Access Control and Pilot Allocation for Machine-Type Communications in Crowded Massive MIMO Systems

Abstract

Massive machine-type communication (mMTC) in 5G New Radio (5G-NR) or the Internet of Things (IoT) is a network of physical devices such as vehicles, smart meters, sensors, and smart appliances, which can communicate and interact in real time without human intervention. In IoT systems, the number of networked devices is expected to be in the tens of billions, while radio resources remain scarce. To connect the massive number of devices with limited bandwidth, it is crucial to develop new access solutions that can improve resource efficiency and reduce control overhead as well as access delay. The key idea is controlling the number of arrival devices that want to access the system, and then allowing only the strongest device (that has the largest channel gain and each device is able to check whether it is the strongest device) be able to transit to BS. In this paper, we consider a random access problem in massive MIMO context for the collision resolution, in which the access class barring (ACB) factor is dynamically adjusted in each time slot to maximize access success rate for the strongest-user collision resolution (SUCRe) protocol. We propose the dynamic ACB scheme to find optimal ACB factor in the next time slot and then apply SUCRe protocol to achieve a good performance. This method is called dynamic access class barring combined strongest-user collision resolution (DACB-SUCR). In addition, we investigate two different ACB schemes that consist of the fixed ACB and the traffic-aware ACB to compare with the proposed dynamic ACB. Analysis and simulation results demonstrate that, compared with SUCRe protocol, the proposed DACB-SUCR method can remarkably reduce pilot collision, and increase access success rate. It is also shown that the dynamic ACB gives better performance than the fixed ACB and the traffic-aware ACB.