2026· IEEE Transactions on Network and Service Management· Vol 23, pp. 6049-6062· 0 citations· 32 references
Computer Science
Abstract
Coordinating multiple heterogeneous xApps in Open Radio Access Networks (O-RAN) is challenging because real-time inter-xApp synchronization introduces communication overhead, latency, and scalability bottlenecks under live deployments. This paper proposes an attention-based Heterogeneous-Agent Proximal Policy Optimization (HAPPO) framework that coordinates four heterogeneous xApps—power control, resource-block allocation, access control, and beam selection—through a Digital Twin (DT)-enabled training pipeline. The framework combines 1) hybrid actor heads supporting mixed continuous/discrete actions; 2) task-aware self-attention for implicit coordination without predefined communication graphs; and 3) DT-based asynchronous experience collection with delay modelling, online correction, and safe rollback. We further validate that the learned attention aligns with the physical coupling between xApps (Pearson <inline-formula> <tex-math notation="LaTeX">$\rho = 0.78 \pm 0.04$ </tex-math></inline-formula>), establishing an interpretable link between the policy and underlying network dependencies. On a VIAVI O-RAN RIC emulator across 10 random seeds, the proposed scheme attains a 91.2% aggregate QoS score and 1.05 ms inference latency, improves per-slice SLA satisfaction by up to 6.4% over strong MARL baselines (FACMAC, MAPPO, CommNet), and reaches the 80%-of-final performance level <inline-formula> <tex-math notation="LaTeX">$1.49\times $ </tex-math></inline-formula> faster than the attention-free counterpart. Improvements are statistically significant (<inline-formula> <tex-math notation="LaTeX">$p\lt 0.05$ </tex-math></inline-formula>, paired two-sided Welch <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-tests with Holm–Bonferroni correction across baselines; under fluctuating UE demands and diverse service-level requirements).
With the rapid expansion of the Internet of Things (IoT) and heterogeneous wireless networks, Age of Information (AoI) has emerged as a critical metric for evaluating information freshness in real-time systems. AoI-oriented access optimization in heterogeneous multiple access networks is challenging because legacy access mechanisms, such as TDMA and ALOHA, may coexist over a shared channel, while conventional rule-based and learning-based methods often suffer from limited adaptability, slow convergence, and poor interpretability. In this paper, we propose Reflex-Core, an LLM-agent-based framework for AoI-oriented adaptive access in heterogeneous wireless networks. Reflex-Core adopts an “Observe-Reflect-Decide-Execute” closed-loop mechanism to refine transmission strategies through semantic feedback and historical memory. To provide an analytical foundation for reflection-guided strategy refinement, we derive a drift-plus-penalty design principle and construct a reflection-cycle-level reward target that jointly captures weighted AoI reduction and collision cost. This reward target guides reflection selection, reward model training, and PPO-based post-training. Based on Reflex-Core, we develop the Reflexive Multiple Access (RMA) protocol and a priority-aware RMA variant for differentiated freshness requirements. We further discuss an asynchronous edge-assisted implementation, where LLM-based reflection can be offloaded without blocking slot-level random access. Simulation results show that RMA reduces AoI by up to 14.9% compared with representative baselines and maintains robust performance in dynamic and priority-aware scenarios. Additional scalability and backbone-sensitivity experiments further confirm that Reflex-Core remains effective in a 20-node heterogeneous scenario with varied ALOHA loads and is robust when LongChat-7B-16k is replaced by Qwen2.5-7B-Instruct.
Fang Liu, Erchao Zhu, Jiedan Tan et al.· IEEE Open Journal of the Com...· 0 citations
Open Radio Access Network (O-RAN) allows independently developed xApps to control RAN functions through the Near-Real-Time RAN Intelligent Controller (Near-RT RIC). When xApps with conflicting objectives operate concurrently, they may issue incompatible actions that degrade network performance. This paper addresses a direct conflict in which an energy-saving (ES) xApp and a coverage/throughput-oriented (CTO) xApp request different downlink transmit-power settings for the same cell. We formulate conflict resolution as online selection of a continuous blend of the two proposals, maximizing an energy-aware utility that jointly considers throughput and power consumption. A network digital twin (NDT) predicts this utility for candidate actions before live deployment, but selecting the highest twin-predicted utility becomes ineffective when the twin drifts. We therefore propose a twin-fidelity-aware hard-switching arbiter that monitors the error between predicted and observed utilities using an exponentially weighted moving average. While the error remains below a threshold, the arbiter follows the NDT-selected action; otherwise, it switches to the best previously observed action learned online. The arbiter is lightweight, training-free, and requires no oracle knowledge of the optimal policy. System-level 5G evaluations show that it achieves the closest throughput-power trade-off to the optimum across operator energy priorities, yielding normalized utility regret of $0.017 \pm 0.006$, versus $0.159 \pm 0.052$ for a COMIX-style twin-based selector. Under severe NDT drift (10 dB), it reduces utility regret from $11.19 \pm 3.58$ to $0.55 \pm 0.25$. These results show that online twin-fidelity monitoring enables robust digital-twin-assisted xApp conflict resolution while preserving utility-aware throughput-power optimization.
Akram A. Almohammedi, Mohammed Balfaqih, Sam Darshi et al.· 0 citations
A compact reasoning model trained with verifier-based self-verification and periodically refined online via shadow updates is deployed, showing manageable, near-linear control-plane overhead as domains scale and during domain joins, and robust decision quality, including recovery after objective changes.
Masoud Shokrnezhad, T. Taleb· IEEE Network· 0 citations
X-CODE is an explainable offline MARL that operates offline without environmental interaction, nor inter-agent communication, nor inter-agent communication, and exploits explainability-aware reward shaping to modify the relative preference among joint offline transitions during centralized training to improve decentralized resource-allocation behavior.
This paper presents a comprehensive framework for artificial intelligence (AI)-enabled autonomous network slicing optimization in 6G systems and investigates the application of advanced machine learning paradigms specifically deep reinforcement learning, federated learning, and generative AI to orchestrate dynamic resource provisioning, cross-slice isolation, and proactive SLA (Service Level Agreement) enforcement.
N. P J, Jeeva Jothi· International Journal of Com...· 0 citations
Time-Sensitive Networking (TSN) and Mobile Edge Computing (MEC) hold strong potential for enabling ultra-reliable low-latency communication for time-sensitive applications, such as eXtended Reality (XR). However, the widespread adoption of XR introduces significant challenges due to co-located services in MEC environments, leading to contention for shared network resources. Moreover, XR traffic types have distinct characteristics and criticality in terms of timing requirements, further increasing the complexity and dynamics of such environments. Although reinforcement learning has shown promise for TSN scheduling optimization in dynamic network scenarios, existing approaches rely on centralized or high-level multi-agent designs and are typically tailored to periodic and predictable industrial traffic, limiting their applicability to XR workloads. As a result, these approaches suffer from (i) limited ability to capture inter-queue dependencies due to coarse-grained control, and (ii) poor adaptability to highly dynamic and heterogeneous XR traffic. To address these gaps, we propose a multi-agent reinforcement learning approach for queue-level XR traffic scheduling. We adopt the multi-agent transformer (MAT) to model inter-queue dependencies via attention over agents'observations and actions, enabling implicit coordination across heterogeneous co-located XR applications. Our simulation results show that the proposed method outperforms baselines, achieving up to 71.42% latency reduction and up to 83.2% reduction in failure rate, while consistently achieving high reliability across all queues.
Marcos Carvalho, Fatih Temiz, Shavbo Salehi et al.· 0 citations