【摘要】 在探讨正交频分复用(Orthogonal Frequency Division Multiplexing, OFDM)系统的优化中，一个显著挑战在于其信号检测性能的相对不足。同时，针对基于深度神经网络的索引调制(Deep Neural Network Based Index Modulation, DNN-IM)检测算法，普遍存在着误码率及损失值偏高的问题。为了弥补上述难题，文中提出一种基于多层感知机(Multilayer Perceptron, MLP)的索引调制检测算法，即MLP-IM算法。该算法采用融合两个连接层与一个输出层的架构设计，通过挑选的激活函数实现对OFDM索引调制系统中数据比特的精准还原。首先将OFDM索引调制系统的基础理论巧妙应用于数据的预处理阶段，随后利用仿真数据集对MLP神经网络模型进行全面而深入的离线训练，确保模型的稳健性与准确性。在检测阶段，通过MLP-IM检测算法实现了对OFDM索引调制系统的高效检测。仿真结果表明，所提出的MLP-IM算法在误码率控制和损失值两个方面的性能表现与最大似然检测算法相媲美，甚至在某些场景下超越了现有DNN-IM算法的性能，其性能改善幅度在0.2～6 dB的区间内。更多还原

【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.更多还原