Abstract
This paper investigates the three-dimensional (3D) downlink sparse channel estimation for tethered aerial platform-enabled multi-user communication systems operating in a frequency division duplexing mode with large-scale antenna arrays. To this end, we design a non-identical Bernoulli-Gaussian distribution-based channel model that reflects the potential common sparsity caused by distant scatterers in a low-altitude environment. A low-complexity channel estimation algorithm without the need for prior knowledge of channel sparsity is proposed. It first applies a greedy pursuit algorithm to roughly estimate the common support set (CSS). Given the initial CSS, multi-user channels are then iteratively estimated and refined using a novel neighborhood-based channel estimation refinement scheme, which includes a decentralized sparse channel estimator to recover sparse channels accurately under non-i.i.d channel sparsity priors with low computation burden. Simulation results indicate that the proposed algorithm outperforms its existing counterparts and approaches the performance limit with perfect knowledge of the CSS.