Reconfigurable intelligent surface (RIS): Eigenvalue Decomposition-Based Separate Channel Estimation

Reconfigurable intelligent surface (RIS) has recently drawn significant attention in wireless communication technologies. However, identifying, modeling, and estimating the RIS channel in multiple-input multiple-output (MIMO) systems are considered challenging in recent studies. In this paper, a disassembled channel estimation framework for the RIS-MIMO system is proposed based on the eigenvalue decomposition (EVD) concept to separate the cascaded channel links and estimate each link separately. This estimation is based on modeling the RIS-MIMO channel as a keyhole MIMO system model. Numerical results show that the proposed estimation method has a low estimation time overhead while providing less estimation error.Comment: Published in: 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC

Reconfigurable Intelligent Surface-Empowered MIMO Systems

Reconfigurable intelligent surface (RIS)-assisted communications appear as a promising candidate for future wireless systems due to its attractive advantages in terms of implementation cost and end-to-end system performance. In this paper, two new multiple-input multiple-output (MIMO) system designs using RISs are presented to enhance the performance and boost the spectral efficiency of state-of-the-art MIMO communication systems. Vertical Bell Labs layered space-time (VBLAST) and Alamouti's schemes have been considered in this study and RIS-based simple transceiver architectures are proposed. For the VBLAST-based new system, an RIS is used to enhance the performance of the nulling and canceling-based sub-optimal detection procedure as well as to noticeably boost the spectral efficiency by conveying extra bits through the adjustment of the phases of the RIS elements. In addition, RIS elements have been utilized in order to redesign Alamouti's scheme with a single radio frequency (RF) signal generator at the transmitter side and to enhance its bit error rate (BER) performance. Monte Carlo simulations are provided to show the effectiveness of our system designs and it has been shown that they outperform the reference schemes in terms of BER performance and spectral efficiency.Comment: To appear in IEEE SYSTEMS JOURNAL, 9 pages, 6 figures, and 1 tabl

Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low-power configuration, which has increased potential in enabling massive connectivity and low-latency communications. Accurate and low-overhead channel estimation in RIS-based systems is one of the most critical challenges due to the usually large number of RIS unit elements and their distinctive hardware constraints. In this paper, we focus on the downlink of a RIS-empowered multi-user Multiple Input Single Output (MISO) downlink communication systems and propose a channel estimation framework based on the PARAllel FACtor (PARAFAC) decomposition to unfold the resulting cascaded channel model. We present two iterative estimation algorithms for the channels between the base station and RIS, as well as the channels between RIS and users. One is based on alternating least squares (ALS), while the other uses vector approximate message passing to iteratively reconstruct two unknown channels from the estimated vectors. To theoretically assess the performance of the ALS-based algorithm, we derived its estimation Cram\'er-Rao Bound (CRB). We also discuss the achievable sum-rate computation with estimated channels and different precoding schemes for the base station. Our extensive simulation results show that our algorithms outperform benchmark schemes and that the ALS technique achieve the CRB. It is also demonstrated that the sum rate using the estimated channels reached that of perfect channel estimation under various settings, thus, verifying the effectiveness and robustness of the proposed estimation algorithms