# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
### Carrier
#### 3.7.1. _O_fdm  
The carrier waveform OFDM is very helpful in hybrid beamforming.  
\(\bullet\) **OFDM Radar Communication**  
Orthogonal frequency-division multiplexing (OFDM) maximizes band utilization in dual-function radar communication (DFRC) systems while simultaneously ensuring communication stability against multipath fading and improved radar estimate accuracy. A completely digital beamforming solution would be costly due to the fact that each receiver has its own RF link. For the OFDM-DFRC technique to work, mixed beamforming is required [72].  
\(\bullet\) **Shared Array Antenna with OFDM**  
The capacity of millimeter-wave frequency bands to accommodate very high data rates for the upcoming 5G mobile communications system is attracting a lot of interest. On the other hand, hybrid beamforming systems that include both digital precoding and analog beamforming have been considered as potential answers that might provide an optimal trade-off between functionality and complexity. Examining the overall average transmission power limit for multi-beam broadcasts and the peak-to-mean envelope power ratio across the antenna components, the hybrid beamforming design with a \"shared array antenna\" is chosen. The hybrid beamforming architecture under consideration should use the non-trivial normalization factors in order to achieve the optimal average transmission power while staying within the total power restriction. The hybrid beamforming system that makes use of a shared array antenna accounts for total power normalization. In order to evaluate the efficacy of multi-beam transmission diversity in comparison to single-beam analog beamforming, several modeling outcomes are presented [73].  
\(\bullet\) **Hybrid Sub-system Beamforming using OFDM**  
Meeting the practical requirements of hybrid beamforming for multi-carrier systems, orthogonal frequency-division multiplexing systems with numerous inputs and outputs for indoor mm-wave channels use the sub-system SVD (SS) hybrid beamforming architecture. The channel estimate becomes less difficult in a realistic setting with less channel state information (CSI) based on the statistical properties of indoor mm-wave channels. Using the SS hybrid beamforming algorithm as a foundation, the limited feedback SS (LSS) system constructs broadcast beamformers using the receiver's limited information. Also developed is a quick codeword selection mechanism to facilitate the LSS algorithm's search. Despite its simplicity, the SS algorithm achieves results comparable to those of conventional full-digital beamforming. However, although having less CSI, the LSS method outperforms the prior one and is simpler to implement [74].  
\(\bullet\) **Weighted Optimization using OFDM**  
An alternate maximization scheme based on Riemannian manifold optimization is employed to enhance the analog precoding in the hybrid beamforming solutions, which are designed for huge multiple-input multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) systems, in order to obtain the best weighted spectral efficiency. Using a locally optimal technique to enhance digital precoding yields an optimum alternating maximization algorithm. Another maximization process that is both less effective and simpler to develop each time is an iterative approach for digital precoding that is based on weighted minimal mean square error (MMSE). It is shown that when the user-specified weights are not significantly different, the two alternating maximization approaches perform similarly. The upper limit of the weighted geometric and arithmetic means of mean square errors (MSEs) is used to do this. Digital beamforming makes use of matrix decomposition, whereas analog beamforming makes use of weighted MMSE. Unlike the two iterative alternating maximization techniques, it achieves the goal of maximizing weighted spectral efficiency with little performance penalty. Because of this, it is an excellent jumping off point for time-saving iterative algorithms [75].