Tejas Networks has been granted a patent for a radio mapping architecture that integrates machine learning with mobile wireless networks. This system utilizes user equipment and a spectrum monitoring unit to capture and update radio parameters in a database, enhancing network performance through dynamic predictions and optimizations. GlobalData’s report on Tejas Networks gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Tejas Networks, Network traffic analysis was a key innovation area identified from patents. Tejas Networks's grant share as of July 2024 was 60%. Grant share is based on the ratio of number of grants to total number of patents.
Radio mapping architecture using machine learning for wireless networks
The granted patent US12052583B2 introduces a sophisticated radio mapping architecture designed for wireless networks. This architecture includes a User Equipment (UE) or a spectrum monitoring unit that continuously measures and captures various radio parameters. These parameters are stored in a radio mapping database, which is periodically updated to reflect changes in the environment and user conditions. The architecture also features a server that manages the radio mapping database, extracting and geo-tagging parameters from both the UE and the spectrum monitoring unit. A Machine Learning (ML) module plays a crucial role in training models based on the stored parameters, predicting dynamically changing radio parameters, and refining the ML model as new data is collected. The system is designed to optimize performance by generating tailored signal waveforms for the UE based on its location and the predicted radio conditions.
The patent further details the types of parameters involved, including location, RF, network, and physical layer parameters, which are essential for accurate radio mapping. The architecture allows for advanced functionalities such as estimating Doppler shifts when the UE is in motion, determining UE movement through various sensors, and utilizing alternative location determination methods when GPS is unavailable. Additionally, the system can beamform to the UE by querying the radio mapping database for current channel state information. The architecture also incorporates a method for populating and updating the radio mapping database, which involves capturing radio parameters, exchanging data with a cellular core network, and refining prediction models based on real-time updates. Overall, this patent outlines a comprehensive approach to enhancing wireless network performance through intelligent radio mapping and machine learning techniques.
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