URLLC service provision to maximize the utility of mIoT slices and that of bursty URLLC slices. The main contributions of this paper can be summarized as the following:

-   We revisit the frame and minislot structure for mIoT transmission to accommodate more RA requests from a massive number of IoT devices.

-   We adopt a queueing model to track the IoT packet arrival, accumulate and departure processes and analyze the queue evolution process by employing probability and stochastic geometry theories. Based on the analysis result, we derive the closed-form expression of the RA success probability of a randomly chosen IoT device.

-   We define mIoT slice utility and bursty URLLC slice utility and formulate the RAN slicing for mIoT and bursty URLLC service multiplexing as a resource optimization problem. The objective of the optimization problem is to maximize the total mIoT and URLLC slice utilities, subject to limited physical resource constraints. The mitigation of this problem is difficult due to the existence of indeterministic objective function and thorny non-convex constraints and the requirement of tackling a two timescale issue as well.

-   To mitigate this thorny optimization problem, we propose a slice resource optimization (SRO) algorithm. In this algorithm, we 