Organizations: Centre for Wireless Communications (CWC), University of Oulu, Finland · i2CAT Foundation, Spain · Research Institute for Digital Future, Khalifa University, 127788 Abu Dhabi, UAE
The recent advances toward sixth-generation (6G) and beyond-6G networks have accelerated the need for intelligent resource management mechanisms capable of supporting heterogeneous services under shared infrastructures in network slicing. However, resource allocation in network slicing naturally forms a resource-coupled cooperative optimization problem with competing slice demands. Slices compete for limited resources to minimize individual latencies while coordinating to avoid conflicts and underutilization. Although multi-agent reinforcement learning (MARL) has shown promising performance in such settings, existing online formulations remain costly, unsafe, and difficult to deploy due to their reliance on environmental interactions and communication among agents. In this work, we present explainable artificial intelligence (XAI)-guided conservative decentralized execution (X-CODE). X-CODE is an explainable offline MARL that operates offline without environmental interaction, nor inter-agent communication. It exploits explainability-aware reward shaping to modify the relative preference among joint offline transitions during centralized training to improve decentralized resource-allocation behavior. In deployment, the agents operate independently without signaling exchange among the agents. Simulation results demonstrate that the proposed approach achieves zero observed resource-conflict events in the evaluated test episodes while minimizing per-slice latencies. Moreover, the proposed framework exhibits lower signaling overhead and reduces effective inference latency by 88 % under the considered communication-delay model compared to the online baselines. Source codes and datasets are available through: https://github.com/Eslam211/xcode-ran-slicing.
Figures & tables
Fig. 1: A diagram illustrating the proposed X-CODE Algorithm. An offline dataset and pretrained VDN Q-networks are used to perform reward shaping and construct the explainable dataset. Then, the proposed X-CODE training is performed via value-decomposed CQL-VDN training. Finally, coordinated agents execute their learned policies online without any inter-agent communication.
Fig. 2: Performance of online training compared to the heuristic baseline in terms of: (a) per-step reward, (b) average latency, and (c) conflict rate.
Parameter
Value
Parameter
Value
K
3
T
40
fmax
40
fth
[15,15,10] Gcycles/s
τ
0.01 s
U
5×10−3 bits/cycle
α
1
μ
1
λc
30
λl
150
λa
0.05
Replay buffer
10,000
TABLE I: Simulation parameters and hyperparameters
Fig. 3: A comparison of the action coverage of datasets collected during training the online baselines compared to the uniform random-walk dataset.
Fig. 4: The performance of the proposed X-CODE compared to the baselines in terms of the latency and conflict CDFs, respectively.
Fig. 5: The performance of the proposed X-CODE compared to MARL baselines in terms of the latency CDF and the CPU utilization.
Fig. 6: The performance of the proposed X-CODE while sweeping: (a) CQL parameter α , and (b) explainability strength μ .
Fig. 7: The attribution of each feature to the predictions represented in terms of the KDE plot and SHAP values.
Fig. 8: The complexity analysis of the proposed X-CODE compared to the DQN (Comm + IB) in terms of the signaling overhead and the inference time under the considered delay model.
Ultra-low latency and high throughput are required for Virtual Reality (VR) services in 6G networks, which presents critical challenges for Software-Defined Radio Access Networks (SD-RANs) dynamic resource management. This work propose a mobility-driven, privacy-aware Multi-Agent Reinforcement Learning (MARL) framework for VR slice management, in which cooperative agents maximize resource distribution over end-to-end VR links while protecting the privacy of user data. Our approach incorporates mobility prediction and an information bottleneck encoder to facilitate effective and secure agent collaboration. In simulations, comparisons with traditional methods are studied which show up to 34% throughput improvement, 28% fewer resources, and 85% less privacy leakage, guaranteeing dependable immersive VR experiences in future 6G environments.
Khaled M. Naguib, Soumaya Cherkaoui, Mahmoud M. Elmesalawy +2
Department of Computer and Software Engineering, Polytechnique Montr´eal, Montr´eal, Canada · Electronics and Communications, Helwan Univ. Cairo, Egypt
Next-generation (NextG) cellular networks are designed to support emerging applications with diverse data rate and latency requirements, such as immersive multimedia services and large-scale Internet of Things deployments. A key enabling mechanism is radio access network (RAN) slicing, which dynamically partitions radio resources into virtual resource blocks to efficiently serve heterogeneous traffic classes, including enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). In this paper, we study the impact of adversarial attacks on AI-driven RAN slicing decisions, where a budget-constrained adversary selectively jams slice transmissions to bias deep reinforcement learning (DRL)-based resource allocation, and quantify the resulting service level agreement (SLA) violations and post-attack recovery behavior. Our results indicate that budget-constrained adversarial jamming can induce severe and slice-dependent steady-state SLA violations. Moreover, the DRL agent's reward converges toward the clean baseline only after a non-negligible recovery period.
Deemah H. Tashman, Soumaya Cherkaoui
Department of Computer and Software Engineering, Polytechnique Montreal, Montreal, Canada
The physical resource block (PRB) allocation in Radio Access Networks (RANs) traditionally relies on case-by-case manual problem construction or, more recently, learning-based artificial intelligence (AI) methods. However, the sixth-generation (6G) RAN environments confront unprecedented service diversity and exponential dynamics, featuring volatile fluctuations in active base stations (BSs), user scale, and stringent Quality-of-Service (QoS) requirements. Faced with such conditions, both manual models and standard AI algorithms remain fundamentally rigid, lacking the flexibility to adapt and self-evolve. To provide a one-size-fits-all solution, we propose treating the PRB allocation problem as an Optimization-as-a-Service (OaaS) provided by a large language model multi-agent (LLM-MA) system. This fundamentally reshapes RAN resource allocation by utilizing agents to dynamically construct optimization problems and automatically determine objectives tailored to real-time scenarios. Our closed-loop architecture, integrating scene understanding, objective generation, solver, and reflection agents, enables context-aware, self-correcting formulation. To eliminate the computational latency of iterative reflection, we introduce a one-shot reflection distillation mechanism, training a lightweight student model to directly predict refined objective parameters. We theoretically bound the performance gap of this one-shot policy. Experimental results demonstrate our framework achieves near-optimal resource allocation with ultra-low inference latency.
Chaoqun You, Yueyue Dai, Xingqiu He +3
Fudan University · Huazhong University of Science and Technology · University of Surrey +1