# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 3 Receiver Design Considerations for LIS: Proposed Improvements in Channel Estimation
### Optimized Pilot Design and Enhancing the Quality of Pilot Data
#### 3.1.2 Hadamard as Orthogonal Pilot Sequences
## 6 Future Research

Future research will focus on the practical implementation of these methods in real-world LIS systems and on optimizing their performance under various and specific channel conditions. Future work may also focus on extending the proposed methods to time-varying channels and incorporating advanced estimation techniques, such as MMSE and deep learning-based approaches, for further performance gains. Methodology, R.D. ; Validation, M.M.d.S. ; Investigation, A.G. All authors contributed equally to the article. All authors have read and agreed to the published version of the manuscript. This work was funded by FCT/MCTES through national funds and, where applicable, co-funded EU funds under the projects UIDB/EEA/50008/2020 and 2022.03897. PTDC. Not applicable. The data were obtained using Monte Carlo simulations and implemented using MATLAB R2024b. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. We acknowledge the support of FCT/MCTES, as described above in \"Funding\". We also acknowledge the support of Autonoma TechLab for providing an interesting environment to carry out this research. The authors declare no conflicts of interest. ## References

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\begin{tabular}{c c c c c c c} \hline \hline
**Receiver** & **BER** & **Complexity** & **Energy** & **Interference** & **Spectral** & **Accuracy** \\
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**EGC** & Moderate & Low & High & Low & Moderate & Moderate \\ \hline \hline \end{tabular}
\end{table} 
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