# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**S. No.** & **Algorithm/Method** & **Year** & **Reference** & **Performance Metrics** & **Characteristics** & **Limitations** & **Performance Evaluations** \\ \hline \hline \multirow{9}{*}{7} & \multirow{9}{*}{7} & \multirow{9}{*}{2022} & \multirow{9}{*}{222} & \multirow{9}{*}{222} & \multirow{9}{*}{222} & The steady-best & \multirow{9}{*}{222} & \multirow{9}{*}{222} \\  & & & & SE vs. Number of & & & beamforming gain, user priority, & \\  & & & & SE vs. Number of & The research investigates a & and error factor, while & \\  & & & & test horizons & robust beamforming design & advantageous, requires careful & The research showcases enhanced & wireless communication \\  & & & SE vs. Number of user & for RIS-assisted & tuning and optimization under & & performance with a desirable \\  & & & & SE vs. Transmit power & imperfect CSI, optimizing & achieve optimal system & & rateof-among beamforming \\  & & & SE vs. Number of & multiple parameters through a & performance, which could & gain, user priority, and error \\  & & & reflecting elements & MEA approach. & impact the generalizability of & factor \\  & & & & & the research findings across & \\  & & & & & different deployment scenarios. & \\ \hline \multirow{9}{*}{9} & \multirow{9}{*}{6} & \multirow{9}{*}{2023} & \multirow{9}{*}{2023} & \multirow{9}{*}{[PERSON] et al. [19]} & \multirow{9}{*}{SNR} & The research presents a novel & \multirow{9}{*}{222} \\  & & & & Inertial number & utilizing reconfigurable & \\  & & & Relative frequency vs. & intelligent surfaces (IRS) in a & The proposed MM and ADMM & & \\  & & & Realized MSE & millimeter-wave system, with & methods, while faster than & The proposed research & \\  & & & Uncoded BER vs. Pilot & efficient inner & existing algorithms, still face & outperforms existing methods by \\  & & & & majorization-minimization & scalability issues and practical & offering faster computational \\  & & & Transmit power vs. & (MM) and an alternating & constraints when applied to & times through MM and ADMM \\  & & & Iteration number & direction method of & large-scale network & algorithms. \\  & & & Feasibility rate vs. MSE & multiples (ADMM) & deployments. & \\  & & & & RIS computation time & algorithms for analog and & \\  & & & vs. RIS element number & digital transmitter & optimization. & \\ \hline \multirow{9}{*}{9} & \multirow{9}{*}{6} & \multirow{9}{*}{2023} & \multirow{9}{*}{2023} & \multirow{9}{*}{2023} & NMSE vs. Measure & Stacked Intelligent & The design involves & By optimizing the number of \\  & & & layers & Messaturates (SMs) are & low-precision analog-to-digital & phase shifters and having multiple \\  & & & & NMSE vs. Meta atoms & responsible for carrying out & converters and vice versa but & layers of metasurfaces, using the \\  & & & & Capacity vs. Measure & signal processing directly in & needs multiple layers of & GD method the capacity \\  & & & the electromagnetic (EM) & metasurfaces for holographic & improvement is 150\% as \\  & & & Capacity vs. Meta atoms & wave system. & beamforming. & compared with a conventional RIS. \\ \hline \multirow{2}{*}{10} & \multirow{2}{*}{Neural Network} & \multirow{2}{*}{2024} & \multirow{2}{*}{[PERSON] et al. [21]} & \multirow{2}{*}{Accuracy vs. Received & The SIM-based & Despite the advantages of a & \\  & & & optical-electronic neural & novel framework: it has physical & The beamforming provides a & reduction in hardware cost and \\  & & & SNR & low latency for wireless & hardware imperfections. & coverage capability is improved. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._