## 2 Related Works  
In [29], a real-time testbed for M-MIMO named LuMaMiis developed by researchers at Lund University. It employs 100 antennas to serve 10 users simultaneously. The system achieves a throughput of 384 Gbps. Similarly, in [30], researchers at Bristol University developed a 128-antenna M-MIMO testbed that serves 22 users simultaneously in an indoor environment, achieving a SE of 145.6 bits/s/Hz. To address pilot contamination, several mitigation techniques have been developed. In [31], the pilot sequence reuse technique is designed to avoid pilot contamination within a cell cluster. A blind pilot decontamination scheme is developed, where, without the requirement of pilot data, subspace projection is used for channel estimation [32]. In [33], the interference alignment-based pilot decontamination technique exploits multiple antennas at the user side for pilot decontamination. Precoding allows for multi-stream transmission in M-MIMO systems. Many efficient linear and non-linear precoders have been developed. The effects of hardware impairments on the performance of M-MIMO systems are extensively studied in [34]. The usage of the machine and deep learning algorithms to address several challenges, such as beamforming, channel estimation, and signal detection, is studied in [35]. The authors of [36] investigated error modeling of the downlink channel and validated computational and experimental results by altering the BS array size. In the past few years, extensive research has been done on mmWave M-MIMO systems, addressing the various benefits offered and challenges faced. Because of signal processing and hardware architecture constraints, conventional digital precoding techniques and analog beamforming cannot be extended for mmWave M-MIMO systems. Hence, in [37], a hybrid precoding scheme is introduced, an amalgamation of analog and digital precoding. This precoding technique achieves antenna array gain, spatial multiplexing gain, and high sum rate performance with fewer RF chains. Estimating channels in mmWave MIMO systems before beamforming presents difficulties due to the massive antennas and the lower SNR. To understand the behavior and properties of M-MIMO channels at mmWave frequencies, practical field measurements should be conducted, through which researchers can derive statistical and/or deterministic models to represent the properties and behavior of channels. Sounding, the process of transmitting known waveforms and analyzing the channel-impacted received signals, has become a prevalent approach for wireless channel modeling. Real-world channel measurements were conducted at 28 GHz and 73 GHz frequencies in New York City to develop detailed spatial statistical channel models. These statistically derived models were then leveraged to theoretically evaluate mmWave micro-cell and picocell networks in a dense urban deployment [38]. Similar work was carried out in Germany by the mmMAGIC group for 10-100 GHz frequencies and in China at 32 GHz [39; 40]. Reference [41] presents novel methods for employing ray-tracing (RT) to establish a digital twin representation of radio propagation across various frequency bands, from microwave frequencies to the visible light spectrum. It suggests techniques for characterizing material electromagnetic properties through a combination of material characteristics and field measurements. A super-resolution modeling method is formed by fusing RT and artificial intelligence algorithms. In [17], the electromagnetic and scattering properties of materials are evaluated for HST mmWave communication between 26.5 GHz and 40 GHz. The study involves creating 3D models for outdoor and tunnel HST environments in the mmWave spectrum. The authors establish fundamental HST mmWave channel modeling principles using extensive RT simulations. The system model is validated with six main objects: buildings, tracks, tunnels, etc. In [42], two case studies aim to simulate real-worldHST channels in the context of 5G mmWave technology.