mlsanityData to 14 Oct 2026

Intelligent Radio Resource Slicing for 6G In-Body Subnetworks

arXiv 2603.28529 ↗First posted 30 Mar 2026eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

6G In-body Subnetworks (IBSs) represent a key enabler for supporting standalone eXtended Reality (XR) applications. IBSs are expected to operate as an underlay to existing cellular networks, giving rise to coexistence challenges when sharing radio resources with other cellular users, such as enhanced Mobile Broadband (eMBB) users. Such resource allocation problem is highly dynamic and inherently non-convex due to heterogeneous service demands and fluctuating channel conditions. In this paper, we propose an intelligent radio resource slicing strategy based on the Soft Actor-Critic (SAC) deep reinforcement learning algorithm. The proposed SAC-based slicing method addresses the coexistence challenge between IBSs and eMBB users by optimizing a refined reward function that explicitly incorporates XR cross-modal delay alignment to ensure immersive experience while preserving eMBB service guarantees. Extensive system-level simulations are performed under realistic network conditions and the results demonstrate that the proposed method can enhance user experience by 12-85% under different network densities compared to baseline methods while maintaining the target data rate for eMBB users.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 24 references that are in the map. First posted on arXiv 30 Mar 2026.

Builds on

6 of 24 references.

References · 6

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2021ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platformscited by 35
  3. 20185G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic Viewcited by 18
  4. 2022Standardization of Extended Reality (XR) over 5G and 5G-Advanced 3GPP New Radiocited by 4
  5. 2025Goal-Oriented Interference Coordination in 6G In-Factory Subnetworkscited by 1
  6. 2025Proactive Radio Resource Allocation for 6G In-Factory Subnetworkscited by 1

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  1. 2026SliceFed: Federated Constrained Multi-Agent DRL for Dynamic Spectrum Slicing in 6G ↗
  2. 2024Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods
  3. 2024On Enhancing Network Throughput using Reinforcement Learning in Sliced Testbeds
  4. 2026GAN-Enhanced Deep Reinforcement Learning for Semantic-Aware Resource Allocation in 6G Network Slicing
  5. 2025Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning
  6. 2025Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning
  7. 2026Interpretable Attention-Based Multi-Agent PPO for Latency Spike Resolution in 6G RAN Slicing
  8. 2023Advancing RAN Slicing with Offline Reinforcement Learning