arXiv subjects

Hamed Ahmadi

Publications and source records attributed to Hamed Ahmadi.

At least 19 recordsLinked to original sources

arXiv2026

Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN

The deployment of unmanned aerial vehicles (UAV) as open radio units (O-RUs) in 6G cellular systems presents a promising opportunity to achieve scalable and adaptive network coverage. However, optimizing UAV trajectories in dynamic and unfamiliar environments remains a critical challenge, particularly due to the need for extensive retraining in each new scenario. In this paper, we introduce a novel UAV trajectory optimization framework that integrates enhanced continual transfer learning within the O-RAN architecture. The proposed system maintains a library of pre-trained models and employs a model selection mechanism to identify and transfer knowledge from the most relevant environments, minimizing adaptation time and improving efficiency. When no sufficiently similar model is available, a fallback model empowered by continuous refinements ensures baseline performance. The framework leverages real-world city maps and ray tracing techniques to enhance learning reliability and improve trajectory planning. Simulation results demonstrate that the proposed model selection-based transfer learning approach reduces convergence time by 44% to 56% compared to retraining from scratch, and up to 40% compared to traditional transfer learning without model selection.

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arXiv2026

Scalable machine learning-based approaches for energy saving in densely deployed Open RAN

Densely deployed base stations are responsible for the majority of the energy consumed in Radio access network (RAN). While these deployments are crucial to deliver the required data rate in busy hours of the day, the network can save energy by switching some of them to sleep mode and maintain the coverage and quality of service with the other ones. Benefiting from the flexibility provided by the Open RAN in embedding machine learning (ML) in network operations, in this work we propose Deep Reinforcement Learning (DRL)-based energy saving solutions. Firstly we propose 3 different DRL-based methods in the form of xApps which control the Active/Sleep mode of up to 6 radio units (RUs) from Near Real time RAN Intelligent Controller (RIC). We also propose a further scalable federated DRL-based solution with an aggregator as an rApp in None Real time RIC and local agents as xApps. Our simulation results present the convergence of the proposed methods. We also compare the performance of our federated DRL across three layouts spanning 6--24 RUs and 500--1000\,m regions, including a composite multi-region scenario. The results show that our proposed federated TD3 algorithm achieves up to 43.75\% faster convergence, more than 50\% network energy saving and 37. 4\% lower training energy versus centralized baselines, while maintaining the quality of service and improving the robustness of the policy.

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arXiv2026

Failure-Aware Access Point Selection for Resilient Cell-Free Massive MIMO Networks

This paper presents a Failure-Aware Access Point Selection (FAAS) method aimed at improving hardware resilience in cell-free massive MIMO (CF-mMIMO) networks. FAAS selects APs for each user by jointly considering channel strength and the failure probability of each AP. A tunable parameter $α\in [0,1]$ scales these failure probabilities to model different levels of network stress. We evaluate resilience using two key metrics: the minimum-user spectral efficiency, which captures worst-case user performance, and the outage probability, defined as the fraction of users left without any active APs. Simulation results show that FAAS maintains significantly better performance under failure conditions compared to failure-agnostic clustering. At high failure levels, FAAS reduces outage by over 85\% and improves worst-case user rates. These results confirm that FAAS is a practical and efficient solution for building more reliable CF-mMIMO networks.

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arXiv2026

Interpretable Attention-Based Multi-Agent PPO for Latency Spike Resolution in 6G RAN Slicing

Sixth-generation (6G) radio access networks (RANs) must enforce strict service-level agreements (SLAs) for heterogeneous slices, yet sudden latency spikes remain difficult to diagnose and resolve with conventional deep reinforcement learning (DRL) or explainable RL (XRL). We propose \emph{Attention-Enhanced Multi-Agent Proximal Policy Optimization (AE-MAPPO)}, which integrates six specialized attention mechanisms into multi-agent slice control and surfaces them as zero-cost, faithful explanations. The framework operates across O-RAN timescales with a three-phase strategy: predictive, reactive, and inter-slice optimization. A URLLC case study shows AE-MAPPO resolves a latency spike in $18$ms, restores latency to $0.98$ms with $99.9999\%$ reliability, and reduces troubleshooting time by $93\%$ while maintaining eMBB and mMTC continuity. These results confirm AE-MAPPO's ability to combine SLA compliance with inherent interpretability, enabling trustworthy and real-time automation for 6G RAN slicing.

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arXiv2026

HAPS-RIS and UAV Integrated Networks: A Unified Joint Multi-objective Framework

Future 6G non-terrestrial networks aim to deliver ubiquitous connectivity to remote and undeserved regions, but unmanned aerial vehicle (UAV) base stations face fundamental challenges such as limited numbers and power budgets. To overcome these obstacles, high-altitude platform station (HAPS) equipped with a reconfigurable intelligent surface (RIS), so-called HAPS-RIS, is a promising candidate. We propose a novel unified joint multi-objective framework where UAVs and HAPS-RIS are fully integrated to extend coverage and enhance network performance. This joint multi-objective design maximizes the number of users served by the HAPS-RIS, minimizes the number of UAVs deployed and minimizes the total average UAV path loss subject to quality-of-service (QoS) and resource constraints. We propose a novel low-complexity solution strategy by proving the equivalence between minimizing the total average UAV path loss upper bound and k-means clustering, deriving a practical closed-form RIS phase-shift design, and introducing a mapping technique that collapses the combinatorial assignments into a zone radius and a bandwidth-portioning factor. Then, we propose a dynamic Pareto optimization technique to solve the transformed optimization problem. Extensive simulation results demonstrate that the proposed framework adapts seamlessly across operating regimes. A HAPS-RIS-only setup achieves full coverage at low data rates, but UAV assistance becomes indispensable as rate demands increase. By tuning a single bandwidth portioning factor, the model recovers UAV-only, HAPS-RIS-only and equal bandwidth portioning baselines within one formulation and consistently surpasses them across diverse rate requirements. The simulations also quantify a tangible trade-off between RIS scale and UAV deployment, enabling designers to trade increased RIS elements for fewer UAVs as service demands evolve.

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arXiv2025

Green O-RAN Operation: a Modern ML-Driven Network Energy Consumption Optimisation

The increasing energy demand of next-generation mobile networks, especially 6G, is becoming a major concern, particularly due to the high power usage of base station components RU, which often remain active even during low traffic periods. To tackle this challenge, our study focuses on improving energy efficiency in O-RAN systems using intelligent control strategies. TD3 leverages a continuous action space to overcome the limitations of traditional discrete-action methods like DQN. By avoiding exponential growth in action space, TD3 enables more precise control of RU sleep modes in dense and large radio environments. Simulation results show that our approach consistently achieves over 50% energy savings compared to the always-on baseline, with TD3 outperforming DQN-based methods by up to 6%, while also offering better stability and faster convergence.

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arXiv2025

An Explainable AI Framework for Dynamic Resource Management in Vehicular Network Slicing

Effective resource management and network slicing are essential to meet the diverse service demands of vehicular networks, including Enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low-Latency Communications (URLLC). This paper introduces an Explainable Deep Reinforcement Learning (XRL) framework for dynamic network slicing and resource allocation in vehicular networks, built upon a near-real-time RAN intelligent controller. By integrating a feature-based approach that leverages Shapley values and an attention mechanism, we interpret and refine the decisions of our reinforcementlearning agents, addressing key reliability challenges in vehicular communication systems. Simulation results demonstrate that our approach provides clear, real-time insights into the resource allocation process and achieves higher interpretability precision than a pure attention mechanism. Furthermore, the Quality of Service (QoS) satisfaction for URLLC services increased from 78.0% to 80.13%, while that for eMBB services improved from 71.44% to 73.21%.

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arXiv2025

Digital Twin-Assisted Resilient Planning for mmWave IAB Networks via Graph Attention Networks

Digital Twin (DT) technology enables real-time monitoring and optimization of complex network infrastructures by creating accurate virtual replicas of physical systems. In millimeter-wave (mmWave) 5G/6G networks, the deployment of Integrated Access and Backhaul (IAB) nodes faces highly dynamic urban environments, necessitating intelligent DT-enabled optimization frameworks. Traditional IAB deployment optimization approaches struggle with the combinatorial complexity of jointly optimizing coverage, connectivity, and resilience, often leading to suboptimal solutions that are vulnerable to network disruptions. With this consideration, we propose a novel Graph Attention Network v2 (GATv2)-based reinforcement learning approach for resilient IAB deployment in urban mmWave networks. Specifically, we formulate the deployment problem as a Markov Decision Process (MDP) with explicit resilience constraints and employ edge-conditioned GATv2 to capture complex spatial dependencies between heterogeneous node types and dynamic connectivity patterns. The attention mechanism enables the model to focus on critical deployment locations to maximize coverage and ensure fault tolerance through redundant backhaul connections. To address the inherent vulnerability of mmWave links, we train the GATv2 policy using Proximal Policy Optimization (PPO) with a carefully designed balance between coverage, cost, and resilience. Comprehensive simulations across three urban scenarios demonstrate that our method achieves 98.5-98.7 percent coverage with 14.3-26.7 percent fewer nodes than baseline approaches, while maintaining 87.1 percent coverage retention under 30 percent link failures, representing 11.3-15.4 percent improvement in fault tolerance compared to state-of-the-art methods.

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arXiv2025

Maximising Energy Efficiency in Large-Scale Open RAN: Hybrid xApps and Digital Twin Integration

The growing demand for high-speed, ultra-reliable, and low-latency communications in 5G and beyond networks has significantly driven up power consumption, particularly within the Radio Access Network (RAN). This surge in energy demand poses critical operational and sustainability challenges for mobile network operators, necessitating innovative solutions that enhance energy efficiency without compromising Quality of Service (QoS). Open Radio Access Network (O-RAN), spearheaded by the O-RAN Alliance, offers disaggregated, programmable, and intelligent architectures, promoting flexibility, interoperability, and cost-effectiveness. However, this disaggregated approach adds complexity, particularly in managing power consumption across diverse network components such as Open Radio Units (RUs). In this paper, we propose a hybrid xApp leveraging heuristic methods and unsupervised machine learning, integrated with digital twin technology through the TeraVM AI RAN Scenario Generator (AI-RSG). This approach dynamically manages RU sleep modes to effectively reduce energy consumption. Our experimental evaluation in a realistic, large-scale emulated Open RAN scenario demonstrates that the hybrid xApp achieves approximately 13% energy savings, highlighting its practicality and significant potential for real-world deployments without compromising user QoS.

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arXiv2025

Sovereign AI for 6G: Towards the Future of AI-Native Networks

The advent of Generative Artificial Intelligence (GenAI), Large Language Models (LLMs), and Large Telecom Models (LTM) significantly reshapes mobile networks, especially as the telecom industry transitions from 5G's cloud-centric to AI-native 6G architectures. This transition unlocks unprecedented capabilities in real-time automation, semantic networking, and autonomous service orchestration. However, it introduces critical risks related to data sovereignty, security, explainability, and regulatory compliance especially when AI models are trained, deployed, or governed externally. This paper introduces the concept of `Sovereign AI' as a strategic imperative for 6G, proposing architectural, operational, and governance frameworks that enable national or operator-level control over AI development, deployment, and life-cycle management. Focusing on O-RAN architecture, we explore how sovereign AI-based xApps and rApps can be deployed Near-RT and Non-RT RICs to ensure policy-aligned control, secure model updates, and federated learning across trusted infrastructure. We analyse global strategies, technical enablers, and challenges across safety, talent, and model governance. Our findings underscore that Sovereign AI is not just a regulatory necessity but a foundational pillar for secure, resilient, and ethically-aligned 6G networks.

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arXiv2025

EcoFL: Resource Allocation for Energy-Efficient Federated Learning in Multi-RAT ORAN Networks

Federated Learning (FL) enables distributed model training on edge devices while preserving data privacy. However, FL deployments in wireless networks face significant challenges, including communication overhead, unreliable connectivity, and high energy consumption, particularly in dynamic environments. This paper proposes EcoFL, an integrated FL framework that leverages the Open Radio Access Network (ORAN) architecture with multiple Radio Access Technologies (RATs) to enhance communication efficiency and ensure robust FL operations. EcoFL implements a two-stage optimisation approach: an RL-based rApp for dynamic RAT selection that balances energy efficiency with network performance, and a CNN-based xApp for near real-time resource allocation with adaptive policies. This coordinated approach significantly enhances communication resilience under fluctuating network conditions. Experimental results demonstrate competitive FL model performance with 19\% lower power consumption compared to baseline approaches, highlighting substantial potential for scalable, energy-efficient collaborative learning applications.

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arXiv2025

Exploring O-RAN Compression Techniques in Decentralized Distributed MIMO Systems: Reducing Fronthaul Load

This paper explores the application of uplink fronthaul compression techniques within Open RAN (O-RAN) to mitigate fronthaul load in decentralized distributed MIMO (DD-MIMO) systems. With the ever-increasing demand for high data rates and system scalability, the fronthaul load becomes a critical bottleneck. Our method uses O-RAN compression techniques to efficiently compress the fronthaul signals. The goal is to greatly lower the fronthaul load while having little effect on the overall system performance, as shown by Block Error Rate (BLER) curves. Through rigorous link-level simulations, we compare our quantization strategies against a benchmark scenario with no quantization, providing insights into the trade-offs between fronthaul data rate reduction and link performance integrity. The results demonstrate that our proposed quantization techniques not only lower the fronthaul load but also maintain a competitive link quality, making them a viable solution for enhancing the efficiency of next-generation wireless networks. This study underscores the potential of quantization in O-RAN contexts to achieve optimal balance between system capacity and performance, paving the way for more scalable and robust DD-MIMO deployments.

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arXiv2025

Enhancing Open RAN Digital Twin Through Power Consumption Measurement

The increasing demand for high-speed, ultra-reliable and low-latency communications in 5G and beyond networks has led to a significant increase in power consumption, particularly within the Radio Access Network (RAN). This growing energy demand raises operational and sustainability challenges for mobile network operators, requiring novel solutions to enhance energy efficiency while maintaining Quality of Service (QoS). 5G networks are evolving towards disaggregated, programmable, and intelligent architectures, with Open Radio Access Network (O-RAN) spearheaded by the O-RAN Alliance, enabling greater flexibility, interoperability, and cost-effectiveness. However, this disaggregated approach introduces new complexities, especially in terms of power consumption across different network components, including Open Radio Units (RUs), Open Distributed Units (DUs) and Open Central Units (CUs). Understanding the power efficiency of different O-RAN functional splits is crucial for optimising energy consumption and network sustainability. In this paper, we present a comprehensive measurement study of power consumption in RUs, DUs and CUs under varying network loads, specifically analysing the impact of Physical resource block (PRB) utilisation in Split 8 and Split 7.2b. The measurements were conducted on both software-defined radio (SDR)-based RUs and commercial indoor and outdoor RU, as well as their corresponding DU and CU. By evaluating real-world hardware deployments under different operational conditions, this study provides empirical insights into the power efficiency of various O-RAN configurations. The results highlight that power consumption does not scale significantly with network load, suggesting that a large portion of energy consumption remains constant regardless of traffic demand.

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arXiv2025

Optimizing 6G Dense Network Deployment for the Metaverse Using Deep Reinforcement Learning

As the Metaverse envisions deeply immersive and pervasive connectivity in 6G networks, Integrated Access and Backhaul (IAB) emerges as a critical enabler to meet the demanding requirements of massive and immersive communications. IAB networks offer a scalable solution for expanding broadband coverage in urban environments. However, optimizing IAB node deployment to ensure reliable coverage while minimizing costs remains challenging due to location constraints and the dynamic nature of cities. Existing heuristic methods, such as Greedy Algorithms, have been employed to address these optimization problems. This work presents a novel Deep Reinforcement Learning ( DRL) approach for IAB network planning, tailored to future 6G scenarios that seek to support ultra-high data rates and dense device connectivity required by immersive Metaverse applications. We utilize Deep Q-Network (DQN) with action elimination and integrate DQN, Double Deep Q-Network ( DDQN), and Dueling DQN architectures to effectively manage large state and action spaces. Simulations with various initial donor configurations demonstrate the effectiveness of our DRL approach, with Dueling DQN reducing node count by an average of 12.3% compared to traditional heuristics. The study underscores how advanced DRL techniques can address complex network planning challenges in 6G-enabled Metaverse contexts, providing an efficient and adaptive solution for IAB deployment in diverse urban environments.

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arXiv2025

Towards Sustainability in 6G and beyond: Challenges and Opportunities of Open RAN

The transition to 6G is expected to bring significant advancements, including much higher data rates, enhanced reliability and ultra-low latency compared to previous generations. Although 6G is anticipated to be 100 times more energy efficient, this increased efficiency does not necessarily mean reduced energy consumption or enhanced sustainability. Network sustainability encompasses a broader scope, integrating business viability, environmental sustainability, and social responsibility. This paper explores the sustainability requirements for 6G and proposes Open RAN as a key architectural solution. By enabling network diversification, fostering open and continuous innovation, and integrating AI/ML, Open RAN can promote sustainability in 6G. The paper identifies high energy consumption and e-waste generation as critical sustainability challenges and discusses how Open RAN can address these issues through softwarisation, edge computing, and AI integration.

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arXiv2025

Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences

This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions and user experiences, especially in the context of 6G systems. It highlights the development and deployment of Large Telecom Models (LTMs), which are tailored AI models designed to address the complex challenges faced by modern telecom networks. The paper covers a wide range of topics, from the architecture and deployment strategies of LTMs to their applications in network management, resource allocation, and optimization. It also explores the regulatory, ethical, and standardization considerations for LTMs, offering insights into their future integration into telecom infrastructure. The goal is to provide a comprehensive roadmap for the adoption of LTMs to enhance scalability, performance, and user-centric innovation in telecom networks.

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arXiv2025

Testbed Development: An Intelligent O-RAN based Cell-Free MIMO Network

Cell-free multiple input multiple output (CF-MIMO) systems improve spectral and energy efficiencies using distributed access points (APs) to provide reliable service across an area equivalent to multiple conventional cells. This paper presents a novel design and implementation of a CF-MIMO network leveraging the open radio access network (O-RAN) architecture based testbed to enhance the performance of interference-prone user. The proposed prototype is developed based on open source software components and unlike many other prototypes, our testbed is able to serve commercial 5G user equipment (UE). The RAN intelligent controller (RIC) allows the cell-free (CF) network to access the embedded artificial intelligence and benefit from the network optimisation techniques that O-RAN brings. The testbed includes an intelligent antenna association xApp which determines the antenna group that serves each UE based on the live key performance measurements. The paper demonstrates the deployment and operation of the CF network and the xApp and discusses how the CF networks can benefit from the O-RAN architecture.

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arXiv2025

Energy Consumption Reduction for UAV Trajectory Training : A Transfer Learning Approach

The advent of 6G technology demands flexible, scalable wireless architectures to support ultra-low latency, high connectivity, and high device density. The Open Radio Access Network (O-RAN) framework, with its open interfaces and virtualized functions, provides a promising foundation for such architectures. However, traditional fixed base stations alone are not sufficient to fully capitalize on the benefits of O-RAN due to their limited flexibility in responding to dynamic network demands. The integration of Unmanned Aerial Vehicles (UAVs) as mobile RUs within the O-RAN architecture offers a solution by leveraging the flexibility of drones to dynamically extend coverage. However, UAV operating in diverse environments requires frequent retraining, leading to significant energy waste. We proposed transfer learning based on Dueling Double Deep Q network (DDQN) with multi-step learning, which significantly reduces the training time and energy consumption required for UAVs to adapt to new environments. We designed simulation environments and conducted ray tracing experiments using Wireless InSite with real-world map data. In the two simulated environments, training energy consumption was reduced by 30.52% and 58.51%, respectively. Furthermore, tests on real-world maps of Ottawa and Rosslyn showed energy reductions of 44.85% and 36.97%, respectively.

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