# BRNN-LSTM for Initial Access in Millimeter Wave Communications
###### 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. m +
Footnote †: journal: Electronics  
0000-0002-2000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-0  
direction of the main lobe. However, this scheme is limited to line-of-sight (LoS) and single-ray channel. Moreover, work in [6] exploits grating lobes for simultaneous transmission to increase directivity. However, this scheme features a complex beamforming structure with a large number of antennas and high-power requirements. Furthermore, the geolocation-assisted access scheme in [7] utilizes global positioning system (GPS) at the MS to determine the BS location. However, this context-based scheme is limited to outdoor settings with permanent GPS connectivity, and it requires the BS to conduct exhaustive beam search to allocate the MS. The work in [8] proposes a single RF chain architecture for multi-user that uses downlink (DL)-uplink (UL) and DL-DL beamforming techniques. A subset beam group is trained here in a single time slot. However, the use of a single RF chain at the BS limits the number of connected MS and scalability. Finally, the work in [9] proposes a subarray-cooperation multiresolution codebook design. It features a beam alignment scheme that adaptively selects initial layers based on various simultaneous signal-to-noise (SNR) levels. Hence, it quickly aligns the desired beam pairs under single dominant path channels by using hybrid beamforming. Overall, the aforementioned schemes still yield great computational complexity, prolonged access times and high power and energy requirements. Various studies have used deep learning to solve the problem of beam management for mmWave communication systems. First, the authors of [10] use deep neural network (DNN) to predict the beam direction at the BS, while implementing an omnidirectional antenna at the MS. The work only studies the accuracy of the DNN algorithm using 24 beams at the BS and aims to outperform the exhaustive beam mechanism. Additionally, the work in [10] considers omnidirectional MS and directional BS. However, the omni-directional mode at the MS presents various challenges in terms of signal quality and throughput (requires further investigation). By contrast, the proposed work in this paper uses 64 beams and considers system performance including access times, power and energy consumption. Furthermore, results are compared to the fastest beam access schemes reported in the literature. In addition, this paper proposes beamforming models at the MS and BS, which then uses the deep learning network to study comprehensive performance metrics. Furthermore, deep-learning-based beam selection is proposed in [11] to reduce the time overhead by exploiting sub-6 GHz channel information. A DNN algorithm is used to estimate the power delay profile (PDP) of a sub-6 GHz channel, then acting an input of the DNN.