基于用于空间移动的机器学习模型的通道估计 (Channel Estimation Based on Machine Learning Paradigm for Spatial Modulation OFDM)

from arxiv, 4 pages, 5 figures, 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA

In this paper, deep neural network (DNN) is integrated with spatial modulation-orthogonal frequency division multiplexing (SM-OFDM) technique for end-to-end data detection over Rayleigh fading channel. This proposed system directly demodulates the received symbols, leaving the channel estimation done only implicitly. Furthermore, an ensemble network is also proposed for this system. Simulation results show that the proposed DNN detection scheme has a significant advantage over classical methods when the pilot overhead and cyclic prefix (CP) are reduced, owing to its ability to learn and adjust to complicated channel conditions. Finally, the ensemble network is shown to improve the generalization of the proposed scheme, while also showing a slight improvement in its performance.

翻译：在本文中,深神经网络(DNN)与Rayleigh 淡化频道上端至端数据探测的空间调制-orthoodroy District division division (SM-OFDM)技术(SM-OFDM)相融合,这一拟议系统直接对收到的符号进行降排,使频道估计只能暗中完成,此外,也为这个系统提议了一个混合网络。模拟结果表明,在试点间接费用和圆形前缀(CP)由于能够学习和适应复杂的频道条件而减少时,拟议的DNN检测计划比古典方法有很大的优势。最后,组合网络显示将改进拟议方案的一般化,同时显示其性能略有改善。