The Next Communication Paradigm Led by AI-Based Semantic Communication

Shannon's Law

Contemporary communication has evolved based on Shannon's Law. This theory defines the limit of network communication, and attempts have been made to increase the maximum channel capacity of the formula described below. Examples include technologies such as MIMO (Multiple Input Multiple Output). These technologies are part of an effort to achieve higher channel capacity, grounded in 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 terms represent Channel Capacity (C), Bandwidth (B), and Signal-to-Noise Ratio (S/N), respectively. To explain it simply in words, the Maximum Communication Rate (C) is the total physical frequency resource available to the system (B) multiplied by the **Efficiency ( $\log_{2}(1 + S/N)$ 50e45f92ab02a6e49994c

With the emergence of this law for calculating channel capacity, the telecommunications industry began to concentrate efforts on increasing channel capacity. Consequently, the innovations in communication over approximately 70 years have largely stemmed from 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. A limit has begun to arise 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 it is near impossible for the current communication network to withstand this. Simply laying more cables and installing more antennas reaches an economic and physical limit.

Thus, a paradigm shift is being researched, moving away from the conventional paradigm of accurately transmitting every bit, and instead aiming to transmit only the context by introducing intelligence into communication. (*The concept itself has existed for several decades.) This is a change necessitated by the powerful advancement of recent intelligent models and the need for communication of increasingly massive data.

This is referred to as Semantic Communication, which involves the exchange of meaning (意味).

Semantic Communication

While conventional communication transmitted the entire data, 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, and they categorized 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 desired 'meaning'?
3. Effectiveness Problem: How effectively does the conveyed meaning influence the receiver's actions?

The evolution of communication has largely resolved the technical problem, and the current task involves translating 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," the method conveys only the core 'meaning' such as "Fire outbreak, immediate dispatch required." This intentionally omits 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 dramatically reduce the amount of data transmission needed for situational awareness.

The core encoding/decoding logic of this semantic communication operates on the application layer, although it is a communication paradigm. 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 take the form of an inference model or similar with the same knowledge base, enabling communication that exchanges semantics without transmitting massive data.

Naturally, this is guaranteed by the completeness of the existing communication paradigm. First, it must be technically possible to transmit symbols accurately, and this level of maturity has already been achieved. The primary challenge now is how well the transmitted symbols convey and interpret the semantics of the information, and research is only just beginning in this area.

However, unlike conventional syntactic communication systems, communication systems based on this semantic context are highly likely to encounter problems because they rely on AI or similar for reliability. Even if they possess the same Knowledge Base, different interpretations can emerge from the black-box area of the models.

Postscript

It is anticipated that 6G (Sixth Generation) mobile communication will incorporate this semantic communication, leading to an intelligent internet system. However, there is a question mark over why a paradigm operating on the application layer becomes a research topic for mobile network operators. My intuition suggests that mobile network operators are responsible for guaranteeing maturity Level 1, where symbols and bits are transmitted accurately 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 a new paradigm can exist in communication technology, which must be fundamentally based on reliability. I also harbor these doubts and personally hold a somewhat negative stance.

Nevertheless, the reason I write this article is that I find the next paradigm of mobile communication to be unfolding in a quite interesting manner. The introduction of satellite internet to expand channel capacity is virtually a foregone conclusion 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.