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The widespread adoption of electronic devices in daily life has raised concerns about the increase in radio noise and its adverse impact on the performance of radiocommunication systems. Recognizing the scarcity of radio noise measurements, a long-term data collection campaign is conducted in the U.K. to monitor recent trends in indoor radio noise for frequencies above 400 MHz. Measurements taken across various indoor premises confirm the strong correlation between noise levels and the density of active noise sources. As anticipated, noise levels tend to rise when more devices are active and decrease when they are idle or turned off. The data also indicate that radio noise generally decreases as frequency increases, and beyond 1 GHz, the average noise levels remain below the typical minimum sensitivity threshold of wireless equipment. Building on the extensive measurement data analysis, this article introduces a method for predicting the probability distribution of indoor radio noise levels. The model covers all three components of radio noise (white Gaussian, single carrier and impulse) and incorporates key parameters such as frequency, device density, activity, proximity, and average emission of noise sources. It also captures the temporal and spatial variability of radio noise. The proposed model offers a robust framework that can be calibrated and adapted to a range of indoor scenarios.