Radio map estimation using a CycleGAN-based learning framework for 6G wireless communication

As the 6G era approaches, wireless communication faces challenges such as massive user numbers, high mobility, and spectrum resource sharing. Radio maps are crucial for network design, optimization, and management, providing essential channel information. In this paper, we propose an innovative learning framework for Radio Map Estimation (RME) based on cycle-consistent generative adversarial networks. Traditional RME methods are often constrained by model complexity and interpolation accuracy, while learning-based methods require strictly paired datasets, making their practical application difficult. Our method overcomes these limitations by enabling training with unpaired data, efficiently converting local features into radio maps. Our experimental results demonstrate the effectiveness of the proposed method in two scenarios: accurate map data and map data with dynamic errors. To address dynamic interference, we designed a two-stage learning process that uses sparse observations to correct local details in the radio map, and the model's accuracy and practicality.