Clipping noise estimation in OFDM systems: A greedy-based approach

Abstract A major drawback of orthogonal frequency division multiplexing (OFDM) is the high peak-to-average power ratio (PAPR) which drives transmitter’s power amplifier into saturation. One of the simplest methods for mitigating PAPR is to clip signal prior to the amplifier. However, this technique suffers from noise caused by clipping operation and limits its practical application. To investigate this issue, we have proposed a greedy algorithm based on orthogonal matching pursuit (OMP) utilizing a distance metric and a threshold parameter in order to define a backward condition to achieve better results in clipping noise estimation. The defined distance metric and threshold parameter are jointly used to determine whether the recovered noise indices are valid or not. Besides, a closed form equation for threshold parameter has been obtained in terms of channel statistics and has been validated by extensive numerical simulations. According to such appropriate choice of threshold values, simulations demonstrate that the proposed algorithm outperforms state-of-the-art methods such as OMP, l 1 -minimization and classic methods, especially when SNR grows higher. Also, in terms of computational complexity, proposed algorithm due to its structure imposes a slightly further complexity to the receiver in comparison with conventional OMP algorithm.

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

[2]  Kamal Mohamed-pour,et al.  Clipping noise estimation in uniform tone reservation scenario using OMP algorithm , 2016, 2016 8th International Symposium on Telecommunications (IST).

[3]  Jong-Seon No,et al.  Clipping Noise Cancelation for OFDM Systems Using Reliable Observations Based on Compressed Sensing , 2015, IEEE Transactions on Broadcasting.

[4]  Jian Wang,et al.  Multipath Matching Pursuit , 2013, IEEE Transactions on Information Theory.

[5]  Xiaodong Li,et al.  Effects of clipping and filtering on the performance of OFDM , 1998, IEEE Communications Letters.

[6]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[7]  Dmitry M. Malioutov,et al.  A sparse signal reconstruction perspective for source localization with sensor arrays , 2005, IEEE Transactions on Signal Processing.

[8]  D. L. Donoho,et al.  Compressed sensing , 2006, IEEE Trans. Inf. Theory.

[9]  K. Mohamed-Pour,et al.  Modified compressive sensing reconstruction algorithm for clipping noise estimation in OFDM systems , 2016, 2016 24th Iranian Conference on Electrical Engineering (ICEE).

[10]  Tareq Y. Al-Naffouri,et al.  Peak Reduction and Clipping Mitigation in OFDM by Augmented Compressive Sensing , 2012, IEEE Transactions on Signal Processing.

[11]  Holger Boche,et al.  The PAPR Problem in OFDM Transmission: New Directions for a Long-Lasting Problem , 2012, IEEE Signal Processing Magazine.

[12]  Robert F. H. Fischer,et al.  Peak-to-Average Power Ratio Reduction in OFDM via Sparse Signals: Transmitter-Side Tone Reservation vs. Receiver-Side Compressed Sensing , 2012 .

[13]  Alexander M. Haimovich,et al.  Iterative estimation and cancellation of clipping noise for OFDM signals , 2003, IEEE Communications Letters.

[14]  Lutz H.-J. Lampe,et al.  Compressive Sensing Recovery of Nonlinearly Distorted OFDM Signals , 2011, 2011 IEEE International Conference on Communications (ICC).

[15]  Shengli Zhou,et al.  Application of compressive sensing to sparse channel estimation , 2010, IEEE Communications Magazine.

[16]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[17]  Tao Jiang,et al.  Norm-adaption penalized least mean square/fourth algorithm for sparse channel estimation , 2016, Signal Process..

[18]  Tareq Y. Al-Naffouri,et al.  On Reducing the Complexity of Tone-Reservation Based PAPR Reduction Schemes by Compressive Sensing , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[19]  Marc E. Pfetsch,et al.  The Computational Complexity of the Restricted Isometry Property, the Nullspace Property, and Related Concepts in Compressed Sensing , 2012, IEEE Transactions on Information Theory.

[20]  Masanori Hamamura,et al.  Zero‐attracting variable‐step‐size least mean square algorithms for adaptive sparse channel estimation , 2015 .

[21]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[22]  Si Liu,et al.  A Low-Complexity Compressive Sensing Algorithm for PAPR Reduction , 2014, Wirel. Pers. Commun..

[23]  Yasir Rahmatallah,et al.  Peak-To-Average Power Ratio Reduction in OFDM Systems: A Survey And Taxonomy , 2013, IEEE Communications Surveys & Tutorials.

[24]  Christian Jutten,et al.  A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed $\ell ^{0}$ Norm , 2008, IEEE Transactions on Signal Processing.

[25]  J.A.C. Bingham,et al.  Multicarrier modulation for data transmission: an idea whose time has come , 1990, IEEE Communications Magazine.