Accelerating Beam Sweeping in mmWave Standalone 5G New Radios using Recurrent Neural Networks

TL;DR

This paper introduces a method using Gated Recurrent Units (GRUs) to predict optimal beam sweeping patterns in mmWave 5G networks, leveraging user distribution data from call detail records to improve cell discovery efficiency.

Contribution

It presents a novel approach combining RNNs with cellular data to enhance beam sweeping patterns for faster cell search in mmWave 5G systems.

Findings

GRU-based predictions accurately estimate user locations.

Predicted patterns improve efficiency of beam sweeping.

Method leverages existing CDR data for real-time optimization.

Abstract

Millimeter wave (mmWave) is a key technology to support high data rate demands for 5G applications. Highly directional transmissions are crucial at these frequencies to compensate for high isotropic pathloss. This reliance on di- rectional beamforming, however, makes the cell discovery (cell search) challenging since both base station (gNB) and user equipment (UE) jointly perform a search over angular space to locate potential beams to initiate communication. In the cell discovery phase, sequential beam sweeping is performed through the angular coverage region in order to transmit synchronization signals. The sweeping pattern can either be a linear rotation or a hopping pattern that makes use of additional information. This paper proposes beam sweeping pattern prediction, based on the dynamic distribution of user traffic, using a form of recurrent neural networks (RNNs) called Gated Recurrent Unit (GRU). The spatial distribution of users is inferred from data in call detail records (CDRs) of the cellular network. Results show that the users spatial distribution and their approximate location (direction) can be accurately predicted based on CDRs data using GRU, which is then used to calculate the sweeping pattern in the angular domain during cell search.