# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
### User
#### 3.1.2 Muti-User  
In multi-user mm-wave networks, the analytical results show the trade-offs between power consumption, coverage, and rate. For millimeter-wave communications, multi-user MIMO is usually the best alternative to spatial multiplexing or single-user analog beamforming. This is because multiuser MIMO takes advantage of the transmitter's perfect channel state information [25]. One important step in improving connectivity and easing the spectrum bottleneck in the wireless communication network is to employ the underutilized mm-wave frequencies. To improve the spectrum efficiency of mm-wave relay networks, hybrid (analog/digital) beamforming of several data streams is often used when dealing with long-distance communication or very dynamic environments. We take a look at a hybrid beamforming method and the decode-and-forward approach for multi-user MIMO relay networks with mixed topologies and full-connected frameworks. Optimizing hybrid beamforming of relay communication is an optimization issue that aims to maximize the total rate of the entire network [26]. * **Users Constraint beamforming** In scenarios where the number of users stays below the number of linked RF chains, the performance metric known as the maximum-minimum user rate performs at its best for completely digital beamforming. The channel capacity is relatively unaffected by the modest number of RF connections that are sufficient. When there are half as many RF lines as users, a time-fraction mm-wave approach may be used to determine a distinct maximum and minimum user rate. This approach may achieve a respectable user rate regardless of the amount of inter-cell interference, provided that the number of RF chains is half that of the users per cell at the base station or fewer [27]. * **Zero-Forcing Beamforming** Consider the scenario where the channel state information is ideal; radio frequency beamforming, a mixed beamforming technique, is optimal for multi-user MIMO. The Robust regularized zero-forcing (RRZF) technique is used when the amount of channel information is restricted [28]. * **SINR Constraint Beamforming** For a multi-user MIMO network to reduce interference and noise, an effective beamforming method is needed. This non-convex issue is solved using singular-value decomposition (SVD) with a modest level of complexity, where the number of RF chains is either larger than or equal to the quantity of users [29]. * **Power-Efficient Beamforming** Hybrid digital-analog beamforming is famous due to low power demand as compared to the fully digital design in multi-user MIMO. Different types of connections have different impacts on power efficiency, cost-effectiveness, and service quality. One extreme case is the fully connected design, where all antenna components of the array are linked to each port of the radio frequency chain. The alternative is the one-stream-per-subarray (OSPS) layout, which uses links between individual subarrays and the antenna ports of radio frequency chains. In order to acquire the beam, the transmitter and receiver are thought of using codebook-based beamforming. The user's equipment's channel gain angle of arrival/departure pairs are the basis of this codebook. Data transmission makes use of three precoding options: analog maximum ratio transmission (MR-ZF), beamsteering (BMS), and analog maximum ratio baseband zero-forcing (BBM). Because it reduces complexity and delays problems, the MR-ZF is preferable for real-world applications [30]. * **DFT-based Beamforming** In multi-user hybrid beamforming, the discrete Fourier transform (DFT) is used as an analog beam selection approach. The study of attainable rates in maximum ratio combining and zero-forcing receivers in the uplink system is carried out in the context of the Rician fading environment. In order to improve the possible downlink and uplink rates, the beam selection approach is based on mathematical equations and approximations [31].