Jun 2026· Radio Electronics, Computer Science, Control· 0 citations· 14 references
Abstract
Context. The rapid deployment of 5G networks and the emergence of 6G architectures introduce unprecedented traffic heterogeneity and burstiness across radio, edge, and core domains. Meanwhile, the energy footprint of mobile infrastructure is becoming a major sustainability concern, as carbon emissions increasingly shape network operation policies.Objective. This work aims to design a predictive carbon-aware multi-layer resource slicing framework for RAN – edge – core 5G/6G networks that jointly optimizes latency, cost, energy, and carbon emissions under bursty traffic conditions.Method. The proposed approach integrates an M/G/1-based queuing model for accurate representation of heavy-tailed service times and bursty arrival patterns; hybrid short-term/long-term forecasting of both traffic load and regional carbon intensity; and multi-objective optimization for carbon-aware VNF placement and traffic steering across network layers. A proactive – reactive orchestration mechanism performs predictive resource pre-allocation and runtime scaling.Results. Trace-driven simulations on a representative multi-layer testbed demonstrate a 34% reduction in CO2 emissions compared to latency-first orchestration, alongside a 22% decrease in operational cost and <1% SLA violation rate. Tail latency remains within slice-specific thresholds even under bursty loads, confirming that carbon reductions can be achieved without service degradation.Conclusions. Predictive, carbon-aware orchestration across RAN-edge-core domains substantially improves environmental and economic efficiency while preserving QoS guarantees. The results highlight the importance of integrating forecast-drivenoptimization and realistic traffic modeling into next-generation slicing architectures.
A constrained optimization model that supports different management goals through alternative objective functions (latency-aware or power-aware) while enforcing operational constraints, including node capacities, slice-specific latency bounds, and explicit limits on VNF migrations/relocations between scheduling periods is proposed.
R. Moreno-Vozmediano, E. Huedo, R. Montero et al.· Journal of Network and Syste...· 0 citations
A QoE-aware framework for Multi-Access Edge Computing-enabled Open Radio Access Network (O-RAN) architectures, combining a graph attention network (GAT) encoder, distributed multi-agent DRL, and privacy-preserving FL, while transitioning control from Quality of Service (QoS) to QoE metrics is proposed.
Manoj Prasad Kunasegran, Wai Leong Pang, S. K. Phang· IEEE Access· 0 citations
Accurate prediction of traffic demand and slice-level key performance indicators (KPIs) is essential for enabling proactive resource management in next-generation radio access networks. However, most existing studies focus on aggregate traffic modeling and provide limited insight into slice-level dynamics under varying mobility and traffic conditions. This paper proposes a slice-aware deep learning framework for the joint prediction of traffic demand and multiple KPIs within a simulation-driven environment. An ns-3-generated multivariate time-series dataset is constructed to capture traffic demand, throughput, goodput, latency, and packet-level statistics across eMBB, URLLC, and mMTC slices under heterogeneous mobility patterns, stochastic UE populations, varying traffic loads, and adaptive radio configurations. This enables a controllable and reproducible evaluation of slice-level traffic and KPI dynamics under diverse service conditions. LSTM, GRU, CNN, and a traditional linear regression baseline are comparatively evaluated under a unified preprocessing and time-series validation framework. Experimental results demonstrate that CNN consistently achieves lower prediction errors across most KPIs, while recurrent models provide competitive performance for smoother traffic patterns. The results further show that deep learning models more effectively capture nonlinear slice-level dynamics compared with traditional forecasting approaches. Latency prediction remains the most challenging task due to its sensitivity to rapid traffic fluctuations and varying network conditions. Beyond prediction, the proposed framework provides a foundation for learning slice-level network dynamics and supporting future digital twin–oriented network representations and closed-loop optimization in 6G-ready wireless systems.
Sultan Ertas, B. Cavusoglu· IEEE Access· 0 citations
Content Delivery Networks (CDNs) and edge platforms are increasingly expected to reduce energy consumption while preserving strict Quality of Service (QoS) and Service Level Agreement (SLA) targets under highly variable demand. In practice, operators often keep excess capacity online to ab-sorb sudden spikes and to hedge against warm-up delays, which leads to persistent energy waste during off-peak periods. This paper presents a forecast driven orchestration framework that couples short-horizon workload prediction with a practical server life-cycle controller managing active, warm-standby, and off pools. The controller converts uncertainty-aware forecasts into risk-calibrated capacity decisions, using hysteresis and warm-up queue dynamics to avoid oscil-lations and to prevent transient under-provisioning. We evaluate the approach in closed-loop simulation using real Points of Presence (PoP) level traces and statis-tically constrained synthetic scenarios generated with a Large Language Model (LLM) to stress-test bursty and heavy-tailed regimes beyond the observed data. Results show that probabilistic, tail-aware provisioning improves reliability under volatile demand while still enabling meaningful energy reductions compared to static provisioning.
Zakarya Farou, Jiyan Salim Mahmud, Imre Lendák· 2026 6th International Confe...· 0 citations
A new paradigm for satisfying the ever-growing demands of real-time Sixth Generation (6G) applications is Mobile Edge Computing (MEC). Additionally, base stations and Internet of Things devices that incorporate renewable energy harvesting capabilities have the potential to lower grid energy use. To maximize system potential and lower carbon emissions, it is crucial to make effective decisions about job offloading and resource allocation. A carbon-aware MEC architecture that uses both grid and renewable energy sources is proposed in this paper. Our goal is to jointly manage resource allocation and task offloading while monitoring carbon emissions and task queue delays to optimize system behavior under uncertainty, specifically for stochastic workloads and variable renewable generation. To balance these two cost components (emissions and queue length), we create a combined optimization problem. We develop a deep deterministic policy gradient (DDPG)-based joint optimization technique to address this issue in a constantly changing environment. In the optimization, we consider greedy policy (GP) and full offloading (FO), as well as time-average carbon emission (TACE) and time-average queue length (TAQL) as performance metrics, and time-average queue length (TAQL) and full execution (FE) as baseline strategies; we also evaluate normalized time-average cumulative reward (NTACR). This method uses continuous-action reinforcement learning to generate efficient, real-time control policies. For the proposed MEC network, numerical statistics show that our approach can lead to effective offloading and lower carbon emissions.
M. Saeed, Rashid A. Saeed, M. A. Ahmed et al.· 2026 6th International Confe...· 0 citations
Efficient long-term network evolution is becoming increasingly critical in dense 5G-Advanced and beyond cellular systems, where persistent traffic imbalances and localized congestion pose significant challenges that conventional short-term radio resource management alone cannot fully mitigate. This paper proposes a digital twin (DT)-enabled non-real-time (NRT) network evolution framework integrated with a large language model (LLM). Within this architecture, the digital twin provides a high-fidelity, controllable environment for evaluating infrastructure actions, while the LLM serves as a strategic orchestration engine that recommends cost-efficient network upgrades based on observed network states. Unlike traditional optimization methods that require exhaustive mathematical reformulations for each specific scenario, the proposed framework leverages the reasoning capabilities of LLMs to interpret operator objectives and constraints in natural language, generating structured evolution plans. The considered NRT action space encompasses antenna upgrades, bandwidth expansion, and new base station (BS) deployment. A techno-economic formulation is introduced to jointly evaluate load reduction performance and overall economic expenditure. Numerical results in a dense cellular scenario demonstrate that the framework effectively reduces peak resource utilization and provides diverse, coordinated evolution strategies tailored to varying network conditions.
Yukai Wang, Janghee Woo, G. Hahm et al.· International Conference on...· 0 citations