  Radio-frequency coverage maps (RF maps) are extensively utilized in wireless
networks for capacity planning, placement of access points and base stations,
localization, and coverage estimation. Conducting site surveys to obtain RF
maps is labor-intensive and sometimes not feasible. In this paper, we propose
radio-frequency adversarial deep-learning inference for automated network
coverage estimation (RADIANCE), a generative adversarial network (GAN) based
approach for synthesizing RF maps in indoor scenarios. RADIANCE utilizes a
semantic map, a high-level representation of the indoor environment to encode
spatial relationships and attributes of objects within the environment and
guide the RF map generation process. We introduce a new gradient-based loss
function that computes the magnitude and direction of change in received signal
strength (RSS) values from a point within the environment. RADIANCE
incorporates this loss function along with the antenna pattern to capture
signal propagation within a given indoor configuration and generate new
patterns under new configuration, antenna (beam) pattern, and center frequency.
Extensive simulations are conducted to compare RADIANCE with ray-tracing
simulations of RF maps. Our results show that RADIANCE achieves a mean average
error (MAE) of 0.09, root-mean-squared error (RMSE) of 0.29, peak
signal-to-noise ratio (PSNR) of 10.78, and multi-scale structural similarity
index (MS-SSIM) of 0.80.
