Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model

TL;DR

This paper reviews the application of big AI models to wireless networks, analyzing differences from language models and proposing methodologies for developing wireless-native BAIM technologies.

Contribution

It provides a comprehensive analysis of how BAIM techniques can be adapted for wireless systems, highlighting key differences and proposing specific development methodologies.

Findings

Wireless BAIM has unique challenges compared to NLP models.

Developing wireless-native BAIM requires tailored technical approaches.

The paper offers methodologies for integrating BAIM into wireless systems.

Abstract

Research on leveraging big artificial intelligence model (BAIM) technology to drive the intelligent evolution of wireless networks is emerging. However, breakthroughs in generalization brought about by BAIM techniques mainly occur in natural language processing. There is a lack of a clear technical direction on how to efficiently apply BAIM techniques to wireless systems, which typically have many additional peculiarities. To this end, this paper reviews recent research on BAIM for wireless systems and assesses the current state of the field. It then analyzes and compares the differences between language intelligence and wireless intelligence on multiple levels, including scientific foundations, core usages, and technical details. It highlights the necessity and scientific significance of developing wireless native BAIM technologies, as well as specific issues that need to be considered for technical implementation. Finally, by synthesizing the evolutionary laws of language models with the particularities of wireless systems, this paper provides several instructive methodologies for developing wireless native BAIM.