# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
###### Abstract  
Millimeter wave (mmWave) bands formulate the standalone (SA) operation mode in the new radio (NR) access technology of 5G systems. These bands rely on beamforming architectures to aggregate antenna array gains that compensate for dynamic channel fluctuations and propagation impairments. However, beamforming results in directional transmission and reception, thus resulting in beam management challenges, foremost initial access, handover, and beam blockage recovery. Here, beam establishment and maintenance must feature ultra-low latencies in the control and data planes to meet network specifications and standardization. Presently, existing schemes rely on arrays redundancy, multi-connectivity, such as dual-beam and carrier aggregation, and out-of-band information. These schemes still suffer from prolonged recovery times and aggregated power consumption levels. Along these lines, this work proposes a fast beam restoration scheme based on deep learning in SA mmWave networks. Once the primary beam is blocked, it predicts alternative beam directions in the next time frame without any reliance on out-of-band information. The scheme adopts long short-term memory (LSTM) due to the robust memory structure, which uses past best beam observations. The scheme achieves near-instantaneous recovery times, i.e., maintaining communications sessions without resetting beam scanning procedures. m m  
high frequency (EHF) bands, i.e., 27.5-28.35 GHz and 37-40 GHz ranges for commercial 5G cellular deployment. In general, this trend follows the standardization of unlicensed bands, such as 57-71 GHz for wireless local area networks (WLANs). Overall contiguous spectrum ranges offer very suitable means for wideband single-carrier transmission. As a result, mmWave technologies can eliminate some of the key challenges associated with 3.9G long term evolution (LTE) and 4G LTE-advanced (LTE-A) networks, such as adaptive subcarrier channelization and assignment, frequency synchronization, and phase noise. However, mmWave spectral regions suffer from notable propagation impairments, e.g., path loss, oxygen absorption, atmospheric attenuation, penetration losses, diffraction, and high reflection coefficients. The latter can result in further isolation between indoor and outdoor networks. Furthermore, mmWave systems are also characterized as power- and noise-limited rather than bandwidth- and interference-limited for conventional cellular networks. Finally, mmWave bands suffer from high noise power and poor channel characteristics, i.e., a sparse structure. In general, some of these effects can be mitigated using antenna arrays and beamforming solutions that offer aggregated array factors. This increases the signal strength, thus improving channel capacity and the scattering profile, which in turn increases the channel rank. As a result, compact substrate frontends are designed for mmWave systems, thereby facilitating the use of beamforming architectures at a mobile station (MS) and base station (BS) to overcome channel and propagation deficiencies. Despite the benefits of beamforming in mitigating propagation impairments and dynamic channel fluctuations, it introduces multiple challenges to mmWave networks. Foremost among these, beamforming yields reduced footprints, complexity in control-plane signaling, and requirements for link maintenance. Most importantly, beamforming results in significant beam association challenges due to the directional transmission and reception modes. This directional mode compels MS and BS to perform spatial searches to determine the spatial direction that returns optimum signal values for use in control and data planes. The search for the best direction results in excessive computational complexity, extended latencies, and aggregated power and energy consumption. This contradicts the ultra-low latency communications (URLLC) use case defined in 5G standards [1].