# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 2 Related Work
The autoencoder architecture is used in DeepJSCC [18] to map the source image directly to channel inputs, and the decoder is trained to reconstruct the image from the input. The proposed method [19] for dimension reduction and image reconstruction involves an architecture that is different from other deepnetworks that use iterative learning. Instead, the hidden layers of this method are obtained through four distinct steps. This approach results in higher learning efficiency compared to deep networks. The work in [20] addresses the task of real-time dynamic medical image reconstruction from limited samples, employing an autoencoder to 'learn' the reconstruction process from a training set. This approach is based on the ability of neural networks to approximate universal functions. In a similar vein, the article [21] proposes a solution to the image reconstruction problem in ECT using a supervised autoencoder neural network. The proposed network consists of an encoder and a decoder. The authors utilize a simulation-based dataset comprising 40,000 pairs of instances, each containing a capacitance vector and a corresponding permittivity distribution vector, for training and evaluating the autoencoder's performance. The training and testing are conducted using 10-fold cross-validation on this dataset. While autoencoder-based deep learning techniques have been extensively employed in previous research on image compression, none of these studies have explored autoencoders for source coding under varying levels of physical channel noise. The work presented in [22] introduces AESC, a SC scheme for wireless relay channels. AESC employs an autoencoder module to encode and decode sentences at the semantic level, ensuring robustness against system noise. Moreover, a semantic forward mode is introduced to enable the relay node to transmit semantic information directly. In the study described in [23], a goal-oriented SC framework is proposed for VANETs. This framework utilizes a DAE to capture semantic information from traffic signs, which is then transmitted to connected autonomous vehicles. In [24], the causes of semantic noise are analyzed, and an adversarial training approach is proposed to incorporate samples with semantic noise into the training dataset. A masked autoencoder is designed as the architecture of a robust SC system, where a portion of the input is masked to mitigate the effect of semantic noise. In [25], a zero-shot learning model based on an SAE is introduced. The SAE model employs a simple and computationally efficient linear projection function and incorporates an additional reconstruction objective to learn a more generalizable projection function. The techniques proposed in [26] focus on transmitting audio semantic information, capturing the contextual features of audio signals. They introduce a wave-to-vector (wav2 vec) architecture-based autoencoder, utilizing CNNs to extract semantic information from audio signals. In [27], a CSAEC is presented. CSAEC aims to map data from different modalities to a shared low-dimensional space while preserving semantic information. To achieve this, an autoencoder is employed to establish the association between feature projections and semantic code vectors, considering the similarities across modalities. This approach facilitates the retention of semantic information while aligning representations across different modalities. Notably, none of the mentioned works have explored semantically enabled image transmission over noisy channels. Therefore, in summary, the existing literature suggests that autoencoders are not typically used for image transmission or SC over a noisy channel, which highlights the main novelty of this paper. On the other hand, in this paper, we address the major issues with our previous work on semantically enabled GAN-based image transmission systems as explained above.