A DRL-Based Automated Algorithm Selection Framework for Cross-Layer QoS-Aware Scheduling and Antenna Allocation in Massive MIMO Systems

Chih Wei Huang, Ibrahim Althamary, Yen Cheng Chou, Hong Yunn Chen, Cheng Fu Chou

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Massive multiple-input-multiple-output (MIMO) systems support advanced applications with high data rates, low latency, and high reliability in next-generation mobile networks. However, using machine learning in massive MIMO resource allocation is challenging due to quality of service (QoS) and network complexity across layers. This work presents a novel framework for adapting the scheduling and antenna allocation in massive MIMO systems using deep reinforcement learning (DRL). Rather than directly assigning execution parameters, the proposed framework utilizes DRL to select combinations of algorithms based on the current traffic conditions. The DRL model is trained using a specialized Markov decision process (MDP) model with a componentized action structure and is evaluated in realistic traffic scenarios. The results show that the proposed framework increases satisfied users by 7.2% and 12.5% compared to static algorithm combinations and other cross-layer adaptation methods. This demonstrates the effectiveness of the framework in managing and optimizing resource allocation in a flexible and adaptable manner.

Original languageEnglish
Pages (from-to)13243-13256
Number of pages14
JournalIEEE Access
Volume11
DOIs
StatePublished - 2023

Keywords

  • Mobile network
  • QoS
  • automated algorithm selection
  • deep reinforcement learning
  • massive MIMO
  • resource allocation

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