Deterministic under-sampling with error correction in OFDM systems
September 2013
Pages 47 - 54
Abstract
The use of an under-sampling technique is presented in this paper allowing the reconstruction of the sparse information transmitted over Orthogonal Frequency Division Multiplexing (OFDM). The inherent data sparsity and the properties of the Discrete Fourier Transform employed by OFDM modulation allows the estimation of several samples from others that have already been retrieved at the side of the receiver. The Forward Error Correction techniques employed can further assist the information recovery with fewer samples. The presented method allows the estimation of up to 1/4 of the values required by the Discrete Fourier Transform at the side of the receiver from other values that have already been sampled. The proposed method can save buffer memory size for sample storage by up to 25% while the power consumption of the Analog Digital Converter at the side of the receiver is also reduced by slowing down the sampling rate at known time intervals. The presented simulations demonstrate the efficiency of different error correcting schemes like Viterbi and Reed-Solomon for information recovery with the proposed method.
References
[1]
Donoho, D. L. 2006. Compressed Sensing. IEEE Trans. On Information Theory 52, 4 (Apr. 2006), 1289--1306. 10.1109/TIT.2006.871582
[2]
Candès, E. J., and Wakin, M. B. 2008. An Introduction To Compressive Sampling. IEEE Signal Processing Magazine 25, 2 (Mar. 2008), 25--30. 10.1109/MSP.2007.914731
[3]
Tropp, J., Laska, J., Duarte, M., Romberg, J., and Baraniuk, M. 2010. Beyond Nyquist: Efficient Sampling of Sparse Band Limited Signals. IEEE Transactions on Information Theory 56, 1 (Jan. 2010), 520--544. 10.1109/TIT.2009.2034811
[4]
Mishali, M., and Eldar, Y. 2010. From Theory to Practice: Sub-Nyquist Sampling of the Sparse Wideband Analog Signals. IEEE Journal of Selected Topics in Signal Processing 4, 2 (Feb. 2010), 375--391. 10.1109/JSTSP.2010.2042414
[5]
Mahalanobis, A., and Muise, R. 2009. Object Specific Image Reconstruction using a Compressive Sensing Architecture for Application in Surveillance Systems. IEEE Trans. On Aerospace and Electronic Systems 45, 3 (Sep. 2009), 1167--1180. 10.1109/TAES.2009.5259191
[6]
Lustig, M., Donoho, D., Santos, J., and Pauly, J. 2008. Compressed Sensing MRI. IEEE Signal Processing Magazine 25, 2 (Mar. 2008), 72--82. 10.1109/MSP.2007.914728
[7]
Luo, J., Xiang, L., and Rosenberg, C. 2010. Does Compressed Sensing Improve the Throughput of Wireless Sensor Networks? In Proceedings of the IEEE International Conference on Communications 2010 (Cape Town, South Africa, 23-27 May 2010) ICC '10, IEEE, 1--6. 10.1109/ICC.2010.5502565
[8]
Pudlewski, S., Melodia, T., and Prasanna, A. 2012. Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Network. IEEE Transactions on Mobile Computing 11, 6 (Apr. 2012), 1060--1072. 10.1109/TMC.2011.175
[9]
Xu, L., and Liang, Q. 2012. Compressive Sensing fin Radar Sensor Networks Using Pulse Compression Waveforms. In Proceedings of the IEEE International Conference on Communications 2012 -- Ad hoc and Sensor Networking Symposium(Ottawa, Canada, 10-15 June 2012) ICC '12, IEEE, 794--798. 10.1109/ICC.2012.6363658
[10]
Fazel, F., Fazel, M., and Stojanovic, M. 2011. Random Access Compressed Sensing of Energy-Efficient Underwater Sensor Networks. IEEE Journal on Selected Areas in Communications 29, 8 (Aug. 2011), 1660--1670. 10.1109/JSAC.2011.110915
[11]
Qi C., and Wu, L. 2011. A Hybrid Compressed Sensing Algorithm for Sparse Channel Estimation in MIMO OFDM Systems. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing 2011 (Prague, Czech Republic, 22-27 May 2011) ICASSP '11, IEEE, 3488--3491. 10.1109/ICASSP.2011.5946229
[12]
Hormati, A., and Vetterli, M. 2011. Compressive Sampling of Multiple Sparse Signals Having Common Support Using Finite Rate of Innovation Principles. IEEE Signal Processing Letters 18, 5 (Mar. 2011), 331--334. 10.1109/LSP.2011.2131649
[13]
Schniter, P. 2011. A Message-Passing Receiver for BICM-OFDM Over Unknown Clustered-Space Channels. IEEE Journal of Selected Topics in Signal Processing 5, 8 (Sep. 2011), 1462--1474. 10.1109/JSTSP.2011.2169232
[14]
Cheng, P., Gui, L., Tao, M., Guo, Y., Huang, X., and Rui, Y. 2012. Sparse Channel Estimation for OFDM Transmission over Two-Way Relay Networks. In Proceedings of the IEEE International Conference on Communications 2012 -- Wireless Communications Symposium (Ottawa, Canada, 10-15 June 2012) ICC '12, IEEE, 3948--3953. 10.1109/ICC.2012.6364641
[15]
Petrellis, N. 2012. Compressive Sampling Methods and Their Implementation Issues. Journ. Bentham Recent Patents on Signal Processing 2 (2012), 127--139.
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- Deterministic under-sampling with error correction in OFDM systems
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Published In
September 2013
359 pages
ISBN:9781450319690
DOI:10.1145/2491845
- General Chairs:
- Panayiotis H. Ketikidis,
- Kostas Margaritis,
- Ioannis Vlahavas,
- Program Chairs:
- Alexandros Chatzigeorgiou,
- George Eleftherakis,
- Ioannis Stamelos
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- University of Macedonia
- Aristotle University of Thessaloniki
- The University of Sheffield: The University of Sheffield
- Alexander TEI of Thessaloniki
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 September 2013
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PCI 2013
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- The University of Sheffield
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