Clipping Noise Cancelation for OFDM Systems Using Reliable Observations Based on Compressed Sensing

In this paper, a clipping noise cancelation scheme using compressed sensing (CS) technique is proposed for orthogonal frequency division multiplexing systems. The proposed scheme does not need reserved tones or pilot tones, which is different from the previous works using CS technique. Instead, observations of the clipping noise in data tones are exploited, which leads to no loss of data rate. Also, in contrast with the previous works, the proposed scheme selectively exploits the reliable observations of the clipping noise instead of using whole observations, which results in minimizing the bad influence of channel noise. From the selected reliable observations, the clipping noise in time domain is reconstructed and canceled by using CS technique. Simulation results show that the proposed scheme performs well compared to other conventional clipping noise cancelation schemes and shows the best performance in some cases.

[1]  Sailes K. Sengijpta.  Fundamentals of Statistical Signal Processing: Estimation Theory , 1995 .

[2]  Paolo Banelli,et al.  Theoretical analysis and performance of OFDM signals in nonlinear AWGN channels , 2000, IEEE Trans. Commun..

[3]  Gordon L. Stüber,et al.  Clipping noise mitigation for OFDM by decision-aided reconstruction , 1999, IEEE Communications Letters.

[4]  Hideki Ochiai,et al.  Performance analysis of deliberately clipped OFDM signals , 2002, IEEE Trans. Commun..

[5]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[6]  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.

[7]  Seok-Joong Heo,et al.  An overview of peak-to-average power ratio reduction schemes for OFDM signals , 2009, Journal of Communications and Networks.

[8]  Chintha Tellambura,et al.  Analysis of Clipping Noise and Tone-Reservation Algorithms for Peak Reduction in OFDM Systems , 2008, IEEE Transactions on Vehicular Technology.

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

[10]  Robert J. Baxley,et al.  Constrained Clipping for Crest Factor Reduction in OFDM , 2006, IEEE Transactions on Broadcasting.

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

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

[13]  Dongsik Kim,et al.  Amplitude clipping and iterative reconstruction of MIMO-OFDM signals with optimum equalization , 2009, IEEE Trans. Wirel. Commun..

[14]  Lie Wang,et al.  Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise , 2011, IEEE Transactions on Information Theory.

[15]  Jong-Seon No,et al.  Clipping Noise Cancellation for OFDM and OFDMA Systems Using Compressed Sensing , 2011, ArXiv.

[16]  Gabriel E. Arrobo,et al.  Improving the performance of OFDM-based vehicular systems through diversity coding , 2013, Journal of Communications and Networks.

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

[18]  Elena Costa,et al.  Impact of amplifier nonlinearities on OFDM transmission system performance , 1999, IEEE Communications Letters.

[19]  Hyun-Bae Jeon,et al.  Bit-Based SLM Schemes for PAPR Reduction in QAM Modulated OFDM Signals , 2009, IEEE Transactions on Broadcasting.

[20]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[21]  Yung-Fang Chen,et al.  A Low-Complexity PAPR Reduction Scheme for OFDMA Uplink Systems , 2011, IEEE Transactions on Wireless Communications.

[22]  Tareq Y. Al-Naffouri,et al.  Pilotless recovery of clipped OFDM signals by compressive sensing over reliable data carriers , 2012, 2012 IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[23]  Yajun Wang,et al.  Genetic Algorithm Based Nearly Optimal Peak Reduction Tone Set Selection for Adaptive Amplitude Clipping PAPR Reduction , 2012, IEEE Transactions on Broadcasting.

[24]  Ying Chen,et al.  Estimation and compensation of clipping noise in OFDMA systems , 2010, IEEE Transactions on Wireless Communications.

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

[26]  Farrokh Marvasti,et al.  Clipping noise cancellation in OFDM systems using oversampled signal reconstruction , 2002, IEEE Communications Letters.

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

[28]  Holger Rauhut,et al.  Random Sampling of Sparse Trigonometric Polynomials, II. Orthogonal Matching Pursuit versus Basis Pursuit , 2008, Found. Comput. Math..

[29]  Masoud Salehi,et al.  Communication Systems Engineering , 1994 .

[30]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[31]  Babak Hossein Khalaj,et al.  On the peak-to-average power of OFDM signals based on oversampling , 2003, IEEE Trans. Commun..

[32]  Stephen J. Wright,et al.  Sparse Reconstruction by Separable Approximation , 2008, IEEE Transactions on Signal Processing.

[33]  Paeiz Azmi,et al.  Iterative reconstruction-based method for clipping noise suppression in OFDM systems , 2005 .

[34]  Robert F. H. Fischer,et al.  OFDM with reduced peak-to-average power ratio by multiple signal representation , 1997, Ann. des Télécommunications.