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.

翻译：在6G时代，实时无线电资源监测与管理对于支持多样化无线赋能应用至关重要，这要求对无线电资源分布（通常由地理环境上空闲信号功率强度表征的无线地图）进行快速高精度估计。本文提出一种基于生成对抗网络（GAN）的协作式无线地图估计方法，命名为GAN-CRME，该方法无需发射器信息即可实现快速高精度的无线地图估计。通过利用深度神经网络估计器挖掘移动用户处分布式接收信号强度（RSS）测量值与地理地图之间的交互关系，实现无线地图推断，显著降低了数据采集成本与计算复杂度。进一步，提出基于GAN的学习算法，借助生成式人工智能的潜力增强深度神经网络估计器的推断能力。仿真结果表明，所提GAN-CRME在地理地图信息不准确时甚至具备粗粒度误差校正能力。