基于级联ELM的莱斯衰落信道联合帧同步和信道估计
Cascaded ELM-based Joint Frame Synchronization and Channel Estimation over Rician Fading Channel with Hardware Imperfections
摘要：
由于帧同步（FS）和信道估计（CE）的相互依赖性，联合FS和CE（JFSCE）方案被提出来增强它们的功能，从而提高无线通信系统的整体性能。针对传统的JFSCE方案在处理硬件缺陷（HI）和确定性视距（LOS）路径时存在的不足，提出了一种基于级联ELM的JFSCE方案，用于在莱斯衰落信道下消除HI的影响。具体地说，传统的JFSCE方法首先被用来提取初始特征，从而形成非神经网络（NN）的解决方案FS和CE，分别。然后，ELM为基础的网络，命名为FS-NET和CE-NET，级联捕获FS和CE的NN解决方案。仿真和分析结果表明，与传统的JFSCE方法相比，所提出的基于ELM的级联JFSCE方法能够显著降低FS的错误概率和CE的归一化均方误差（NMSE），即使在参数变化的影响下也是如此.
英文摘要：
Due to the interdependency of frame synchronization (FS) and channel estimation (CE), joint FS and CE (JFSCE) schemes are proposed to enhance their functionalities and therefore boost the overall performance of wireless communication systems. Although traditional JFSCE schemes alleviate the influence between FS and CE, they show deficiencies in dealing with hardware imperfection (HI) and deterministic line-of-sight (LOS) path. To tackle this challenge, we proposed a cascaded ELM-based JFSCE to alleviate the influence of HI in the scenario of the Rician fading channel. Specifically, the conventional JFSCE method is first employed to extract the initial features, and thus forms the non-Neural Network (NN) solutions for FS and CE, respectively. Then, the ELM-based networks, named FS-NET and CE-NET, are cascaded to capture the NN solutions of FS and CE. Simulation and analysis results show that, compared with the conventional JFSCE methods, the proposed cascaded ELM-based JFSCE significantly reduces the error probability of FS and the normalized mean square error (NMSE) of CE, even against the impacts of parameter variations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
