MACOL-x: Multi-Agent Conflict Learning for xApp Coordination in Open RAN
Year: 2026
Type: conference-paper
Abstract: Concurrent xApp operation on the Near-Real Time RAN Intelligent Controller (Near-RT RIC) in Open Radio Access Networks (O-RAN) frequently produces conflicting control decisions that degrade network performance and user experience. We propose MACOL-x, a lightweight decentralised multi-agent coordination framework for Open RAN xApp conflict resolution. Each xApp independently encodes the recent actions of its peers as a coordination context and learns-without any central orchestrator-which action-context combinations are conflictprone. A hybrid two-tier context memory provides$O(1)$exact lookup for warm contexts and$K$-Nearest Neighbour interpolation for cold starts, feeding a conflict-targeted backoff gate that suppresses conflict-creating actions while leaving conflict-neutral ones intact. Evaluations against MAB, Q-Learning, MACOL, and QMIX baselines in a heterogeneous O-RAN HetNet show that MACOL-x achieves 95% conflict reduction, 91% fewer handovers, and$11 \times$lower user-plane interruption time, with reward comparable to QMIX (+89.5 vs. +89.6) while generating$6 \times$fewer conflicts and operating at$10 \times$lower per-step computation (4.56 ms vs. 45.99 ms) -without any neural network, replay buffer, or central coordinator.
Author: Abdulkadir Kose
Institution: Abdullah Gül University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Award: G472043668
OpenAlex ID: W7167794080