Risk-Aware Resource Allocation for URLLC: Challenges and Strategies with Machine Learning
This addresses a critical challenge in 5G wireless networks for applications requiring stringent delay and reliability, though it appears incremental as it builds on existing ML and resource management techniques.
The paper tackles the problem of enabling coexistence of scheduled and non-scheduled ultra-reliable low-latency communications (URLLC) traffic in 5G networks by proposing a distributed risk-aware machine learning solution for radio resource management, achieving a 75% increase in data rate for scheduled traffic while maintaining 99.99% reliability for both traffic types.
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.