DRL-Based AoI Minimization for RSMA in Finite-Blocklength MU-MISO
Abstract
This letter investigates age-of-information (AoI) minimization in multi-user wireless networks operating in the finite-blocklength (FBL) regime, which is critical for low-latency transmission of short state-update packets. While rate-splitting multiple access (RSMA) provides a powerful and flexible framework for interference management in multi-user FBL systems, the joint optimization of its parameters, such as precoding vectors, power allocation, and rate-splitting ratios, to guarantee information freshness results in analytically intractable complexity. To address this challenge, we propose an actor--critic deep reinforcement learning (DRL) framework to learn dynamic resource-allocation policies in multi-user multiple-input single-output (MU-MISO) broadcast channels. Simulation results show that the proposed RSMA-RL framework achieves consistently lower AoI than the state-of-the-art benchmarks, with substantial gains observed at low signal-to-noise ratio (SNR) and short blocklengths, while matching benchmark performance at high SNR with significantly lower online complexity via a single neural-network forward pass at execution.
Versions
Related
- Finite-Bit CSI Compression Under Feedback Constraints: Exact PCA Error Decomposition and Robust Dimension–Bit Allocation for MISO-OFDM
- Empirical coordination in the finite blocklength regime: an achievability result---Extended version
- Breakdown of Edgeworth Expansion in Finite-Blocklength Regime and Exact Absorption via q-Deformation
- Non-asymptotic Sphere-Packing Bounds for Finite-Blocklength Scalar Gaussian Multiple-Access and Broadcast Channels
- Shaping and Finite-Blocklength Analysis of Quaternionic Alamouti-Type Codes
BibTeX
@misc{kaya2026drlbased,
title = {{DRL-Based AoI Minimization for RSMA in Finite-Blocklength MU-MISO}},
author = {Enes Kaya and Mehmet Alp Demircioğlu and Elif Tugce Ceran and Melda Yuksel},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.11396},
eprint = {2610.11396},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.11396},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - DRL-Based AoI Minimization for RSMA in Finite-Blocklength MU-MISO
AU - Enes Kaya
AU - Mehmet Alp Demircioğlu
AU - Elif Tugce Ceran
AU - Melda Yuksel
PY - 2026
DA - 2026/10/08
PB - arXiv
DO - 10.48550/arxiv.2610.11396
UR - https://arxiv.org/abs/2610.11396
AB - This letter investigates age-of-information (AoI) minimization in multi-user wireless networks operating in the finite-blocklength (FBL) regime, which is critical for low-latency transmission of short state-update packets. While rate-splitting multiple access (RSMA) provides a powerful and flexible framework for interference management in multi-user FBL systems, the joint optimization of its parameters, such as precoding vectors, power allocation, and rate-splitting ratios, to guarantee information freshness results in analytically intractable complexity. To address this challenge, we propose an actor--critic deep reinforcement learning (DRL) framework to learn dynamic resource-allocation policies in multi-user multiple-input single-output (MU-MISO) broadcast channels. Simulation results show that the proposed RSMA-RL framework achieves consistently lower AoI than the state-of-the-art benchmarks, with substantial gains observed at low signal-to-noise ratio (SNR) and short blocklengths, while matching benchmark performance at high SNR with significantly lower online complexity via a single neural-network forward pass at execution.
ER -