  Supporting ultra-reliable low-latency communications (URLLC) is a major
challenge of 5G wireless networks. Stringent delay and reliability requirements
need to be satisfied for both scheduled and non-scheduled URLLC traffic to
enable a diverse set of 5G applications. Although physical and media access
control layer solutions have been investigated to satisfy only scheduled URLLC
traffic, there is a lack of study on enabling transmission of non-scheduled
URLLC traffic, especially in coexistence with the scheduled URLLC traffic.
Machine learning (ML) is an important enabler for such a co-existence scenario
due to its ability to exploit spatial/temporal correlation in user behaviors
and use of radio resources. Hence, in this paper, we first study the
coexistence design challenges, especially the radio resource management (RRM)
problem and propose a distributed risk-aware ML solution for RRM. The proposed
solution benefits from hybrid orthogonal/non-orthogonal radio resource slicing,
and proactively regulates the spectrum needed for satisfying delay/reliability
requirement of each URLLC traffic type. A case study is introduced to
investigate the potential of the proposed RRM in serving coexisting URLLC
traffic types. The results further provide insights on the benefits of
leveraging intelligent RRM, e.g. a 75% increase in data rate with respect to
the conservative design approach for the scheduled traffic is achieved, while
the 99.99% reliability of both scheduled and nonscheduled traffic types is
satisfied.
