The Next Communication Paradigm Led by AI-Based Semantic Communication

Shannon's Law

Contemporary communication has evolved based upon Shannon's Law. This constitutes a theory that determines the limitations of network communication, and the attempts have been to increase the maximum channel capacity of the formula described subsequently. Examples include technologies such as MIMO (Multiple Input Multiple Output). These technologies are part of an effort to achieve higher channel capacity, based on Shannon's theory.

Shannon's Law is expressed by the following formula for calculating channel capacity:

$$C = B \cdot \log_{2}(1 + \frac{S}{N})$$ author: yoonhyunwoo

These respectively denote Channel Capacity, Bandwidth, and Signal-to-Noise Ratio. In simpler terms, the Maximum Communication Speed (C) is the product of the total amount of physical frequency resources that the system can utilize (B) and the **Efficiency ( $\log_{2}(1 + S/N)$ afe9fa2bfe0934a415fbc

With the emergence of this law for calculating channel capacity, the telecommunications industry began to concentrate efforts on increasing channel capacity. Consequently, for approximately 70 years, most innovations in communication have been achieved through the improvement of channel capacity.

However, in the current era, processing resources have advanced significantly. Communication has progressed from sending text to now transmitting spatial vectors. Limitations have begun to arise in reliably splitting and transmitting all this data. For instance, the data generated by an autonomous vehicle can amount to several terabytes per day, and it is nearly impossible for the current communication network to sustain this. Simply laying more cables and installing more antennas reaches economic and physical limitations.

Thus, a paradigm shift is occurring, moving away from the existing paradigm of accurately transmitting every bit, to one that seeks to transmit only the context by introducing intelligence into communication. (*The concept itself has existed for decades.) This change is driven by the recent powerful advancement of intelligent models and the increased necessity for the communication of larger volumes of data.

This is referred to as Semantic Communication, as it involves the exchange of meaning.

Semantic Communication

Semantic communication aims to transmit only the core meaning, or context, contained within the data, in contrast to the traditional method of transmitting the entire data set.

This issue was already raised in the communication model of Shannon and Weaver, who separated the maturity of communication into three levels.

1. Technical Problem: How accurately can a symbol be transmitted? (This is the core domain of my theory.)
2. Semantic Problem: How accurately does the transmitted symbol convey the intended 'meaning'?
3. Effectiveness Problem: How effectively does the conveyed meaning influence the receiver's behavior?

The advancement of communication has largely resolved the technical problem, and the current task is to address the semantic and effectiveness problems.

The difference between maturity level 1 and levels 2 and 3 (semantic communication) is typically illustrated using the example of a burning house.

A house is on fire.

In the current communication paradigm, this scene is meticulously converted into data and transmitted as a photograph.

In semantic communication, instead of sending all the data, such as "black smoke is coming out of the window and flames are visible," it transmits only the core 'meaning,' such as "Fire outbreak, immediate dispatch required." This method involves boldly omitting unnecessary information, focusing on causing the receiver to take a specific action (dispatch).

If the communication is between endpoints sharing the same knowledge base within the category of firefighting, this can drastically reduce the volume of data transmission required for situational awareness.

The core encoding/decoding logic of such semantic communication is a communication paradigm, but it operates on top of the application layer. On the transmitting side, a semantic encoder transforms the given data into semantic data, and on the receiving side, a semantic decoder processes it into a format usable by the backend source. Both will likely take the form of an inference model possessing the same knowledge base, enabling communication that exchanges semantics without the need for massive data transfer.

Naturally, this is guaranteed based on the completeness of the existing communication paradigm. First, the technical capability to accurately transmit symbols must be ensured, and this level of maturity has already been achieved. The main challenge now is how well the transmitted symbol conveys and interprets the semantics of the information, and research into this is just commencing.

However, unlike the existing syntactic communication system, a communication system based on such semantic context is highly likely to encounter issues because it relies on AI, etc., for reliability. Even with a shared Knowledge Base, different interpretations may arise from the black box area of the model.

Postscript

It is suggested that 6G (6th generation) mobile communication will adopt semantic communication to become an intelligent internet system, but there is a question mark regarding why a paradigm operating on the application layer becomes a research subject for mobile telecommunication companies. My intuition suggests that telecommunication companies are responsible for ensuring maturity level 1, where symbols and bits are transmitted accurately from a technical perspective, and the point at which semantic communication operates is already within the domain of application programs.

Furthermore, there is a question as to whether this constitutes a new paradigm in communication technology, which must inherently prioritize reliability as a fundamental value. I also harbor such doubts and am personally somewhat skeptical.

Nevertheless, the reason for writing this article is the belief that the next paradigm of mobile communication is unfolding in a quite intriguing manner. The introduction of satellite internet for expanding channel capacity is virtually a foregone conclusion with the emergence of Project Kuiper, Starlink, etc., and the attempt to overcome the limitations constrained by Shannon's Law in a novel manner was quite fascinating.