# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
###### Abstract  
Next-generation wireless communications aim to utilize mmWave/subTHz bands. In this regime, signal propagation is vulnerable to interferences and path losses. To overcome this issue, a novel technology has been introduced, which is called reconfigurable intelligent surface (RIS). RISs control digitally the reflecting signals using many passive reflector arrays and implement a smart and modifiable radio environment for wireless communications. Nonetheless, channel estimation is the main problem of RIS-assisted systems because of their direct dependence on the system architecture design, the transmission channel configuration and methods used to compute channel state information (CSI) on a base station (BS) and RIS. In this paper, a concise survey on the up-to-date RIS-assisted wireless communications is provided and includes the massive multiple input-multiple output (mMIMO), multiple input-single output (MISO) and cell-free systems with an emphasis on effective algorithms computing CSI. In addition, we will present the effectiveness of the algorithms computing CSI for different communication systems and their techniques, and we will represent the most important ones. channel estimation; channel state information (CSI); optimized algorithm; multi input-multi output (MIMO) systems; multiple input-single output (MISO) systems; reconfigurable intelligent surface (RIS) 202  
contribution of the RIS is represented in [4] for reducing transmitted power at the BS using signal-to-interference-plus-noise ratio (SINR) constraints in multi-user MISO (MU-MISO) RIS-assisted systems for the joint improvement of the precoder and reflecting beamforming matrices (RBMs); in [5], the total rate reached the maximum under the transmit power limitation; in [6], the total rate was improved by considering the associated attenuation of Rayleigh waves and the usual hardware faults in both the transceiver and RIS; in [7], reaching the maximum of the minimum user equipment (UE) rate was investigated in the case of a large amount of antennas; in [8], the effect of hardware faults was assessed; in [9], the effect of incomplete CSI in the possibility of shutdown was demonstrated. Furthermore, there are discussions on how the technology of RIS contributes to the efficient transmission of energy consumption on the internet of vehicle (IoV) networks [10] and in passive meta surface-coated devices [11]. In addition, a new RIS structure, which reflects and absorbs the transmitted signal through an absorbing agent equipped with radio frequency chains, performs signal processing techniques, configures its phase shift matrix, and does not need incoming signal control [2]. It is called a hybrid RIS (HRIS) and research has been presented in [12; 13; 14; 15]. In [16], concerning sparse antenna activation based on a channel extension strategy in a HRIS, a full one-hop channel optimization and beam searching scheme design for inclusion in channel subsampling are proposed. Beamforming (i.e., precoding in transmission and combining in reception) is a key signal processing technique to ensure reliable communication between the transmitter and receiver. Although conventional MIMO systems widely consider single-stage all-digital beamforming, two critical challenges for mMIMO systems arise: high hardware costs due to a single dedicated radio frequency (RF) chain per antenna and large channel estimation overheads [17]. In several studies, such as [18; 19; 20; 21], the hybrid beamforming (HBF) solution is proposed, which generates the RF stage through slow time-varying angular information and the digital baseband (BB)-stage through reduced-dimensional CSI. HBF has been studied, mainly in RIS-assisted mMIMO systems, to achieve the requirements for full CSI estimation [22; 23]. In [22], the spectral performance is improved by the joint optimization of the RIS phase shift and hybrid precoder/compressor two-stage algorithm, while cost and energy consumption are reduced.