# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
## 2 Related Work
### Beam Recovery Schemes Based on Deep Learning Networks  
Recently, deep learning networks have received a great deal of attention in beam management for mmWave communications (see Table 2). The authors of [9] gauge the signal level of beam directions in the proximity of a failed main beam, albeit at prolonged test times that extend the recovery process and yield session drops. Moreover, the authors of [10] present a data forwarding algorithm for intermediate nodes between MS and BS that relay data if the main beam degrades or fails. Furthermore, a backup beam is leveraged in [11] as a reactive recovery method in the occurrence of link failure. This presents a form of redundancy in the design of beamforming architecture, which doubles the amount of power consumption in favor of low recovery times. Note that the direction of the backup beam is retrieved using microwave bands, thus making this solution limited to the non-standalone (NSA) standard. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Algorithm** & **Advantages** & **Limitations** \\ \hline \multirow{2}{*}{[12]} & Machine learning for reliable & Proactive approach enables fast beam & Limited blockage parameters, marginal \\  & mmWave systems. & switching times between BSs. & link improvements. \\ \hline \multirow{3}{*}{[13;14]} & Situational awareness [13] and & \multirow{3}{*}{Consideration for power at the beam index, high prediction precision.} & Location awareness of MS to train the network, accuracy challenges. \\  & data-driven [14] prediction & & \\  & approaches. & & \\ \hline \multirow{3}{*}{[15]} & Alignment method for multi-user & \multirow{3}{*}{Eliminates the need for MS location (reducing overhead).} & \multirow{3}{*}{Lacks modeling the beam access/recovery times and power consumption levels.} \\  & mmWave massive MIMO system. & & \\ \hline \multirow{3}{*}{[16]} & inter-band beam switching scheme & \multirow{3}{*}{Resiliency through dynamic temporal setting and blockage probabilities.} & Location awareness, multi-connectivity, magnified power consumption. \\  & multi-armed bandits (MAB) model. & & \\ \hline \multirow{3}{*}{[17]} & Out-of-band beam prediction & \multirow{3}{*}{Reduce the learning time overhead.} & High complexity due to functions \\  & leverages transfer learning. & & \\ \hline \multirow{3}{*}{[18]} & Deep neural network for scheduling & \multirow{3}{*}{Improved prediction accuracy, reduced complexity, user mobility.} & Based on sub-6 GHz channel knowledge, limited to downlink transmission. \\  & prediction at BS. & & \\ \hline \multirow{3}{*}{[19; 20; 21]} & Beam cooperation and delegation & \multirow{3}{*}{Mobility-aware, better node visibility and QoS, sustaining communication sessions.} & Multi-connectivity (redundancy) requires high power levels, lack of delay models. \\  & indoor mmWave networks. & & \\ \hline \multirow{3}{*}{[23]} & Vision-assisted scheme for beam & \multirow{3}{*}{Robust and accurate recovery scheme.} & Dependence on sub-6 GHz channels, large number of installed cameras in dense network deployment. \\  & blockage problem. & & \\ \hline \multirow{3}{*}{[24]} & Intelligent reflecting surface & \multirow{3}{*}{Mobility awareness, reduced system overhead, high handover success rate.} & High computational complexity, lack of power model, requires massive infrastructure scalability. \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Existing research on beam recovery techniques based on deep learning networks. Furthermore, deep learning methods have been applied in wireless communications, such as in [27], that leverages the LSTM algorithm for content prediction in virtual reality (VR)-based mobile edge computing (MEC). The work aims to minimize the system energy consumption and latency by using the LSTM autoencoder network to predict content popularity leveraged in caching replacement and deterministic offloading in order to better utilize the caching and computing resource of a system.