The current design of 5G Core Network (5G CN) adopts a cloud-native service-based architecture, where Network Functions (NFs) are exposed as services that can be dynamically composed and managed to achieve high flexibility. These NFs are interconnected via interfaces that Standardization Development Organizations (SDOs) like 3GPP have standardized. The complexity of the interconnections and data sensitivity make these interfaces vulnerable. In this letter, we advocate the use of extended Berkeley Packet Filter (eBPF) to monitor the 5G CN interfaces activities. eBPF programs run in kernel space of the host machine, thereby providing visibility of all programs and this is especially convenient for observability of 5G CN NFs. With a specific use case implemented in Open Air Interface (OAI), we demonstrate the benefits of the eBPF framework to identify session deletion attacks and mitigate associated risks.
{"title":"Monitoring 5G Core Networks Vulnerabilities With eBPF","authors":"Gabriele Nunziati;Claudio Fiandrino;Luca Foschini;Paolo Bellavista","doi":"10.1109/LNET.2025.3577184","DOIUrl":"https://doi.org/10.1109/LNET.2025.3577184","url":null,"abstract":"The current design of 5G Core Network (5G CN) adopts a cloud-native service-based architecture, where Network Functions (NFs) are exposed as services that can be dynamically composed and managed to achieve high flexibility. These NFs are interconnected via interfaces that Standardization Development Organizations (SDOs) like 3GPP have standardized. The complexity of the interconnections and data sensitivity make these interfaces vulnerable. In this letter, we advocate the use of extended Berkeley Packet Filter (eBPF) to monitor the 5G CN interfaces activities. eBPF programs run in kernel space of the host machine, thereby providing visibility of all programs and this is especially convenient for observability of 5G CN NFs. With a specific use case implemented in Open Air Interface (OAI), we demonstrate the benefits of the eBPF framework to identify session deletion attacks and mitigate associated risks.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 3","pages":"220-223"},"PeriodicalIF":0.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-02DOI: 10.1109/LNET.2025.3575716
Akanksha Sharma;Sharda Tripathi
Recently, Rate-Splitting Multiple Access (RSMA) has emerged as a powerful paradigm for meeting the demanding performance requirements of 6G wireless networks through non-orthogonal high-rate data transmission. However, uplink access in RSMA necessitates optimizing the decoding order, which can lead to significant search latency. Besides, the process overlooks the Quality-of-Service (QoS) constraints of different traffic types, making current RSMA methods inadequate, especially for low-latency communication. Here, we address this issue by proposing QORA, short for QoS-aware One-shot Rate-splitting multiple Access, a multi-agent Deep Q-Network (DQN) framework that leverages a novel QoS-aware transmit power allocation and decoding order policy in uplink RSMA that achieves remarkable performance improvements while maintaining low latency and high admission rates.
{"title":"A Quality-of-Service-Centric Uplink Rate-Splitting Approach for Next-Generation Multiple Access","authors":"Akanksha Sharma;Sharda Tripathi","doi":"10.1109/LNET.2025.3575716","DOIUrl":"https://doi.org/10.1109/LNET.2025.3575716","url":null,"abstract":"Recently, Rate-Splitting Multiple Access (RSMA) has emerged as a powerful paradigm for meeting the demanding performance requirements of 6G wireless networks through non-orthogonal high-rate data transmission. However, uplink access in RSMA necessitates optimizing the decoding order, which can lead to significant search latency. Besides, the process overlooks the Quality-of-Service (QoS) constraints of different traffic types, making current RSMA methods inadequate, especially for low-latency communication. Here, we address this issue by proposing QORA, short for QoS-aware One-shot Rate-splitting multiple Access, a multi-agent Deep Q-Network (DQN) framework that leverages a novel QoS-aware transmit power allocation and decoding order policy in uplink RSMA that achieves remarkable performance improvements while maintaining low latency and high admission rates.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 3","pages":"195-199"},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-22DOI: 10.1109/LNET.2025.3563434
Daniel Commey;Sena G. Hounsinou;Garth V. Crosby
The growth of IoT in healthcare generates massive sensitive data. This necessitates a secure and privacy-preserving distributed network to transport and process the data. Federated learning (FL) offers privacy-preserving model training, while blockchain ensures data integrity through transparency and immutability. Yet, quantum computing threatens cryptographic schemes like ECDSA, endangering long-term data confidentiality. This paper integrates post-quantum cryptography (PQC) with blockchain-based FL for healthcare analytics. We evaluate three signature-based PQC algorithms—Falcon, Dilithium (ML-DSA-65), and SPHINCS+ (SPHINCS+-SHA2-128s)—to assess their impact on blockchain transaction costs and latency. Benchmarks on a local Ethereum testnet show that lattice-based schemes, particularly ML-DSA-65, achieve verification under 10 ms with acceptable gas costs. Our findings indicate that smart contract signature verification is the primary gas consumer, offering guidelines for deploying quantum-resistant FL systems. These findings justify and potentially create a foundation for building complete systems that integrate PQC into Blockchain-based FL systems.
{"title":"Post-Quantum Secure Blockchain-Based Federated Learning Framework for Healthcare Analytics","authors":"Daniel Commey;Sena G. Hounsinou;Garth V. Crosby","doi":"10.1109/LNET.2025.3563434","DOIUrl":"https://doi.org/10.1109/LNET.2025.3563434","url":null,"abstract":"The growth of IoT in healthcare generates massive sensitive data. This necessitates a secure and privacy-preserving distributed network to transport and process the data. Federated learning (FL) offers privacy-preserving model training, while blockchain ensures data integrity through transparency and immutability. Yet, quantum computing threatens cryptographic schemes like ECDSA, endangering long-term data confidentiality. This paper integrates post-quantum cryptography (PQC) with blockchain-based FL for healthcare analytics. We evaluate three signature-based PQC algorithms—Falcon, Dilithium (ML-DSA-65), and SPHINCS+ (SPHINCS+-SHA2-128s)—to assess their impact on blockchain transaction costs and latency. Benchmarks on a local Ethereum testnet show that lattice-based schemes, particularly ML-DSA-65, achieve verification under 10 ms with acceptable gas costs. Our findings indicate that smart contract signature verification is the primary gas consumer, offering guidelines for deploying quantum-resistant FL systems. These findings justify and potentially create a foundation for building complete systems that integrate PQC into Blockchain-based FL systems.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 2","pages":"126-129"},"PeriodicalIF":0.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-16DOI: 10.1109/LNET.2025.3561336
Piotr Lechowicz;Carlos Natalino;Farhad Arpanaei;Stefan Melin;Renzo Diaz;Anders Lindgren;David Larrabeiti;Paolo Monti
Machine learning (ML) is emerging as a promising tool for estimating the Quality of Transmission (QoT) in optical networks, especially for unestablished lightpaths where traditional methods are limited. However, inaccuracies in ML-based QoT predictions—typically expressed in terms of generalized signal-to-noise ratio (GSNR)—can significantly affect network operation. Overestimation may lead to retransmissions due to overly aggressive modulation format choices, while underestimation results in underutilized spectral resources. To address this, we propose a novel ML architecture that incorporates an embedding layer for link-level features alongside path- and service-level inputs. Using data generated from an accurate analytical model, we show that our approach reduces prediction error by up to 34% compared to standard architectures. Simulated deployment scenarios further demonstrate operational benefits, with a 15.9% decrease in incorrect and a 34.8% reduction in overly conservative modulation format selections.
{"title":"Toward Better QoT Estimation: An ML Architecture With Link-Level Embedding Layers","authors":"Piotr Lechowicz;Carlos Natalino;Farhad Arpanaei;Stefan Melin;Renzo Diaz;Anders Lindgren;David Larrabeiti;Paolo Monti","doi":"10.1109/LNET.2025.3561336","DOIUrl":"https://doi.org/10.1109/LNET.2025.3561336","url":null,"abstract":"Machine learning (ML) is emerging as a promising tool for estimating the Quality of Transmission (QoT) in optical networks, especially for unestablished lightpaths where traditional methods are limited. However, inaccuracies in ML-based QoT predictions—typically expressed in terms of generalized signal-to-noise ratio (GSNR)—can significantly affect network operation. Overestimation may lead to retransmissions due to overly aggressive modulation format choices, while underestimation results in underutilized spectral resources. To address this, we propose a novel ML architecture that incorporates an embedding layer for link-level features alongside path- and service-level inputs. Using data generated from an accurate analytical model, we show that our approach reduces prediction error by up to 34% compared to standard architectures. Simulated deployment scenarios further demonstrate operational benefits, with a 15.9% decrease in incorrect and a 34.8% reduction in overly conservative modulation format selections.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 2","pages":"122-125"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10966418","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308547","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}
Pub Date : 2025-04-14DOI: 10.1109/LNET.2025.3560459
Chaouki Ben Issaid;Mehdi Bennis
This letter presents a novel decentralized matching algorithm (DEMA) for pairing data and algorithm providers in AI ecosystems. DEMA addresses scalability, stability, and matching utility challenges in large-scale environments. Formulated as a two-sided matching game, our decentralized solution enables autonomous decision-making based on local information. Simulations demonstrate DEMA‘s near-optimal matching quality and almost perfect stability. Furthermore, DEMA exhibits excellent scalability with execution times and memory usage growing much more slowly than centralized matching as the number of providers increases.
{"title":"A Decentralized Matching Theory Framework to Match Data and Algorithms Providers","authors":"Chaouki Ben Issaid;Mehdi Bennis","doi":"10.1109/LNET.2025.3560459","DOIUrl":"https://doi.org/10.1109/LNET.2025.3560459","url":null,"abstract":"This letter presents a novel decentralized matching algorithm (DEMA) for pairing data and algorithm providers in AI ecosystems. DEMA addresses scalability, stability, and matching utility challenges in large-scale environments. Formulated as a two-sided matching game, our decentralized solution enables autonomous decision-making based on local information. Simulations demonstrate DEMA‘s near-optimal matching quality and almost perfect stability. Furthermore, DEMA exhibits excellent scalability with execution times and memory usage growing much more slowly than centralized matching as the number of providers increases.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 2","pages":"140-144"},"PeriodicalIF":0.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964373","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308554","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}
Pub Date : 2025-03-30DOI: 10.1109/LNET.2025.3575096
Jason K. Bingham;Md Sadman Siraj;Eirini Eleni Tsiropoulou
Recently, Intelligent Reflecting Surfaces (IRSs) with controllable substructures have attracted attention due to their ability to manipulate electromagnetic wave reflections. A key benefit of IRSs is their capacity to enhance signal gain at the receiver. In letter, we propose a general channel gain model suitable for various wireless communication setups. We start with the gain models for the basic Single-Input Single-Output (SISO) case, progressing to the general model in the Multiple-Input Multiple-Output (MIMO) scenario. The models simplify for specific parameter choices. Also, we determine the optimal phase of IRS atoms for maximizing gain.
{"title":"Channel Gain Modeling Through an Intelligent Reflecting Surface","authors":"Jason K. Bingham;Md Sadman Siraj;Eirini Eleni Tsiropoulou","doi":"10.1109/LNET.2025.3575096","DOIUrl":"https://doi.org/10.1109/LNET.2025.3575096","url":null,"abstract":"Recently, Intelligent Reflecting Surfaces (IRSs) with controllable substructures have attracted attention due to their ability to manipulate electromagnetic wave reflections. A key benefit of IRSs is their capacity to enhance signal gain at the receiver. In letter, we propose a general channel gain model suitable for various wireless communication setups. We start with the gain models for the basic Single-Input Single-Output (SISO) case, progressing to the general model in the Multiple-Input Multiple-Output (MIMO) scenario. The models simplify for specific parameter choices. Also, we determine the optimal phase of IRS atoms for maximizing gain.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 3","pages":"200-204"},"PeriodicalIF":0.0,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-26DOI: 10.1109/LNET.2025.3573855
I Nyoman Apraz Ramatryana
This letter proposes a contention-based random access for air-to-ground communications, which is based on a slotted ALOHA protocol with a relay control scheme (RCS-ALOHA). In RCS-ALOHA, idle UAVs are exploited as relay UAVs. By encouraging relay operation with low transmit power, the proposed RCS-ALOHA increases the likelihood that distance UAVs will succeed. Each relay UAV is connected to some active UAVs and forwards information through fixed slots with scheduling. Next, a ground control station as the receiver implements iterative cancelation for the decoding process. The throughput of RCS-ALOHA is derived to validate the superiority of RCS-ALOHA over slotted ALOHA.
{"title":"Slotted ALOHA With Relay Control Scheme for Air-to-Ground Communications","authors":"I Nyoman Apraz Ramatryana","doi":"10.1109/LNET.2025.3573855","DOIUrl":"https://doi.org/10.1109/LNET.2025.3573855","url":null,"abstract":"This letter proposes a contention-based random access for air-to-ground communications, which is based on a slotted ALOHA protocol with a relay control scheme (RCS-ALOHA). In RCS-ALOHA, idle UAVs are exploited as relay UAVs. By encouraging relay operation with low transmit power, the proposed RCS-ALOHA increases the likelihood that distance UAVs will succeed. Each relay UAV is connected to some active UAVs and forwards information through fixed slots with scheduling. Next, a ground control station as the receiver implements iterative cancelation for the decoding process. The throughput of RCS-ALOHA is derived to validate the superiority of RCS-ALOHA over slotted ALOHA.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 3","pages":"181-184"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-22DOI: 10.1109/LNET.2025.3572513
Xujie Li;Fei Shao;Ying Sun;Haotian Li;Jiayi Huang
The identification of critical nodes in networks is of substantial practical significance. For instance, it can expedite information propagation within networks, target vulnerable links to enhance robustness, and optimize resource allocation by reducing redundancy and lowering costs. To improve the accuracy of critical node identification, we propose an algorithm that integrates complex networks, propagation models, and deep learning techniques. The algorithm generates low-complexity features that include the characteristics of nodes and their neighboring nodes. A ResNet-CBAM network is then designed to identify critical nodes. To assess node importance, a method has been proposed that considers both propagation range and propagation efficiency, using their product as the evaluation criterion. Experimental results show that, compared to various centrality-based algorithms and other deep learning methods, our proposed algorithm outperforms others in terms of recognition accuracy across different types of networks.
{"title":"Critical Nodes Identification Algorithm Based on ResNet-CBAM","authors":"Xujie Li;Fei Shao;Ying Sun;Haotian Li;Jiayi Huang","doi":"10.1109/LNET.2025.3572513","DOIUrl":"https://doi.org/10.1109/LNET.2025.3572513","url":null,"abstract":"The identification of critical nodes in networks is of substantial practical significance. For instance, it can expedite information propagation within networks, target vulnerable links to enhance robustness, and optimize resource allocation by reducing redundancy and lowering costs. To improve the accuracy of critical node identification, we propose an algorithm that integrates complex networks, propagation models, and deep learning techniques. The algorithm generates low-complexity features that include the characteristics of nodes and their neighboring nodes. A ResNet-CBAM network is then designed to identify critical nodes. To assess node importance, a method has been proposed that considers both propagation range and propagation efficiency, using their product as the evaluation criterion. Experimental results show that, compared to various centrality-based algorithms and other deep learning methods, our proposed algorithm outperforms others in terms of recognition accuracy across different types of networks.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 2","pages":"103-107"},"PeriodicalIF":0.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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