RadioRS: A Sampling-Free Low-Altitude Wireless Networks Leveraging Radio Maps and Rate-Splitting

The Low-Altitude Wireless Networks (LAWNs) has emerged as a cornerstone of next-generation mobile due to their flexibility and adaptability in providing on-demand connectivity. However, ensuring reliable and high-throughput aerial drone communication remains a major challenge, mainly due to the dynamic mobility of aerial drones and the complexity of the wireless propagation environment. Traditional LAWNs rely heavily on channel sampling and real-time feedback, which introduce latency and communication overhead. In this work, we proposeRadioRS, a novel sampling-free aerial drone communication framework that combines Radio Map (RM) prediction with Rate-Splitting Multiple Access (RSMA) to enable robust and efficient communication without requiring explicit channel estimation during flight. RadioRS leverages a RM that provides location-aware predictions of channel state. To enhance the accuracy and generalization of these predictions under complex propagation conditions, we develop a generative model based on the Mamba architecture, which efficiently captures fine-grained correlations in the radio environment. Building on the RM, RSMA is employed to flexibly manage interference and improve spectral efficiency. In addition, we design a Mamba-powered controller that adapts beamforming strategies from the RM directly, further improving link reliability and throughput. Comprehensive simulation results demonstrate that the proposed RadioRS framework significantly outperforms conventional channel-sampling-based approaches in terms of both communication reliability and throughput.