Wavelet networks for reducing the envelope fluctuations in WirelessMan-OFDM systems

Abstract The IEEE 802.16d standard specified Orthogonal Frequency Division Multiplexing (OFDM) modulation for the Worldwide Interoperability for Microwave Access (WiMAX) physical layer. However, the main weakness of OFDM is the high Peak-to-Average Power Ratio (PAPR). In this paper, we present two new approaches based on Wavelet Networks (WNs) for reducing the PAPR in the fixed WiMAX system. The training data is obtained from the ACE-AGP algorithm. The results of the simulations show the effectiveness of the proposed schemes even for high order modulation such as 64-QAM. Furthermore, the proposals allow reduction in the complexity and convergence time in comparison with other methods.

[1]  Achilleas Zapranis,et al.  Wavelet Neural Networks: A Practical Guide , 2011, Neural Networks.

[2]  Víctor P. Gil Jiménez,et al.  Reduction of the Envelope Fluctuations of Multi-Carrier Modulations using Adaptive Neural Fuzzy Inference Systems , 2011, IEEE Trans. Commun..

[3]  Víctor P. Gil Jiménez,et al.  Reduction of Power Envelope Fluctuations in OFDM Signals by using Neural Networks , 2010, IEEE Communications Letters.

[4]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[5]  Antonios K. Alexandridis,et al.  Wavelet Neural Networks , 2014 .

[6]  Jeffrey G. Andrews,et al.  Broadband wireless access with WiMax/802.16: current performance benchmarks and future potential , 2005, IEEE Communications Magazine.

[7]  Jeffrey G. Andrews,et al.  Fundamentals of WiMAX: Understanding Broadband Wireless Networking , 2007 .

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

[9]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[10]  Hsien-Yu Tseng,et al.  A reduced-complexity PTS scheme for peak-to-average power ratio reduction in OFDM systems , 2008, 2008 16th European Signal Processing Conference.

[11]  Gérard Dreyfus,et al.  Initialization by selection for wavelet network training , 2000, Neurocomputing.

[12]  Stephen A. Billings,et al.  A new class of wavelet networks for nonlinear system identification , 2005, IEEE Transactions on Neural Networks.

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

[14]  Thomas Eriksson,et al.  Peak-to-Average-Power-Ratio (PAPR) reduction in WiMAX and OFDM/A systems , 2011, EURASIP J. Adv. Signal Process..