### Related Work  
A combination of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) deep learning models have been used to predict future blockages and beams for mmWave systems. This helps with proactive handovers between base stations to ensure uninterrupted connectivity for users [16]. Diverse approaches have been tried for link quality prediction, including using camera images [17]. Predicting link blockages is important to ensure seamless connectivity. The predictions help to proactively switch beams and handoff (HO). Computer vision has been used to conduct these predictions based on camera images in yet another work [18]. Transfer learning using deep neural networks was attempted to predict the optimal beams for multi-links, resulting in reduced interference and training overhead [19]. EHF waves suffer from low spectral efficiency, narrow coverage, and difficulty in Non-Line-of-Sight (NLoS) propagation. It is therefore important to model path loss accurately for optimal base station placement. There are three types of path loss modeling methods: empirical, deterministic, and machine learning-based. Machine learning-based modeling uses measured data to train a model to predict path loss. A novel machine learning scheme called multi-way local attentive learning has been proposed to model and predict path loss [20]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Environment & Urban microcell indoor \\ Area & 200 \(\times\) 200 m\({}^{2}\) \\ Carrier frequency & 28 GHz \\ Path loss model & 5G urban microcell indoor \\ Number of base station antennas & 64 \\ Number of user equipments & 8 \\ Bandwidth & 400 MHz \\ Radius of base station & 100 m \\ Height of base station & 10 m \\ Height of user equipment & 1.7 m \\ Blocker dimensions & 1.7 m \(\times\) 0.3 m \\ Transmission power & 23 dBm \\ Noise figure & 9 dB \\ Sampling interval & 20 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 1: NYUSIM simulation parameters for generating the data. By understanding the environment using a combination of analytical models such as geometric analysis to recognize the shadowed regions that separate Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) scenarios and deep neural networks, researchers have built systems that can proactively allocate resources for better performance [21]. The allocation is based on link quality predictions as described in this work but using deep neural networks for regression. Not just deep learning, but traditional machine learning techniques such as Support Vector Regression (SVR), Random Forest (RF), and Gradient Tree Boosting (GTB) have also been used for the task of mmWave link prediction [22]. In yet another novel approach, researchers extract a sparse feature representation using non-deterministic quantization and apply deep neural network (DNN) to learn from those features for mmWave beam prediction [23]. Channel State Information (CSI) is fused with information about the user's location to predict the beam [24]. The prediction is still carried out using a neural network that adopts Adjustable Feature Fusion Learning (AFFL). Another approach [25] combines Deep Neural Networks (DNNs) with Long Short-Term Memory (LSTM) networks to create a new prediction method. This approach considers both past channel information and the position of the device. The proposed method can predict both large-scale trends and small-scale fluctuations in mmWave channel features. The approach achieved over 4.5% improvement in accuracy compared to existing approaches. To detect the motion of blockers such as a walking person close to the Line-of-Sight (LoS) path, an mmAlert system was proposed [26] using the passive sensing technique. It could predict 90% of the LoS blockage, with a sensing time of 1.4 s being sufficient enough to provide a timely warning. Privacy is important in such applications. To preserve privacy, instead of cameras, point clouds have been used for predicting signal strength in millimeter-wave communication systems [27].