# Combined Improved CEEMDAN and Wavelet Transform Sea Wave Interference Suppression
## 4 Experimental Results
The proposed method could remove the interference signal, but the filtering method could not effectively eliminate the interference. The encoding adopts 16 QAM. Table 3 shows the BER of the two different methods under strong wind. In Figure 9, the black asterisk represents the decoding result of the filtering, and the BER was 0.07%. The red plus sign represents the processing result of the proposed algorithm, and the BER was 17.14%. Under the condition of sea wave interference, the proposed method could effectively reduce the BER. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Bit Error Rate** \\ \hline Proposed algorithm & 0.07\% \\ Filtering method & 17.14\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: BER of the proposed algorithm and filtering (strong wind). Figure 8: (**a**) The proposed algorithm’s result (strong wind); (**b**) time–frequency result; (**c**) proposed algorithm’s result (strong wind); (**d**) time–frequency result. Due to the limitation of the experimental conditions, the sea wave interference caused by the wind did not exceed 0.5 m. Under the two different conditions of weak wind and strong wind, the proposed algorithm could well eliminate the wave interference and ensure a low BER. Compared with the proposed algorithm, the filtering method needs to know the interference frequency distribution to design the corresponding filter. However, the sea wave frequencies are in a wide range, and the sea wave frequencies may be within the range of the communication frequency band, which results in greater difficulty in the design of the filter. The proposed algorithm is not affected by the interference in the communication frequency band. It mainly searches for the IMFs corresponding to the reconstruction scale, which contain the characteristic information of the WSAW and reconstructs it to obtain the WSAW signals. In practical applications, the method proposed in this paper can adapt to more complex sea surfaces than the filtering method. In addition, the above related experiments were all based on a fixed distance from the radar to water and the distance from the speaker to water. However, in actual situations, the actual distance exceeds the set distance by the water tank experiment. Recall the AEIC, the receiver employs a millimeter wave radar to measure the WSAW to realize the information transmission from underwater to above water. The radar transmits the FMCW signals and measures their reflection off the water's surface. The radar measures the change in the distance by estimating the phase of the beat signal after the mixed frequency of the transmitted signal and the received signal. The phase of the beat signal can be expressed as Equation (8). In Figure 5a, the phase changes include the WSAW signals and the sea wave signals. The extracted phase changes are shown in Figure 5b, which are the input of the proposed algorithm. Therefore, we can increase the transmission power to ensure that the radar detects the water within an effective distance. As for the distance from the speaker to the water, it has little influence on the results. In Equation (3), the frequency of the WSAW is consistent with the transmitting frequency of the speaker, and it is not affected by the distance. The essence of AEIC is to estimate the frequency of the WSAW. Therefore, the preprocessed phase signals contain the WSAW signals, and we can separate the WSAW signal from the sea wave signals. Figure 9: The results of the constellations of the ideal, proposed algorithm and filtering (strong wind).