It has been observed over last several decades that bandwidth greediness of applications never gets fulfilled. Hence, scientists, researchers, and engineers keep working on new ways of providing higher bandwidth. Recently, a new modulation technique called Orthogonal Frequency Division Multiplexing (OFDM) has been introduced which provides very high data rates. In OFDM the high frequency input signal is modulated over a large number of low frequency sub-carrier signals which are orthogonal to each other. This feature makes it very robust against efficiency degradation at higher frequencies. That is the reason why OFDM is a choice for the modern high and ultra-high data rate communication systems. However, it suffers from high levels of the peak power to the average power also called Peak-to-Average Power Ratio or PAPR. Reducing PAPR in OFDM is a hot research area. There are many schemes available which attempt to reduce PAPR. Some are in fact able to reduce PAPR but not sufficient enough to make these feasible. Others do reduce it but increase its complexity to an extent that these become unfeasible to realize. From literature it has been identified that SLM performs better than other methods in terms of computational complexity at the same performance level. The main motivation behind this research effort is to find a mechanism which reduces PAPR for OFDM systems and has a reasonable level of complexity so that it may be realizable. As an outcome of this research activity, a novel framework based on Artificial Neural Networks (ANN) and Selective Mapping (SLM) is proposed. The kernel used by the ANN in proposed framework is a modified version (proposed by us) of an already available kernel called Novel Kernel Based ? Radial Basis Function (NKB-RBF). We show through simulations results that our proposed kernel, Modified NKB-RBF (MNKB-RBF), is more efficient than NKB-RBF and gives better results in selection of low frequency sub-carriers with lowest PAPR.
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