Systems that employ multicarrier code division multiple access, commonly known as MC-CDMA, produce outstanding results in terms of both the performance of the system as a whole and the efficiency with which it uses the spectrum. However, multiple access strategies are susceptible to interference despite their high spectrum efficiency. This work aims to reduce multiple access interference (MAI) by developing an MC-CDMA receiver. When MC-CDMA deteriorates nonlinearly, standard receivers, namely, zero forcing (ZF), maximal ratio combining (MRC), minimum mean square error (MMSE), and equal gain combining (EGC), are unable to cancel MAI. Neural network (NN) receivers are a better option due to their nonlinear nature. Based on the simulation results, the suggested deep neural network- (DNN-)based schemes outperform the current baselines in terms of error handling and usability. This research explores the viability and effectiveness of a DNN-based receiver designed for MC-CDMA with nonlinearity degradations. The focus of this research is on MC-CDMA.

中文翻译：

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采用深度神经网络优化 MC-CDMA 接收器中的非线性失真和干扰

采用多载波码分多址（通常称为 MC-CDMA）的系统在系统整体性能和频谱使用效率方面都取得了出色的成果。然而，尽管多址接入策略的频谱效率很高，但仍容易受到干扰。这项工作旨在通过开发 MC-CDMA 接收机来减少多址干扰 (MAI)。当MC-CDMA非线性恶化时，标准接收机，即迫零(ZF)、最大比合并(MRC)、最小均方误差(MMSE)和等增益合并(EGC)，无法消除MAI。由于其非线性特性，神经网络 (NN) 接收器是更好的选择。根据模拟结果，建议的基于深度神经网络（DNN）的方案在错误处理和可用性方面优于当前基线。本研究探讨了针对具有非线性退化的 MC-CDMA 设计的基于 DNN 的接收器的可行性和有效性。本次研究的重点是MC-CDMA。