Accelerating Beam Sweeping in mmWave Standalone 5G New Radios using Recurrent Neural Networks
This addresses the cell discovery problem in 5G networks for improved efficiency, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the challenge of accelerating beam sweeping in mmWave 5G networks by predicting beam sweeping patterns based on user traffic distribution using GRU recurrent neural networks, achieving accurate prediction of user spatial distribution from call detail records.
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.