Comparative Performance Analysis of Beam Sweeping Using a Deep Neural Net and Random Starting Point in mmWave 5G New Radio

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 directional 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 compares recently proposed beam sweeping pattern prediction, based on the dynamic distribution of user traffic, using a form of recurrent neural networks (RNNs) called a Gated Recurrent Unit (GRU), and random starting point sweeping to measure the synchronization delay distribution. Results show that user spatial distribution and their approximate location (direction) can be accurately predicted based on Call Detail Records (CDRs) data using a GRU, which is then used to calculate the sweeping pattern in the angular domain during cell search. Moreover, the proposed beam sweeping pattern prediction enable the UE to initially assess the gNB in approximately 0.41 of a complete scanning cycle with probability 0.9 in a sparsely distributed UE scenario.