Context Aware Initial Search in 5G mmWave Systems

Abstract

The wide bandwidth available at the millimeter wave (mm Wave) frequencies is expected to offer high data rates in the fifth generation (5G) of cellular networks. The technology implements directional transmission to overcome increased path loss at high frequencies. The dependence on directionality urges to establish new control layer protocols because the algorithms implemented in omnidirectional long term evolution (LTE) systems are not suitable for these networks. The mmWave base station (BS) and user equipment (UE) need to be properly aligned for directional communication constituting long-lasting initial access (IA) phase. Recently, several research works have been done to devise smart IA procedures for mmWave systems. Some of these schemes periodically sweep across the cell area while others make use of the contextual information regarding BS and UE profiles and propagation environment to establish the link. This paper proposes a smart machine learning-based context-aware sequential algorithm for IA in 5G mm Wave systems and analyzes its performance in comparison to the conventional exhaustive and iterative search algorithms. The algorithm is shown to provide a comparatively lower misdetection probability and a smaller discovery delay.