# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 1 Introduction  
The use of multimedia services for everyday activities such as taking pictures, streaming, broadcasting, teleconferencing, video-on-demand, and peer-to-peer video sharing has undergone unprecedented growth in recent years. It has been forecasted that by 2024, almost 95% of global data traffic will be image and video, driven mainly by the vast number of consumer communication devices being introduced to the market, coupled with users' increasing consumption of multimedia services. Crucially, the quality of the received media is of prime importance to both users and service providers, regardless of where the media is generated or how users are connected to the service. As these users are increasingly mobile, providing the necessary capacity to handle this ever-increasing media traffic poses significant challenges for the future communications infrastructure, especially in mobile-wireless systems where the spectrum capacity and handset resources (e.g., battery capacity) are limited. The capacity-efficiency challenge is also evident in image/video coding and connected processing. Even though the latest standards, such as 5G technologies, increasepeak data rates in the downlink to as much as 10 Gbps, the state-of-the-art video coding standards, such as VVC, improve coding efficiency by approximately 50% compared to their predecessor, H.265/HEVC. However, this improvement will still not be sufficient in practical networks where capacity is shared between multiple users for voice, image, data, and video services. For example, new XR applications are expected to demand spatial resolutions of 15,360 \(\times\) 7680, frame rates of up to 300 fps, and color depths of up to 12 bits, in contrast to conventional 4 K video with frame rates of 50 fps and color depths of 8 or 10 bits. These XR applications require huge data rates that cannot be supported by 5G and VVC. Therefore, these emerging video formats pose a significant challenge even to state-of-the-art video coding standards and mobile communication standards. This is especially true for bandwidth-hungry and resource-intensive multimedia applications that will adopt upcoming high-resolution video formats, such as UHD, SHD, HDR, 360-degree videos, 6-DOF video content, and real-time interactive multimedia applications like ACTION-TV [1], which aim to provide a superior visual experience over existing conventional formats and technologies. SC is a communication paradigm that has gained attention from both academia and industry, as it offers potential advantages over classical communication systems in bandwidth-limited channels [2; 3; 4]. This paradigm aims to deliver the semantic meaning of a message, rather than its exact form, by utilizing common prior knowledge and semantically encoded messages. It is expected to outperform traditional communication techniques by significantly reducing the physical bandwidth required between the transmitter and receiver to convey intended messages. While the benefits of SC are evident in high-bandwidth applications such as 16 K video, 3D video, and AR/VR/MR streaming, its efficacy in M2M communication and IoT remains unclear. However, SC has the potential to reduce bandwidth and complexity, increase range, and enable longer operational cycles for battery-powered devices in IoT and M2M communications. The conventional approach to communication focuses on transmitting the minimum number of bits with minimal errors between two points. This approach is based on [PERSON]'s 1948 paper [5], which established the concept of channel capacity and demonstrated that data rates below this capacity can be achieved without incurring an exponentially higher number of errors at the receiver. However, this method does not explicitly leverage the information about the source available at the transmitter. SC is a paradigm that addresses the second layer of communication, known as the semantic problem, by delivering the semantic meaning of the message rather than its exact form.