## 2 Related Works
The first case study focuses on tunnels within the 30 GHz band, while the second examines outdoor HST environments in the 90 GHz band. Both studies include RT simulations, stochastic channel modeling, implementation, and cross-validation. The authors of [43] assessed the precoding and beamforming of mmWave M-MIMO, focusing on beamforming approaches for the practical application of 5G wireless networks and trade-offs between performance and complexity. The authors of [44] suggested a hybrid beamforming (HBF) approach with good accuracy and low complexity for mmWave M-MIMO. An experimental demonstration was accomplished on an actual 5G mmWave setup following the 3 GPP standard with two distinct users. Because of its minimal complexity and high performance, the suggested technology is well-suited for B5G. In [45], the authors provided a detailed description of the working principles of RIS. They also provided a mathematical model for signal transmission using RIS as a smart reflector (SR) in a dual-hop communication scenario and RIS as a low-complex energy-efficient transmitter. In addition, a system model for multiuser downlink transmission over an RIS was described. Closed-form expressions for symbol error probability (SEP) were also developed for the communication scenarios under consideration. RIS can be integrated with other potential technologies to reap the combined benefits and enhance SE, EE, reliability, and signal coverage. In [23], RIS was integrated with NOMA to improve SE and connectivity. The uplink sum capacity was maximized using optimal power allocation coefficients for NOMA. The intelligent RIS-assisted fixed NOMA is investigated in [46], also assessing outage probabilities and optimizing transmit power allocation. The work in [23] is further developed in [22], which considers blind RIS-assisted ordered NOMA rather than fixed NOMA. The potential benefits of employing RIS across several frequency bands, from sub-6 GHz to mmWave, for indoor and outdoor setups are studied in [47]. Empirical models are also used to investigate the path loss exponent. Most current RIS-assisted channel models consider NLoS propagation. However, in [48], the authors analyzed the RIS-assisted channel model by considering the LoS path between the transmitter and the receivers. When RIS array size increases, mmWave channels can be modeled as a spatially correlated Rayleigh fading channel, leading to an improved mmWave scattering environment [49]. In [25], a channel model for a system with RIS operating at mmWave frequencies is described, which is usable in various indoor and outdoor environments and covers the physical properties of propagation by taking 3 GPP 5G standard radio channel models into account. The authors further developed SimRIS, an open-source channel simulator that generates channel coefficients according to the 3 GPP 5G radio standards for indoor and outdoor scenarios. This work is expanded in [28], where the authors developed a channel simulator, SimRIS version 2, which presents a channel model for RIS-assisted mmWave MIMO. The simulation results in [50] show the immense benefits enabled by the RIS compared to increasing the number of BS antennas. The authors of [51] suggested employing an RIS for MIMO wireless communication by modulating data onto a carrier signal reflected by the RIS. Each RIS element corresponds to a distinct data stream. A non-linear modulation technique is devised to independently regulate harmonic amplitudes and phases under a constant envelope constraint to enable high-order quadrature amplitude modulation (QAM). A prototype was also built for RIS-based 2 \(\times\) 2 MIMO 16-QAM transmission at 4.25 GHz. They achieved a 20 Mbps data rate with 0.7 W power consumption. A RIS-aided angular-based HBF design for mmWave M-MIMO systems and a three-stage architecture are presented in [52]. Comprehensive simulations show that employing RIS can result in significant rate gains, especially when the direct Tx-Rx link is blocked, and RIS can provide an alternate, reliable path.