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RL-based RAN Slicing Optimization for 5G Networks

> optimizing latency via Q-learning in distributed O-RU units...

check_circle REAL O-RAN 5G

JD

AS

J. Doe, A. Smith et al.

Oct 12

Generative Models for Synthetic Channel Estimation

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smart_toy SYNTH GenAI MIMO

MK

M. Kim

Sep 28

Beamforming Strategies for 6G Terahertz Communications

> comparative analysis of hybrid vs digital beamforming in THz...

check_circle REAL 6G THz

L

T

+2

L. Zhang, T. O'Neil...

Sep 15

Anomaly Detection in Core Network Traffic using Autoencoders

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RP

R. Patel

Aug 30

Real Data

Paper Details

RL-based RAN Slicing Optimization for 5G Networks

Published Oct 12, 2023

Source IEEE Access

Relevance 98% Match

Abstract

This paper proposes a deep reinforcement learning (DRL) framework for dynamic resource allocation in O-RAN slicing. By leveraging proximal policy optimization (PPO) agents deployed at the Near-RT RIC, we demonstrate a 24% reduction in latency for URLLC slices while maintaining eMBB throughput. We also introduce a synthetic data augmentation pipeline to handle data sparsity in early deployment phases.

Authors

JD

John Doe, PhD

Ericsson Research

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AS

Alice Smith, MD

MIT CSAIL

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