IEEE Open Journal of the Communications Society最新文献

{"title":"Unified Spatial Analytical Framework for Confirmed Uplinks in Multi-Gateway LoRaWAN Under Duty-Cycle Constraints","authors":"Hong-Nhat Hoang;Kien Nguyen;Hiroo Sekiya;Jong-Deok Kim","doi":"10.1109/OJCOMS.2026.3667708","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3667708","url":null,"abstract":"Confirmed uplinks in LoRaWAN improve reliability by requiring acknowledgment feedback from the network. However, gateway constraints such as half-duplex operation and duty-cycle limitations often prevent timely acknowledgment delivery, triggering retransmissions and degrading performance. These challenges are compounded in multi-gateway deployments, where overlapping coverage, correlated reception, and spatial interference create tightly coupled uplink-downlink dynamics. While prior analytical models capture parts of this behavior, a unified framework that jointly accounts for multi-gateway topology, heterogeneous bi-directional traffic, and spatial interference remains limited. In this paper, we propose a Unified Spatial Analytical Framework for confirmed-uplinks in multi-gateway LoRaWAN under percentage-based duty-cycle constraints. The framework serves as a fast and scalable what-if diagnostic engine for network planning and configuration. It combines a hex-cell spatial abstraction with imperfect spreading-factor orthogonality and heterogeneous traffic classes (NAPP/PAPP/CAPP), and couples these with a bi-directional M/G/1/1 renewal model to capture gateway transmission availability. Validation against NS-3 shows high fidelity in predicting key performance indicators (uplink success, downlink success, and retransmission overhead), with errors below 10% at fine spatial resolution. Finally, diagnostic case studies reveal that performance collapse is primarily driven by downlink capacity starvation (rather than uplink collisions) and expose the limitation regimes and fairness trade-offs of representative mitigation levers, including load-balance downlink assignment, gateway densification, and downlink airtime reduction.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1901-1922"},"PeriodicalIF":6.3,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11408859","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scalable Deep Learning for RF Fingerprinting: The MADE Architecture for Robust Physical-Layer Device Identification","authors":"Nordine Quadar;Abdellah Chehri;Benoit Debaque","doi":"10.1109/OJCOMS.2026.3667450","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3667450","url":null,"abstract":"Radio Frequency (RF) fingerprinting stands out as a powerful technique for device identification, exploiting intrinsic hardware-induced signal variations that are difficult to mimic and thus provide a robust layer of security in wireless systems. In recent years, deep learning techniques have been increasingly integrated with RF fingerprinting to enhance device identification and strengthen security against spoofing and unauthorized access. Nevertheless, existing deep learning approaches face severe scalability challenges in large-scale wireless environments. Conventional classification-based frameworks are constrained by closed-set assumptions that require predefined device identities, thereby limiting adaptability to unknown devices and causing exponential performance degradation as device populations expand. These limitations undermine the practicality of current solutions in dynamic, heterogeneous deployments. This paper introduces the MAsked Denoising autoEncoder (MADE), a novel architecture designed to overcome these fundamental constraints through embedding-based similarity matching and dual-objective training. Beyond conventional approaches, MADE advances the field by enabling scalable and robust physical-layer device identification which is an essential prerequisite for authentication systems that can ensure that each device’s unique RF signature serves as a reliable basis for recognition even in large-scale and dynamic environments. The architecture integrates masked denoising autoencoder pre-training with transformer-based feature extraction, combining reconstruction learning with classification fine-tuning to support both supervised device identification and open-set recognition. Extensive experimental validation across WiFi (WiSIG) and UAV controller datasets demonstrates MADE’s effectiveness: 99.9% accuracy under optimal conditions, 92.9% cross-transmission generalization on UAV controllers, and controlled 5.0% performance degradation across 16-fold device scaling (5 to 80 devices). In addition, ablation analysis confirms that cyclostationary-based features, when combined with denoising, yield optimal performance. Finally, MADE achieves practical deployment feasibility with 12–15ms inference latency and sub-linear computational scaling, establishing a scalable foundation for next-generation wireless security applications.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1973-1993"},"PeriodicalIF":6.3,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11408831","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing the Achievable Sum-Rate in OFDM-Based Multi-User MIMO Systems Assisted by Multiple Beyond-Diagonal RISs","authors":"Diogo Mendes;Nuno Souto;João Pedro Pavia;João Silva","doi":"10.1109/OJCOMS.2026.3666997","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3666997","url":null,"abstract":"Massive multiple-input, multiple-output (MIMO) systems operating in the millimeter wave (mmWave) and terahertz (THz) frequency bands offer high data rates and spatial multiplexing, yet they face significant propagation challenges. Reconfigurable intelligent surfaces (RISs) and their different architectures have emerged as a promising solution to these challenges, having the potential to enhance system performance. This paper addresses the joint sum-rate maximization problem in multi-user, multi-stream, multi-carrier MIMO systems aided by multiple parallel RIS panels. To minimize the inter-user interference, we adopt a problem formulation that adds a regularization term. To solve the resulting problem, we then propose a regularized cyclic block proximal gradient (MU-RCBPG) algorithm, which can jointly optimize precoders and RIS phase shifts without increasing the complexity compared to traditional single-valued decomposition (SVD)-based methods. The resulting algorithm has a flexible design that allows it to support configurations with beyond-diagonal RIS (BD-RIS), conventional diagonal RIS (D-RIS), and active D-RIS. Numerical results demonstrate that the MU-RCBPG algorithm outperforms existing RIS-aided schemes in various scenarios.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1843-1860"},"PeriodicalIF":6.3,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11406146","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting Out-of-Order Control Messages in O-RAN: Dataset, Benchmarks, and Early-Warning Models","authors":"Hams Gelban;Rouaa Naim;Ahmed Badawy","doi":"10.1109/OJCOMS.2026.3666940","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3666940","url":null,"abstract":"The Open Radio Access Network (O-RAN) architecture introduces openness, programmability, and multi-vendor xApp innovation through the Near-Real-Time RAN Intelligent Controller (Near-RT RIC). However, this flexibility also increases the risk of control-plane inconsistencies, particularly out-of-order control messages (OOM) that disrupt state synchronization and degrade control efficiency. Despite their impact, OOM behaviors remain insufficiently examined, and no datasets exist for benchmarking detection mechanisms. In this work, we present the first reproducible framework for generating, tracing, and analyzing OOM events in O-RAN using an instrumented FlexRIC platform. We modify the E2 Application Protocol (E2AP) stack, message handlers, and routing logic to record fine-grained sequencing metadata, enabling construction of a ground-truth dataset containing over one hundred thousand OOM events across diverse stress scenarios involving multi-xApp, multi-node, and subscription-conflict conditions. Our Key Performance Indicator (KPI)-driven evaluation reveals consistent degradation patterns such as increasing processing cost, Stream Control Transmission Protocol (SCTP) backpressure, and RIC state corruption leading to termination under heavy load. We further benchmark classical baselines against sequence-aware models for automated OOM detection and early warning. We introduce a Temporal-Aware Attention Model with Hierarchical Feature Fusion (TAAM-HFF), which achieves high recall across both rare and common OOM scenarios, underscoring the importance of temporal modeling for detecting emerging control-plane instability. The resulting dataset and benchmarking framework provide a foundation for resilient xApp operation and robust O-RAN control-plane security.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1941-1957"},"PeriodicalIF":6.3,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11406132","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reducing the Energy Footprint of 5G: A gNB on MPSoC With Low-Power FFT Acceleration","authors":"Abdelghani Bourenane;Emilio Paolini;Nicola Andriolli;Luca Valcarenghi","doi":"10.1109/OJCOMS.2026.3666573","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3666573","url":null,"abstract":"Open, virtualized architectures are reshaping cellular networks, but purely software-based 5G deployments often struggle to meet strict real-time requirements. Thus, accelerators are often utilised even at the expense of a high energy consumption, in part due to the overhead introduced by host-to-device data transfers. In this paper, we propose and demonstrate an energy-efficient solution that accelerates Fast Fourier Transform computations by placing Low-PHY functions in an MPSoC FPGA. Moreover, by placing High-PHY layer functions in the CPU of the same MPSoC, we eliminate host-to-accelerator bottlenecks (e.g., accelerator to processor transfer time) that typically limit hardware speedups and contribute to unnecessary energy expenditure. Though implemented into a single MPSoC, the solution is compatible with Open RAN solutions, such as the one proposed by the Small Cell Forum in the 5G function application platform interface. Experimental results using OpenAirInterface demonstrate that the MPSoC-based approach achieves up to <inline-formula> <tex-math>$13times $ </tex-math></inline-formula> speedup compared to CPU-based implementations, particularly for larger Fast Fourier Transform sizes, which directly contributes to reduced energy consumption per processed task. Furthermore, a detailed power consumption analysis shows that, while the processing system remains a significant energy consumer, hardware acceleration enhances overall energy efficiency by shifting computationally intensive tasks to dedicated hardware optimized for parallel, energy-efficient processing, substantially reducing their energy consumption and paving the way for greener 5G deployments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1889-1900"},"PeriodicalIF":6.3,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11404157","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Resource Allocation in Relay-Assisted ISAC Systems via Deep Reinforcement Learning","authors":"Hamidreza Hojjati;Ali Rezapour;S. Mohammad Razavizadeh","doi":"10.1109/OJCOMS.2026.3666829","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3666829","url":null,"abstract":"This article studies joint beamforming and power allocation in a full-duplex decode-and-forward (FD-DF) relay-assisted monostatic integrated sensing and communication (ISAC) system. The base station performs monostatic sensing in the presence of clutter, residual self-interference (SI), and uplink transmissions, while it must satisfy downlink and uplink quality-of-service (QoS) requirements under total power constraints. Unlike prior FD-ISAC or relay-aided works that neglect monostatic sensing geometry, explicit clutter modeling, or dynamic environments, we formulate a nonconvex optimization problem capturing coupled beamforming vectors, user powers, and sensing signal covariance, with end-to-end DF-relay rate bottlenecks and SI-aware sensing signal-to-interference-plus-noise ratio (SINR) maximization. To support real-time adaptation in dynamic environments, we model the design as a Markov decision process (MDP) and apply a deep deterministic policy gradient (DDPG) algorithm with a tailored state representation (dynamic user/echo channels), continuous actions (beamformers and powers), and a constraint-aware reward that combines piecewise-linear sensing SINR gains above 5 dB with normalized QoS violation penalties. We study several system setups, including non-relay, half-duplex (HD) relay, and FD relay-assisted configurations, and show that the proposed FD relay-assisted ISAC scheme achieves better overall performance. Extensive simulations indicate that the proposed DDPG-based policy achieves comparable or improved average performance relative to a successive convex approximation (SCA) benchmark under the considered system settings, including up to 0.7 dB higher mean sensing SINR, substantially reduced performance variance under strong self-interference, and near-100% QoS satisfaction across diverse channel realizations, while enabling lower online inference complexity. These results highlight the practical effectiveness of deep reinforcement learning (DRL) for resource allocation in relay-enhanced ISAC networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1861-1872"},"PeriodicalIF":6.3,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11404128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Energy-Aware Framework for WSNs: Clustering, Routing, and Optimization Using PSO, Fuzzy Logic, and SOM","authors":"H. N. Vishwas;T. K. Ramesh","doi":"10.1109/OJCOMS.2026.3666185","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3666185","url":null,"abstract":"The recent applications of Wireless Sensor Networks (WSNs) have attracted significant interests because of their broad application in industrial automation, healthcare monitoring, and environmental monitoring. Nevertheless, a significant issue in WSNs is how to maximize the energy use to increase the lifetime of the network without compromises in data delivery. The classical methods of clustering and routing, e.g., LEACH and its derivatives, tend to consider the selection of cluster heads in isolation and the optimization of routing in isolation, which results in uneven energy consumption, untimely nodes death, and inefficiency of the network as a whole. Furthermore, a lot of current metaheuristic techniques cannot dynamically adjust to the evolving network conditions, which leads to poor energy management. This paper presents an integrated approach that combines Particle Swarm Optimization (PSO), Fuzzy Logic, and Self-Organizing Maps (SOM) to achieve energy conservating approach using clustering and routing in WSNs. PSO is used for optimizing the clustering process by selecting optimal cluster heads, thus ensuring balanced energy distribution and reducing communication overhead. Fuzzy Logic is utilized to dynamically adjust the parameters of the network, such as node reliability and transmission power, based on real-time conditions, ensuring efficient resource utilization. Additionally, SOM is used to self-organize the network, facilitating the identification of optimal paths for data routing, which minimizes energy consumption and maximizes network lifetime. Moreover, the proposed SOM guided approach introduces a cross-layer interaction mechanism which helps to enhance the clustering stability and energy balance resulting in improved network lifetime. The incorporation of these techniques delivers a robust and adaptive solution to the energy challenges in WSNs. Simulation results demonstrate that the suggested approach significantly outperforms traditional clustering and routing strategies, offering enhanced energy efficiency, better network lifetime, and reduced packet loss.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1827-1842"},"PeriodicalIF":6.3,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11400533","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Complexity and Low-Energy RNN-Enhanced Kalman Filter for Channel Estimation: A Case Study","authors":"Antoine Siebert;Bertrand Le Gal;Guillaume Ferre;Aurélien Fourny","doi":"10.1109/OJCOMS.2026.3665311","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3665311","url":null,"abstract":"In radiocommunications, multipath propagation often induces Inter-Symbol Interference (ISI), which requires accurate channel estimation and equalization. Neural networks have recently shown promising performance for this task but remain limited by their high computational cost and lack of interpretability, hindering their adoption in mission-critical systems. To overcome these limitations, this study proposes a hybrid estimation architecture that combines a linear Kalman filter with two lightweight neural networks that dynamically and deterministically adjust the noise covariance matrices of the filter, enabling efficient and explainable channel tracking. Experimental results show performance gains of up to 5 dB at low SNR and 3 dB at high SNR compared with the Least Squares (LS) algorithm. This low-dimensional design enables real-time deployment on energy-constrained programmable platforms. Implementations on an ARM Cortex-A72 CPU and Artix-7/Virtex-7 FPGAs demonstrate throughputs of 53.7 Mbps and 8-20 Mbps, respectively, with FPGA energy consumption over <inline-formula> <tex-math>$20times $ </tex-math></inline-formula> lower than on the CPU. These results confirm the effectiveness of the proposed NN-enhanced Kalman estimator for interpretable and energy-efficient channel estimation in embedded and defense applications.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"2119-2129"},"PeriodicalIF":6.3,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11397385","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectre-Fed: Evolving Federated Edge Intelligence From FedEdge-ID to Robust-Private IoT Intrusion Detection via Hybrid Adversarial Training","authors":"Saeed Ullah;Junsheng WU;Mian Muhammad Kamal;Mohammed K. Alzaylaee;Mohammad Alibakhshikenari","doi":"10.1109/OJCOMS.2026.3665325","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3665325","url":null,"abstract":"The growing number of Internet of Things (IoT) devices requires decentralized Edge Intelligence solutions. As the current FL-based IDS systems are decentralized solutions for privacy protection, face two major problems: 1) network traffic manipulation through adversarial evasion attacks 2) privacy threats from gradient-based inference attacks and 3) server-side Robustness issue. The current methods which use Differential Privacy (DP) or adversarial training result in 5-15% accuracy reduction which makes them unsuitable for deployment. The key novelty of our work is the integration of a novel dual-defense framework that uniquely reconciles the conflict between differential privacy noise and adversarial gradient requirements, effectively eliminating the conventional “accuracy tax” along with server-side Robust Aggregation. Our research develops an enhanced two-stage federated system which is robust and protects privacy while delivering secure IoT edge intelligence solutions. The core system FedEdge-ID provides 99.73% detection performance across different edge devices. Spectre-Fed enhances the FedEdge-ID framework via three key defenses: (1) Hybrid Loss Adversarial Training (<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>=0.5) to fortify decision boundaries against evasion, (2) Gradient-Guided Adaptive Privacy with decreasing noise injection (<inline-formula> <tex-math>$sigma _{0}$ </tex-math></inline-formula>=0.0005, <inline-formula> <tex-math>$gamma $ </tex-math></inline-formula> =0.95) for secure gradient updates, and (3) Robust Trimmed Mean Aggregation to counter Byzantine poisoning. Experiments demonstrate that Spectre-Fed’s client-side (Layer 1) defense achieves 99.72% clean accuracy with only a 0.01% utility loss versus the non-private baseline. It shows strong adversarial resilience, retaining 99.34% accuracy against FGSM attacks (<inline-formula> <tex-math>$epsilon $ </tex-math></inline-formula> =0.01), a mere 0.38% degradation from the clean state. When integrated with server-side Robust Aggregation (Layer 2), the system sustains 99.59% accuracy even under active label-flipping attacks from 20% of clients, while preserving high utility compared to the baseline. The system achieves optimal privacy-utility balance through its formal privacy protection and its ability to resist adversarial attacks which makes it suitable for zero-trust IoT systems.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1994-2012"},"PeriodicalIF":6.3,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11397383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Evaluation of LEO to HAP Communication Under Wobbling, Pointing Error, and Hardware Impairments","authors":"Yerulan Zikibayev;Behrouz Maham;Refik Caglar Kizilirmak;Hossein Safi;Dimitrios Zorbas","doi":"10.1109/OJCOMS.2026.3664668","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3664668","url":null,"abstract":"This study assesses the performance of communication links between low Earth orbit (LEO) satellites and high-altitude platforms (HAPs), which are critical for emerging global communication infrastructures. These links are inherently susceptible to various propagation and system-level impairments that degrade their overall effectiveness. We quantitatively investigate the impact of these impairments on link performance, focusing on two critical communication metrics: delay-outage probability and ergodic capacity. Our comprehensive modeling incorporates essential factors such as LEO satellite visibility, accounting for geometric constraints and elevation masks, and integrates the effects of transmit and receive hardware impairments (HWI), beamforming, wobbling dynamics, and pointing errors, Doppler shift alongside precisely characterized LEO orbital parameters, and integration of multiple LEO satellites’ availability over the HAP. Closed-form analytical expressions for delay outage probability and ergodic capacity have been derived for both cache-assisted and cache-free relaying scenarios under the combined influence of hardware impairments, pointing errors, and wobbling. These analytical models are further validated and complemented by a rigorous Monte Carlo simulation methodology. Using the developed system model, we simulated the combined effects of the considered impairments across three carrier-frequency scenarios. The results demonstrate how varying the number of available satellites influences overall link performance and which impairments are more sensitive to increased satellite diversity. Not all impairments respond similarly to changes in satellite availability. For example, pointing error can be partially mitigated through multi-satellite connectivity, as greater spatial diversity reduces the likelihood of severe misalignment. In contrast, hardware impairments show significantly less improvement under increased satellite availability, since distortion noise remains inherent to the transceiver hardware and affects all links uniformly.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1568-1585"},"PeriodicalIF":6.3,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11395988","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}