Channel Estimation and Symbol Detection for UAV-RIS Assisted IoT Systems via Tensor Decomposition

Utilizing unmanned aerial vehicle (UAV) technology in communication holds promise for meeting the increasing data rate demands in future wireless systems due to its flexibility. Meanwhile, reconfigurable intelligent surface (RIS) has garnered increased attention for their potential to enhance wireless communication performance through intelligent control of the transmission environment. In this paper, we first combine the UAV and the RISs to construct an Internet of Things (IoT) uplink transmission system, where the UAV serves as an aerial relay to collect data from IoT terminal (IT) and forward it to base stations (BS), while RISs assist communication to reduce congestion. Then, a parallel factor (PARAFAC) tensor model is formulated at the BS. At last, the iterative alternating least squares (ALS) algorithm and the closed-form singular value decomposition (SVD) algorithm are derived to fit the constructed tensor model for joint channel estimation and symbol detection. Compared with the competitive algorithms, the two proposed algorithms offer lower computational complexity and superior channel estimation performance. Furthermore, the proposed algorithms exhibit good symbol detection capabilities even at higher transmission rates. The numerical results demonstrate the effectiveness of the proposed algorithms.