Enabling LTE RACH Collision Multiplicity Detection via Machine Learning

Abstract

The collision resolution mechanism in the Random Access Channel (RACH) procedure of the Long-Term Evolution (LTE) standard is known to represent a serious bottleneck in case of Machine-Type Communication (MTC). Its main drawbacks are seen in the facts that Base Stations (eNBs) typically cannot infer the number of collided User Equipments (UEs) and that collided UEs learn about the collision only implicitly, through the lack of the feedback in the later stage of the RACH procedure. The collided UEs then restart the procedure, increasing the RACH load and making the system more prone to collisions. In this paper, we leverage machine learning techniques to design a system that, besides outperforming the state-of-the-art schemes in preamble detection for the LTE RACH procedure, is able to estimate the collision multiplicity and thus gather information about how many devices chose the same preamble. This data can be used by the eNB to resolve collisions, increase the supported system load and reduce transmission latency. Besides LTE, the presented approach is applicable to novel 3GPP standards that target massive Internet of Things (IoT), e.g., LTE-M and NB-IoT, as well as 5G, since their RACH procedures are based on the same principles.