Radio map presents communication parameters of interest, e.g., received signal strength, across a geographical region in a specific frequency band. It can be leveraged to improve the efficiency of spectrum utilization. With the rapid development of radio technology and the proliferation of radioenabled devices, there is an increasing demand for finer granularity (higher resolution) in radio maps. However, the problem of fine-grained radio map construction is uniquely challenging, as it requires to utilize an extremely small number of radio samples collected by sparsely distributed sensor devices to infer the map. To address the challenge, we propose a hybrid device-edge-cloud collaboration paradigm called CollaboRadio. CollaboRadio first groups sensor devices into multiple clusters, with cluster locations optimized based on principles of radio propagation. Next, it leverages a small AI model on each edge server to generate a local radio map for the respective cluster region from radio samples collected by intra-cluster sensors. Finally, it employs a large AI model in the cloud to construct a global radio map for the entire region from the local maps produced by edge servers of different clusters. For the implementation of CollaboRadio, we develop an UNet-based small model for the edge server and a Transformerbased large model for the cloud. Extensive simulations show that CollaboRadio is capable of constructing fine-grained radio maps from an ultra-low sampling rate of 0.1%, and significantly outperforms state-of-the-art.

Radio map presents communication parameters of interest, e.g., received signal strength, across a geographical region. It can be leveraged to improve the efficiency of spectrum utilization. With the rapid development of radio technology and the proliferation of radio-enabled devices, there is an increasing demand for finer granularity (higher resolution) in radio maps. However, the problem of fine-grained radio map construction is uniquely challenging, as it requires to utilize an extremely small number of radio samples collected by sparsely distributed sensors to infer the map. To address the problem, we propose a novel edge-cloud model collaboration paradigm. This paradigm first groups sensors into multiple clusters, with cluster locations optimized based on principles of radio propagation. Next, a small model on each edge device generates a local radio map for the respective cluster region using intra-cluster radio samples. Finally, a large model in the cloud constructs a global radio map for the entire region from the local maps produced by edge devices of different clusters. To evaluate the proposed paradigm, we also develop an UNet-based small model for the edge device and a Transformer-based large model for the cloud. Extensive simulations show that our paradigm is capable of constructing fine-grained radio maps from an ultra-low sampling rate of 0.1%, and significantly outperforms state-of-the-art.

Radio map presents communication parameters of interest, e.g., radio power, across a geographical region in a specific frequency band. It can be leveraged to improve the efficiency of spectrum utilization. The problem of constructing a radio map involves utilizing measurements from sparsely distributed sensors to infer the parameter of interest at every point in the region. One major challenge of solving this problem is ensuring a strong generalization capability for the solution, i.e., ensuring that the solution is able to make accurate inferences even in the presence of unknown system variables that can affect radio propagation, such as obstacle layouts and weather conditions. This paper focuses on analyzing and optimizing the generalization capability of radio map construction, an aspect that has been neglected in prior research as far as we know. We design RobUNet—a UNet-based algorithm that captures multi-scale features of radio maps. It further leverages mechanisms of residual connection, channel attention, and pixel attention to enhance its generalization capability. Simulations based on real-world dataset show that (i) the radio maps constructed by RobUNet are much more accurate than those constructed by many existing solutions in the presence of unknown system variables, and (ii) the accuracy of the radio maps constructed by RobUNet when system variables are unknown is close to that of the radio maps constructed by RobUNet when system variables are known. As a result, the generalization capability of RobUNet is much stronger than that of state-of-the-art.