###### Abstract
[5] propose a structured approach to defining semantic information, semantic noise, and the fundamental limits of semantic coding. It appears that recent advances have shown the significance of deep learning (DL) and the impacts of physical-layer (PHY) communications. These perspectives lead to end-to-end learned transmission models, adaptive modulation techniques, and AI-driven semantic communication. Different from conventional rule-based systems, deep learning facilitates global optimization across transmitter, channel, and receiver components, thereby paving the way for semantic-aware communication models. [PERSON] & [PERSON] [6] introduced the concept of autoencoder-based communication, where a transmitter, channel, and receiver are jointly optimized as a deep neural network. Their work shows that deep learning can substitute traditional encoding, modulation, and decoding blocks, which offers improved performance under complex channel conditions. [PERSON] et al. [7] investigated the potential of deep neural networks (DNNs) to enhance modulation, signal compression, and detection processes. Early works such as DeepSC have demonstrated the potential of end-to-end deep learning-based semantic communication models [8]. They have proposed a system that is capable of jointly performing semantic channel coding for text transmission using neural networks (NNs). Furthermore, DeepSC-S extends semantic communication to speech signals [9]. The utilization of semantic speech information has been shown to facilitate more efficient transmission compared to conventional systems. Specifically, the NN-based joint design of speech coding and channel coding has been developed to facilitate the learning and extraction of essential speech information. L-DeepSC [10] has been introduced as a lightweight alternative to DeepSC, with the objective of reducing computational complexity for Internet of Things (IoT) applications. This objective is pursued by employing model compression techniques such as quantization and model pruning. [PERSON] et al. [11] investigated Federated Learning (FL) for Audio Semantic Communication (ASC), introducing a wav2 vec-based autoencoder that extracts semantic speech features for transmission over wireless networks. Although there exist remarkable studies in this context, these models often assume fully neural transceivers, making them impractical for real-world integration with existing wireless infrastructure. They lack structured feature discretization and remain heavily dependent on black-box AI models. Numerous studies suggest the concept of analog modulation which involves the transmission of continuous signals without discretization into constellation symbols, allowing for the representation of any value. However, this ideal assumption regarding modulation poses significant challenges in practical implementation due to limitations inherent in hardware components, such as power amplifiers. With regard to digital semantic communications, [PERSON] et al. [12] introduced D\({}^{2}\)-JSCC, a digital deep joint source-channel coding (JSCC) framework for semantic communication by utilizing deep source coding to extract and encode semantic features before transmission and employing adaptive density models to jointly optimize digital source and channel coding for image-based transmission. [PERSON] et al. [13] proposed a Joint Coding-Modulation (JCM) framework based on Variational Autoencoders (VAEs) to facilitate digital semantic communication by using the Gumbel Softmax method [14] to create a differentiable constellation of symbols for image semantic communications. [PERSON] et al. [15] introduced HybridBSC, a hybrid bit and semantic communication system that enables the co-existence of semantic information and bit information within the same transmission framework for image transmission. Alongside these works, there are numerous surveys and tutorials designed to give a broad overview of semantic communications [16; 17; 18; 19; 20].