Shaking-aware and Fast Channel Estimation for Large-scale RIS-Aided HAP Networks

The emerging high-altitude platform (HAP) networks are envisioned as critical components in space-air-ground integrated networks. This paper investigates the uplink channel estimation for large-scale reconfigurable intelligent surface (RIS)-aided HAP networks. To overcome the HAP shaking effect and high computational overhead of massive passive arrays,we propose a shaking-aware fast three-stage channel estimation (SA-FTCE) algorithm in the angular domain, tailored for uniform planar arrays (UPAs). SA-FTCE achieves a computationally efficient estimate by progressively pruning the angular channel matrix to a lower dimension by eliminating inactive azimuth and elevation angular regions. Specifically, in Stage 1, we derive the angle-of-arrival (AoA) interval for the RIS-HAP link through the spatial relationship between the AoA variation and HAP attitude shaking, and introduce a shaking-aware AoA search for initial pruning. In Stage 2, a novel Kronecker variational Bayesian inference (Kronecker-VBI) algorithm is proposed for the low-complexity detection of the effective angular region (EFAR) for further pruning. Finally, the channel estimation is efficiently obtained by a VBI based estimator within the drastically reduced angular space. The simulation results show that the proposed SA-FTCE scheme is faster than its counterparts and achieves comparable estimation accuracy.