# Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services

arXiv: https://arxiv.org/abs/2202.06435 · HTML: https://mlsanity.com/paper/2202.06435/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2022.

## Built on by (most cited first, 12 of 12)

- [Performance Optimization of Energy-Harvesting Underlay Cognitive Radio Networks Using Reinforcement Learning](https://mlsanity.com/paper/2505.14581/) (2025)
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- [Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks](https://mlsanity.com/paper/2604.01238/) (2026)
- [Slicing for AI: An Online Learning Framework for Network Slicing Supporting AI Services](https://mlsanity.com/paper/2411.02412/) (2024)
- [Near-Optimal Reinforcement Learning with Shuffle Differential Privacy](https://mlsanity.com/paper/2411.11647/) (2024)
- [Joint Link Adaptation and Device Scheduling Approach for URLLC Industrial IoT Network: A DRL-based Method with Bayesian Optimization](https://mlsanity.com/paper/2512.23493/) (2025)
- [Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN](https://mlsanity.com/paper/2507.18111/) (2025)
- [AWaRe-SAC: Proactive Slice Admission Control under Weather-Induced Capacity Uncertainty](https://mlsanity.com/paper/2601.05978/) (2026)
- [Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery](https://mlsanity.com/paper/2604.01049/) (2026)
- [When Critics Disagree: Adaptive Reward Poisoning Attacks in RIS-Aided Wireless Control System](https://mlsanity.com/paper/2605.20037/) (2026)

## Abstract (arXiv)

Radio access network (RAN) slicing is a key technology that enables 5G network to support heterogeneous requirements of generic services, namely ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB). In this paper, we propose a two time-scales RAN slicing mechanism to optimize the performance of URLLC and eMBB services. In a large time-scale, an SDN controller allocates radio resources to gNodeBs according to the requirements of the eMBB and URLLC services. In a short time-scale, each gNodeB allocates its available resources to its end-users and requests, if needed, additional resources from adjacent gNodeBs. We formulate this problem as a non-linear binary program and prove its NP-hardness. Next, for each time-scale, we model the problem as a Markov decision process (MDP), where the large-time scale is modeled as a single agent MDP whereas the shorter time-scale is modeled as a multi-agent MDP. We leverage the exponential-weight algorithm for exploration and exploitation (EXP3) to solve the single-agent MDP of the large time-scale MDP and the multi-agent deep Q-learning (DQL) algorithm to solve the multi-agent MDP of the short time-scale resource allocation. Extensive simulations show that our approach is efficient under different network parameters configuration and it outperforms recent benchmark solutions.