Graph-Based Element Deployment and Channel Estimation for IRS-Aided Wireless Communications

To address the channel estimation challenge in semi-passive Intelligent Reflecting Surface (IRS)-aided communication systems, a graph-based active element deployment and channel estimation method is proposed, combining graph smoothing with atomic norm minimization. The semi-passive IRS channel is modeled as a graph signal, and the element deployment is reformulated as a graph sampling problem. Then, spectral clustering is applied, and the vertex with minimum total variation in each cluster is sampled. Based on that, a channel estimation method is developed by incorporating the smoothness priors of the graph signal with atomic norm constraints, resulting in an atomic norm minimization under smoothness constraints. Simulations reveal the performance improvement of the proposed scheme in channel estimation for semi-passive IRS-aided systems.