arXiv subjects

Halim Yanikomeroglu

Publications and source records attributed to Halim Yanikomeroglu.

At least 55 records · Page 3Linked to original sources

arXiv2026

DQN Based Joint UAV Trajectory and Association Planning in NTN Assisted Networks

Advanced Air Mobility (AAM) has emerged as a key pillar of next-generation transportation systems, encompassing a wide range of uncrewed aerial vehicle (UAV) applications. To enable AAM, maintaining reliable and efficient communication links between UAVs and control centers is essential. At the same time, the highly dynamic nature of wireless networks, combined with the limited onboard energy of UAVs, makes efficient trajectory planning and network association crucial. Existing terrestrial networks often fail to provide ubiquitous coverage due to frequent handovers and coverage gaps. To address these challenges, geostationary Earth orbit (GEO) satellites offer a promising complementary solution for extending UAV connectivity beyond terrestrial boundaries. This work proposes an integrated GEO terrestrial network architecture to ensure seamless UAV connectivity. Leveraging artificial intelligence (AI), a deep Q network (DQN) based algorithm is developed for joint UAV trajectory and association planning (JUTAP), aiming to minimize energy consumption, handover frequency, and disconnectivity. Simulation results validate the effectiveness of the proposed algorithm within the integrated GEO terrestrial framework.

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arXiv2026

Outage Probability Analysis of NOMA Enabled Hierarchical UAV Networks with Non-Linear Energy Harvesting

Uncrewed aerial vehicles (UAVs) are expected to enhance connectivity, extend network coverage, and support advanced communication services in sixth-generation (6G) cellular networks, particularly in public and civil domains. Although multi-UAV systems enhance connectivity for IoT networks more than single-UAV systems, energy-efficient communication systems and the integration of energy harvesting (EH) are crucial for their widespread adoption and effectiveness. In this regard, this paper proposes a hierarchical ad hoc UAV network with non-linear EH and non-orthogonal multiple access (NOMA) to enhance both energy and cost efficiency. The proposed system consists of two UAV layers: a cluster head UAV (CHU), which acts as the source, and cluster member UAVs (CMUs), which serve as relays and are capable of harvesting energy from a terrestrial power beacon. For the considered IoT network architecture, the outage probability expressions of ground Internet of things (IoT) devices, each CMU, and the overall outage probability of the proposed system are derived over Nakagami-m fading channels with practical constraints such as hardware impairments and non-linear EH. We compare the proposed system against a non EH system, and our findings indicate that the proposed system outperforms the benchmark in terms of outage probability.

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arXiv2026

HAPS-RIS-assisted IoT Networks for Disaster Recovery and Emergency Response: Architecture, Application Scenarios, and Open Challenges

Reliable and resilient communication is essential for disaster recovery and emergency response, yet terrestrial infrastructure often fails during large-scale natural disasters. This paper proposes a High-Altitude Platform Station (HAPS) and Reconfigurable Intelligent Surfaces (RIS)-assisted Internet of Things (IoT) communication system to restore connectivity in disaster-affected areas. Distributed IoT sensors collect critical environmental data and forward it to nearby gateways via short-range links, while the HAPS-RIS system provides backhaul to these gateways. To overcome the severe double path loss of passive RIS at high altitudes, we propose a dynamically adjustable sub-connected active RIS architecture that can reconfigure the number of elements connected to each power amplifier through switching mechanisms. Simulation results demonstrate substantial gains in downlink and uplink data rates, as well as system energy efficiency, compared with conventional passive RIS schemes. Moreover, a 1 dB increase in ground-station transmit power yields approximately 20-30 Mbps improvement in gateway data rates. These findings confirm that HAPS-RIS technology offers an effective and energy-efficient approach for resilient IoT backhaul in 6G non-terrestrial networks, particularly in line-of-sight (LoS)-dominant HAPS-ground backhaul scenarios.

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arXiv2026

Two-Phase Cell Switching in 6G vHetNets: Sleeping-Cell Load Estimation and Renewable-Aware Switching Toward NES

This paper proposes a two phase framework to improve the sustainability in vertical heterogeneous networks that integrate various types of base stations~(BSs), including terrestrial macro BSs~(MBSs), small BSs~(SBSs), and a high altitude platform station super MBS (HAPS SMBS). In Phase I, we address the critical and often overlooked challenge of estimating the traffic load of sleeping SBSs, a prerequisite for practical cell switching, by introducing three methods with varying data dependencies: (i) a distance based estimator (no historical data), (ii) a multi level clustering (MLC) estimator (limited historical data), and (iii) a long short term memory~(LSTM) based temporal predictor (full historical data). In Phase II, we incorporate the most accurate estimation results from Phase I into a renewable energy aware cell switching strategy, explicitly modeling solar powered SBSs in three operational scenarios that reflect realistic hybrid grid renewable deployments. This flexible design allows the framework to adapt switching strategies based on renewable availability and storage conditions, making it more practical and robust for real world networks. Using a real call detail record dataset from Milan, simulation results show that the LSTM method achieves a mean absolute percentage error (MAPE) below 1% in Phase I, while in Phase II, the threshold based solar integration scenario achieves up to 23% network energy saving (NES) relative to conventional cell switching. Overall, the proposed framework bridges the gap between theoretical cell switching models and practical, sustainable 6G radio access network~(RAN) operation, enabling significant energy saving without compromising quality of service.

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arXiv2026

AI-Enhanced Spatial Cellular Traffic Demand Prediction with Contextual Clustering and Error Correction for 5G/6G Planning

Accurate spatial prediction of cellular traffic demand is essential for 5G NR capacity planning, network densification, and data-driven 6G planning. Although machine learning can fuse heterogeneous geospatial and socio-economic layers to estimate fine-grained demand maps, spatial autocorrelation can cause neighborhood leakage under naive train/test splits, inflating accuracy and weakening planning reliability. This paper presents an AI-driven framework that reduces leakage and improves spatial generalization via a context-aware two-stage splitting strategy with residual spatial error correction. Experiments using crowdsourced usage indicators across five major Canadian cities show consistent mean absolute error (MAE) reductions relative to location-only clustering, supporting more reliable bandwidth provisioning and evidence-based spectrum planning and sharing assessments.

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arXiv2026

Towards Intelligent Spectrum Management: Spectrum Demand Estimation Using Graph Neural Networks

The growing demand for wireless connectivity, combined with limited spectrum resources, calls for more efficient spectrum management. Spectrum sharing is a promising approach; however, regulators need accurate methods to characterize demand dynamics and guide allocation decisions. This paper builds and validates a spectrum demand proxy from public deployment records and uses a graph attention network in a hierarchical, multi-resolution setup (HR-GAT) to estimate spectrum demand at fine spatial scales. The model captures both neighborhood effects and cross-scale patterns, reducing spatial autocorrelation and improving generalization. Evaluated across five Canadian cities and against eight competitive baselines, HR-GAT reduces median RMSE by roughly 21% relative to the best alternative and lowers residual spatial bias. The resulting demand maps are regulator-accessible and support spectrum sharing and spectrum allocation in wireless networks.

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arXiv2026

A Graph-Based Approach to Spectrum Demand Prediction Using Hierarchical Attention Networks

The surge in wireless connectivity demand, coupled with the finite nature of spectrum resources, compels the development of efficient spectrum management approaches. Spectrum sharing presents a promising avenue, although it demands precise characterization of spectrum demand for informed policy-making. This paper introduces HR-GAT, a hierarchical resolution graph attention network model, designed to predict spectrum demand using geospatial data. HR-GAT adeptly handles complex spatial demand patterns and resolves issues of spatial autocorrelation that usually challenge standard machine learning models, often resulting in poor generalization. Tested across five major Canadian cities, HR-GAT improves predictive accuracy of spectrum demand by 21% over eight baseline models, underscoring its superior performance and reliability.

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arXiv2026

AI-Enabled Data-driven Intelligence for Spectrum Demand Estimation

Accurately forecasting spectrum demand is a key component for efficient spectrum resource allocation and management. With the rapid growth in demand for wireless services, mobile network operators and regulators face increasing challenges in ensuring adequate spectrum availability. This paper presents a data-driven approach leveraging artificial intelligence (AI) and machine learning (ML) to estimate and manage spectrum demand. The approach uses multiple proxies of spectrum demand, drawing from site license data and derived from crowdsourced data. These proxies are validated against real-world mobile network traffic data to ensure reliability, achieving an R$^2$ value of 0.89 for an enhanced proxy. The proposed ML models are tested and validated across five major Canadian cities, demonstrating their generalizability and robustness. These contributions assist spectrum regulators in dynamic spectrum planning, enabling better resource allocation and policy adjustments to meet future network demands.

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arXiv2026

Towards Flexible Spectrum Access: Data-Driven Insights into Spectrum Demand

In the diverse landscape of 6G networks, where wireless connectivity demands surge and spectrum resources remain limited, flexible spectrum access becomes paramount. The success of crafting such schemes hinges on our ability to accurately characterize spectrum demand patterns across space and time. This paper presents a data-driven methodology for estimating spectrum demand variations over space and identifying key drivers of these variations in the mobile broadband landscape. By leveraging geospatial analytics and machine learning, the methodology is applied to a case study in Canada to estimate spectrum demand dynamics in urban regions. Our proposed model captures 70\% of the variability in spectrum demand when trained on one urban area and tested on another. These insights empower regulators to navigate the complexities of 6G networks and devise effective policies to meet future network demands.

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arXiv2026

Energy Efficient Traffic Scheduling For Optical LEO Satellite Downlinks

In recent years, the number of satellites in orbit has increased rapidly, with megaconstellations like Starlink providing near-global, delay-sensitive communication services. However, not all satellite communication use cases have stringent delay requirements; services such as Earth observation (EO) and remote Internet of Things (IoT) fall into this category. These relaxed delay quality of service (QoS) objectives allow services to be delivered using sparse constellations, enabled by delay-tolerant networking protocols. In the context of rapidly growing data volumes that must be delivered through satellite networks, a key challenge is having sufficient space-to-ground link capacity. This has led to proposals for using free-space optical (FSO) communications, which offer high data rates. However, FSO communications are highly vulnerable to weather-related disruptions. This results in certain communication opportunities being energy inefficient. Given the energy-constrained nature of satellites, developing schemes to improve energy efficiency is highly desirable. In this work, both static and adaptive schemes were developed to balance maintaining the delivery ratio and maximizing energy efficiency. The proposed schemes fall into the following categories: threshold schemes, heuristic sorting algorithms, and reinforcement learning-based schemes. The schemes were evaluated under a variety of different data volumes and cloud cover distribution configurations as well as a case study using historical weather data. It was found that static schemes suffered from low delivery ratio performance under dynamic conditions when compared to adaptive techniques. However, this performance improvement came at the cost of increased complexity and onboard computations.

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arXiv2026

Optimized Deployment of HAPS Systems for GNSS Localization Enhancement in Urban Environments

While high altitude platform stations (HAPS) have been primarily explored as network infrastructure for communication services, their advantageous characteristics also make them promising candidates for augmenting GNSS localization. This paper proposes a metaheuristic framework to jointly optimize the number and placement of HAPS for GNSS enhancement in dense urban environments, considering practical constraints such as elevation masks, altitude limits, and ray-traced visibility from 3D city models. The problem is highly nonconvex due to the discrete HAPS count and the environment-dependent 3D Cramer-Rao lower bound (CRLB). To address this, we develop a tailored version of the adaptive special-crowding distance non-dominated sorting genetic algorithm II (ASDNSGA-II). Simulations show the method successfully identifies the minimum number of HAPS needed to satisfy a CRLB threshold and selects the configuration with the lowest CRLB within that minimum, offering a cost-effective and scalable solution for future HAPS-aided positioning systems.

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arXiv2026

QoS-Aware Energy Optimization via Cell Switching in Heterogeneous Networks

The growing demand for mobile data services in dense urban areas has intensified the need for energy-efficient radio access networks (RANs) in future 6G systems. In this context, one promising strategy is cell switching (CS), which dynamically deactivates underutilized small base stations (SBSs) to reduce power consumption. However, while previous research explored CS primarily based on traffic load, ensuring user quality of service (QoS) under realistic channel conditions remains a challenge. In this paper, we propose a novel optimization-driven CS framework that jointly minimizes network power consumption and guarantees user QoS by enforcing a minimum received power threshold as part of offloading decisions. In contrast to prior load-based or learning-based approaches, our method explicitly integrates channel-aware information into the CS process, thus ensuring reliable service quality for offloaded users. Furthermore, flexibility of the proposed framework enables operators to adapt system behavior between energy-saving and QoS-preserving modes by tuning a single design parameter. Simulation results demonstrate that the proposed approach achieves up to 30% power savings as compared to baseline methods while fully maintaining QoS under diverse network conditions. Scalability and robustness of the proposed method in realistic heterogeneous networks (HetNets) further highlight its potential as a practical solution for sustainable 6G deployments.

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arXiv2026

Sustainable Vertical Heterogeneous Networks: A Cell Switching Approach with High Altitude Platform Station

The rapid growth of radio access networks (RANs) is increasing energy consumption and challenging the sustainability of future systems. We consider a dense-urban vertical heterogeneous network (vHetNet) comprising a high-altitude platform station (HAPS) acting as a super macro base station, a terrestrial macro base station (MBS), and multiple small base stations (SBSs). We propose a HAPS-enhanced cell-switching algorithm that selectively deactivates SBSs based on their traffic load and the capacity and channel conditions of both the MBS and HAPS. The resulting energy-minimization problem, subject to an outage-based quality-of-service (QoS) constraint, is formulated as a mixed-integer nonlinear program and reformulated into a mixed-integer program for efficient solution. Using realistic 3GPP channel models, simulations show substantial energy savings versus All-ON, terrestrial cell switching, and sorting benchmarks. Relative to All-ON, the proposed method reduces power consumption by up to 77% at low loads and about 40% at high loads; a NoQoS variant achieves up to 90% and 47%, respectively. The approach maintains high served-traffic levels and provides a tunable trade-off between power efficiency and outage-based QoS, supporting scalable and sustainable 6G deployments.

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arXiv2026

Solutions for Sustainable and Resilient Communication Infrastructure in Disaster Relief and Management Scenarios

As natural disasters become more frequent and severe, ensuring a resilient communications infrastructure is of paramount importance for effective disaster response and recovery. This disaster-resilient infrastructure should also respond to sustainability goals by providing an energy-efficient and economically feasible network that is accessible to everyone. This paper provides a comprehensive exploration of the technological solutions and strategies necessary to build and maintain resilient communications networks that can withstand and quickly recover from disaster scenarios. The paper starts with a survey of existing literature and related reviews to establish a solid foundation, followed by an overview of the global landscape of disaster communications and power supply management. We then introduce the key enablers of communications and energy resource technologies to support communications infrastructure, examining emerging trends that improve the resilience of these systems. Pre-disaster planning is emphasized as a critical phase where proactive communication and energy supply strategies can significantly mitigate the impact of disasters. We explore the essential technologies for disaster response, focusing on real-time communications and energy solutions that support rapid deployment and coordination in times of crisis. The paper presents post-disaster communication and energy management planning for effective rescue and evacuation operations. The main findings derived from the comprehensive survey are also summarized for each disaster phase. This is followed by an analysis of existing vendor products and services as well as standardization efforts and ongoing projects that contribute to the development of resilient infrastructures. A detailed case study of the Turkiye earthquakes is presented to illustrate the practical application of these technologies and strategies.

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arXiv2025

Distributed Beamforming in Massive MIMO Communication for a Constellation of Airborne Platform Stations

Non-terrestrial base stations (NTBSs), including high-altitude platform stations (HAPSs) and hot-air balloons (HABs), are integral to next-generation wireless networks, offering coverage in remote areas and enhancing capacity in dense regions. In this paper, we propose a distributed beamforming framework for a massive MIMO network with a constellation of aerial platform stations (APSs). Our approach leverages an entropy-based multi-agent deep reinforcement learning (DRL) model, where each APS operates as an independent agent using imperfect channel state information (CSI) in both training and testing phases. Unlike conventional methods, our model does not require CSI sharing among APSs, significantly reducing overhead. Simulations results demonstrate that our method outperforms zero forcing (ZF) and maximum ratio transmission (MRT) techniques, particularly in high-interference scenarios, while remaining robust to CSI imperfections. Additionally, our framework exhibits scalability, maintaining stable performance over an increasing number of users and various cluster configurations. Therefore, the proposed method holds promise for dynamic and interference-rich NTBS networks, advancing scalable and robust wireless solutions.

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arXiv2025

Beamforming for Massive MIMO Aerial Communications: A Robust and Scalable DRL Approach

This paper presents a distributed beamforming framework for a constellation of airborne platform stations (APSs) in a massive Multiple-Input and Multiple-Output (MIMO) non-terrestrial network (NTN) that targets the downlink sum-rate maximization under imperfect local channel state information (CSI). We propose a novel entropy-based multi-agent deep reinforcement learning (DRL) approach where each non-terrestrial base station (NTBS) independently computes its beamforming vector using a Fourier Neural Operator (FNO) to capture long-range dependencies in the frequency domain. To ensure scalability and robustness, the proposed framework integrates transfer learning based on a conjugate prior mechanism and a low-rank decomposition (LRD) technique, thus enabling efficient support for large-scale user deployments and aerial layers. Our simulation results demonstrate the superiority of the proposed method over baseline schemes including WMMSE, ZF, MRT, CNN-based DRL, and the deep deterministic policy gradient (DDPG) method in terms of average sum rate, robustness to CSI imperfection, user mobility, and scalability across varying network sizes and user densities. Furthermore, we show that the proposed method achieves significant computational efficiency compared to CNN-based and WMMSE methods, while reducing communication overhead in comparison with shared-critic DRL approaches.

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arXiv2025

Anchor-and-Connect: Robotic Aerial Base Stations Transforming 6G Infrastructure

Despite the significant attention that aerial base stations (ABSs) have received recently, their practical implementation is severely weakened by their limited endurance due to the battery constraints of drones. To overcome this fundamental limitation and barrier for wider adoption, we propose the concept of robotic aerial base stations (RABSs) that are equipped with energy-neutral anchoring end-effectors able to autonomously grasp or perch on tall urban landforms. Thanks to the energy-efficient anchoring operation, RABSs could offer seamless wireless connectivity for multiple hours compared to minutes of the typical hovering-based ABSs. Therefore, the prolonged service capabilities of RABSs allowing them to integrate into the radio access network and augment the network capacity where and when needed. To set the scene, we discuss the key components of the proposed RABS concept including hardware, workflow, communication considerations, and regulation issues. Then, the advantages of RABSs are highlighted which is followed by case studies that compare RABSs with terrestrial micro BSs and other types of non-terrestrial communication infrastructure, such as hovering-based, tethered, and laser-powered ABSs.

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arXiv2025

HAPS-ISAC for 6G: Architecture, Design Trade-offs, and a Practical Roadmap

To meet the ambitious goals of next-generation 6G networks, including ultra-high data rates and ubiquitous coverage, we propose a novel high-altitude platform station (HAPS)-based integrated sensing and communication (ISAC) architecture. Operating in the stratosphere, the HAPS functions as both a powerful communication hub and an advanced environmental sensor. Combined with a fleet of cooperative uncrewed aerial vehicles (UAVs), this dual-purpose system forms a scalable and intelligent 3D network. Simulation results indicate that this approach significantly boosts network performance, improves sensing accuracy, and ensures a fairer service distribution across users, outperforming conventional UAV-only baselines. We conclude by outlining the prospective applications and a deployment roadmap for this technology for smart cities and other large-scale environments.

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