arXiv subjects

Mohammad Farhoudi

Publications and source records attributed to Mohammad Farhoudi.

8 recordsLinked to original sources

arXiv2026

Multi-Agent Reinforcement Learning for SLA-Aware Network Slicing in UAV-Enabled MEC

Unmanned Aerial Vehicle (UAV)-enabled Mobile Edge Computing (MEC) offers flexible capacity provisioning for heterogeneous network slices, including Hyper-Reliable and Low-Latency Communication (HRLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine-Type Communications (mMTC). However, guaranteeing slice-level Service-Level Agreements (SLAs) under dynamic user mobility, stochastic task arrivals, and constrained onboard energy and computing resources remains a fundamental challenge. This paper proposes a predictive multi-agent Reinforcement Learning (RL) framework that proactively maintains SLA stability in UAV-enabled MEC through coordinated trajectory control and computation resource allocation. A lightweight prediction module forecasts near-future user mobility, enabling UAVs to anticipate congestion and reposition before SLA violations occur. We design an SLA-aware reward function that explicitly penalizes both violation probability and duration across slices, alongside total energy consumption. UAV agents are trained using Multi-Agent Proximal Policy Optimization (MAPPO) with centralized training and decentralized execution, enabling scalable online decision-making. Event-driven simulations with realistic mobility traces demonstrate that the proposed framework significantly improves SLA stability compared with baselines while maintaining competitive energy efficiency and delay performance, approaching oracle-level performance with sufficiently accurate predictive information.

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arXiv2026

Quality-Aware Personalized AI Service Provisioning in UAV-Assisted 6G Networks

In sixth-generation (6G) artificial intelligence (AI) services, two quality dimensions should be jointly addressed: conventional quality (e.g., latency) and Quality of AI Services (QoAIS; output fidelity, continuity, personalization). Existing methods emphasize conventional quality, while neglecting QoAIS, particularly for personalized outputs in dynamic aerial-terrestrial settings. This paper introduces HyPE, a Hybrid Predictive-in-context-lEarning framework for holistically quality-aware personalized AI service provisioning in Unmanned Aerial Vehicle (UAV)-assisted 6G networks. HyPE integrates: (i) mobility-aware prediction to forecast spatio-temporal request distributions, (ii) learning-augmented decision leveraging Large Language Model (LLM)-based reasoning to optimize UAV trajectories and inference assignments, and (iii) pre-/post-processing service placement and routing using heuristics. We formulate an optimization problem for joint trajectory planning, service placement, and routing, and present HyPE as a scalable alternative to intractable optimal solutions. Simulations with empirical mobility traces and heterogeneous AI workloads show near-optimal coverage, reduced end-to-end latency, sustained QoAIS-driven, and continuity-based service personalization versus optimization and state-of-the-art baselines. The results highlight the promise of predictive learning-augmented provisioning for elastic, user-centric AI in 6G.

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arXiv2026

Energy Efficient Orchestration in Multiple-Access Vehicular Aerial-Terrestrial 6G Networks

The proliferation of users, devices, and novel vehicular applications - propelled by advancements in autonomous systems and connected technologies - is precipitating an unprecedented surge in novel services. These emerging services require substantial bandwidth allocation, adherence to stringent Quality of Service (QoS) parameters, and energy-efficient implementations, particularly within highly dynamic vehicular environments. The complexity of these requirements necessitates a fundamental paradigm shift in service orchestration methodologies to facilitate seamless and robust service delivery. This paper addresses this challenge by presenting a novel framework for service orchestration in Unmanned Aerial Vehicles (UAV)-assisted 6G aerial-terrestrial networks. The proposed framework synergistically integrates UAV trajectory planning, Multiple-Access Control (MAC), and service placement to facilitate energy-efficient service coverage while maintaining ultra-low latency communication for vehicular user service requests. We first present a non-linear programming model that formulates the optimization problem. Next, to address the problem, we employ a Hierarchical Deep Reinforcement Learning (HDRL) algorithm that dynamically predicts service requests, user mobility, and channel conditions, addressing the challenges of interference, resource scarcity, and mobility in heterogeneous networks. Simulation results demonstrate that the proposed framework outperforms state-of-the-art solutions in request acceptance, energy efficiency, and latency minimization, showcasing its potential to support the high demands of next-generation vehicular networks.

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arXiv2025

Service Registration, Indexing, Discovery & Selection; An Architectural Survey Toward a GenAI-Driven Future

The emergence of sixth-generation (6G) networks marks a paradigm shift: by unifying an edge-to-cloud computing continuum with ultra-high-performance networking, 6G will enable capabilities far beyond today's boundaries. As use-case diversity grows exponentially and user adoption drives traffic to unprecedented and highly dynamic levels, novel service orchestration mechanisms are indispensable. In this paper, we adopt an architectural viewpoint, examining Service Registration, Indexing, Discovery, and Selection (SRIDS) as fundamental elements of 6G service provision. We first establish the theoretical foundations of SRIDS in 6G by defining its core concepts, detailing its end-to-end workflow, reviewing current standardization efforts, and projecting its future design objectives, including reliability, scalability, automaticity and adaptability, determinism, efficiency, sustainability, semantic-awareness, security, privacy, and trust. We then perform a comprehensive literature review and gap analysis encompassing both existing surveys and recent research efforts, identifying conceptual and methodological gaps that hinder unified SRIDS in 6G. Next, we introduce a taxonomy that classifies SRIDS mechanisms into centralized, distributed, decentralized, and hybrid architectures, and systematically examine the relevant studies within each category. Each work is evaluated against the extracted design objectives. Building on these findings, we propose a hybrid architectural framework, combining centralized data management to ensure consistency and agility with distributed coordination to enhance scalability in emerging 6G use cases. The framework incorporates innovative technologies, such as Generative Artificial Intelligence (GenAI). We conclude by highlighting open challenges and suggesting directions for future research.

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arXiv2025

Adaptive Multiple Access and Service Placement for Generative Diffusion Models

Generative Diffusion Models (GDMs) have emerged as key components of Generative Artificial Intelligence (GenAI), offering unparalleled expressiveness and controllability for complex data generation tasks. However, their deployment in real-time and mobile environments remains challenging due to the iterative and resource-intensive nature of the inference process. Addressing these challenges, this paper introduces a unified optimization framework that jointly tackles service placement and multiple access control for GDMs in mobile edge networks. We propose LEARN-GDM, a Deep Reinforcement Learning-based algorithm that dynamically partitions denoising blocks across heterogeneous edge nodes, while accounting for latent transmission costs and enabling adaptive reduction of inference steps. Our approach integrates a greedy multiple access scheme with a Double and Dueling Deep Q-Learning (D3QL)-based service placement, allowing for scalable, adaptable, and resource-efficient operation under stringent quality of service requirements. Simulations demonstrate the superior performance of the proposed framework in terms of scalability and latency resilience compared to conventional monolithic and fixed chain-length placement strategies. This work advances the state of the art in edge-enabled GenAI by offering an adaptable solution for GDM services orchestration, paving the way for future extensions toward semantic networking and co-inference across distributed environments.

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arXiv2025

Deep Learning Based Service Composition in Integrated Aerial-Terrestrial Networks

The explosive growth of user devices and emerging applications is driving unprecedented traffic demands, accompanied by stringent Quality of Service (QoS) requirements. Addressing these challenges necessitates innovative service orchestration methods capable of seamless integration across the edge-cloud continuum. Terrestrial network-based service orchestration methods struggle to deliver timely responses to growing traffic demands or support users with poor or lack of access to terrestrial infrastructure. Exploiting both aerial and terrestrial resources in service composition increases coverage and facilitates the use of full computing and communication potentials. This paper proposes a service placement and composition mechanism for integrated aerial-terrestrial networks over the edge-cloud continuum while considering the dynamic nature of the network. The service function placement and service orchestration are modeled in an optimization framework. Considering the dynamicity, the Aerial Base Station (ABS) trajectory might not be deterministic, and their mobility pattern might not be known as assumed knowledge. Also, service requests can traverse through access nodes due to users' mobility. By incorporating predictive algorithms, including Deep Reinforcement Learning (DRL) approaches, the proposed method predicts ABS locations and service requests. Subsequently, a heuristic isomorphic graph matching approach is proposed to enable efficient, latency-aware service orchestration. Simulation results demonstrate the efficiency of the proposed prediction and service composition schemes in terms of accuracy, cost optimization, scalability, and responsiveness, ensuring timely and reliable service delivery under diverse network conditions.

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arXiv2024

Discovery of 6G Services and Resources in Edge-Cloud-Continuum

The advent of 6G networks will present a pivotal juncture in the evolution of telecommunications, marked by the proliferation of devices, dynamic service requests, and the integration of edge and cloud computing. In response to these transformative shifts, this paper proposes a service and resource discovery architecture as part of service provisioning for the future 6G edge-cloud-continuum. Through the architecture's orchestration and platform components, users will have access to services efficiently and on time. Blockchain underpins trust in this inherently trustless environment, while semantic networking dynamically extracts context from service requests, fostering efficient communication and service delivery. A key innovation lies in dynamic overlay zoning, which not only optimizes resource allocation but also endows our architecture with scalability, adaptability, and resilience. Notably, our architecture excels at predictive capabilities, harnessing learning algorithms to anticipate user and service instance behavior, thereby enhancing network responsiveness and preserving service continuity. This comprehensive architecture paves the way for unparalleled resource optimization, latency reduction, and seamless service delivery, positioning it as an instrumental pillar in the unfolding 6G landscape. Simulation results show that our architecture provides near-optimal timely responses that significantly improve the network's potential, offering scalable and efficient service and resource discovery.

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arXiv2023

QoS-Aware Service Prediction and Orchestration in Cloud-Network Integrated Beyond 5G

Novel applications such as the Metaverse have highlighted the potential of beyond 5G networks, which necessitate ultra-low latency communications and massive broadband connections. Moreover, the burgeoning demand for such services with ever-fluctuating users has engendered a need for heightened service continuity consideration in B5G. To enable these services, the edge-cloud paradigm is a potential solution to harness cloud capacity and effectively manage users in real time as they move across the network. However, edge-cloud networks confront a multitude of limitations, including networking and computing resources that must be collectively managed to unlock their full potential. This paper addresses the joint problem of service placement and resource allocation in a network-cloud integrated environment while considering capacity constraints, dynamic users, and end-to-end delays. We present a non-linear programming model that formulates the optimization problem with the aiming objective of minimizing overall cost while enhancing latency. Next, to address the problem, we introduce a DDQL-based technique using RNNs to predict user behavior, empowered by a water-filling-based algorithm for service placement. The proposed framework adeptly accommodates the dynamic nature of users, the placement of services that mandate ultra-low latency in B5G, and service continuity when users migrate from one location to another. Simulation results show that our solution provides timely responses that optimize the network's potential, offering a scalable and efficient placement.

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