Green Resource Allocation in Cloud-Native O-RAN Enabled Small Cell Networks

2024/07/16 by Rana Muhammad Sohaib, Syed Tariq Shah, Sohaib, Rana M. +7
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Communication Technologies #Cooperative Communication and Network Coding #FOS: Electrical engineering #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2407.11563

openalex publication_date 2024/07/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In the rapidly evolving landscape of 5G and beyond, cloud-native Open Radio Access Networks (O-RAN) present a paradigm shift towards intelligent, flexible, and sustainable network operations. This study addresses the intricate challenge of energy efficient (EE) resource allocation that services both enhanced Mobile Broadband (eMBB) and ultra-reliable low-latency communications (URLLC) users. We propose a novel distributed learning framework leveraging on-policy and off-policy transfer learning strategies within a deep reinforcement learning (DRL)--based model to facilitate online resource allocation decisions under different channel conditions. The simulation results explain the efficacy of the proposed method, which rapidly adapts to dynamic network states, thereby achieving a green resource allocation.

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