# Blind Turbo Equalization of Short CPM Bursts for UAV-Aided Internet of Things
## 4 EMLVA for Blind Channel Equalization
### The EMVA/EMLVA Blind Equalizer
(**a**) BER as a function of \(E_{b}/N_{0}\) for \(\alpha=1.0\); (**b**) BER as a function of \(E_{b}/N_{0}\) for \(\alpha=0.9\); (**c**) BER as a function of \(E_{b}/N_{0}\) for \(\alpha=0.8\); (**d**) BER as a function of \(E_{b}/N_{0}\) for different exchange methods of extrinsic information with the HDA stopping criterion. ## 6 Discussion

In this paper, we focused our attention on the physical layer design of a UAV-aided IoT communications system. In recent years, an emerging and revolutionizing technology, RIS, can significantly improve communication performance by smartly reconfiguring the wireless propagation environment [38]. In the non-LOS scenarios of UAV-aided IoT, the RIS can be applied to maximize the received power of the user to keep the connection. In the presence of eavesdroppers, the

Figure 9: The performance of the EMLVA when the channel length is overestimated. (**a**) BER as a function of \(E_{b}/N_{0}\) for the blind equalizer in a Ricean fading channel when the channel length at the receiver is known (\(\tilde{N}_{c}=0\)) and when it is overestimated (\(\tilde{N}_{c}=1,2,3\)). BER as a function of \(E_{b}/N_{0}\) when the channel length is overestimated. (**b**) Average number of expanded trellis nodes per information symbol as a function of \(E_{b}/N_{0}\) when the channel length at the receiver is known (\(\tilde{L}_{c}=0\)) and when it is overestimated in terms of the symbol time (\(\tilde{L}_{c}=1,2,3\)). Average trellis nodes per symbol as a function of \(E_{b}/N_{0}\) when the channel length is overestimated. reflected signal by the RIS can be tuned to cancel out the signal from the sensors node at the eavesdropper by smartly adjusting the reflection coefficients [39]. Apart from this, when the RIS is applied in the UAV-aided IoT communications system, the structure of the transmitter and receiver can be simplified, further meeting the requirements of low power and cost [40]. Predictably, the application of the RIS to future UAV-aided IoT communications systems will fundamentally change their architecture and significantly improve their performance. ## 7 Conclusions

In this paper, the EMLVA based on the HMM was proposed as an efficient approach to blind equalization of short CPM bursts in a UAV-aided IoT communications scenario. The proposed method significantly outperformed the HMM blind equalization based on the standard Viterbi algorithm in complexity, while keeping a similar BER performance as the conventional one. An initializer was developed based on the BCA method, which ensured good convergence of channel estimation, called the BCA initializer. The EMLVA equalization can be further improved by using the decoder output to propose the turbo EMLVA equalization. Considering the convergence and relatively large decoding delay in outer turbo iterations, weighted extrinsic information with the HDA stopping criterion is proposed for iterative detection. The simulation results showed that the proposed blind turbo equalization achieved an excellent trade-off between complexity and performance, verifying its advantages and practical values against other conventional methods. Writing--original draft preparation, Z.P. ; writing--review and editing, C.X., H.W., Y.W. and D.G. ; visualization, Z.P. ; Methodology, D.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. ## 8 Acknowledgments

This work was supported by the National Science Foundation of China (Grant No. 10000000). ## References

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