Search for: All records

Radiomap characterizes geographical radio spectrum coverage and can facilitate resource allocation and management of wireless networks. One practical radiomap estimation (RME) task is to form a full radiomap from sparse samples collected by sensors or mobile devices. Often, traditional RME approaches focus on statistical data distributions without exploiting the underlying spatial correlations among sparse observations. Utilizing geometric/geographical path correlation, this letter proposes a novel dual-phase RME method based on graph neural networks. In this Dual-phase Graph-based Radiomap Estimation (Dual-GRE) framework, the first phase integrates graph attention (GAT) networks with radio propagation models to construct a coarse-resolution (CR) radiomap to embed the spatial information and physical principles. Phase 2 utilizes a deep convolution neural network that uses the CR radiomap and landscape information to derive fine-resolution radiomaps. Our experimental results demonstrate the power of physics-integrated GAT in capturing the spatial spectrum information, together with the efficiency of the proposed Dual-GRE in radiomap estimation.