Adaptive Predistortions Based on Neural Networks Associated with Levenberg-Marquardt Algorithm for Satellite Down Links

This paper presents adaptive predistortion techniques based on a feed-forward neural network (NN) to linearize power amplifiers such as those used in satellite communications. Indeed, it presents the suitable NN structures which give the best performances for three satellite down links. The first link is a stationary memoryless travelling wave tube amplifier (TWTA), the second one is a nonstationary memoryless TWT amplifier while the third is an amplifier with memory modeled by a memoryless amplifier followed by a linear filter. Equally important, it puts forward the studies concerning the application of different NN training algorithms in order to determine the most prefermant for adaptive predistortions. This comparison examined through computer simulation for 64 carriers and 16-QAM OFDM system, with a Saleh's TWT amplifier, is based on some quality measure (mean square error), the required training time to reach a particular quality level, and computation complexity. The chosen adaptive predistortions (NN structures associated with an adaptive algorithm) have a low complexity, fast convergence, and best performance.

[1]  Janusz Pochmara.  Improving compensation of nonlinear distortions in OFDM system using recurrent neural network with conjugate gradient algorithm , 2004, 2004 IEEE 15th International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE Cat. No.04TH8754).

[2]  Mohamed Ibnkahla,et al.  Applications of neural networks to digital communications - a survey , 2000, Signal Process..

[3]  G. Baudoin,et al.  Filter Look Up Table method for Power Amplifiers Linearization , 2003 .

[4]  Daniel Roviras,et al.  Equalization of satellite UMTS channels using neural network devices , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[5]  Mohamed Ibnkahla,et al.  Neural network predistortion technique for digital satellite communications , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[6]  Yiwen Wang,et al.  Comparison of TDNN training algorithms in brain machine interfaces , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[7]  Shun-ichi Amari,et al.  Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.

[8]  A. Gatherer,et al.  Controlling clipping probability in DMT transmission , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[9]  A. S. Madhukumar,et al.  A novel two-layered suboptimal approach to partial transmit sequences , 2003, The 57th IEEE Semiannual Vehicular Technology Conference, 2003. VTC 2003-Spring..

[10]  Jian Li,et al.  Adaptive Volterra predistorters for compensation of non-linear effects with memory in OFDM transmitters , 2006, 4th Annual Communication Networks and Services Research Conference (CNSR'06).

[11]  R. Baxley.  Analyzing Selected Mapping for Peak-to-Average Power Reduction in OFDM , 2005 .

[12]  T.J. Brazil,et al.  An adaptive Volterra predistorter for the linearization of RF high power amplifiers , 2002, 2002 IEEE MTT-S International Microwave Symposium Digest (Cat. No.02CH37278).

[13]  Tong Wang,et al.  Compensation of nonlinear distortions with memory effects in OFDM transmitters , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[14]  D. Roviras,et al.  Comparison of neural network adaptive predistortion techniques for satellite down links , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[15]  Raviv Raich,et al.  A Hammerstein predistortion linearization design based on the indirect learning architecture , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[16]  William H. Tranter,et al.  Nonlinear tapped delay line digital predistorter for power amplifiers with memory , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[17]  Daniel Roviras,et al.  An adaptive neural network pre-distorter for non stationary HPA in OFDM systems , 2007, 2007 15th European Signal Processing Conference.

[18]  Jae Hong Lee,et al.  An overview of peak-to-average power ratio reduction techniques for multicarrier transmission , 2005, IEEE Wireless Communications.

[19]  Wang Gang,et al.  A nonlinearity predistortion technique for HPA with memory effects in OFDM systems , 2007 .

[20]  Adel A. M. Saleh,et al.  Frequency-Independent and Frequency-Dependent Nonlinear Models of TWT Amplifiers , 1981, IEEE Trans. Commun..

[21]  R. Bäuml,et al.  Reducing the peak-to-average power ratio of multicarrier modulation by selected mapping , 1996 .

[22]  Geneviève Baudoin,et al.  Filter Lookup Table Method for Power Amplifier Linearization , 2007, IEEE Transactions on Vehicular Technology.

[23]  Amparo Alonso-Betanzos,et al.  A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis , 2006, J. Mach. Learn. Res..

[24]  J. Tellado,et al.  Multicarrier Modulation with Low Par: Applications to DSL and Wireless , 2000 .

[25]  Douglas L. Jones,et al.  PAR reduction in OFDM via active constellation extension , 2003, IEEE Trans. Broadcast..

[26]  Udo Seiffert,et al.  Training of Large-Scale Feed-Forward Neural Networks , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[27]  Sergios Theodoridis,et al.  A Novel Efficient Cluster-Based MLSE Equalizer for Satellite Communication Channels with-QAM Signaling , 2006, EURASIP J. Adv. Signal Process..

[28]  Mohamed Ibnkahla,et al.  A neural network MLSE receiver based on natural gradient descent: application to satellite communications , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

[29]  Mohamed Ibnkahla,et al.  Neural network based channel estimation and performance evaluation of time varying multipath satellite channel , 2005, 3rd Annual Communication Networks and Services Research Conference (CNSR'05).