arXiv subjects

Nima Afraz

Publications and source records attributed to Nima Afraz.

11 recordsLinked to original sources

arXiv2026

AI-Driven Multi-Modal Adaptive Handover Control Optimization for O-RAN

Handover optimization in O-RAN faces growing challenges due to heterogeneous user mobility patterns and rapidly varying radio conditions. Existing ML-based handover schemes typically operate at the near-RT layer, which lack awareness of the mobility-mode and struggle to incorporate a longer-term predictive context. This paper proposes a multi-modal mobility-aware optimization framework in which all predictive intelligence, including mobility mode classification, short-horizon trajectory and RSRP forecasting, and a PPO Actor--Critic policy, runs entirely inside an rApp in the non-RT RIC. The rApp generates per-UE ranked neighbour-cell recommendations and delivers them to the existing handover xApp through the A1 interface. The xApp combines these rankings with instantaneous E2 measurements and performs the final standards-compliant handover decision. This hierarchical design preserves low-latency execution in the xApp while enabling the rApp to supply richer and mode-specific predictive guidance. Evaluation using mobility traces demonstrates that the proposed approach reduces ping-pong handover events and improves handover reliability compared to conventional 3GPP A3-based and ML-based baselines.

cs.NI↗

arXiv2026

AI-Powered Conflict Management in Open RAN: Detection, Classification, and Mitigation

Open Radio Access Network (RAN) was designed with native Artificial Intelligence (AI) as a core pillar, enabling AI- driven xApps and rApps to dynamically optimize network performance. However, the independent ICP adjustments made by these applications can inadvertently create conflicts- direct, indirect, and implicit, which lead to network instability and KPI degradation. Traditional rule-based conflict management becomes increasingly impractical as Open RAN scales in terms of xApps, associated ICPs, and relevant KPIs, struggling to handle the complexity of multi-xApp interactions. This highlights the necessity for AI-driven solutions that can efficiently detect, classify, and mitigate conflicts in real-time. This paper proposes an AI-powered framework for conflict detection, classification, and mitigation in Open RAN. We introduce GenC, a synthetic conflict generation framework for large-scale labeled datasets with controlled parameter sharing and realistic class imbalance, enabling robust training and evaluation of AI models. Our classification pipeline leverages GNNs, Bi-LSTM, and SMOTE-enhanced GNNs, with results demonstrating SMOTE-GNN's superior robustness in handling imbalanced data. Experimental validation using both synthetic datasets (5-50 xApps) and realistic ns3-oran simulations with OpenCellID-derived Dublin topology shows that AI-based methods achieve 3.2x faster classification than rule-based approaches while maintaining near-perfect accuracy. Our framework successfully addresses Energy Saving (ES)/Mobility Robustness Optimization (MRO) conflict scenarios using realistic ns3-oran and scales efficiently to large-scale xApp environments. By embedding this workflow into Open RAN's AI-driven architecture, our solution ensures autonomous and self-optimizing conflict management, paving the way for resilient, ultra-low-latency, and energy-efficient 6G networks.

cs.NI↗

arXiv2025

RU Energy Modeling for O-RAN in ns3-oran

This paper presents a detailed and flexible power consumption model for Radio Units (RUs) in O-RAN using the ns3-oran simulator. This is the first ns3-oran model supporting xApp control to perform the RU power modeling. In contrast to existing frameworks like EARTH or VBS-DRX, the proposed framework is RU-centric and is parameterized by hardware-level features, such as the number of transceivers, the efficiency of the power amplifier, mmWave overheads, and standby behavior. It enables simulation-driven assessment of energy efficiency at various transmit power levels and seamlessly integrates with ns-3's energy tracking system. To help upcoming xApp-driven energy management strategies in O-RAN installations, numerical research validates the model's capacity to represent realistic nonlinear power scaling. It identifies ideal operating points for effective RU behavior.

cs.NI↗

arXiv2024

xApp-Level Conflict Mitigation in O-RAN, a Mobility Driven Energy Saving Case

This paper investigates the emerging challenges of conflict detection and mitigation in Open Radio Access Network (O-RAN). Conflicts between xApps can arise that affect network performance and stability due to the disaggregated nature of O-RAN. This work provides a detailed theoretical framework of Extended Application (xApp)-level conflicts, i.e., direct, indirect, and implicit conflicts. Leveraging conflict graphs, we further highlight how conflicts impact Key Performance Indicators (KPIs) and explore strategies for conflict detection using Service Level Agreements (SLAs) and Quality of Service (QoS) thresholds. We evaluate the effectiveness of several mitigation strategies in a simulated environment with Mobility Robustness Optimization (MRO) and Energy Saving (ES) xApps and present experimental results showing comparisons among these strategies. The findings of this research provide significant insights for enhancing O-RAN deployments with flexible and efficient conflict management.

cs.NI↗

arXiv2024

subMFL: Compatiple subModel Generation for Federated Learning in Device Heterogenous Environment

Federated Learning (FL) is commonly used in systems with distributed and heterogeneous devices with access to varying amounts of data and diverse computing and storage capacities. FL training process enables such devices to update the weights of a shared model locally using their local data and then a trusted central server combines all of those models to generate a global model. In this way, a global model is generated while the data remains local to devices to preserve privacy. However, training large models such as Deep Neural Networks (DNNs) on resource-constrained devices can take a prohibitively long time and consume a large amount of energy. In the current process, the low-capacity devices are excluded from the training process, although they might have access to unseen data. To overcome this challenge, we propose a model compression approach that enables heterogeneous devices with varying computing capacities to participate in the FL process. In our approach, the server shares a dense model with all devices to train it: Afterwards, the trained model is gradually compressed to obtain submodels with varying levels of sparsity to be used as suitable initial global models for resource-constrained devices that were not capable of train the first dense model. This results in an increased participation rate of resource-constrained devices while the transferred weights from the previous round of training are preserved. Our validation experiments show that despite reaching about 50 per cent global sparsity, generated submodels maintain their accuracy while can be shared to increase participation by around 50 per cent.

cs.LG↗

arXiv2024

Data Sharing at the Edge of the Network: A Disturbance Resilient Multi-modal ITS

Mobility-as-a-Service (MaaS) is a paradigm that encourages the shift from private cars to more sustainable alternative mobility services. MaaS provides services that enhances and enables multiple modes of transport to operate seamlessly and bringing Multimodal Intelligent Transport Systems (M-ITS) closer to reality. This requires sharing and integration of data collected from multiple sources including modes of transports, sensors, and end-users' devices to allow a seamless and integrated services especially during unprecedented disturbances. This paper discusses the interactions among transportation modes, networks, potential disturbance scenarios, and adaptation strategies to mitigate their impact on MaaS. We particularly discuss the need to share data between the modes of transport and relevant entities that are at the vicinity of each other, taking advantage of edge computing technology to avoid any latency due to communication to the cloud and privacy concerns. However, when sharing at the edge, bandwidth, storage, and computational limitations must be considered.

cs.DC↗

arXiv2024

QACM: QoS-Aware xApp Conflict Mitigation in Open RAN

The advent of Open Radio Access Network (RAN) has revolutionized the field of RAN by introducing elements of native support of intelligence and openness into the next generation of mobile network infrastructure. Open RAN paves the way for standardized interfaces and enables the integration of network applications from diverse vendors, thereby enhancing network management flexibility. However, control decision conflicts occur when components from different vendors are deployed together. This article provides an overview of various types of conflicts that may occur in Open RAN, with a particular focus on intra-component conflict mitigation among Extended Applications (xApps) in the Near Real Time RAN Intelligent Controller (Near-RT-RIC). A QoS-Aware Conflict Mitigation (QACM) method is proposed that finds the optimal configuration of conflicting parameters while maximizing the number of xApps that have their Quality of Service (QoS) requirements met. We compare the performance of the proposed QACM method with two benchmark methods for priority and non-priority cases. The results indicate that our proposed method is the most effective in maintaining QoS requirements for conflicting xApps.

cs.NI↗

arXiv2024

An Adaptive System Architecture for Multimodal Intelligent Transportation Systems

Multimodal intelligent transportation systems (M-ITS) encompass a range of transportation services that utilise various modes of transport and incorporate intelligent technologies for enhanced efficiency and user experience. There are several challenges in M-ITS including data integration, Interoperability, scalability, user experience, etc. To address these challenges, such a system requires an adaptive system architecture that enables M-ITS to operate as an integrated ecosystem. In this paper, we provide an adaptive, user-centric, and layered architecture for multimodal transportation systems. The proposed architecture ensures scalability for seamless interactions of various subcomponents, that are often managed by different stakeholders. Concurrently, the data architecture is detailed, covering diverse data sources, advanced analytics, and stringent governance, providing a robust basis for intelligent decision-making. We provide two example use cases of the proposed architecture, showing how the data architecture and the system architecture can be fused and serve multimodal intelligent transport services.

eess.SY↗

arXiv2024

On the Sum Secrecy Rate Maximisation for Wireless Vehicular Networks

Wireless communications form the backbone of future vehicular networks, playing a critical role in applications ranging from traffic control to vehicular road safety. However, the dynamic structure of these networks creates security vulnerabilities, making security considerations an integral part of network design. We address these security concerns from a physical layer security aspect by investigating achievable secrecy rates in wireless vehicular networks. Specifically, we aim to maximize the sum secrecy rate from all vehicular pairs subject to bandwidth and power resource constraints. For the considered problem, we first propose a solution based on the successive convex approximation (SCA) method, which has not been applied in this context before. To further reduce the complexity of the SCA-based method, we also propose a low-complexity solution based on a fast iterative shrinkage-thresholding algorithm (FISTA). Our simulation results for SCA and FISTA show a trade-off between convergence and runtime. While the SCA method achieves better convergence, the FISTA-based approach is at least 300 times faster than the SCA method.

eess.SY↗

arXiv2023

Conflict Management in the Near-RT-RIC of Open RAN: A Game Theoretic Approach

Open Radio Access Network (RAN) was introduced recently to incorporate intelligence and openness into the upcoming generation of RAN. Open RAN offers standardized interfaces and the capacity to accommodate network applications from external vendors through extensible applications (xApps), which enhance network management flexibility. The Near-Real-Time Radio Intelligent Controller (Near-RT-RIC) employs specialized and intelligent xApps for achieving time-critical optimization objectives, but conflicts may arise due to different vendors' xApps modifying the same parameters or indirectly affecting each others' performance. A standardized Conflict Management System (CMS) is absent in most of the popular Open RAN architectures including the most prominent O-RAN Alliance architecture. To address this, we propose a CMS with independent controllers for conflict detection and mitigation between xApps in the Near-RT-RIC. We utilize cooperative bargain game theory, including Nash Social Welfare Function (NSWF) and the Equal Gains (EG) solution, to find optimal configurations for conflicting parameters. Experimental results demonstrate the effectiveness of the proposed Conflict Management Controller (CMC) in balancing conflicting parameters and mitigating adverse impacts in the Near-RT-RIC on a theoretical example scenario.

cs.NI↗

arXiv2023

The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistencies

Blockchain promises to enhance distributed machine learning (ML) approaches such as federated learning (FL) by providing further decentralization, security, immutability, and trust, which are key properties for enabling collaborative intelligence in next-generation applications. Nonetheless, the intrinsic decentralized operation of peer-to-peer (P2P) blockchain nodes leads to an uncharted setting for FL, whereby the concepts of FL round and global model become meaningless, as devices' synchronization is lost without the figure of a central orchestrating server. In this paper, we study the practical implications of outsourcing the orchestration of FL to a democratic setting such as in a blockchain. In particular, we focus on the effects that model staleness and inconsistencies, endorsed by blockchains' modus operandi, have on the training procedure held by FL devices asynchronously. Using simulation, we evaluate the blockchained FL operation by applying two different ML models (ranging from low to high complexity) on the well-known MNIST and CIFAR-10 datasets, respectively, and focus on the accuracy and timeliness of the solutions. Our results show the high impact of model inconsistencies on the accuracy of the models (up to a ~35% decrease in prediction accuracy), which underscores the importance of properly designing blockchain systems based on the characteristics of the underlying FL application.

cs.NI↗