M.Hawej Channel Estimation for Reconfigurable Intelligent Surfaces (RIS) Assisted based on Orthogonal Matching Pursuit (OMP) algorithm
Keywords:Reconfigurable Intelligent Surfaces (RIS), Time Division Duplex (TDD)-RIS assisted system, Compressed Sensing CS Algorithm, Normalized Mean Square Error (NMSE) performance.
The tremendous demand for reliable high-speed broadband wireless links is expected to continue growing due to the rapid increase in the number of users, amount of data traffic, and number of applications. The Reconfigurable Intelligent Surfaces (RIS) has been promised as a potential technique for future Sixth-Generation (6G) communication systems. The RIS can passively phase-shift the electromagnetic waves to enhance coverage and capacity at low power and hardware costs. It can provide high beamforming gain that requires accurate channel state information (CSI). The CSI acquisition is too hard to develop for two reasons: First, the passive nature of RIS does not allow the transfer and processing of pilot signals. Second, the dimensions of the cascaded channel between transceivers increase with the large number of RIS elements, which yields high training overhead and computational complexity. In this project, a Time Division Duplex (TDD)-RIS-assisted system is considered, where the CSI in the downlink can be obtained based on the estimated uplink channel. In addition, the cascaded channel i.e. (The cascade of User-RIS and RIS-base station (BS) channels) in RIS-assisted systems shows the sparsity when transformed into the angular domain. Therefore, the channel estimation in the TDD-RIS-assisted system is formulated as a sparse signal recovery problem, which Compressed Sensing (CS) algorithms can solve, especially Orthogonal Matching Pursuit (OMP) algorithm and Double Structure-OMP (DS-OMP) approach. The Simulation results demonstrate our proposed OMP approach has significantly improvement compared to the conventional least square estimator. Furthermore, the DS-OMP approach has a slight improvement over the OMP algorithm in terms of Normalized Mean Square Error (NMSE) performance. On the other hand, the OMP has less complexity than the DS-OMP.