mlsanityData to 14 Oct 2026

Combining Improvements in Uplink AI-RAN

arXiv 2610.05936 ↗First posted 5 Oct 2026eess.SP cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

One of the major transformative factors in 6G will be the integration of Artificial Intelligence (AI) to become a native part of Radio Access Network (RAN). While most physical-layer AI features have so far been evaluated in isolation using link-level simulations, their combined behavior in a realistic multi-cell, multi-UE deployment has remained largely unexplored. In this paper, we present system-level performance results when multiple uplink AI features are enabled together, achieved by integrating accurate link-level and system-level simulators. To infer state-of-the-art deep-learning-aided Multiple Input Multiple Output (MIMO) receivers under the dynamic allocations produced by a realistic uplink scheduler, we propose a mirrored data augmentation method that decouples receiver performance from scheduled allocation size. In addition to these Physical Layer (PHY) receiver features, we combine several recent advances in deep reinforcement learning to train uplink power control and link adaptation that outperform a heuristic baseline and further boost the gains obtainable from the AI receiver alone. The system-level results show that the combined AI features improve the mean uplink user throughput by roughly 27% compared to a non-AI baseline, confirming that the individual PHY and Medium Access Control (MAC) AI features provide complementary gains when deployed jointly.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 21 references that are in the map. First posted on arXiv 5 Oct 2026.

Builds on

10 of 21 references.

References · 10

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 786
  3. 2019Soft Actor-Critic for Discrete Action Settingscited by 174
  4. 2023The Dormant Neuron Phenomenon in Deep Reinforcement Learningcited by 174
  5. 2022Sionna: An Open-Source Library for Next-Generation Physical Layer Researchcited by 145
  6. 2020DeepRx: Fully Convolutional Deep Learning Receivercited by 52
  7. 2020D2RL: Deep Dense Architectures in Reinforcement Learningcited by 42
  8. 2025Generalization in Reinforcement Learning for Radio Access Networkscited by 3
  9. 2024From Simulation to Practice: Generalizable Deep Reinforcement Learning for Cellular Schedulerscited by 2
  10. 2026EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receivercited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026A Survey on AI for 6G: Challenges and Opportunities
  2. 2025Practical Policy Distillation for Reinforcement Learning in Radio Access Networks
  3. 2024Adapting MLOps for Diverse In-Network Intelligence in 6G Era: Challenges and Solutions
  4. 2025Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning
  5. 2025Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning
  6. 2025Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocation
  7. 2026Agents Should Replace Narrow Predictive AI as the Orchestrator in 6G AI-RAN
  8. 2023Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler