# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 2 Literature Survey
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Method** & **Advantages** & **Limitations** \\ \hline
[16] & Precoding/beamforming survey & A thorough classification of & Pilot pollution lacks \\  & & 5G methods & practical remedies \\ \hline
[17] & Reinforcement learning (RL) & Improves throughput and QoS & High computational cost for \\  & algorithm and optimization theory & Improves throughput and QoS & real-time decisions \\ \hline
[18] & Anti-jamming hybrid beamforming & Robust to imperfect CSI & Limited single-user environment \\ \hline
[19] & Unmanned aerial vehicle-relayed & Removes LoS obstructions & High complexity in channel estimation \\  & mmWave MIMO & Uses proactive handover to avoid RLF & Needs a lot of previous signal data \\ \hline
[20] & I-dual active protocol stack (DAPS) & Minimal disruption to the movement & Dual DNNs are needed, which \\  & handover & Minimal disruption to the movement & raises overhead \\ \hline
[21] & Alternating optimization-based & Joint IRS/beamforming design & Assumes stable reflection angles and \\  & RIS optimization & & passive RIS \\ \hline
[23] & Hybrid analog and digital & Eliminates interference between and & Only functions with HBF designs that \\  & BeamForming & within users & are completely linked \\ \hline
[24] & Intelligent reflecting & Enhances the energy efficiency & Limited to static users \\  & surface-assisted mmWave NOMA & & \\ \hline
[25] & Neural network-based full-duplex CE & Reduces full-duplex pilot overhead & TX-RX antenna separation is \\  & & necessary (hardware limitation) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the literature survey. The authors of [29] examined the channel estimates for RIS-assisted millimeter-wave communications. By assuming that homogeneous arrays provide \"the array manifolds of the base station antennas and the RIS\", an efficient two-stage channel estimate strategy was proposed that depended on array signal processing techniques. Then, some practical issues are examined, including complexity, faulty RIS hardware, and channel path counting. * More pilot symbols are required when there are direct channels, as they only consider indirect channel estimation. The proposed approach for channel parameter estimation has a small training overhead suitable in both cases with and without direct channels. The authors of [30] proposed deep reinforcement learning (DRL)-based network partitioning techniques. Furthermore, they created a novel hybrid analogy beam-steering digital beamforming model that simultaneously maximizes the instantaneous sum rate of all UEs within each sub-network and zero-forces interference amongst different cell-free sub-networks to mitigate interference between them. The HB model is implemented using a special coupled DRL-convex optimization method. * Changing the CSI has a big effect on the variance and convergence rate of DRL methods, such as the SARSA algorithm. The authors of [31] intended to assess the performance of an adaptive beamforming technique. Here, beamforming is accomplished under the guidance of a predefined set of configurations that, by appropriately producing highly directional beams on demand, can manage a spectrum of traffic conditions. Concurrent with this, a machine learning (ML) beamforming method is also investigated. This technique uses the k-nearest neighbors (k-NN) approximation and is trained to provide suitable beamforming configurations based on the spatial distribution of throughput demand. * In this instance, thorough beamforming designs that would possibly permit the admission of additional users in the orientation are excluded. The authors of [32], because of the super-resolution technology, suggested a low overhead analog beam selection strategy based on deep learning. To put it in practical terms, beam quality estimation based on partial beam observations was carried out by deep neural networks. Our proposed technique covers all directions of arriving signals with little overhead by means of codebooks with different beam widths.