Risk-Aware Resource Allocation for URLLC: Challenges and Strategies with Machine Learning

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 scheduled URLLC traffic, there is 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 to manage this coexistence scenario due to its ability to exploit spatial/temporal correlation in user behaviors and use of radio resources. In this article, 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 the 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. For example, a 75 percent increase in data rate with respect to the conservative design approach for the scheduled traffic is achieved, while the 99.99 percent reliability of both scheduled and non-scheduled traffic types is satisfied.