# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 1 Introduction
Currently, there is no standardized transmission strategy for SC, so systems must be designed within the existing communication framework. The challenge is to ensure that the transmitter's semantic information is preserved at the receiver while transmitting through the physical channel. Further research is needed to explore how different media types can be effectively transmitted using SC over conventional communication standards. This research aims to develop an autoencoder-based [6] SC system to transmit images over a noisy channel, optimizing bandwidth while maintaining image quality at the receiver for an M2M application. Figure 1 illustrates the SC framework used in this research. A multi-layered autoencoder, which serves as the semantic encoder/decoder pair in this study, generates a latent vector (LV) for a given image. This LV is then channel-encoded before being transmitted over a noisy channel to the decoder. At the decoder, the semantic bitstream is channel-decoded, and the estimated bitstream is used as input to the autoencoder decoder to reconstruct the desired image. Common knowledge, in the form of the dataset on which the autoencoder is trained, is shared between both the encoder and decoder. To reduce the amount of data transmitted, only the LV of a given image is sent through the noisy channel. This approach significantly reduces energy demand and wireless bandwidth usage, contributing to a more sustainable M2M communication network. To protect the LV, we specifically select polar codes as our channel coding scheme, as they demonstrate exceptional performance in achieving channel capacity, offer low-complexity decoding operations, and exhibit resilience against varying channel conditions. The code words are modulated using the simple yet effective BPSK [7] scheme over a well-studied AWGN channel. At the receiver end, the noisy LLRs are fed to the decoder, which computes the estimated codewords. We then increase the induced noise in the channel to evaluate the effect of white noise on SC and studied several scenarios. In the first scenario, the autoencoder is trained without any channel noise, and the impact of channel noise on the autoencoder is studied across a range of SNRs. In the second phase, the autoencoder is trained with different levels of channel noise and tested with varying SNRs. In the third scenario, we extend our experiments to include a more complex dataset, where a single decoder is trained under several BERs to observe the impact of operating under different noise levels. In all scenarios, the peak signal-to-noise ratio (PSNR) is calculated for the received images to measure the objective quality of the proposed architecture. Since there are no established semantic quality measurement techniques available, a CNN model is used to identify the semantic meaning of the received images, with its detection accuracy serving as an indicator of the subjective quality. Finally, a comparable JPEG image transmission system (defined in Section 4) is used to benchmark the proposed system. Results indicate that the proposed system outperforms the JPEG-compressed system by up to 17 dB in coding gain. The main novel contributions of the paper can be summarized as follows:  
1. A semantically trained autoencoder-based system is proposed for image transmission over resource-constrained, error-prone channels. 2. The effect of physical channel noise on designing autoencoder-based SC systems with existing physical communication channels is investigated, and important insights are derived. 3. A semantically enabled autoencoder-based image transmission system for M2M communication is demonstrated. The performance of the proposed architecture is compared to a comparable JPEG image transmission system. 4. The decoder of the proposed system is trained with noise-added latent vectors, aiding in learning a robust representation.