A Pseudo-Bayesian Subframe based Framework for Grant-Free Massive Random Access in 5G NR Networks

Although the number of massive machine type communications (mMTC) devices covered by a fifth generation (5G) cell could be huge, not all devices are always active. For radio resource allocation, it is vital that a base station has the knowledge on the number of active mMTC devices in each frame. In this paper, we develop a mathematical framework to perform pseudo-Bayesian estimation on the number of active devices within a 5G new radio (NR) subframe. The estimation is performed by the base station on a subframe-by-subframe basis based upon which a transmission permission probability is imposed on active devices for their transmissions in the next subframe. The active devices include both backlogged devices and new arrivals in the current subframe that will attempt for medium access in the next subframe relying on a combination of NR radio resources in both time and frequency domains. Such an estimation constitutes an important component for the design of grant-free access schemes for mMTC uplink traffic.