# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 5 Conclusions and Future Work  
This paper proposes an autoencoder-based semantic image communication system that compresses and transmits images over an error-prone channel, with the goal of decoding and reconstructing the original data at the receiver under varying channel noise levels. To exploit the semantics of the information, joint training of the encoder and decoder in the autoencoder is performed, generating a highly reduced dimensional vector called the latent vector. Once trained, the encoder and decoder are placed at the transmitter and receiver, respectively. The transceivers are connected through an error-prone channel, and the latent vector is transmitted over this channel under different SNRs. A polar channel encoder and a BPSK modulator are used on the transmitter side, with corresponding decoders/demodulators at the receiver. The proposed autoencoder is trained under different channel noise levels to minimize reconstruction error at the decoder, enabling it to better reconstruct the original data from noisy inputs. This approach ultimately improves the system's performance in noisy channels.  
The simulation results illustrate that the proposed system maintains excellent image quality and very high classification accuracy above 3.5 dB channel SNR. Below 3.5 dB, the autoencoder trained with different noise levels performs much better than the autoencoder trained with zero errors. The reason for this behavior is that channel errors introduced during training helped the model mitigate the impact of channel noise at lower SNRs. All autoencoders fail to produce good image quality at very low channel SNRs. This is expected, as in any communication system, where decoders struggle to reconstruct images at low SNRs. The results are compared against an equivalent JPEG transmission system, showing that the proposed system's performance is far superior to the JPEG system across all channel SNRs under similar constraints. Both image quality and compression performance are significantly better in the proposed system than in the JPEG system. We also tested the proposed framework on a complex dataset, as explained in Section 2, and observed similar performance enhancements. Therefore, we can conclude that the proposed SC system is independent of specific datasets and performs equally well under various conditions.  
Future work will aim to more comprehensively represent the complexities of real-world images, better reflecting the diversity and challenges encountered in practical applications. The concept and approach presented in this paper are not limited to these datasets. We chose MNIST and CIFAR-10 as representative benchmarks. The experiments conducted on these two datasets serve to validate the methodology, and the same principles can be extended to more complex datasets in future work. Furthermore, we plan to extend this work by implementing a scalable semantic image communication system that can operate efficiently over error-prone channels. Additionally, we aim to develop a similar system for video transmission.  
Methodology, P.S. and H.R.; software, P.S.; validation, P.S.; formal analysis, P.S. and A.F.; investigation, P.S.; resources, P.S.; data curation, P.S.; writing--original draft preparation, P.S. and M.L.; writing--review and editing, P.S., M.L., T.S. and A.F.; supervision, N.R. and A.F. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the University of Strathclyde, UK.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
6-DOF  
AESC  
AI  
AWRGN  
6 Degrees of Freedom  
AESC  
Auteonceder-Based Semantic Communication  
Atificial Intelligence  
Additive White Gaussian Noise