arXiv subjects

Nurul Huda Mahmood

Publications and source records attributed to Nurul Huda Mahmood.

At least 19 recordsLinked to original sources

arXiv2026

Performance Analysis of HAPS-Assisted Downlink RSMA under Orthogonal and Full Frequency Reuse

This paper analyzes the outage performance of a high-altitude platform station (HAPS)-assisted downlink employing rate-splitting multiple access (RSMA). A realistic link budget accounting for free-space path loss, rain attenuation, and atmospheric absorption is combined with elevation-angle-dependent shadowed Rician fading. Closed-form outage probability and throughput expressions are derived for orthogonal frequency allocation and full frequency reuse, where the aggregate inter-beam interference is approximated by a moment-matched Gamma random variable, yielding a tractable finite-sum expression via its Laplace transform. The analytical expressions are validated through Monte Carlo simulations, showing close agreement. Numerical results show that the optimal common stream power allocation depends on the transmit power, while inter-beam interference under frequency reuse introduces an outage floor absent under orthogonal allocation, providing practical design insights for HAPS-RSMA systems

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arXiv2026

Outage Analysis of RSMA-Enabled Terrestrial Users Under Co-Channel Interference from HAPS

The coexistence of terrestrial networks (TNs) with high-altitude platform station (HAPS)-based non-terrestrial networks (NTNs) is a promising approach for extending 6G connectivity, but the resulting cross-network interference can significantly affect TN reliability. This paper investigates an rate-splitting multiple access (RSMA)-based TN operating under interference from a multibeam HAPS-NTN system. The desired TN links are modeled using Nakagami-m fading, while the aggregate HAPS interference is characterized by shadowed-Rician fading. Closed-form outage probability expressions are derived by characterizing the aggregate interference and accounting for both perfect and imperfect successive interference cancellation (SIC). Monte Carlo simulations are used to verify the analytical results and to examine the effects of HAPS transmit power, the number of interfering beams, and SIC imperfections. The results demonstrate that the interference-to-signal power scaling is a key factor determining TN outage behavior, with jointly scaled HAPS interference leading to interference-limited performance. Moreover, RSMA provides improved outage performance compared with non-orthogonal multiple access (NOMA), particularly in the presence of HAPS interference

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arXiv2026

Enhancing SDVN Performance via Policy-Driven Lightweight Control-Plane Resizing Strategies

Software-defined vehicular networks (SDVNs) under high mobility and fluctuating traffic demand offer programmable, centralized control for latency-sensitive intelligent transportation systems. However, data-plane Quality of Service (QoS) is often degraded by control-plane overload due to frequent handovers and dense vehicle-to-infrastructure (V2I) contacts. To address this, we propose two lightweight mechanisms for low-latency control-plane resizing in multi-controller SDVNs. The first - \textit{Control-plane Centric Control-plane Resizing Mechanism} - proactively offloads roadside units from overloaded controllers to underloaded or idle ones when a predefined load threshold is exceeded, preventing prolonged overload with minimal decision latency. The second - \textit{Data-plane Centric Control-plane Resizing Mechanism} - triggers resizing based on observable data-plane QoS degradation, such as average round-trip time exceeding a QoS threshold, aligning control-plane adaptation with V2I service experience. Both mechanisms are implemented and evaluated on Mininet-WiFi emulation testbeds with realistic worst-case vehicles mobility. Compared to fixed single-controller and static multi-controller benchmarks, the proposed algorithms significantly reduce end-to-end delay and packet loss while improving load balancing rate.

cs.NI↗

arXiv2026

Predicting Train Delays in Finland Using Machine Learning and Weather Data

Reliable railway operations depend increasingly on real-time environmental intelligence delivered through wireless sensor infrastructures, a capability that 6G networks will substantially enhance through integrated sensing and edge computing. Adverse weather, particularly in Arctic regions with extreme temperatures and heavy precipitation, remains a leading cause of train delays, yet most prediction approaches rely on raw meteorological inputs without exploiting domain-informed feature engineering. This paper investigates machine learning for train delay prediction using the Finland Integrated Train-Weather (FI-TW) dataset, which fuses railway operational records with observations from the Finnish Meteorological Institute's nationwide sensor network of approximately 200 stations communicating over wireless links. We evaluate three feature configurations using XGBoost at Oulu central station (101,146 observations): full weather features, instant weather observations only, and derived weather category scenarios. The category-based approach, employing hierarchical classifications such as Blizzard, Heavy Snow, and Extreme Cold, achieved an R^2 of 0.78, root mean squared error of 8.5 minutes, and mean absolute error of 3.7 minutes, representing an 11% R^2 improvement and 10% error reduction over alternative configurations. These results demonstrate that compact, domain-informed features derived from sensor streams outperform raw meteorological observations, offering bandwidth-efficient representations suitable for edge deployment over current and emerging wireless infrastructures.

cs.AI↗

arXiv2026

Performance Analysis of LoRa and LR-FHSS in 3GPP LEO Satellite and HAPS Channel Scenarios for Remote Area IoT Coverage

In this paper, we analyze and compare the performance of LoRa and LR-FHSS in a remote area Internet-of-Things (IoT) connectivity scenario using a low-earth orbit (LEO) satellites and high-altitude platform station (HAPS)-mounted gateway. We conducted a link budget analysis to evaluate communication stability to assess the ability of the technologies to support the different distances and behaviors using a 3GPP channel model for non-terrestrial networks. Several parameters are considered, such as altitude, minimum elevation angle as well as the impact of different types of loss, such as shadow and fast fading, ionosphere scintillation loss, for example. Our findings highlight the performance gap between LR-FHSS and LoRa in terms of both connectivity. We also highlight how lower HAPS altitudes can benefit certain LoRa settings compared to LEO satellites. We show that HAPS can enable the use of lower LoRa spreading factors, which improves scalability and energy efficiency, while LR-FHSS and a higher spreading factor LoRa remain the solution for LEO satellites.

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arXiv2026

A Novel Geometry-Aware GPR-Based Energy-Efficient and Low-Overhead Channel Estimation Scheme

Accurate channel state information (CSI) acquisition under tight pilot and training-energy constraints is essential for next-generation wireless networks. In this work, we model the wireless channel as a proper complex Gaussian process over the transmit and receive antenna arrays, reducing pilot overhead and training energy by estimating the CSI from partial observations. We formulate the CSI acquisition problem as a highly underdetermined Bayesian linear inverse problem. We develop a Gaussian process regression (GPR) framework that reconstructs the full CSI from sparse and noisy observations by extrapolating to the unknown entries. To incorporate propagation information into the GPR prior, we introduce a novel array-geometry-based kernel and prove that it is Hermitian positive semidefinite. The proposed kernel better captures the channel spatial correlations through richer hyperparameters. Our GPR-based CSI extrapolation approach learns the channel hyperparameters online from sparse, noisy pilot measurements within each coherence block. Numerical results show that the proposed estimator reduces pilot overhead by up to 75 percent and total training energy by up to 93.75 percent, while maintaining lower normalized mean-square error and higher spectral efficiency in the low-to-moderate signal-to-noise-ratio regime.

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arXiv2026

FI-TW: An Open Train-Weather Dataset for Railway Delay Analysis in Finland

Train delays result from complex interactions between operational, technical, and environmental factors. While weather impacts railway reliability, particularly in Nordic regions, existing datasets rarely integrate meteorological information with operational train data. This study presents the first publicly available dataset combining Finnish railway operations with synchronized meteorological observations from 2018-2024. The dataset integrates operational metrics from Finland Digitraffic Railway Traffic Service with weather measurements from 209 environmental monitoring stations, using spatial-temporal alignment via Haversine distance. It encompasses 28 engineered features across operational variables and meteorological measurements, covering approximately 38.5 million observations from Finland's 5,915-kilometer rail network. Preprocessing includes strategic missing data handling through spatial fallback algorithms, cyclical encoding of temporal features, and robust scaling of weather data to address sensor outliers. Analysis reveals distinct seasonal patterns, with winter months exhibiting delay rates exceeding 25\% and geographic clustering of high-delay corridors in central and northern Finland. Furthermore, the work demonstrates applications of the data set in analysing the reliability of railway traffic in Finland. A baseline experiment using XGBoost regression achieved a Mean Absolute Error of 2.73 minutes for predicting station-specific delays, demonstrating the dataset's utility for machine learning applications. The dataset enables diverse applications, including train delay prediction, weather impact assessment, and infrastructure vulnerability mapping, providing researchers with a flexible resource for machine learning applications in railway operations research.

cs.LG↗

arXiv2026

On the Impact of Electromagnetic Interference and Inter-RIS Reflections in Indoor Factory Local 6G Networks

The Sixth Generation (6G) radio technology is expected to include local 6G networks as a special use case, extending the capabilities of `generic' 6G networks towards more demanding performance requirements. Reconfigurable intelligent surfaces (RISs) offer a novel paradigm for next-generation wireless communications, especially in the context of local 6G networks, enabling advanced signal propagation control through intelligent phase-shift configurations. However, in practical deployments, their performance can be adversely affected by electromagnetic interference (EMI) from external sources and inter-RIS reflections (IRR) caused by signal reflections between multiple colocated RIS units. This paper presents a comprehensive analysis of the joint impact of EMI and IRR in a multi-RIS multi-cell system deployed within an indoor factory environment. A detailed evaluation study is first carried out to investigate their impact on system performance. System-level simulations demonstrate that the joint impact of EMI and IRR degrades system performance more significantly than their individual effects, particularly as RIS dimensions and transmit power increase. To address these adverse effects, an alternate optimization algorithm using the Riemannian conjugate gradient method is then proposed. The novel algorithm optimizes the phase shifts of the RIS elements considering the spatial correlation among their associated channels, and is found to provide up to several orders of magnitude gains in terms of the system sum rate and the outage probability.

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arXiv2026

Improved GPR-Based CSI Acquisition via Spatial-Correlation Kernel

Accurate channel estimation with low pilot overhead and computational complexity is key to efficiently utilizing multi-antenna wireless systems. Motivated by the evolution from purely statistical descriptions toward physics- and geometry-aware propagation models, this work focuses on incorporating channel information into a Gaussian process regression (GPR) framework for improving the channel estimation accuracy. In this work, we propose a GPR-based channel estimation framework along with a novel Spatial-correlation (SC) kernel that explicitly captures the channel's second-order statistics. We derive a closed-form expression of the proposed SC-based GPR estimator and prove that its posterior mean is optimal in terms of minimum mean-square error (MMSE) under the same second-order statistics, without requiring the underlying channel distribution to be Gaussian. Our analysis reveals that, with up to 50% pilot overhead reduction, the proposed method achieves the lowest normalized mean-square error, the highest empirical 95% credible-interval coverage, and superior preservation of spectral efficiency compared to benchmark estimators, while maintaining lower computational complexity than the conventional MMSE estimator.

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arXiv2026

Rethinking Passive RIS: Finite Blocklength Reliability Analysis Under Thermal Noise

Short-packet communication alters the fundamental performance limits of reconfigurable intelligent surface (RIS)-assisted systems, making conventional analyses based on the infinite blocklength regime insufficient. This work investigates RIS-assisted transmission in the finite blocklength (FBL) regime while explicitly incorporating thermal noise generated by passive RIS elements, an effect commonly neglected in existing models. A unified analytical framework is developed to characterize the block-error rate (BLER), its asymptotic behavior, and the resulting goodput under both uniform and non-uniform RIS reflection coefficients. Our results show that ignoring RIS thermal noise leads to a pronounced overestimation of reliability with the mismatch increasing as the number of reflecting elements grows. Furthermore, increasing the RIS size does not always improve performance, particularly in the low transmit power regime where accumulated noise becomes dominant. Overall, the results highlight fundamental limitations of idealized RIS models and demonstrate the need for incorporating thermal noise for accurate system evaluation.

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arXiv2026

Passive RIS Is Not Silent: Revisiting Performance Limits Under Thermal Noise

Reconfigurable intelligent surfaces (RISs) have emerged as a promising solution for enabling energy-efficient and flexible spectrum usage in wireless communication, particularly in the context of sixth-generation (6G) networks. While passive RIS architectures are widely regarded as virtually noiseless due to the lack of active components, this idealized assumption can lead to misleading performance evaluations. In this paper, we revisit this assumption and demonstrate that the thermal noise generated by passive RIS elements, though often neglected, can significantly affect system performance. We propose a tractable approximated analytical framework that incorporates RIS-induced thermal noise into the system and derive closed-form expressions for key performance metrics, such as outage probability and throughput. Simulation results validate our approximated analysis and highlight the substantial performance discrepancies that arise when RIS thermal noise is ignored. Our results offer valuable insights into the trade-offs between receiver and RIS noise, guiding the development of robust and efficient 6G communication systems.

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arXiv2026

A Novel Reinforcement Learning Based Framework for Scalable MIMO Interference Alignment

Interference alignment (IA) is a widely recognized approach for mitigating inter-cell interference in multi-user multiple-input multiple-output (MIMO) networks. Despite its effectiveness, practical deployment remains constrained by two major challenges, i.e., the need for global channel state information (CSI) at each transmitter and the complexity of deriving closed-form solutions for intricate MIMO systems. This work aims to maximize network throughput by effectively mitigating interference using an IA-inspired learning algorithm that addresses its aforementioned challenges. First, we propose a predictive, transformer-based IA framework that estimates CSI to reduce signaling overhead in small-scale MIMO systems. Next, we formulate the IA problem as a multi-objective optimization problem based on subspace coordination and develop two reinforcement learning-based algorithms to enhance the scalability of IA in large-scale MIMO systems. Simulation results demonstrate that the proposed methods significantly outperform conventional baselines with up to 30% average user throughput gains over the best performing baseline.

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arXiv2026

Impact of CSIR, SIC, and Hardware Impairments on the Ergodic Rate of Downlink RSMA

This work investigates the ergodic rate performance analysis of rate-splitting multiple access (RSMA) in a downlink communication system under practical impairments. Closed-form expressions are derived for key performance metrics such as ergodic rate, energy efficiency, sum-rate, and Jains fairness index, capturing the joint effects of imperfect channel state information at the receiver (CSIR), imperfect successive interference cancellation (SIC), and hardware impairments. Numerical simulations validate the accuracy of the analytical expressions and reveal several insightful trends. At low transmit powers, imperfect CSIR is the dominant performance-limiting factor, followed by hardware impairments and imperfect SIC. However, as the transmit power increases, hardware impairments become the primary bottleneck, with the impact of imperfect CSIR gradually diminishing, and imperfect SIC becoming a more prominent bottleneck. Moreover, RSMA consistently outperforms non-orthogonal multiple access (NOMA) in terms of ergodic rate, fairness, and sum-rate, even under severe non-idealities. These findings underscore the importance of incorporating fairness as a core design objective alongside rate and energy efficiency, positioning RSMA as a robust and strong multiple access candidate for next-generation wireless networks.

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arXiv2026

RSMA-Aided Full-Duplex Networks Under Imperfect CSI and SIC: Performance Evaluation

This work investigates a full-duplex (FD)-enhanced Rate-Splitting Multiple Access (RSMA) system under practical constraints, including imperfect channel state information (CSI) and successive interference cancellation (SIC). We derive closed-form expressions for key performance metrics, such as outage probability and throughput, for both uplink and downlink users. The analysis considers co-channel interference (CCI) from uplink to downlink users and models the self-interference (SI) channel as a random variable. Monte Carlo simulations validate the analytical results and highlight the impact of system imperfections on RSMA-FD performance. At low transmit power, imperfect CSI significantly affects the system, though this effect weakens as power increases. In contrast, imperfect SIC becomes more detrimental at high transmit power, causing severe degradation. Additionally, neglecting CCI and assuming perfect SI cancellation leads to substantial overestimation of performance. Lastly, we demonstrate that the SI cancellation factor must be carefully selected to suppress interference effectively. Otherwise, a poor choice limits the full potential of FD technology.

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arXiv2026

Integrating HAPS, LEO, and Terrestrial Networks: A Cost-Performance Study for IoT Connectivity

This work evaluates the potential of High-Altitude Platform Stations (HAPS) and Low Earth Orbit (LEO) satellites as alternative or complementary systems to enhance Internet of Things (IoT) connectivity. We first analyze the transmission erasure probability under different connectivity configurations, including only HAPS or LEO satellites, as well as hybrid architectures that integrate both aerial/spatial and terrestrial infrastructures. To make the analysis more realistic, we considered movement of LEO satellites regarding a fixed region, elevation angle between gateway and devices, and different fading models for terrestrial and non-terrestrial communication. We also analyze LR-FHSS (Long-Range Frequency Hopping Spread Spectrum) random access uplink technology as a potential use case for IoT connectivity, showing the scalability impact of the scenarios. The simulation results demonstrate that HAPS can effectively complement sparse terrestrial networks and improve the performance of satellite-based systems in specific scenarios. Furthermore, considering the deployment and operational costs, respectively, CAPEX and OPEX, the economic analysis reveals that although HAPS exhibits higher costs, these remain within a comparable order of magnitude to LEO and terrestrial deployments. In addition, specific use cases, such as natural disasters, transform HAPS into a competitive technology for conventional infrastructures.

cs.NI↗

arXiv2026

Dependable Connectivity for Industrial Wireless Communication Networks

Dependability - a system's ability to consistently provide reliable services by ensuring safety and maintainability in the face of internal or external disruptions - is a fundamental requirement for industrial wireless communication networks (IWCNs). While 5G ultra-reliable low-latency communication (URLLC) addresses some aspects of this challenge, its evolution toward holistic dependability in 6G must encompass reliability, availability, safety, and security. This paper provides a comprehensive framework for dependable IWCNs, bridging theory and practice. We first establish the theoretical foundations of dependability, including outlining its key attributes and presenting analytical tools to study it. Next, we explore practical enablers, such as adaptive multiple access schemes leveraging real-time monitoring and time-sensitive networking to ensure end-to-end determinism. A case study demonstrates how intelligent wake-up protocols improve event detection probability by orders of magnitude compared to conventional duty cycling. Finally, we outline open challenges and future directions for a 6G-driven dependable IWCN.

cs.NI↗

arXiv2025

Low-Overhead CSI Prediction via Gaussian Process Regression

Accurate channel state information (CSI) is critical for current and next-generation multi-antenna systems. Yet conventional pilot-based estimators incur prohibitive overhead as antenna counts grow. In this paper, we address this challenge by developing a novel framework based on Gaussian process regression (GPR) that predicts full CSI from only a few observed entries, thereby reducing pilot overhead. The correlation between data points in GPR is defined by the covariance function, known as kernel. In the proposed GPR-based CSI estimation framework, we incorporate three kernels, i.e., radial basis function, Mat'ern, and rational quadratic, to model smooth and multi-scale spatial correlations derived from the antenna array geometry. The proposed approach is evaluated across two channel models with three distinct pilot probing schemes. Results show that the proposed GPR with 50% pilot saving achieves the lowest prediction error, the highest empirical 95% credible-interval coverage, and the best preservation of spectral efficiency relative to the benchmarks.

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arXiv2025

Resilient-By-Design: A Resiliency Framework for Future Wireless Networks

Our future society will be increasingly digitalized, hyper-connected and globally data driven. The sixth generation (6G) and beyond 6G wireless networks are expected to bridge the digital and physical worlds by providing wireless connectivity as a service to different vertical sectors, making the society increasingly dependent on wireless networks. Thus, any disruption to these networks would have a significant impact with far-reaching consequences. Disruptions can occur for a variety of reasons, including planned outages, natural disasters, and deliberate cybersecurity attacks. Resilience against such disruptions is expected to be one of the most important defining features of future wireless networks. This paper first discusses a generic framework for designing future resilient wireless networks. A novel resilient-by-design framework consisting of four building blocks, namely predict, preempt, protect and progress, is then proposed as a specific example.

cs.NI↗