# eMBB-URLLC Resource Slicing: A Risk-Sensitive Approach

arXiv: https://arxiv.org/abs/1902.01648 · HTML: https://mlsanity.com/paper/1902.01648/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2019.

## Built on by (most cited first, 5 of 5)

- [Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction](https://mlsanity.com/paper/2302.07675/) (2023)
- [IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture](https://mlsanity.com/paper/2209.07382/) (2022)
- [LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk](https://mlsanity.com/paper/2511.19175/) (2025)
- [Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic](https://mlsanity.com/paper/2103.01801/) (2021)
- [Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistence](https://mlsanity.com/paper/2103.10419/) (2021)

## Abstract (arXiv)

Ultra Reliable Low Latency Communication (URLLC) is a 5G New Radio (NR) application that requires strict reliability and latency. URLLC traffic is usually scheduled on top of the ongoing enhanced Mobile Broadband (eMBB) transmissions (i.e., puncturing the current eMBB transmission) and cannot be queued due to its hard latency requirements. In this letter, we propose a risk-sensitive based formulation to allocate resources to the incoming URLLC traffic while minimizing the risk of the eMBB transmission (i.e., protecting the eMBB users with low data rate) and ensuring URLLC reliability. Specifically, the Conditional Value at Risk (CVaR) is introduced as a risk measure for eMBB transmission. Moreover, the reliability constraint of URLLC is formulated as a chance constraint and relaxed based on Markov's inequality. We decompose the formulated problem into two subproblems in order to transform it into a convex form and then alternatively solve them until convergence. Simulation results show that the proposed approach allocates resources to the incoming URLLC traffic efficiently while satisfying the reliability of both eMBB and URLLC.