Environment Reconstruction in Multi-Bounce Channels with Array Partial Blockage

Abstract: Extremely-large antenna arrays (ELAA) are important in applications requiring high angular resolution. However, a prominent issue is the spatial non-stationary (SNS) channels due to partial blockage to the ELAA. In this paper, we address the scatterer localization and subsequent environment reconstruction considering partially blocked SNS channels. Specifically, the SNS effects are parametrically modeled through spatial-varying amplitudes with sparsity. Based on the established signal model, the graph-based dictionary-aided multi-bounce space-alternating generalized expectation-maximization (GM-SAGE) algorithm is applied to estimate the channel parameters and the channel sparsity is empirically detected along with amplitude estimation. To validate the proposed approach, we generate multi-bounce paths through ray tracing (RT) simulations, where the SNS channels caused by partial blockage could be configured flexibly. The simulation results demonstrate the robustness of the proposed approach in dealing with the SNS channels.