Точка Синхронізації

Researchers specializing in wireless communications published a technical paper on the arXiv preprint server in February 2025, introducing a novel framework that utilizes Large Language Models (LLMs) to optimize the physical layer of 6G Internet of Things (IoT) networks. The study proposes a system called Prompt-Engineering-Based Real-Time Feedback and Verification (PE-RTFV), designed to bridge the gap between generative artificial intelligence and high-speed telecommunications infrastructure. The primary goal of this research is to solve complex optimization tasks in wireless systems by treating LLMs not just as text processors, but as real-time decision-makers within the hardware-adjacent layers of a network. The PE-RTFV framework relies on an iterative process that leverages the closed-loop feedback naturally present in wireless communication systems. By using prompt engineering to guide the LLM, the system can analyze current signal performance and hardware constraints to adjust parameters dynamically. This approach represents a significant shift from traditional optimization methods, which often rely on rigid mathematical models that may struggle to adapt to the highly volatile environments characteristic of dense 6G IoT deployments. The researchers argue that the inherent reasoning capabilities of LLMs can lead to more efficient resource allocation and better signal integrity. Furthermore, the paper highlights how this architectural integration could streamline the development of future autonomous networks. By implementing real-time verification, the proposed framework ensures that the LLM-generated optimizations are technically sound and performance-enhancing before they are fully deployed across the network. This research marks a critical step toward the realization of 'AI-native' 6G networks, where artificial intelligence is embedded directly into the physical infrastructure rather than acting merely as an external application, ultimately aiming to support the massive connectivity requirements of next-generation smart cities and industrial automation.