METHOD AND DEVICE FOR SAVING POWER USING CONTEXT INFORMATION-BASED ARTIFICIAL INTELLIGENCE IN WIRELESS COMMUNICATION SYSTEM

Smart overview of the Invention

The proposed method and device aim to enhance power efficiency in wireless communication systems by utilizing context information-based artificial intelligence. The approach involves the use of an AI model to derive preferred parameter values, such as discontinuous reception (DRX) control values, which are then communicated between a terminal and a base station. This interaction is facilitated through the establishment of a radio resource control (RRC) connection, optimizing the communication process and reducing power consumption.

Technical Field

The invention pertains to wireless communication systems, focusing on power-saving techniques through AI-based control of DRX and bandwidth part (BWP) values. It leverages context information to dynamically adjust these parameters, thereby optimizing resource usage and extending battery life for devices within the network. The system is designed to adapt to varying communication environments and user needs, enhancing overall efficiency.

Background

Modern radio access systems, such as CDMA, FDMA, and TDMA, support multiple users by sharing system resources. As demand for communication capacity grows, technologies like enhanced mobile broadband (eMBB) and massive machine type communications (mITC) have emerged. These developments necessitate advanced solutions for managing resources and ensuring reliable, low-latency service delivery, which is where the proposed AI-driven method comes into play.

Summary

The invention outlines a method for signal transmission and reception that incorporates AI to optimize DRX and BWP control values. By analyzing context information from both the terminal and base station, the system can adjust these parameters to better suit the current communication environment. This process involves learning and updating AI models to improve decision-making and power-saving capabilities over time.

Practical Implementation

In practical terms, the method involves a series of interactions between the terminal and base station. The terminal establishes an RRC connection, gathers context information, and uses an AI model to determine preferred parameters. These are communicated to the base station, which then sends back an RRC reconfiguration message. The terminal acknowledges this with a reconfiguration complete message, allowing data transmission to proceed efficiently. This cycle ensures optimal power usage without compromising communication quality.