# BRNN-LSTM for Initial Access in Millimeter Wave Communications
###### Abstract
Overall, this work relies on the support of sub-6 GHz connections, thus limiting the ability of mmWave networks to operate separately. This becomes efficient for a 5G new radio network operating at FR2. Moreover, the work assumes that the sub-6 GHz link is already established, which makes the access time incomplete, i.e., it is required here to study the time complexity for beam association once a MS joins a network until the start of the data-plane. There is also a lack of comprehensive beamforming designs at the MS and BS, where it is limited to a conventional discrete Fourier transform (DFT)-based codebook. Similarly, the work in [12] also relies on sub-6 GHz channel vectors for initial beam access and blockage in mmWave systems. As opposed to the methodology of [11] which extracts spatial channel characteristics at the sub-6 GHz band and then uses them to reduce the mmWave beam training overhead, here mapping functions are predicted directly from the sub-6 GHz channel. Specifically, the model leverages transfer learning to reduce the learning time overhead. However, the estimation of the mapping functions is often complicated and requires large neural network to achieve accuracy. In addition, the work again relies on sub-6 GHz bands to realize the beam access at mmWave. Namely, dual-band (microwave and mmWave transceivers) systems are needed at the BS and MS. Here the power consumption analysis and access time need to be further investigated. For mmWave vehicular communication, the authors od [13] propose a beam alignment procedure based on fingerprinting approach, over which a set of beam pairs constitute the fingerprint of a given location. Deep learning is deployed at the BS to adapt and update these fingerprints. Moreover, a plurality mechanism is proposed for the beams that meet the received signal strength, i.e., to achieve multiplexing and diversity gains. The outcomesaim to improve the fidelity as compared to exhaustive beam search fingerprinting without deep learning. Another use for DNN is for beam management and interference coordination in indoor dense mmWave networks for IEEE 802.11 ay networks in [14] that optimizes the beam directions, beamwidths and transmit power. The goal is to reduce the computational complexity and time, while obtaining comparable sum-rate to conventional methods. However, it uses a beamforming training mechanism to establish the directional links between mobile access point (MAB) and stationary access point (SAP), which is used subsequently to generate training data for DNN to mitigate interference. Therefore, the deep learning network is not used here for initial access. Moreover, the implementation is limited to indoor wireless local area networks (WLAN) and not applied for outdoor settings at larger separation distances. The authors of [15] describe a specific dataset for beam selection techniques on vehicle-to-infrastructure using millimeter waves. A methodology for channel data generation in mmWave multiple-input multiple-output (MIMO) scenarios is presented that aims to simplify creating data in mobility scenarios by invoking a traffic simulator and a ray-tracing simulator. However, the context here is different and unrelated to initial access between MS and BS cells. Namely, the propagation channel dataset developed here by raytracing is specific for vehicle-to-infrastructure mmWave networks. Overall, the work in [15] focuses on modeling mobility only and lacks the analysis of the downlink performance. By contrast, the proposed work in this paper focuses on standalone mmWave networks, considering link performance with beamforming architectures at the BS and MS. Furthermore, deep learning is also used for beam training in [16] for a mmWave massive MIMO system. The nonlinear properties of channel power leakage are used in the estimation process, where DNN to predict the best beam combination that yields the strongest channel path based on the probability vector in efforts to improve the successful and achievable rates at lower overhead.