A Stochastic Framework for Radio Channel Modeling Incorporating Random Antenna Arrays

The separation of antenna design and channel modeling has made it difficult to capture the intricate interplay between antenna parameters and channel dynamics, resulting in suboptimal performance in real-world environments. To address this challenge, this study introduces a stochastic framework that integrates random antenna array, modeled as one-dimensional Wiener process, with a realistic wireless propagation channel model, i.e., the sixth-generation pervasive channel model (6 GPCM). This framework is grounded in rigorous derivations and incorporates reasonable approximations. It provides a detailed analysis of how antenna mobility, characterized by the increasing variance of the Wiener process over the same time interval, influences the spatial cross-correlation function (SCCF), temporal autocorrelation function (TACF), and channel capacity. Finally, based on the aforementioned derivations, simulations are conducted to validate the theoretical findings, and the results are analyzed to demonstrate their implications for wireless communication system design.