Paper Type |
: |
Research Paper |
Title |
: |
An Efficient Low complexity PAPR Reduction Techniques Using
Neural Networks |
Country |
: |
India |
Authors |
: |
Feby Paulose || Asst. Prof. Shiji Abraham |
Abstract: In Long term evaluation (LTE) systems advanced wireless communication technique is used to
minimize the Multiple Access Interference (MAI). Both transmitter and receiver are responsible for the better
throughput and minimum error rate. Transmitter plays the major role and needs little efficient modification in
terms of transmission power and modulation techniques. Minimum transmission power deliver the good results
and it can be achieved by peak to average power ratio (PAPR) reduction with the help of soft computing
techniques. In this letter, we propose a new method that uses NNs trained on the active constellation extension
(ACE) signals to reduce the PAPR of OFDM signals. Unlike other NN based techniques, the proposed method
employs a receiver NN unit, at the OFDM receiver side, achieving significant bit error rate (BER) improvement
with low computational complexity
Keywords - OFDM, PAPR, ACE, BPNN,Neural Networks.
[1] R. van Nee and R. Prasad, OFDM for Wireless Multimedia Communications. Artech House, 2000.
[2] B. S. Krongold and D. L. Jones, "PAR reduction in OFDM via active constellation extension," IEEE Trans. Broadcast., vol. 49, no.
3, pp. 258- 268, Sep. 2003.
[3] Y. Kou, W.-S. Lu, and A. Antoniou, "New peak-to-average powerratio reduction algorithms for multicarrier communications,"
IEEE Trans. Circuits and Syst., vol. 51, no. 9, pp. 1790-1800, Sep. 2004.
[4] K. Bae, J. G. Andrews, and E. J. Powers, "Adaptive active constellation extension algorithm for peak-to-average ratio reduction in
OFDM," IEEE Commun. Lett., vol. 14, no. 1, pp. 39-41, Jan. 2010.
[5] Y. Jabrane, V. P. G. Jimenez, A. G. Armada, B. A. E. Said, and A. A. Ouahman, "Reduction of power envelope fluctuations in
OFDM signals by using neural networks," IEEE Commun. Lett., vol. 14, no. 7, pp. 599- 601, July 2010.
[6] S. Haykin, Neural Networks, A Comprehensive Foundation Macmillan, 1994.
[7] D. W. Marquardt, "An algorithm for least-squares estimation of nonlinear parameters," J. Soc. Ind. Appl. Math, vol. 11, no. 2, pp.
431-441, 1963.