Research on Prediction Based Emergency Resource Allocation in 5G Uplink

As a typical application scenario of 5G uRLLC, the data transmission delay and reliabilityrequirements of industrial applications are more and more stringent, and the convergent transmission ofdiversified data becomes an urgent problem to be solved. One of the important challenges is the efficientscheduling of wireless resources to ensure the coexistence of various data transmission without interfering witheach other and stable operation of the system. In view of 5G uplink transmission in industrial transmissionscenarios, a prediction-based resource allocation scheme is proposed, which uses Auto Regressive MovingAverage (ARMA) model to predict the activation rates of the next transmission cycle based on the historicaldata. Then the resources are dynamically reserved for periodic and emergency data, so as to minimize theimpact on periodic data transmission under the premise of meeting the emergency data transmission conditions.Experimental results show that, compared with the traditional resource allocation scheme, this scheme caneffectively reduce the impact of emergency data transmission on periodic data and improve the utilization of spectrum resources.