Droege, D.R., “A MAP Estimator for Simultaneous Superresolution and Detector Nonunifomity Correction,” EURASIP Journal on Advances in Signal Processing Article ID 89354, Hindawi Publishing Corp., pp. 1-11, 2007.
  10. Schniter, P., “A Message-Passing Receiver for BICM-OFDM Over Unknown Clustered-Space Channels,” IEEE Journal of Selected Topics in Signal Processing, vol. 5(8), pp. 1462-1474, 2011.
  11. Petrellis, N., “Deterministic Under-sampling with Error Correction in OFDM Systems,” Proceedings of 17th PCI Conference (ACM), pp. 47-54, Sep. 19-21, 2013 Thessaloniki, Greece.
  12. Petrellis, N., “Information Recovery Using Undersampling in Orthogonal Frequency Division Multiplexing Systems,” Proceedings of IEEE DSP 2013, July 1-3, 2013, Santorini, Greece.
  13. Yu, J., Boucheret, M.L., Vallet, R., Duverdier, A., Mesnager, G., “Interleaver Design for Turbo Codes From Convergence Analysis,” IEEE Transactions On Communications, vol. 54(4), pp. 619-624, 2006.
To cite this article

Nikos Petrellis , " Undersampled Information Recovery In Ofdm Environment ", International Journal of Latest Research in Science and Technology . Vol. 3, Issue 3, pp 40-46 , 2014

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