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Performance Optimization of OFDM Communication Systems Using Artificial Neural Networks

Author(s): C.D. Raut | N. P. Giradkar

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: ncipet;
Issue: 6;
Date: 2012;
Original page

Keywords: Clipping | OFDM | peak-to-average ratio. 802.11a WLAN network | ANN network

In order to combat fading in an OFDM communications system, adaptive modulation techniques have been employed which can improve the performance. We propose that an artificial neural network (ANN) can be inserted in an OFDM system that would provide the information necessary to perform an adaptive modulation of the subcarriers. The performance of the system is evaluated in terms of symbol error probability. The results of our simulations allow us to validate our hypothesis. Multicarrier signals are known to suffer from a high peak-to-average power ratio, caused by the addition of a large number of independently modulated subcarriers in parallel at the transmitter. When subjected to a peak-limiting channel, such as a nonlinear power amplifier, these signals may undergo significant spectral distortion, leading to both in-band and out-of-band interference, and an associated degradation in system performance [1], [2]. This paper characterizes the distortion caused by the clipping of multicarrier signals in a peak-limiting (nonlinear) channel. Rather than modeling the effects of distortion as additive noise, as is widespread in the literature, we identify clipping as a rare event and focus on evaluating system performance based on the conditional probability of bit error given the occurrence of such an event [4]. Our analysis is Based on the asymptotic properties of the large excursions of a stationary Gaussian process, and offers important insights into both the true nature of clipping distortion, as well as the consequent design of schemes to alleviate this problem[10], [11].

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