Intercarrier Interference Suppression for OFDM Systems Using Hopfield Neural Network

In Orthogonal frequency division multiplexing (OFDM) transmission system, channel variations within an OFDM symbol destroy orthogonality between subcarriers, resulting in intercarrier interference (ICI), which increases an error floor in proportional to normalized Doppler frequency. To mitigate the effects of channel variations, in this paper, we propose a novel ICI suppression technique, which realizes near maximum likelihood sequence estimation by using continuous Hopfield neural network (HNN). The obvious advantage of using continuous HNN is speeding up the process of signal detection. The each neuron of continuous HNN herein present has multi-level activation function. The number of levels depends on the modulation format adopted in each subcarrier modulation. The performance of the proposed HNN-based detector is evaluated via computer simulations and compared with both conventional detection and optimal detection. It is shown that the HNN detector has low computational complexity and good performance for most Doppler frequency of practical importance.