Abstract: In the pursuit of optimizing orthogonal frequency division multiplexing(OFDM) systems,a notable challenge lies in the relative inadequacy of its detection performance.Meanwhile,deep neural network-based index modulation(DNN-IM) detection algorithms generally suffer from issues such as high bit error rates(BER) and significant loss values.To overcome these difficulties,this paper proposes an index modulation detection algorithm based on multilayer perceptron(MLP),namely MLP-IM algorithm.This algorithm employs an architecture designed with two fused connection layers and an output layer,utilizing meticulously selected activation functions to achieve precise restoration of data bits in OFDM index modulation systems.Firstly,the fundamental theories of OFDM systems are ingeniously applied to the data preprocessing stage.Subsequently,a comprehensive and intensive offline training of the MLP neural network model is conducted using simulated datasets,ensuring the model’s robustness and accuracy.During the detection phase,efficient detection of the OFDM Index Modulation system is achieved through the MLP-IM detection algorithm.Simulation results demonstrate that the proposed MLP-IM algorithm exhibits performance comparable to that of the maximum likelihood detection algorithm in terms of BER control and loss values,and in some scenarios even superior to the existing DNN-IM detection algorithm,with a performance improvement of 0.2～6 dB.

Key words: Orthogonal frequency division multiplexing, Index modulation, Signal detection, Deep learning, Low loss value