Digital Twin Prototype for Real-Time Indoor Wi-Fi Network Optimization
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Abstract
The implementation of a digital twin for Wi-Fi networks arises from the rapid evolution of wireless technologies and the growing global interest in applying cloud computing and big data for intelligent network management. This research presents a digital twin system that integrates the USRP N310 as a software-defined radio node to monitor and control a Wi-Fi network operating in the 2.4 GHz and 5 GHz frequency bands. The system generates, processes, and analyzes OFDM-based signals to automatically evaluate and optimize transmission power levels according to real-time reception conditions. Data flow and control between the physical and virtual layers are managed through Python scripts and a local MQTT server, ensuring synchronization with autonomous system operation and high efficiency. Additionally, a three-dimensional representation developed in Unity enables real-time visualization of transmitter and receiver processes, replicating their interaction within a realistic 3D environment. Experimental results demonstrated that reducing the transmission power to 12 dBm maintained a stable received signal at -29.59 dBm, validating the system’s ability to sustain performance while improving energy efficiency. These outcomes confirm that the proposed digital twin not only enables adaptive control of transmission power but also enhances the understanding of wireless network dynamics under varying operational conditions. Consequently, the system efficiently manages Wi-Fi networks, optimizing critical parameters such as transmission power, energy consumption, and overall network performance.
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