# LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RAN

arXiv: https://arxiv.org/abs/2606.23110 · HTML: https://mlsanity.com/paper/2606.23110/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 27 of its 50 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Exact solutions to the nonlinear dynamics of learning in deep linear neural networks](https://mlsanity.com/paper/1312.6120/) (2013)
- [Responsive Safety in Reinforcement Learning by PID Lagrangian Methods](https://mlsanity.com/paper/2007.03964/) (2020)
- [Residual Reinforcement Learning for Robot Control](https://mlsanity.com/paper/1812.03201/) (2018)
- [Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO](https://mlsanity.com/paper/2005.12729/) (2020)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Constrained Reinforcement Learning Has Zero Duality Gap](https://mlsanity.com/paper/1910.13393/) (2019)
- [The Road Towards 6G: A Comprehensive Survey](https://mlsanity.com/paper/2102.01420/) (2021)
- [dApps: Distributed Applications for Real-time Inference and Control in O-RAN](https://mlsanity.com/paper/2203.02370/) (2022)
- [dApps: Enabling Real-Time AI-Based Open RAN Control](https://mlsanity.com/paper/2501.16502/) (2025)
- [New Radio Physical Layer Abstraction for System-Level Simulations of 5G Networks](https://mlsanity.com/paper/2001.10309/) (2020)
- [Reinforcement Learning for Efficient and Tuning-Free Link Adaptation](https://mlsanity.com/paper/2010.08651/) (2020)
- [X5G: An Open, Programmable, Multi-vendor, End-to-end, Private 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterface](https://mlsanity.com/paper/2406.15935/) (2024)
- [Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks](https://mlsanity.com/paper/1912.04030/) (2019)
- [Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning Algorithm](https://mlsanity.com/paper/2105.12827/) (2021)
- [Generalization in Reinforcement Learning for Radio Access Networks](https://mlsanity.com/paper/2507.06602/) (2025)
- [Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems](https://mlsanity.com/paper/1708.00994/) (2017)
- [InterfO-RAN: Real-Time In-band Cellular Uplink Interference Detection with GPU-Accelerated dApps](https://mlsanity.com/paper/2507.23177/) (2025)
- [Programmable and GPU-Accelerated Edge Inference for Real-Time ISAC on NVIDIA Aerial Testbed](https://mlsanity.com/paper/2512.06493/) (2025)
- [From Simulation to Reality: Practical Deep Reinforcement Learning-based Link Adaptation for Cellular Networks](https://mlsanity.com/paper/2603.00689/) (2026)
- [LibIQ: Toward Real-Time Spectrum Classification in O-RAN dApps](https://mlsanity.com/paper/2505.10537/) (2025)
- [Sim2Field: End-to-End Development of AI RANs for 6G](https://mlsanity.com/paper/2509.23528/) (2025)
- [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)

## Built on by (most cited first, 1 of 1)

- [Improving 5G AI-RAN MCS Selection by Predicting Retransmissions](https://mlsanity.com/paper/2609.09324/) (2026)

## Abstract (arXiv)

Outer-loop link adaptation (OLLA) is widely deployed in 5G NR to track channel variations, yet its reliance on first-order, single-bit feedback degrades performance significantly under high-mobility and fast-varying channels. This paper presents LOLLA (Learned Outer-Loop Link Adaptation), a deep reinforcement learning framework that replaces the conventional OLLA staircase with a learned, continuous SINR offset conditioned on rich PHY/MAC telemetry inaccessible to OLLA. The offset modulates the SINR-to-MCS lookup table, preserving 3GPP-compliant MCS selection and provably subsuming the conventional OLLA update rule. A Proximal Policy Optimization (PPO) policy trained under a Lagrangian block error rate (BLER) constraint automatically enforces tunable reliability targets from 1% to 15% without manual penalty calibration. The framework is realized as the first closed-loop AI-native control dApp on a GPU-accelerated 5G NR stack, achieving end-to-end control latencies under 500 microseconds. Evaluations under 3GPP TDL channel models demonstrate 15% to 92% throughput gains over OLLA across Doppler frequencies up to 400 Hz, while attaining a Pareto frontier that strictly dominates OLLA across all evaluated reliability targets. The learned policy generalizes to unseen channel models and scales to eight concurrent UEs under shared-resource scheduling. In the uplink formulation, the gNB directly observes decoding outcomes, enabling simulation-to-deployment parity.