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

arXiv: https://arxiv.org/abs/1901.04292 · HTML: https://mlsanity.com/paper/1901.04292/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2018.

## Built on by (most cited first, 10 of 10)

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- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [A Hybrid Reinforcement Learning Framework for Hard Latency Constrained Resource Scheduling](https://mlsanity.com/paper/2504.03721/) (2025)
- [Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistence](https://mlsanity.com/paper/2103.10419/) (2021)
- [5G Traffic Prediction with Time Series Analysis](https://mlsanity.com/paper/2110.03781/) (2021)
- [Communication-Efficient Orchestrations for URLLC Service via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2307.13415/) (2023)
- [A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT](https://mlsanity.com/paper/2401.06135/) (2023)
- [Intelligent Hybrid Resource Allocation in MEC-assisted RAN Slicing Network](https://mlsanity.com/paper/2405.17436/) (2024)

## Abstract (arXiv)

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.