The Next Communication Paradigm Led by AI-Driven Semantic Communication

Shannon's Law

Contemporary communication has evolved based on Shannon's Law. This theory defines the limit of network communication and represents attempts 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 the effort to achieve higher channel capacity based on Shannon's theory.

Shannon's Law is expressed by the following formula for calculating channel capacity: $$C = B \cdot \log_{2}(1 + \frac{S}{N})$$

These terms respectively represent Channel Capacity (C), Bandwidth (B), and Signal-to-Noise Ratio (S/N). To explain it in easily understandable terms, the Maximum Communication Speed (C) is calculated by multiplying the total physical frequency resources available to the system (B) by the Efficiency ( $\log_{2}(1 + S/N)$ ), which represents how many bits of information per 1 Hz of resource can be carried depending on the signal quality (S/N).

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 enhancement of channel capacity.

However, in the present era, the resources for processing have advanced significantly. Communication has progressed from transmitting text to transmitting spatial vectors. A limit is beginning to emerge in reliably segmenting and transmitting all this data. For instance, the data generated by an autonomous vehicle can amount to several terabytes per day, and sustaining this with current communication networks is nearly impossible. Simply laying more cables and installing more antennas reaches economic and physical limitations.

Thus, a paradigm shift is being researched, moving away from the existing paradigm of accurately transmitting every bit and instead seeking 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 necessity for the communication of increasingly massive amounts of data.

This is referred to as Semantic Communication, as it is communication that exchanges meaning (意味).

Semantic communication

While traditional communication transmits the entire data set, semantic communication aims to transmit only the core meaning embedded within it, that is, the context.

This issue was already raised in Shannon and Weaver's communication model, where they 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 recipient's behavior?

To date, the evolution of communication has largely solved 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 this data is 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," the method involves transmitting only the core 'meaning,' such as "Fire outbreak, immediate dispatch required." The purpose of this is to drastically omit unnecessary information and prompt the recipient to take a specific action (dispatch).

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

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

Naturally, this is guaranteed by the completeness of the existing communication paradigm. First, the technical ability 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 is only just commencing.

However, a communication system based on such semantic context, unlike the existing syntactic communication system, is highly likely to encounter problems because it relies on AI, etc., for reliability. Even if they share the same Knowledge Base, different interpretations may emerge from the black-box area of the model.

Epilogue

It is anticipated that 6G (Sixth Generation) mobile communication will incorporate this semantic communication to establish an intelligent internet system, but there is a question mark regarding why a paradigm operating on the application layer becomes a research topic for mobile carriers. My intuition suggests that mobile carriers are responsible for ensuring Level 1 maturity, where symbols and bits are accurately transmitted technically, 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 fundamentally prioritize reliability. I, too, harbor these doubts and am personally somewhat negative about it.

Nevertheless, the reason for writing this article is that the next paradigm of mobile communication is unfolding in a quite intriguing manner. The introduction of satellite internet for channel capacity expansion is virtually an established fact with the emergence of Project Kuiper and Starlink, and the attempt to overcome the limitations constrained by Shannon's Law in a new form was quite fascinating.

Since there is no content about GO, I will conclude with a gopher.