Decentralized and Joint Resource Allocation, beamforming and beamcombining for 5G Networks with Heterogeneous MARL | Zendy

Ala'a Al-Habashna | Zendy; Jon Menard | Zendy; Gabriel Wainer | Zendy; Gary Boudreau | Zendy

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Decentralized and Joint Resource Allocation, beamforming and beamcombining for 5G Networks with Heterogeneous MARL

Author(s) -

Ala'a Al-Habashna,

Jon Menard,

Gabriel Wainer,

Gary Boudreau

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3576190

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

In this paper, we propose a novel Multi-Agent Reinforcement Learning (MARL) -based paradigm for distributed and joint resource allocation, beamforming (BF), and beam combining of uplink transmissions in 5G networks. The proposed paradigm employs two types of heterogenous agents that learn to perform and optimize different tasks in order to achieve the main objective of the system, as well as the objective of the individual agents. In the proposed paradigm, UEs can be multi-agents that optimize their own resource allocation and BF. In addition to these multi agents (i.e., UEs), the BS is a different type of agent that optimizes the combining of UEs’ transmissions. We developed three different implementations of our proposal using three different MARL algorithms: Independent Q Learners (IQL), Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and QTRAN. Various experiments were conducted to validate the usability of our proposal. Our results show that the proposed paradigm can successfully optimize the task of joint resource allocation, beamforming, and combining. Furthermore, we provide a comparative analysis of the three different implementations, highlighting noteworthy insights into the strengths and limitations of fully distributed algorithms, such as IQL, in comparison to algorithms employing the Centralized Training with Decentralized Execution (CTDE) framework, exemplified by QTRAN and MADDPG.

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