arXiv subjects

Gunes Karabulut Kurt

Publications and source records attributed to Gunes Karabulut Kurt.

At least 19 recordsLinked to original sources

arXiv2026

CSI-Aging-Aware Functional Split Optimization and KAN-LSTM Prediction for Open-RAN NTN

In this paper, we study functional-split selection for an Open radio access network (RAN) deployed as a non-terrestrial network (NTN), in which a low-Earth-orbit (LEO) satellite hosts the radio unit, so that each candidate split (7.2x, 6, 2) places the RAN at a distinct point of the transparent-to-regenerative axis. We capture the space-ground geometry exactly through an instantaneous link plane, which yields closed-form slant range, elevation, delay, and Doppler for an inclined orbit and a rotating Earth. On this geometry we derive the received-SNR density of the elevation-driven LOS/NLOS mixture channel and, through a split-dependent channel state information (CSI) aging mechanism, an uncoded bit-error-rate (BER) law for each split, establishing that what separates the splits is the freshness of the CSI each brings to its adaptation loops: a loop closed on the ground pays the feeder round-trip and incurs an aging-induced error floor. We formulate split selection as a prefer-centralized feasibility problem over reliability, aging, and fronthaul-capacity constraints, and we show that its exact solution is a closed-form region rule in four state coordinates, validated against exhaustive enumeration without a single mismatch. Because this solution is reactive whereas a split change must be initiated ahead of time, we propose a compact Kolmogorov-Arnold network (KAN)-LSTM, sized for an edge xApp/rApp, that predicts the split schedule seconds ahead; we show that it attains high schedule accuracy at a sub-percent BER-violation probability and delivers nearly all of the ground-truth policy's goodput, outperforming naive and reactive baselines.

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arXiv2026

AI-Enabled Wireless Propagation Modeling and Radio Environment Maps for 5G Aerial Wireless Networks

With the gaining prominence of aerial mobility applications, their success depends on the seamless integration of terrestrial and non-terrestrial network connectivity. However, providing reliable connectivity from terrestrial telecommunication networks remains challenging due to multi-cell interference from base stations (BSs) under line-of-sight (LoS) conditions to unmanned aerial vehicles (UAVs), coverage holes caused by antenna sidelobe degradation, localized multipath fading effects, and the high-speed dynamics of aerial users. To model such complexities, often exacerbated by sparse real-world data, this work proposes a dual-stage radio environment map (REM) framework. Our approach physically decouples the channel modeling, where a spatial Transformer first anchors the deterministic, large-scale path loss geometry, while a gated recurrent unit (GRU) subsequently extrapolates the stochastic, localized fast- fading deviations. By reformulating 3D spatial interpolation as a 1D radial sequence prediction task, the framework inherently aligns with the physics of propagation. We evaluate the proposed framework against state-of-the-art baselines, including 3D Kriging, UNet, Mamba, and Inception, using empirical 5G datasets. The results demonstrate improved intra-site generalization across diverse altitudes, user dynamics, and reference signal received power (RSRP) datasets, achieving signal-strength predictions with errors near 3 dB and REM spatial-similarity indices exceeding 0.75. Finally, we examine the influence of REMs and channel rank conditions on UAV channel quality, underscoring the necessity of reliable channel modeling for robust aerial connectivity.

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arXiv2026

Fourth-Order Co-Channel Interference-Aware OFDM-ISAC Sensing in Cluttered Environments

In this paper, we develop a sensing-centric receiver for monostatic orthogonal frequency-division multiplexing integrated sensing and communication (OFDM-ISAC) in the simultaneous presence of passive clutter and a non-cooperative co-channel OFDM transmitter. We show that removing the known ISAC symbols maps target and clutter echoes to deterministic delay-Doppler components, while the non-cooperative waveform remains a random, non-Gaussian, generically full-rank residual. Guided by this distinction, we combine moving-target indication for zero-Doppler clutter with a centered diagonal fourth-order statistic for interference identification. To obtain an unbiased fourth-order estimate from phase-aligned proper snapshots, we derive additive and multiplicative finite-sample bias corrections and pair the corrected statistic with a second-order moment to separate interference from Gaussian noise. Fourth order provides identification and presence testing, whereas second order provides the diagonal disturbance-power proxy used for weighting. We further introduce diagonally loaded weights that enforce a range-Doppler aperture-efficiency floor. Simulations show that the interference estimate approaches its theoretical profile as coherent support grows and remains insensitive to deterministic echoes, that the presence test resolves interference well below the level at which weighting matters, and that loaded processing improves target detection while controlling point-spread-function degradation.

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arXiv2026

Inverse Maxwell-Based Wall-Aware OFDM-ISAC for Slow-Moving Target Sensing

In this paper, we study integrated sensing and communication (ISAC) for short-range indoor orthogonal frequency-division multiplexing (OFDM) systems in which the sensing path crosses a building wall. The target is a slow-moving user equipment behind the wall, and its echo is embedded in the wall reflection and static indoor clutter. Because the wall adds excess propagation length, attenuation, and internal reflections, a conventional delay transform reports an apparent range. We therefore formulate physical-range estimation as an inverse Maxwell problem for the known wall: the range operator is the distorted-Born Jacobian of the discretized one-dimensional Helmholtz equation, assembled offline from the calibrated wall and applied online as a per-snapshot range inversion, so that the range-Doppler map is indexed by physical rather than apparent range. We then prove that this operator carries a structural limitation: when the two-way wall factor has constant magnitude and linear phase, the wall-aware range image is identically the free-space image on a translated grid, for every ridge level, taper, and noise realization. A free-space branch translated by the same excess length is therefore a required comparison. Sensing-only numerical results for a representative layered wall, with every branch on one range grid and a common post-FFT CSI model, show that the proposed inverse removes the range bias that wall-unaware processing cannot, and that a scalar-corrected free-space inverse tracks it closely, the two differing only marginally in empirical range RMSE and detection probability across the tested sweep.

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arXiv2026

Is Forecasting Accuracy Enough? A Comparative Study of Traffic Forecasters for Beam-Hopping LEO Satellite Networks

We evaluate diverse models for user traffic demand forecasting in Low Earth Orbit (LEO) satellite networks with Beam Hopping (BH), questioning whether predictive accuracy is the right objective for this task. To capture the complex nature of the user traffic demand, we employ a second-order self-similar traffic model, supplemented by a publicly available Wi-Fi dataset to validate the self-similar model against the empirical traffic patterns. We compare forecasters ranging from classical statistical approaches, such as the optimal forecaster for self-similar data and the optimal linear predictor on the discrete sampling grid, to Fractional Auto-Regressive Integrated Moving Average (FARIMA) models, as well as emerging deep learning architectures. The latter category encompasses foundation and domain-specific transformer models, alongside a lightweight neural network consisting solely of linear layers. We assess these models at two levels: in isolation, through the Mean Absolute Scaled Error (MASE), and in context, through a BH simulator in which the forecast drives the illumination plan. On purely self-similar traffic the three self-similarity aware forecasters perform on par with one another and dominate the learned models, whereas on the raw Wi-Fi trace this ordering nearly reverses. Seasonality violates their stationary increment assumption; removing the periodic component restores their comparative accuracy. Crucially, these accuracy differences barely propagate to the system level. Loss ratio and buffer backlog are affected more by system utilization and the planning period than by the choice of forecaster, with the performance gap between forecasters vanishing entirely below 0.90 utilization. This suggests design efforts are better spent optimizing utilization margins and planning periods rather than chasing marginal gains in raw accuracy.

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arXiv2026

Joint Satellite Power Consumption and Handover Optimization for LEO Constellations

In satellite constellation-based communication systems, continuous user coverage requires frequent handoffs due to the dynamic topology induced by the Low Earth Orbit (LEO) satellites. Each handoff between a satellite and ground users introduces additional signaling and power consumption, which can become a significant burden as the size of the constellation continues to increase. This work focuses on the optimization of the total transmission rate in a LEO-to-user system, by jointly considering the total transmitted power, user-satellite associations, and power consumption, the latter being handled through a penalty on handoff events. We consider a system where LEO satellites serve users located in remote areas with no terrestrial connectivity, and formulate the power allocation problem as a mixed-integer concave linear program (MICP) subject to power and association constraints. Our approach can be solved with off-the-shelf solvers and is benchmarked against a naive baseline where users associate to their closest visible satellite. Extensive Monte Carlo simulations demonstrate the effectiveness of the proposed method in controlling the handoff frequency while maintaining high user throughput. These performance gains highlight the effectiveness of our handover-aware optimization strategy, which ensures that user rates improve significantly, by about 40%, without incurring a disproportionate rise in the handoff frequency.

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arXiv2026

Multi-Agent Reinforcement Learning for Joint Handover Management and Power Allocation in Multi-Orbit Satellite Networks

Future sixth-generation non-terrestrial networks are expected to combine low Earth orbit (LEO), medium Earth orbit (MEO), and geostationary Earth orbit (GEO) satellites, whose complementary layers must be coordinated through joint user association, power allocation, and handover management under fast LEO dynamics. This paper studies this problem by formulating it as a mixed-integer nonlinear program and decomposing it into a multi-agent reinforcement learning (MARL) policy that selects the associations and a convex power-allocation subproblem solved exactly at each time slot that defines the reward of the MARL part. The association policy is trained with multi-agent proximal policy optimization (MAPPO) and the targeted multi-agent communication (TarMAC) mechanism, and is made aware of the orbital layer through a state that encodes layer-dependent handover penalties. Evaluated on a realistic multi-constellation scenario built from real two-line element data over Nairobi, Kenya, the proposed policy reaches 92% of the throughput of a greedy signal-to-noise ratio (SNR) maximizing scheme while triggering more than four times fewer handovers, and improves throughput by roughly 14% over a conservative stay heuristic. Compared to an LEO-only learned policy of identical architecture, it attains slightly higher throughput with fewer handovers by offloading a fraction of the users to the MEO and GEO layers, an emergent multi-orbit behavior that drives its favorable throughput and handover trade-off.

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arXiv2026

The Impact of Demand Forecasting on Delay and Jitter in DVB-Based Beam-Hopping LEO Networks

In LEO satellite networks utilizing beam hopping (BH), resource allocation plans must be committed well in advance. This inherent operational delay necessitates predicting future user demand during the planning phase. Such predictive agility is particularly crucial for military applications, where unpredictable tactical environments demand low-latency, resilient communication links. However, existing forecasting models are typically evaluated based on standalone accuracy, ignoring their cross-layer impact on overall network performance. To address this gap, we evaluate two distinct demand forecasting solutions within a comprehensive, full-stack LEO satellite simulation compliant with DVB-S2X standards. Beyond prediction accuracy, we examine how incorporating user demand forecasts into BH plan generation impacts key network metrics, particularly delay and jitter. We evaluate these forecasting solutions alongside a static allocation baseline. Our results demonstrate that forecast-based dynamic planning reduces delay by 10-40% across the beams under certain load conditions compared to static allocation methods. Crucially, marginal improvements in predictive accuracy do not translate into proportional network metric gains. While the evaluated forecasting solutions differ by 14-16% in Normalized Mean Square Error (NMSE), this discrepancy yields less than a 1% reduction in delay and produces nearly identical jitter characteristics. These findings suggest that when designing user demand forecasting solutions for practical LEO deployments, prioritizing system scalability may be more valuable than chasing minor accuracy enhancements.

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arXiv2026

Profit-Driven Pricing and SLA-Aware Reserve Sizing for Multi-Tenant Satellite O-RAN Services

This paper studies a multi-tenant resource allocation problem in a satellite open radio access network (O-RAN) wholesale setting, where heterogeneous traffic classes share a time-varying limited satellite capacity over a finite planning horizon. We formulate a joint pricing and reserve allocation problem from a service provider perspective, where tenant-specific demand exhibits price elasticity and stochastic service requirements subject to strict service-level agreement (SLA) constraints, leading to a coupled economic and reliability-driven bottleneck. A deterministic reformulation is adopted to approximate probabilistic SLA requirements through tractable margin constraints, enabling coordinated control of horizon-wide contract prices and time-varying reserves. The resulting problem is non-convex due to interdependent decisions across tenants, time windows, and service classes. To address this, an alternating optimization (AO) scheme is developed separating pricing and allocation decisions while preserving feasibility and SLA guarantees. Numerical results show that the proposed method achieves near-optimal profit within approximately $1\%$ of a global benchmark, while reducing runtime by up to $22\times$. In contrast, considered baseline schemes incur profit losses exceeding $15\%$ or fail to satisfy SLA constraints. The proposed approach consistently maintains non-positive empirical SLA gaps and achieves up to $30\%$ higher resource utilization than a price-optimization baseline without adaptive reserve control. These results demonstrate that joint economic and resource control enables the provider to efficiently exploit scarce satellite network capacity with reliable service delivery and scalable computation.

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arXiv2026

Space-Based GNSS Radio Frequency Interference Detection Evaluation Through Multi-Satellite Data Integration

Space-based GNSS reflectometry (GNSS-R) can detect terrestrial radio frequency interference (RFI) through elevated noise power in delay-Doppler map forbidden zones. This study evaluates how constellation size affects detection performance using Level 1 delay-Doppler observations from seven CYGNSS spacecraft collected over three months from the NASA PO.DAAC archive. Four metrics are analysed: detection latency, spatial coverage, spatial coherence, and persistence monitoring reliability. Results show that the full seven-satellite constellation reduces median detection latency by a factor of 4.7 compared with a single satellite and increases interception probability for a 5-minute emission from 2\% to 11.5\%. Median footprint revisit time improves from 5.8 hours to under 2.0 hours. Spatial coherence analysis indicates that a single satellite leaves up to 72\% of source structure unresolved. Persistence monitoring confirms interference onset 39 days earlier than single-satellite deployment. The largest gains occur between one and three satellites, establishing three satellites as the minimum effective constellation size.

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arXiv2026

Burst Aware Forecasting of User Traffic Demand in LEO Satellite Networks

In Low Earth Orbit (LEO) satellite networks, Beam Hopping (BH) technology enables the efficient utilization of limited radio resources by adapting to varying user demands and link conditions. Effective BH planning requires prior knowledge of upcoming traffic at the time of scheduling, making forecasting an important sub-task. Forecasting becomes particularly critical under heavy load conditions where an unexpected demand burst combined with link degradation may cause buffer overflows and packet loss. To address this challenge, we propose a burst aware forecasting solution. This challenge may arise in a wide range of wireless networks; therefore, the proposed solution is broadly applicable to settings characterized by bursty traffic patterns where accurate demand forecasting is essential. Our approach introduces three key enhancements to a transformer architecture: (i) a distance from the last burst embedding to capture burst proximity, (ii) two additional linear layers in the decoder to forecast both upcoming bursts and their relative impact, and (iii) use of an asymmetric cost function during model training to better capture burst dynamics. Empirical evaluations in an Earth-fixed cell under high-traffic demand scenario demonstrate that the proposed model reduces prediction error by up to 94% at a one-step horizon and maintains the ability to accurately capture bursts even near the end of longer prediction horizons following Mean Square Error (MSE) metric.

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arXiv2026

A Novel Piecewise Atmospheric Attenuation Model for Free Space Optical Links in Vertical Heterogeneous Networks

Free-space optical (FSO) communication is emerging as a key backhaul technology for next-generation vertical heterogeneous networks (VHetNets), whose architecture spans satellites, high-altitude platform stations (HAPS), unmanned aerial vehicles (UAVs), and terrestrial nodes. Along these vertical and slant paths, optical beams traverse successive atmospheric layers that may contain clouds, fog, rain, and aerosols, conditions that conventional single-coefficient Beer-Lambert models typically handle only in isolation. Instead of such simplified formulas, we present a unified attenuation model that incorporates aerosols, fog, rain, cloud layers, and drizzle, accounts for the zenith angle, and provides a holistic estimate of the cumulative power loss across atmospheric layers. Numerical results show several-decibel attenuation variations across representative weather scenarios, while the difference between the proposed model predictions and the layer-resolved MODTRAN simulations remains within 1 dB, thereby validating the accuracy of the proposed model and its practical relevance for VHetNet link-budget studies.

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arXiv2026

Resilience Through Escalation: A Graph-Based PACE Architecture for Satellite Threat Response

Modern satellite systems face increasing operational risks from jamming, cyberattacks, and electromagnetic disruptions in contested space environments. Traditional redundancy strategies often fall short against such dynamic and multi-vector threats. This paper introduces a resilience-by-design framework grounded in the PACE methodology, which stands for Primary, Alternate, Contingency, and Emergency, originally developed for tactical communications in military operations. It adapts this framework to satellite systems through a layered state-transition model informed by threat scoring frameworks such as CVSS, DREAD, and NASA's risk matrix. We define a dynamic resilience index to quantify system adaptability and implement three PACE variants including static, adaptive, and epsilon-greedy reward-optimized to evaluate resilience under diverse disruption scenarios. Results show that lightweight, decision-aware fallback mechanisms can substantially improve survivability and operational continuity for next-generation space assets.

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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

Online Convex Optimization for On-Board Routing in High-Throughput Satellites

The rise in low Earth orbit (LEO) satellite Internet services has led to increasing demand, often exceeding available data rates and compromising the quality of service. While deploying more satellites offers a short-term fix, designing higher-performance satellites with enhanced transmission capabilities provides a more sustainable solution. Achieving the necessary high capacity requires interconnecting multiple modem banks within a satellite payload. However, there is a notable gap in research on internal packet routing within extremely high-throughput satellites. To address this, we propose a real-time optimal flow allocation and priority queue scheduling method using online convex optimization-based model predictive control. We model the problem as a multi-commodity flow instance and employ an online interior-point method to solve the routing and scheduling optimization iteratively. This approach minimizes packet loss and supports real-time rerouting with low computational overhead. Our method is tested in simulation on a next-generation extremely high-throughput satellite model, demonstrating its effectiveness compared to a reference batch optimization and to traditional methods.

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arXiv2025

Connectivity-Aware Task Offloading for Remote Northern Regions: a Hybrid LEO-MEO Architecture

Arctic regions, such as northern Canada, face significant challenges in achieving consistent connectivity and low-latency computing services due to the sparse coverage of Low Earth Orbit (LEO) satellites. To enhance service reliability in remote areas, this paper proposes a hybrid satellite architecture for task offloading that combines Medium Earth Orbit (MEO) and LEO satellites. We develop an optimization framework to maximize task offloading admission rate while balancing the energy consumption and delay requirements. Accounting for satellite visibility and limited computing resources, our approach integrates dynamic path selection with frequency and computational resource allocation. Because the formulated problem is NP-hard, we reformulate it into a mixed-integer convex form using disjunctive constraints and convex relaxation techniques, enabling efficient use of off-the-shelf optimization solvers. Simulation results show that, compared to a standalone LEO network, the proposed hybrid LEO-MEO architecture improves the task admission rate by 15\% and reduces the average delay by 12\%. These findings highlight the architecture's potential to enhance connectivity and user experience in remote Arctic areas.

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arXiv2025

Advancing Lunar Communication through Inter-domain Space Networks and Dynamic Orchestration

The resurgent era of lunar exploration is defined by a strategic shift from temporary visits to a sustained international and commercial presence, resulting in an unprecedented demand for a robust and continuously available communication infrastructure. The conventional direct-to-Earth communication architecture relies on limited and oversubscribed deep space networks, which are further challenged by the radiative environment and insufficient visibility in certain areas of the cislunar domain. We address these issues by proposing a foundational move toward inter-domain space network cooperation by introducing architectures based on near space networks. They can directly service lunar surface users or, via cislunar relays, by forming a resilient and multi-layered communication backbone. First, we establish a unified link analysis framework incorporating frequently disregarded environmental factors, such as the Moon's variable illumination, to provide a high-fidelity performance evaluation. Second, we assess architectures' reliability based on the outage risk, essential for quantifying the operational robustness of communication links. Finally, to manage the inherent dynamism of architectures, we propose an inter-domain space digital twin: a dynamic decision-making engine that performs real-time analysis to autonomously select the best communication path, ensuring high and stable reliability while simultaneously optimizing power consumption. Overall, our paper provides a holistic architectural and conceptual management framework, emphasizing the necessity of lunar communications to support a permanent human and economic foothold on the Moon.

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arXiv2025

Forecasting Self-Similar User Traffic Demand Using Transformers in LEO Satellite Networks

In this paper, we propose the use of a transformer-based model to address the need for forecasting user traffic demand in the next generation Low Earth Orbit (LEO) satellite networks. Considering a LEO satellite constellation, we present the need to forecast the demand for the satellites in-orbit to utilize dynamic beam-hopping in high granularity. We adopt a traffic dataset with second-order self-similar characteristics. Given this traffic dataset, the Fractional Auto-regressive Integrated Moving Average (FARIMA) model is considered a benchmark forecasting solution. However, the constrained on-board processing capabilities of LEO satellites, combined with the need to fit a new model for each input sequence due to the nature of FARIMA, motivate the investigation of alternative solutions. As an alternative, a pretrained probabilistic time series model that utilizes transformers with a Prob-Sparse self-attention mechanism is considered. The considered solution is investigated under different time granularities with varying sequence and prediction lengths. Concluding this paper, we provide extensive simulation results where the transformer-based solution achieved up to six percent better forecasting accuracy on certain traffic conditions using mean squared error as the performance indicator.

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