Aug 2026· Journal of optical communications· 0 citations· 23 references
TL;DR
A novel Multi-Armed Bandit (MAB) approach is applied to optimize dynamic bandwidth allocation at the ONU layer, enabling increased user density without inducing latency burdens at the OLT and establishes a fault-resilient infrastructure suitable for next-generation converged optical networks.
Abstract
Abstract Recent studies show that global Passive Optical Network (PON) deployments will reach over 1.3 billion subscribers by 2030in Optical Network Unit (ONU) device integration. Despite this, legacy systems suffer from scalability bottlenecks and average latency increments of 15–30 % during user density surges. Existing networks face significant challenges, such as latency spikes at the Optical Line Terminal (OLT) during ONU scalability and insufficient reliability in handling anomalies within converged infrastructures. To address these issues, a novel Multi-Armed Bandit (MAB) approach is applied to optimize dynamic bandwidth allocation (DBA) at the ONU layer, enabling increased user density without inducing latency burdens at the OLT. The MAB-based selection strategy efficiently adapts to varying traffic patterns by learning optimal resource assignment policies in real time, ensuring minimal contention delays and better quality of service (QoS). Network fault tolerance and reliability are enhanced through a Deep Q-Auto Encoder (DQAE)-based anomaly detection model trained to recognize and classify failure signatures across optical and packet layers. This unsupervised deep reinforcement learning model integrates reconstruction loss with Q-learning to identify unknown failure states simultaneously and recommend proactive recovery actions. The combined strategy improves user scalability and service stability and establishes a fault-resilient infrastructure suitable for next-generation converged optical networks.
The rapid evolution of 5G and emerging 6G networks requires optical access systems to support immersive extended reality (XR) services with stringent quality-of-service (QoS) requirements, like ultra-low latency and high bandwidth. However, conventional dynamic bandwidth allocation (DBA) schemes in passive optical networks (PONs) allocate upstream bandwidth solely based on reported queue occupancy, without considering the unique characteristics of XR traffic. To address these limitations, we propose an XR-aware Predictive (XP)-DBA scheme that integrates XR traffic prediction, deadline-aware scheduling, adaptive grant control, and a cycle-controller to proactively allocate bandwidth, prioritize latency-critical packets, and limit polling-cycle growth. We also derive closed-form analytical expressions to characterize XR-specific stability and delay feasibility in PON systems. We evaluate XP-DBA under standardized and burst-enhanced XR traffic models across varying XR user densities and transmission distances of up to 100 km. The results show that XP-DBA will reduce latency, jitter, and polling-cycle time while increasing throughput and supporting higher XR user densities under heavy network loads without violating XR delay bounds. These findings establish XP-DBA as an efficient and scalable scheduling solution for next-generation immersive XR services over long-reach optical access networks.
Akhilesh Patel, Y. Singh· IEEE Transactions on Network...· 0 citations
As Data Center Networks (DCNs) continue to scale, the limitations of traditional centralized Software-Defined Networking (SDN) architectures become increasingly apparent, as they fail to meet the stringent demands for low latency and quality of service (QoS). In this paper, we propose an adaptive traffic-aware load balancing mechanism (ATL), a telemetrydriven in-switch scheme implemented on the programmable data plane (PDP) using P4 and driven by In-band Network Telemetry (INT). The current traffic regime is inferred by analyzing the remaining capacity (RC) of each link and its short-term variation (VAR), and adopts a dual-optimization strategy: (i) separating elephant flows (large flows) and mice flows (small flows) onto disjoint path sets to mitigate head-of-line blocking and packet reordering; (ii) dynamically adjusting the flowlet threshold $\left(F^{*}\right)$ to strike a balance between maximizing parallelism and ensuring in-order delivery. We prototyped and evaluated ATL in a Mininet/BMv2 environment, targeting bandwidth-constrained scenarios representative of IoT and edge deployments. The results show that, compared to existing methods such as ECMP, HULA, AWCMP, and APS, ATL consistently reduces both the average and 99th-percentile AFCT while achieving superior elephant-flow throughput, with notable improvements in traffic stability and packet-ordering preservation. Furthermore, ATL demonstrates a favorable cost-performance trade-off ratio of 1:0.99, confirming its efficiency and feasibility within the resource-constrained P4 switch environment.
The proposed Multi-Path Multi-Level Feedback Queueing (MP-MLFQ) leverages the spatial diversity and regularity of DCNs to realize a scheduler with numerous logical priority levels while occupying as low as 2 physical priority queues within network switches.
Alessandro Cornacchia, Andrea Bianco, Paolo Giaccone et al.· 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
The rapid deployment of fifth-generation mobile networks (5G) is transforming the optical transport from a static capacity layer into a dynamic, service-critical infrastructure that must support cloud radio access, edge computing, network slicing, industrial connectivity and latency-sensitive consumer services. Faults in this layer may degrade fronthaul, midhaul and backhaul performance before a hard alarm appears. Over-conservative engineering margins contribute to cost, energy consumption and wasted spectrum. This paper discusses the potential of artificial intelligence to enable fault prediction and performance optimization in 5G optical transport networks through the integration of optical performance monitoring, telemetry analytics, digital twins, machine learning, software-defined control and closed-loop assurance. We adopt a structured narrative review approach to synthesise the literature published from 2020 to 2025 on optical failure management, quality-of-transmission prediction, loss-of-signal forecasting, soft-failure localisation, traffic forecasting, transport automation, and 5G optical fronthaul design. This paper proposes a practical AI-enabled operating framework to convert streaming measurements to failure probability, time-to-impact, root-cause ranking, optimization recommendations and governed automation actions. The review suggests that the greatest value of AI is to embed models into operational processes, rather than to use them as standalone prediction tools. For telecom operators, the key benefits are reduced mean time to repair, fewer preventable outages, improved spectrum and power efficiency, better service-level assurance and more disciplined capacity expansion. The study concludes with a phased implementation roadmap for operators seeking to move away from reactive maintenance and towards predictive, explainable and policy-controlled optical transport operations.
M. Imran· Global academic journal of e...· 0 citations
Abstract Software-Defined Networking (SDN) increases networking flexibility, scalability, and programmability by separating the control plane from the data plane. Nevertheless, this structure is also vulnerable to DDoS, which may result in performance degradation and service unavailability. We propose a new adaptive hybrid real-time DDoS detection and mitigation framework designed for consumer applications with tight low latency constraints such as telemedicine, online gaming, and video streaming services. The suggested architectural model incorporates four well-known techniques (LSTM-based DeepPredict-DDoS, Reinforcement Learning-based Adaptive DeepPredict-DDoS, Genetic Algorithm-based DeepPredict-GA, and ARIMA-based DeepPredict-ARIMA) through an innovative decision-making engine. Experimental results show an average detection accuracy of 97.08%, reduced latency by 80%, and packet loss rate as low as 0.027%. These properties make the solution scalable, efficient, and effective for consumer-level SDN systems. Graphic Abstract
Sumit Badotra, Sarvesh Tanwar, S. Verma· Scientific Reports· 0 citations