Work

Implementing a Cell-Free 6G Distributed AI Network With the Use of Deep ML Under a Traditional Multi-Cell Mobile Network

Year: 2025

Type: conference-paper

Abstract: The emergence of cell-free networks marks a transformative shift in wireless communication by eliminating rigid cell boundaries and addressing the challenges of dense environments. This study introduces a novel cell-free architecture that integrates advanced clustering algorithms such as Self-Organizing Maps (SOM), Gaussian Mixture Model (GMM), MeanShift, DBSCAN, and KMeans, with Belief-Desire-Intention extended (BDIx) agents for optimized resource allocation. Among the approaches, SOM demonstrates the highest performance, achieving superior clustering metrics and significantly improving network sum rate and energy efficiency, making it ideal for dense networks. The integration of BDIx agents further improves real-time decision-making for collaborative load balancing and resource distribution. Simulation results validate the framework’s alignment with 6G goals, offering a scalable, adaptive, and energy-efficient solution for modern wireless networks and high-bandwidth urban applications.

Source: IEEE Middle East and North Africa Communications Conference (MENACOMM)

DOI: 10.1109/menacomm62946.2025.10910986

Authors: Iacovos Ioannou, Andreas Gregoriades, Christophoros Christophorou, Marios Raspopoulos, Vasos Vassiliou

Institutions: University of Cyprus, Cyprus University of Technology, University of Lancashire, University of Central Lancashire Cyprus

Language: English

Topic: Advanced MIMO Systems Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funders: European Commission, Deputy Ministry of Research, Innovation and Digital Policy