# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 2 Related Work  
In the development of 6G networks, it is anticipated that SC will become a prominent approach for designing E2E communication systems [8; 9; 10; 11]. SC involves integrating the meaning of the data into various tasks related to processing and transmitting data, which is a significant departure from the traditional Shannon paradigm [5]. Although SC is expected to provide significant advances to emerging modern applications like VIoT, 360\({}^{\circ}\) video, AR/VR/MR applications, M2M, and M2H communications, many challenges still exist that must be addressed to make it feasible for real-life applications. These challenges can be divided into the following categories:  
1. How multimedia data can be effectively compressed to suit SC applications. 2. How this source encoding can be better integrated with optimized channel coding for SC applications. 3. How these source and channel coding techniques behave under channel noise. 4. How SC can be applied in M2M and M2H communications. The following texts summarize the related work reported on the above challenges and their limitations. As a starting point, [PERSON] et al. [2] examined the objectives and the strong rationales for implementing SC in 6G networks and presented a summary of the key concepts and crucial technologies that serve as the foundation of SC in 6G networks. Meanwhile, in the field of NLP, deep learning-powered systems have achieved remarkable results in analyzing and comprehending a wide range of linguistic documents. A SC system (DeepSC) introduced in [3] aims for text transmission. Unlike conventional systems that deal with bit or symbol errors, DeepSC aims to recover the meaning of phrases to enhance the system's capacity and reduce semantic errors. Transfer learning is used to accelerate joint transceiver training and improve the model's performance in various communication settings. DeepSC-S [4] employs an attention mechanism in the encoder/decoder structure to learn and extract relevant information. The attention mechanism is used to minimize the distortion of the received signal. Results show that DeepSC-S is more robust to channel noise, especially in the low SNR levels. While the above SC systems explore the text data domain, the importance of exploring SC in the multimedia domain has shown great potential. The authors of [12] consider image corruption during semantic transmission as a form of data augmentation in CL and leverage CL to reduce the semantic distance between the original and the corrupted reconstruction while maintaining the semantic distance among irrelevant images for better discrimination in downstream tasks. The study in [13] proposes a SC system for image transmission that can distinguish between ROI and RONI based on semantic segmentation. The drawback of this method is that it only divides the image into two segments based on bandwidth requirements, not according to the semantic information present. WITT [14] utilizes Swin Transformers as a backbone to extract long-range information specifically optimized for image transmission in wireless channels. WITT introduces a spatial modulation module that scales the latent representations based on channel state information. The above systems propose efficient SC systems to transmit images over noisy channels but have not deeply studied the effect of channel noise on the transmitted semantic data. Recent advancements in E2E communication systems, which leverage deep learning capabilities, have led to the optimization of transceivers jointly [15; 16; 17]. These systems integrate all physical layer components, enabling the development of DeepJSCC for image transmission [18]. Unlike conventional systems, this approach does not rely on separate source and channel coding. Instead, the CNN directly maps image bits into channel input symbols. The CNN encoder and decoder are trained jointly, while the communication channel is an untrainable AWGN channel.