## 1 Introduction
The separation theorem posits that separate source and channel coding (SSCC) is theoretically optimal in terms of distortion, with source coding eliminating data redundancy and channel coding ensuring reliable transmission in noisy and interference-prone environments. Moreover, under certain conditions, incorporating perceptual constraints renders separation suboptimal in the absence of common randomness between the encoder and decoder [32]. Recent research has increasingly focused on AI-empowered communication systems, where communication nodes are enhanced through machine learning techniques, such as generative modeling, to improve the comprehension and generation of high-fidelity content for downstream tasks, including image and video transmission, augmented and virtual reality (AR/VR), the Internet of Things (IoT), and vehicular-to-everything (V2X) applications. These systems are trained end-to-end for downstream tasks using generative objectives that inherently incorporate perceptual measures. Inspired by these information theoretic results, which suggest that common and private randomness at the encoder and decoder provide advantages in terms of the RDP trade-off, AI-empowered generative models can be deployed at the transmitter and receiver nodes, with these randomness sources serving as shared or private random seeds. As the main purpose of this paper, we are devoted to highlighting efforts in the field of information theory aimed at elucidating the fundamental limits of source coding and communication with perceptual-oriented metrics. Inspired by these findings, we demonstrate the utility of common and private randomness in generative modeling architectures, such as variational auto-encoders (VAEs), generative adversarial networks (GANs), diffusion models, and transformers, through the lens of the RDP trade-off. These generative modeling architectures can be designed and deployed at communication nodes to support downstream tasks requiring high perceptual fidelity. We provide a comprehensive overview of common perceptual measures in Section 2. Subsequently, Section 3 discusses the information-theoretic results and their implications with respect to the additional rate required to meet the new perceptual constraint. Section 4 introduces several generative modeling architectures, including VAEs, GANs, diffusion models, and transformers, used in communication systems, emphasizing the importance of high perceptual fidelity in downstream machine learning tasks. Section 5 characterizes the role of generative models in AI-empowered semantic communication systems. Finally, Section 6 explores future directions in information theory and communication. Through the lens of the RDP trade-off, we demonstrate that the introduced perceptual constraint leads to an increase in expected traffic within communication networks. The key takeaways include the following:  
* Perceptual constraints fundamentally alter the rate-distortion trade-off, requiring additional rate even with unlimited common randomness. * Generative models naturally align with RDP objectives, making them powerful tools for developing perceptually optimized communication systems. * The practical implementation of RDP-aware systems faces challenges related to computational complexity and the need for robustness across diverse channel conditions.