Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN
Zezhong Zhang Guangxu Zhu Junting Chen Shuguang Cui
In the 6G era, real-time radio resource monitoring and management are urged
to support diverse wireless-empowered applications. This calls for fast and
accurate estimation on the distribution of the radio resources, which is
usually represented by the spatial signal power strength over the geographical
environment, known as a radio map. In this paper, we present a cooperative
radio map estimation (CRME) approach enabled by the generative adversarial
network (GAN), called as GAN-CRME, which features fast and accurate radio map
estimation without the transmitters' information. The radio map is inferred by
exploiting the interaction between distributed received signal strength (RSS)
measurements at mobile users and the geographical map using a deep neural
network estimator, resulting in low data-acquisition cost and computational
complexity. Moreover, a GAN-based learning algorithm is proposed to boost the
inference capability of the deep neural network estimator by exploiting the
power of generative AI. Simulation results showcase that the proposed GAN-CRME
is even capable of coarse error-correction when the geographical map
information is inaccurate.