electronics Article BRNN-LSTM for Initial Access in Millimeter Wave Communications Adel Aldalbahi 1 , Farzad Shahabi 2 and Mohammed Jasim 3, *   Citation: Aldalbahi, A.; Shahabi, F.; Jasim, M. BRNN-LSTM for Initial Access in Millimeter Wave Communications. Electronics 2021, 10, 1505. https://doi.org/10.3390/ electronics10131505 Academic Editors: Ramón Gonzalo and Adão Silva Received: 10 February 2021 Accepted: 20 April 2021 Published: 22 June 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Department of Electrical Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia; aaldalbahi@kfu.edu.sa 2 Department of Electrical Engineering, School of Engineering, University of South Florida, Tampa, FL 33620, USA; fshahabi@mail.usf.edu 3 School of Engineering, University of Mount Union, Alliance, OH 44601, USA * Correspondence: jaesimad@mountunion.edu; Tel.: +1-330-829-4919 Abstract: The use of beamforming technology in standalone (SA) millimeter wave communications results in directional transmission and reception modes at the mobile station (MS) and base station (BS). This results in initial beam access challenges, since the MS and BS are now compelled to perform spatial search to determine the best beam directions that return highest signal levels. The high number of signal measurements here prolongs access times and latencies, as well as increasing power and energy consumption. Hence this paper proposes a first study on leveraging deep learning schemes to simplify the beam access procedure in standalone mmWave networks. The proposed scheme combines bidirectional recurrent neural network (BRNN) and long short-term memory (LSTM) to achieve fast initial access times. Namely, the scheme predicts the best beam index for use in the next time step once a MS accesses the network, e.g., transition from sleep to active (or idle) modes. The scheme eliminates the need for beam scanning, thereby achieving ultra-low access times and energy efficiencies as compared to existing methods. Keywords: millimeter wave; beamforming; initial beam access; bidirectional recurrent neural net- work; long short-term memory; access times 1. Introduction Millimeter Wave (mmWave) frequencies constitute a major component of SA 5G networks for high data rates support in enhanced mobile broadband (eMBB). One key advantage here is the contiguous available spectrum at these bands. However, the ag- gregated path losses impose the use of beamforming techniques to achieve higher link gains. This results in directional transmission and reception modes, which yields pro- longed access times. Now the International Mobile Telecommunications (IMT) framework specifies 10 millisecond (ms) latency levels for eMBB in 5G systems [1]. Hence, a major challenge here is to provide fast access schemes that feature ultralow times, along with reduced power and energy consumption levels. Additionally, these access schemes need to consider channel fluctuations and variations in link status as a function of blockage, as well as mobility effects. Currently, conventional schemes dictate that the MS and BS perform spatial search over all directions, in order to determine the best beamforming and combining vectors with the highest received signal level. For example, work in [2] proposes a hierarchical codebook for iterative search that uses wide beams in the initial search stages, then refinement is conducted in subsequent stages using narrow beams. However, this technique can suffer from reduced directivity, outages and sensitivity to blockage due to the low gains achieved in the initial codebook stage. Moreover, work in [3,4] uses metaheuristics in efforts to accelerate the access times and reduce energy consumption, e.g., generalized pattern search and Hooke Jeeves methods. The work in [5] exploits the sidelobe information to retrieve the Electronics 2021, 10, 1505. https://doi.org/10.3390/electronics10131505 https://www.mdpi.com/journal/electronics