NBIoT Optimization on massive devices access load control

Abstract

Narrow-band Internet of Things (NBIoT) supports a large number of machine connections, random access congestion comes with burst and uncertainty of terminal access. Considering the four key steps of NBIoT random access, this paper models the NBIoT random access process combing with time slot analysis and coverage level transition mechanism. The collision probability and the number of successfully connected devices are derived. Aiming at the fact that the existing ACB algorithm cannot effectively solve the problem of access load control when congestion is severe, this paper proposes a load access control algorithm based on reinforcement learning. In this algorithm, the base station dynamically learns the changes in the congestion state of the system, and adjusts the access level restriction parameters accordingly to reduce the collision probability. The simulation results show that the proposed algorithm system can quickly converge, effectively reduce the probability of access collisions under congestion conditions, increase the access success rate, and improve system access performance.