In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO), deep learning (DL)-based superimposed channel state information (CSI) feedback has presented promising performance. However, it is still facing many challenges, such as the high complexity of parameter tuning, large number of training parameters, and long training time, etc. To overcome these challenges, an extreme learning machine (ELM)-based superimposed CSI feedback is proposed in this paper, in which the downlink CSI is spread and then superimposed on uplink user data sequence (UL-US) to feed back to base station (BS). At BS, an ELM-based network is constructed to recover both downlink CSI and UL-US. In the constructed ELM-based network, we employ the simplified versions of ELM-based subnets to replace the subnets of DL-based superimposed feedback, yielding less training parameters. Besides, the input weights and hidden biases of each ELM-based subnet are loaded from the same matrix by using its full or partial entries, which significantly reduces the memory requirement. With similar or better recovery performances of downlink CSI and UL-US, the proposed ELM-based method has less training parameters, storage space, offline training and online running time than those of DL-based superimposed CSI feedback.

中文翻译：

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基于ELM的FDD Massive MIMO系统叠加CSI反馈

在频分双工 (FDD) 大规模多输入多输出 (MIMO) 中，基于深度学习 (DL) 的叠加信道状态信息 (CSI) 反馈表现出良好的性能。然而，它仍然面临着诸多挑战，如参数调优复杂度高、训练参数数量多、训练时间长等。为了克服这些挑战，提出了一种基于极限学习机（ELM）的叠加CSI反馈在本文中，将下行 CSI 扩频，然后叠加在上行用户数据序列（UL-US）上反馈给基站（BS）。在 BS，构建了一个基于 ELM 的网络来恢复下行链路 CSI 和 UL-US。在构建的基于 ELM 的网络中，我们使用基于 ELM 的子网的简化版本来替换基于 DL 叠加反馈的子网，产生较少的训练参数。此外，每个基于 ELM 的子网的输入权重和隐藏偏差是通过使用其全部或部分条目从同一矩阵加载的，这显着降低了内存需求。由于下行CSI和UL-US具有相似或更好的恢复性能，所提出的基于ELM的方法比基于DL的叠加CSI反馈具有更少的训练参数、存储空间、离线训练和在线运行时间。