# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 2 Literature Survey  
The authors of [16] examined three 5G technology concepts, implementations, contrasts, and debates, while taking beamforming and precoding techniques into account. However, when a lot of antennas and radio frequencies (RFs) are employed, these technologies become more sophisticated. In addition, the pilot contamination issue, which could restrict the number of scheduled users and reduce the effectiveness of the channel estimation, needs to be addressed by an additional study that takes practical implementation into account. The authors of [17] proposed a unique handover mechanism that ensures the quality of service (QoS) of each of the user equipment (UE) and optimizes both the total system delay and throughput. In particular, optimization theory and the reinforcement learning (RL) algorithm are integrated into the suggested handover system known as O-MAPPO. Determining handover trigger conditions involves an RL technique called multi-agent proximal policy optimization (MAPPO). Moreover, they use a Handover (HO) penalty technique to increase system efficiency by preventing needless HO and lowering the HO rate. The authors of [18] provided dependable wireless communications and examined an anti-jamming HB design in millimeter-wave (mmWave) massive MIMO systems. Unlike the conventional systems designed under the presumption of perfect jamming and channel state information (CSI), we investigate two HBF topologies, including partial connections. Furthermore, they have shown that the LNAPS-based HBF system is free from small communication channel flaws, implying feasible protection against jamming assaults. The authors of [19] suggested a UAV relay-assisted multi-BS mm-wave massive MIMO system with HB architecture, which prevents abrupt link disconnections brought on by high path loss and LoS obstruction in the mm-wave frequency band. This system enables the use of UAV relay-based architecture in serving various ground BS-user pairs. In upcoming work, they will solve the computational complexity issue and investigate possible UAV relaying systems considering channel estimates. The authors of [20] proposed intelligent DAPS (I-DAPS) HO, a unique learning-based DAPS HO technology that avoids abrupt radio link failure (RLF) while maintaining a high data rate The proposed I-DAPS HO uses a Double Deep Q-network (DDQN) deep reinforcement learning (DRL) framework to generate blockage predictions based on historical signal data, therefore enabling RLFs to be actively avoided. In scenario A, the recommended I-DAPS HO completes HO before the RLF; hence, I-DAPS HO experiences a 0 ms MIT and achieves an average throughput gain of 247.29 Mbps and 589.81 Mbps. The authors of [21] proposed many handover (HO) solutions that can provide a near-zero mobility interruption time (MIT) to ensure smooth real-time connectivity for 5G applications. However, we look into how to apply selection schemes by utilizing the results of two different DNNs. The authors of [22] created an algorithm known as alternating optimization (AO). They tackled the resource distribution challenge by maintaining the hybrid beamforming and RIS phase shifts, and the phase shifts, analog beamforming, and transmit beamforming were designed, respectively, using an alternating manifold optimization (AMO)-based approach and a sequential convex approximation (SCA)-based method, given the power allocation matrix. However, because the RIS is realized passively, obtaining the CSI is challenging. Therefore, in subsequent research, we create a reliable transmission scheme in a RIS-assisted mmWave system by fusing the suggested algorithm with the MMSE approach. The authors of [23] downlinked a millimeter wave (mmWave) multi-user massive MIMO system and proposed an HB design framework for interference cancellation (IC). Depending on the provided architecture, three HB methods 34 re developed employing successive interference cancellation (SIC) to resolve intra- and inter-user interference.