Semi-Blind Structure Subspace-Based Channel Estimation for Wideband RIS-Assisted Multi-User mmWave Systems

This paper proposes a novel semi-blind structured subspace-based channel estimation framework for wideband reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-user systems with direct transmission paths. The proposed method exploits the inherent Toeplitz structure of wideband channels induced by the convolutional nature of wireless signal propagation and incorporates a limited number of pilot symbols to construct a semi-blind estimation criterion. Specifically, a hybrid cost function is formulated by integrating a signal subspace constraint derived from the received signal covariance matrix with a least-squares pilot-based component using limited pilot observations. This formulation enables the proposed approach to effectively exploit both the statistical properties of the received signals and the limited pilot information, thereby improving estimation reliability while significantly reducing pilot overhead. The structural properties of the channel and the Kronecker product representation of the system model are leveraged to provide a closed-form solution through the minimization of the resulting quadratic optimization problem. Simulation results demonstrate that the proposed method achieves high channel estimation accuracy with substantially fewer pilot symbols compared to conventional pilot-based approaches, highlighting its potential for efficient and practical implementation in wideband RIS-assisted mmWave communication systems.