and improve resource utilization through non-orthogonally allocating uplink channel resources at the second step of the RA procedure. Additionally, without knowing the statistical characteristic of traffic, a reinforcement learning-based algorithm was proposed in [@jiang2019reinforcement] to determine the uplink resource configuration for RA such that the average number of served IoT devices was maximized while ensuring a high RA success probability.

Except for the IoT service, future networks are envisioned to simultaneously support different services and applications with significantly different requirements on reliability, latency and bandwidth. As a result, researchers are now paying more attention to the service multiplexing of IoT/mMTC and many other services such as eMBB and URLLC. For example, instead of slicing the RAN via orthogonal resource allocation among different services, the work in [@popovski20185g; @budhiraja2019tactile] studied the potential advantages of allowing for non-orthogonal RAN resources sharing in uplink communications from a collection of mMTC, eMBB, and URLLC devices to the same BS. The work in [@ksentini2017toward] developed a two-level scheduling process to allocate dynamically dedicated bandwidth to each network slice according to workload demand and slices’ quality of service (QoS) requirement such that flexible resource allocation could be implemented. The work in [@rost2017network] proposed to maintain slice-specific 