## 5 AI-Empowered Communication with Perceptual Constraint
### AI-Empowered Semantic Communication
#### 5.2.5 Basic Unit for AI-Empowered Communications  
In contemporary communication networks, formatted blocks of data are transmitted across various layers of the protocol stack. For instance, in the physical layer of 5G networks, binary digits (bits) are modulated and transmitted over the air interface. In the transport layer, messages from applications are encapsulated into segments such as TCP segments, which include headers, source and destination port numbers, and check-sums. This segmentation focuses on the accurate transmission of application messages, with the goal of minimizing the average distortion measure, which is additive in terms of segmental distortion.  
In DeepJSCC-like systems, analog signals are employed for end-to-end uncoded transmission [135; 134]. Ref. [134] further integrates uncoded signals into a digital modulation system, which is then transmitted and received by the decoder. In the context of low-latency transmission, where information freshness is crucial, short packets serve as the basic unit [138], and the age-distortion trade-off has been studied [139]. Ref. [105] combines analog signals for JSCC with conventional digital signals to form a hybrid communication scheme.  
In many generative modeling architectures, tokens function as the fundamental unit. Ref. [140] introduces a token-domain multiple-access scheme, which allows multiple devices to transmit over the same multiple-access channel in a non-orthogonal manner. In the realm of source coding, ref. [141] utilizes 1D tokenization to project 2D images into variable-length, ordered 1D token sequences. This method describes images in a coarse-to-fine manner, thereby achieving a trade-off between the number of tokens (rate) and perceptual quality. The SoTA tokenizer FlowMo is a transformer-based diffusion autoencoder that combines mode matching pre-training and mode seeking post-training [142]. In [143], a video tokenizer is developed for both videos and images using a common token vocabulary. These systems are trained end-to-end using losses that incorporate perceptual quality. Furthermore, technologies such as chunking and the semantic compression of token chunks have been developed in [90], leveraging the parallel computational power of GPUs. In this approach, chunks of tokens serve as the fundamental unit. Given the diverse representation of information, the integration of signals such as tokens and architectures such as tokenizers into intelligent communication systems is necessitated through the lens of the RDP framework.  
The integration of generative models into communication systems demonstrates substantial potential for improving perceptual quality while maintaining efficient transmission. Joint source-channel coding approaches like DeepJSCC have shown particular promise by directly optimizing for end-to-end reconstruction quality rather than treating source and channel coding as separate problems. However, practical implementation faces challenges related to computational complexity, training data requirements, and the need for robustness against varying channel conditions.