# BRNN-LSTM for Initial Access in Millimeter Wave Communications
###### Abstract
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Algorithm** & **Metrics** & **Advantages** & **Limitations** \\ \hline
[10] & Deep neural network for beam prediction at BS & Algorithm Accuracy & Improves beam search at the BS versus exhaustive scheme & Omnidirectional mode at the MS, no beam ‘discovery at the MS, wide beams. Lacks time and power models \\ \hline
[11] & Deep neural network & Beam selection accuracy & Ray-tracing simulations and over-the-air experiments & Dependent on sub-6 GHz bands. It lacks time and power models \\ \hline
[12] & Approximation theory, deep neural network & Beam-prediction accuracy, SNR, spectral efficiency & Empirical models for the beam prediction performance & Dependent on sub-6 GHz bands. It lacks delay measurements. \\ \hline
[13] & Deep learning model, multifingerprint database plurality mechanism & Instantaneous RSS for various traffic densities, probability distributions of fingerprints & High spectral efficiency & Similar performance to beam- sweeping approach, lack of beam delay and power analysis and simulation. Requirements for multiple BSs and location dependent. Lack of estimation at the MS. Limited to vehicular networks. \\ \hline
[14] & Deep neural network, beam management and interference coordination & Sum-rate and time ratio & High sum-rates and reducing computational complexity & Limited to indoor environment, lack of outdoor scalability. Lack of latency and power models/simulation. \\ \hline
[15] & Deep learning for vehicle-to-infrastructure & Average reward & Consideration for mobility & Not tested for outdoor cellular networks. Lack of downlink performance. \\ \hline
[16] & Deep neural network & Achievable and success rates for various SNR & Success rates, consideration for power leakage & Lack of beamforming, time and power models \\ \hline
[17] & Deep neural networks & Network convergence and spectral efficiency & Multi-user beam alignment, online prediction & Lack of time and power models/simulation \\ \hline
[18] & Machine learning and situational awareness & Root mean squared and regression errors & Multi-user beam alignment & Location-dependent estimation, lack of time model \\ \hline
[19] & Deep neural network & Effective achievable rate & High achievable rates at different dataset size & Use of omnidirectional antennas at the MS, requirements for multiple BSs, lack of beam estimation at the MS \\ \hline
[20] & Convolutional neural network & Training and validation loss of different depths & High accuracy for different SNR & Dependence on sub-6 GHz bands. Lack of beamforming models and details of the algorithm. Similar spatial features between microwave and mmWave bands \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing Research on Deep Learning Methods for Beam Management in mmWave Communications. In light of the above, this paper proposes a first use of a deep learning network model for initial beam access in mmWave communications, with the goal to develop one of the fastest beam access schemes. The model operates in learning and training modes, which aims to predict the best beam index over subsequent time steps, where these indices are affiliated with specific beamforming and combining vectors. The key practical application for the proposed work is enhancing mmWave networks as part of the 5G FR2 New Radio. Current 5G implementation relies on conventional sub-6 GHz and leverages mmWave bands as a supplementary component, e.g., dual bands and carrier aggregation. However, this is projected only in the first phase of 5G, where the mmWave bands are expected to work independently starting in 2022-2024, contingent upon the development of mature technologies and optimization to support the targeted throughout and latencies. Hence, the mmWave bands are projected to provide standalone service without dependency on microwave (legacy) bands. Thus, the beamforming capability enhances the channel quality and throughput for the user.