Yifei Peng

Traditional multiple-input multiple-output (MIMO) systems are confronted with significant challenges in realizing ubiquitous connectivity for sixth-generation (6G) networks, particularly in environments characterized by severe signal blockage and dynamic co-mobility. While aerial intelligent reflecting surfaces (AIRS) offer a promising paradigm to address these difficulties, the existing channel models often fail to capture fast channel changes, thereby leading to inefficient phase optimization in time-varying scenarios. To address these limitations, a geometric MIMO channel model is proposed for AIRS-assisted communications. This model comprises an indirect link from the base station (BS) via the AIRS to the receiver (Rx) and a direct BS-Rx link, whose direct propagation environment is rigorously characterized by a one-cylinder model specifically designed to tackle the complexities of dynamic co-mobility and intricate propagation. A joint optimization problem is formulated to maximize the achievable rate by optimizing the transmitted signal’s covariance matrix and the AIRS phase shift. Subsequently, an iterative algorithm employing the projected gradient method (PGM) is proposed for its solution, which is tailored for efficient operation in time-varying environments. Furthermore, expressions for the space–time correlation function and Doppler power spectrum are derived to evaluate the overall channel properties. Significant enhancements in achievable rates are demonstrated by AIRS, with substantial gains being observed even for a small number of reflecting elements. Consequently, crucial guidance for the design of robust AIRS-assisted MIMO systems is provided by these findings, and the broad applicability of the proposed algorithm is thereby confirmed.