Undersampling in Orthogonal Frequency Division Multiplexing Telecommunication Systems

Several techniques have been proposed that attempt to reconstruct a sparse signal from fewer samples than the ones required by the Nyquist theorem. In this paper, an undersampling technique is presented that allows the reconstruction of the sparse information that is transmitted through Orthogonal Frequency Division Multiplexing (OFDM) modulation. The properties of the Discrete Fourier Transform (DFT) that is employed by the OFDM modulation, allow the estimation of several samples from others that have already been obtained on the side of the receiver, provided that special relations are valid between the original data values. The inherent sparseness of the original data, as well as the Forward Error Correction (FEC) techniques employed, can assist the information recovery from fewer samples. It will be shown that up to 1/4 of the samples can be omitted from the sampling process and substituted by others on the side of the receiver for the successful reconstruction of the original data. In this way, the size of the buffer memory used for sample storage, as well as the storage requirements of the Fast Fourier Transform (FFT) implementation at the receiver, may be reduced by up to 25%. The power consumption of the Analog Digital Converter on the side of the receiver is also reduced when a lower sampling rate is used.

[1]  Yoshikazu Miyanaga,et al.  An area and power efficient pipeline FFT processor for 8×8 MIMO-OFDM systems , 2011, 2011 IEEE International Symposium of Circuits and Systems (ISCAS).

[2]  Chin-Long Wey,et al.  High-speed, Low Cost Parallel Memory-Based FFT Processors for OFDM Applications , 2007, 2007 14th IEEE International Conference on Electronics, Circuits and Systems.

[3]  Namrata Vaswani,et al.  LS-CS-Residual (LS-CS): Compressive Sensing on Least Squares Residual , 2009, IEEE Transactions on Signal Processing.

[4]  Philip Schniter.  A message-passing receiver for BICM-OFDM over unknown clustered-sparse channels , 2011, SPAWC 2011.

[5]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[6]  Tommaso Melodia,et al.  Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Networks , 2012, IEEE Transactions on Mobile Computing.

[7]  Marie-Laure Boucheret,et al.  Interleaver design for turbo codes from convergence analysis , 2006, IEEE Transactions on Communications.

[8]  Lenan Wu,et al.  A hybrid compressed sensing algorithm for sparse channel estimation in MIMO OFDM systems , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  Daniel Massicotte.  A parallel VLSI architecture of Kalman-filter-based algorithms for signal reconstruction , 2000, Integr..

[10]  MelodiaTommaso,et al.  Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Networks , 2012 .

[11]  Daniel Massicotte,et al.  A higher radix FFT FPGA implementation suitable for OFDM systems , 2011, 2011 18th IEEE International Conference on Electronics, Circuits, and Systems.

[12]  Walt Kester,et al.  Mixed-signal and DSP Design Techniques Analog Devices, Inc. , 2002 .

[13]  Sandeep Kumar,et al.  Appl. Sci , 2013 .

[14]  Daniel Massicotte,et al.  A novel approach for FFT data reordering , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[15]  Yonina C. Eldar,et al.  From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals , 2009, IEEE Journal of Selected Topics in Signal Processing.

[16]  Lin Gui,et al.  Sparse channel estimation for OFDM transmission over two-way relay networks , 2012 .

[17]  Pini Gurfil,et al.  Methods for Sparse Signal Recovery Using Kalman Filtering With Embedded Pseudo-Measurement Norms and Quasi-Norms , 2010, IEEE Transactions on Signal Processing.

[18]  Daniel Massicotte,et al.  A systolic architecture for Kalman-filter-based signal reconstruction using the wave pipeline method , 1998, 1998 IEEE International Conference on Electronics, Circuits and Systems. Surfing the Waves of Science and Technology (Cat. No.98EX196).

[19]  Tara N. Sainath,et al.  Kalman filtering for compressed sensing , 2010, 2010 13th International Conference on Information Fusion.

[20]  Namrata Vaswani,et al.  Kalman filtered Compressed Sensing , 2008, 2008 15th IEEE International Conference on Image Processing.

[21]  Myung Hoon Sunwoo,et al.  A high-speed FFT processor for OFDM systems , 2002, 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353).

[22]  G. Biradar.  Implementation of Area Efficient OFDM Transceiver on FPGA , 2013 .

[23]  J. Tukey,et al.  An algorithm for the machine calculation of complex Fourier series , 1965 .

[24]  R. Muise,et al.  Object Specific Image Reconstruction using a Compressive Sensing Architecture for Application in Surveillance Systems , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[25]  Catherine Rosenberg,et al.  Does Compressed Sensing Improve the Throughput of Wireless Sensor Networks? , 2010, 2010 IEEE International Conference on Communications.