# Cascaded ELM-based Joint Frame Synchronization and Channel Estimation over Rician Fading Channel with Hardware Imperfections

## TL;DR

This paper introduces a cascaded Extreme Learning Machine (ELM) approach for joint frame synchronization and channel estimation in Rician fading channels, effectively mitigating hardware imperfections and LOS path effects to improve wireless communication reliability.

## Contribution

It proposes a novel cascaded ELM-based joint FS and CE scheme that enhances performance over traditional methods, especially under hardware imperfections and Rician fading conditions.

## Key findings

- Significantly reduces error probability of frame synchronization
- Decreases normalized mean square error of channel estimation
- Outperforms conventional JFSCE methods under hardware imperfections

## Abstract

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.