Pub Date : 2025-11-21DOI: 10.1109/OJCOMS.2025.3636436
Mohammad Cheraghinia;Eli De Poorter;Jaron Fontaine;Merouane Debbah;Adnan Shahid
Wireless Technology Recognition (WTR) and localization are essential in modern communication systems, enabling efficient spectrum usage, coexistence across diverse technologies, and accurate positioning in dynamic environments. Real-world deployments must handle signals from different sampling rates, capturing devices, frequency bands, and propagation conditions. Traditional methods, such as energy detection and conventional Deep Learning (DL) models like Convolutional Neural Networks (CNNs), often fail to generalize across unseen technologies, environments, or tasks. In this work, we introduce a Transformer-based foundation model for both WTR and localization, pre-trained in a self-supervised manner on large-scale unlabeled aciq and Channel Impulse Response (CIR) timeseries data. The model aims for reusability and generalizability compared to single-task architectures. It leverages input patching for computational efficiency and employs a two-stage pipeline: self-supervised pre-training to learn general-purpose representations, followed by lightweight fine-tuning for task-specific adaptation. This enables the model to generalize to new wireless technologies and unseen environments using minimal labeled samples. Evaluations across short-range and long-range datasets show superior accuracy in WTR (up to 99.99%), Line-Of-Sight (LOS) detection (up to 100%), and ranging error correction (reducing Mean Absolute Error (MAE) by up to 50%), all while maintaining low computational complexity. These results underscore the potential of a reusable wireless foundation model for multi-task applications with minimal retraining.
{"title":"A Foundation Model for Wireless Technology Recognition and Localization Tasks","authors":"Mohammad Cheraghinia;Eli De Poorter;Jaron Fontaine;Merouane Debbah;Adnan Shahid","doi":"10.1109/OJCOMS.2025.3636436","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3636436","url":null,"abstract":"Wireless Technology Recognition (WTR) and localization are essential in modern communication systems, enabling efficient spectrum usage, coexistence across diverse technologies, and accurate positioning in dynamic environments. Real-world deployments must handle signals from different sampling rates, capturing devices, frequency bands, and propagation conditions. Traditional methods, such as energy detection and conventional Deep Learning (DL) models like Convolutional Neural Networks (CNNs), often fail to generalize across unseen technologies, environments, or tasks. In this work, we introduce a Transformer-based foundation model for both WTR and localization, pre-trained in a self-supervised manner on large-scale unlabeled aciq and Channel Impulse Response (CIR) timeseries data. The model aims for reusability and generalizability compared to single-task architectures. It leverages input patching for computational efficiency and employs a two-stage pipeline: self-supervised pre-training to learn general-purpose representations, followed by lightweight fine-tuning for task-specific adaptation. This enables the model to generalize to new wireless technologies and unseen environments using minimal labeled samples. Evaluations across short-range and long-range datasets show superior accuracy in WTR (up to 99.99%), Line-Of-Sight (LOS) detection (up to 100%), and ranging error correction (reducing Mean Absolute Error (MAE) by up to 50%), all while maintaining low computational complexity. These results underscore the potential of a reusable wireless foundation model for multi-task applications with minimal retraining.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9879-9896"},"PeriodicalIF":6.3,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11264506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674732","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 : 2025-11-19DOI: 10.1109/OJCOMS.2025.3635533
Sandra Aladin;Lena Wosinska;Christine Tremblay
Fast and accurate estimation of lightpaths’ quality of transmission (QoT) is crucial for ensuring quality of service (QoS) and seamless operation in real-world optical networks. Machine learning (ML) algorithms are promising tools for QoT estimation of lightpaths before their establishment. In multi-domain optical networks, where learned QoT estimation models must be transferred between heterogeneous environments with limited target data, deep neural networks (DNNs) have shown promising results. However, DNN-based transfer learning (TL) approaches using fine-tuned artificial neural networks (ANNs) and convolutional neural networks (CNNs), offer limited interpretability. Consequently, little insight into the decision-making process is provided, and large labeled datasets as well as high computational resources are required, limiting their suitability for real-time, large-scale deployment in production networks. To address these challenges, we propose a novel lightweight and interpretable TL framework that integrates the Boruta-SHAP algorithm for automated feature selection (FS) together with two domain adaptation (DA) techniques: Feature Augmentation and Correlation Alignment. In contrast to the existing approaches based on DNN, our strategy leverages interpretable and efficient ML models to enhance the adaptability across diverse datasets in real-world network environments. We show that our random forest (RF)-based models achieve better performance than the ANN-based models, without sacrificing the classification accuracy. The FS via Boruta-SHAP allows for reducing dimensionality as well as training and inference times up to 70.68%, and 41.64%, respectively. Our proposed framework outperforms DA baseline models achieving 99.35% accuracy improvement in domain shift. Moreover, it offers 86% accuracy with a 99.83% reduction in the size of the target domain.
{"title":"Automated, Interpretable and Efficient ML Models for Real-World Lightpaths’ Quality of Transmission Estimation","authors":"Sandra Aladin;Lena Wosinska;Christine Tremblay","doi":"10.1109/OJCOMS.2025.3635533","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3635533","url":null,"abstract":"Fast and accurate estimation of lightpaths’ quality of transmission (QoT) is crucial for ensuring quality of service (QoS) and seamless operation in real-world optical networks. Machine learning (ML) algorithms are promising tools for QoT estimation of lightpaths before their establishment. In multi-domain optical networks, where learned QoT estimation models must be transferred between heterogeneous environments with limited target data, deep neural networks (DNNs) have shown promising results. However, DNN-based transfer learning (TL) approaches using fine-tuned artificial neural networks (ANNs) and convolutional neural networks (CNNs), offer limited interpretability. Consequently, little insight into the decision-making process is provided, and large labeled datasets as well as high computational resources are required, limiting their suitability for real-time, large-scale deployment in production networks. To address these challenges, we propose a novel lightweight and interpretable TL framework that integrates the Boruta-SHAP algorithm for automated feature selection (FS) together with two domain adaptation (DA) techniques: Feature Augmentation and Correlation Alignment. In contrast to the existing approaches based on DNN, our strategy leverages interpretable and efficient ML models to enhance the adaptability across diverse datasets in real-world network environments. We show that our random forest (RF)-based models achieve better performance than the ANN-based models, without sacrificing the classification accuracy. The FS via Boruta-SHAP allows for reducing dimensionality as well as training and inference times up to 70.68%, and 41.64%, respectively. Our proposed framework outperforms DA baseline models achieving 99.35% accuracy improvement in domain shift. Moreover, it offers 86% accuracy with a 99.83% reduction in the size of the target domain.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9785-9801"},"PeriodicalIF":6.3,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11261392","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612078","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}
This paper proposes a multi-agent deep reinforcement learning (DRL) framework based on proximal policy optimization (PPO) for joint resource optimization in full-duplex (FD) reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access (NOMA) integrated sensing and communication (ISAC) systems. The goal is to maximize the minimum beampattern gain under quality-of-service (QoS) constraints for both uplink (UL) and downlink (DL) users. The optimization jointly controls transmit beamforming, RIS phase shift, DL power allocation, and UL transmit power. A centralized training with decentralized execution approach is adopted, where two agents are defined: a DL agent responsible for DL beamforming, RIS configuration, and power allocation, and a UL agent responsible for uplink power control. Each agent interacts with the shared environment, which comprises the base station (BS), RIS, and users, and learns its optimal policy under time-varying channels and mutual interference. Simulation results demonstrate that the proposed multi-agent PPO (MA-PPO) significantly outperforms baseline methods, including single-agent PPO and heuristic schemes, in terms of convergence speed, sum-rate, and beampattern gain. Moreover, the MA-PPO method exhibits superior scalability and performance in FD mode over half-duplex (HD) counterparts under various user densities and RIS configurations, showcasing its effectiveness for real-time joint communication and sensing in next-generation wireless networks.
{"title":"Multi-Agent PPO-Based Resource Optimization for Full-Duplex RIS-Aided NOMA-ISAC Systems","authors":"Nonis Wara;Anal Paul;Keshav Singh;Aryan Kaushik;Wonjae Shin","doi":"10.1109/OJCOMS.2025.3635274","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3635274","url":null,"abstract":"This paper proposes a multi-agent deep reinforcement learning (DRL) framework based on proximal policy optimization (PPO) for joint resource optimization in full-duplex (FD) reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access (NOMA) integrated sensing and communication (ISAC) systems. The goal is to maximize the minimum beampattern gain under quality-of-service (QoS) constraints for both uplink (UL) and downlink (DL) users. The optimization jointly controls transmit beamforming, RIS phase shift, DL power allocation, and UL transmit power. A centralized training with decentralized execution approach is adopted, where two agents are defined: a DL agent responsible for DL beamforming, RIS configuration, and power allocation, and a UL agent responsible for uplink power control. Each agent interacts with the shared environment, which comprises the base station (BS), RIS, and users, and learns its optimal policy under time-varying channels and mutual interference. Simulation results demonstrate that the proposed multi-agent PPO (MA-PPO) significantly outperforms baseline methods, including single-agent PPO and heuristic schemes, in terms of convergence speed, sum-rate, and beampattern gain. Moreover, the MA-PPO method exhibits superior scalability and performance in FD mode over half-duplex (HD) counterparts under various user densities and RIS configurations, showcasing its effectiveness for real-time joint communication and sensing in next-generation wireless networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9802-9820"},"PeriodicalIF":6.3,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11261334","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612060","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 : 2025-11-19DOI: 10.1109/OJCOMS.2025.3635226
Boqing Zhou;Sujun Li;Decheng Miao
Replication nodes can compromise a network by not only stealing confidential data but also by selectively forwarding packets and injecting false data to the base station. This undermines the base station’s decision-making, ultimately allowing attackers to control the network. Scholars have proposed various solutions to deal with this attack. However, network performance is still susceptible to its impact because of their inherent limitations. In this paper, we introduce a novel authentication scheme. In this scheme, the network contains high-energy nodes, mobile sensor nodes (MSNs), and a base station. High energy nodes act as cluster heads. MSNs must be authenticated by a cluster head before they can obtain or provide data to the cluster head. Authentication between intra-cluster nodes relies on key information pre-distributed by the cluster head, leveraging a Bloom filter for efficient verification. Within a cluster, node $a$ will only forward node $b$ ’s data after node $b$ is authenticated by node $a$ . The analysis and simulation validate that the proposed scheme significantly enhances the network’s resilience against replication attacks. The communication probability between high-energy nodes is about 90%, and the probability that high-energy nodes can complete MSNs’ authentication within 1 hop is about 1; after introducing the bloom filter scheme within the cluster, the storage overhead of MSNs can be reduced by 90%, and the impact on the transmission of information from MSNs within the cluster to the cluster head can be ignored.
{"title":"A Two-Layer Authentication Scheme Against Node Replication Attacks in Mobile Heterogeneous Sensor Networks","authors":"Boqing Zhou;Sujun Li;Decheng Miao","doi":"10.1109/OJCOMS.2025.3635226","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3635226","url":null,"abstract":"Replication nodes can compromise a network by not only stealing confidential data but also by selectively forwarding packets and injecting false data to the base station. This undermines the base station’s decision-making, ultimately allowing attackers to control the network. Scholars have proposed various solutions to deal with this attack. However, network performance is still susceptible to its impact because of their inherent limitations. In this paper, we introduce a novel authentication scheme. In this scheme, the network contains high-energy nodes, mobile sensor nodes (MSNs), and a base station. High energy nodes act as cluster heads. MSNs must be authenticated by a cluster head before they can obtain or provide data to the cluster head. Authentication between intra-cluster nodes relies on key information pre-distributed by the cluster head, leveraging a Bloom filter for efficient verification. Within a cluster, node <inline-formula> <tex-math>$a$ </tex-math></inline-formula> will only forward node <inline-formula> <tex-math>$b$ </tex-math></inline-formula>’s data after node <inline-formula> <tex-math>$b$ </tex-math></inline-formula> is authenticated by node <inline-formula> <tex-math>$a$ </tex-math></inline-formula>. The analysis and simulation validate that the proposed scheme significantly enhances the network’s resilience against replication attacks. The communication probability between high-energy nodes is about 90%, and the probability that high-energy nodes can complete MSNs’ authentication within 1 hop is about 1; after introducing the bloom filter scheme within the cluster, the storage overhead of MSNs can be reduced by 90%, and the impact on the transmission of information from MSNs within the cluster to the cluster head can be ignored.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9897-9907"},"PeriodicalIF":6.3,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11261337","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674703","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 : 2025-11-18DOI: 10.1109/OJCOMS.2025.3634331
Hakim Jemaa;Simon Tarboush;Hadi Sarieddeen;Mohamed-Slim Alouini;Tareq Y. Al-Naffouri
Achieving terabit-per-second (Tbps) data rates in terahertz (THz)-band communications requires bridging the complexity gap in baseband transceiver design. This work addresses the signal processing challenges associated with data detection in THz-band multiple-input multiple-output (MIMO) systems. We begin by analyzing the trade-offs between performance and complexity across various detection schemes and THz channel models, demonstrating significant complexity reduction by leveraging spatial parallelism across subspaces of correlated, typically ill-conditioned THz MIMO channels. We also derive accurate theoretical bounds on the detection error probability by incorporating THz-specific channel distributions and accounting for mismatches introduced by subspace decomposition. In addition, we propose a variation of subspace detectors that combines channel-matrix sorting, QR decomposition, and puncturing. Furthermore, under wideband THz UM-MIMO systems, we introduce a channel-matrix reuse strategy that minimizes exhaustive computations while maintaining reliable detection performance within a coherence bandwidth. Simulations over accurate THz channels show that the proposed efficient spatial parallelization schemes yield multi-dB performance gains, while the proposed reuse strategy offers significant computational savings with minimal performance degradation.
{"title":"Performance and Complexity Analysis of Terahertz-Band MIMO Detection","authors":"Hakim Jemaa;Simon Tarboush;Hadi Sarieddeen;Mohamed-Slim Alouini;Tareq Y. Al-Naffouri","doi":"10.1109/OJCOMS.2025.3634331","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3634331","url":null,"abstract":"Achieving terabit-per-second (Tbps) data rates in terahertz (THz)-band communications requires bridging the complexity gap in baseband transceiver design. This work addresses the signal processing challenges associated with data detection in THz-band multiple-input multiple-output (MIMO) systems. We begin by analyzing the trade-offs between performance and complexity across various detection schemes and THz channel models, demonstrating significant complexity reduction by leveraging spatial parallelism across subspaces of correlated, typically ill-conditioned THz MIMO channels. We also derive accurate theoretical bounds on the detection error probability by incorporating THz-specific channel distributions and accounting for mismatches introduced by subspace decomposition. In addition, we propose a variation of subspace detectors that combines channel-matrix sorting, QR decomposition, and puncturing. Furthermore, under wideband THz UM-MIMO systems, we introduce a channel-matrix reuse strategy that minimizes exhaustive computations while maintaining reliable detection performance within a coherence bandwidth. Simulations over accurate THz channels show that the proposed efficient spatial parallelization schemes yield multi-dB performance gains, while the proposed reuse strategy offers significant computational savings with minimal performance degradation.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9821-9839"},"PeriodicalIF":6.3,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11251284","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612052","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}
Applying reconfigurable intelligent surfaces (RISs) to control of propagation paths can lead to more efficient wireless communication networks from the perspectives of spectrum and energy efficiency. For future networks, there have been proposals to employ large number of RISs. However, since RISs consume a certain amount of power, deploying massive amounts of RISs significantly increases the overall power consumption. Furthermore, such large-scale deployments introduce additional control latency. In this paper, we propose and demonstrate a green relay-assisted wireless communication system that maintains received power while reducing RIS activation time. We implement dynamic control of multiple distributed RISs in a real indoor environment. The proposed system adaptively turns RISs on and off based on the radio environment and user location, enabling practical real-time RIS control. Our experiments confirmed that the proposed system could adaptively select RISs distributed across the area based on the position of user equipments and the radio environment map, achieving control within 1 second in about 99% of cases. We further verified that this approach improved the received power by approximately 4.5 dB at the 10% value of the cumulative distribution function while reducing RIS usage time by approximately 89%.
{"title":"Experimental Evaluation of Distributed Reconfigurable Intelligent Surfaces Control for Green Wireless Networks","authors":"Takumi Yoneda;Tomoki Murakami;Yasushi Takatori;Tomoaki Ogawa","doi":"10.1109/OJCOMS.2025.3633316","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3633316","url":null,"abstract":"Applying reconfigurable intelligent surfaces (RISs) to control of propagation paths can lead to more efficient wireless communication networks from the perspectives of spectrum and energy efficiency. For future networks, there have been proposals to employ large number of RISs. However, since RISs consume a certain amount of power, deploying massive amounts of RISs significantly increases the overall power consumption. Furthermore, such large-scale deployments introduce additional control latency. In this paper, we propose and demonstrate a green relay-assisted wireless communication system that maintains received power while reducing RIS activation time. We implement dynamic control of multiple distributed RISs in a real indoor environment. The proposed system adaptively turns RISs on and off based on the radio environment and user location, enabling practical real-time RIS control. Our experiments confirmed that the proposed system could adaptively select RISs distributed across the area based on the position of user equipments and the radio environment map, achieving control within 1 second in about 99% of cases. We further verified that this approach improved the received power by approximately 4.5 dB at the 10% value of the cumulative distribution function while reducing RIS usage time by approximately 89%.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9736-9747"},"PeriodicalIF":6.3,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11250873","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612068","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 : 2025-11-14DOI: 10.1109/OJCOMS.2025.3633162
Abbas Abolfathimomtaz;Hamid Ebrahimzad;Chuandong Li
Recently, data centers (DCs) have been increasingly dedicated to artificial intelligence (AI) training processes. To enable large-scale model training, parallelization schemes such as distributed data parallelism (DDP) and pipeline parallelism (PP) are essential. Both techniques require extensive data transmission through optical communication links within a DC. The latency and power consumption of these links are critical factors affecting DC efficiency for AI training. Although optical links are typically engineered for near-error-free transmission, the impact of data transmission errors during AI training remains insufficiently explored. In this work, we analytically investigate the effect of communication link errors on the learning process under the DDP and PP schemes. Our analysis reveals that link errors introduce bounded noise into the model weights, allowing weight error levels to be controlled by maintaining an appropriate link bit error rate (BER). Relaxing the error-free requirement opens new opportunities for optimizing optical link performance. Specifically, we propose a novel protocol stack layer for optical links, with minimal deviation from the IEEE 802.3bs standard, to enable data transmission with reduced latency. We validate our theoretical findings through simulations by training the ChatGPT2 model, consisting of 124 million parameters. The results highlight several practical implications and confirm the theoretical relationship between link BER and bounded weight noise. For example, our simulations demonstrate that a communication link with a BER less than 1e-4 has negligible impacts on DDP performance, while the PP method requires a link BER less than 1e-5.
{"title":"Impact of Optical Communication Link Error on Large-Scale AI Training in Data Centers","authors":"Abbas Abolfathimomtaz;Hamid Ebrahimzad;Chuandong Li","doi":"10.1109/OJCOMS.2025.3633162","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3633162","url":null,"abstract":"Recently, data centers (DCs) have been increasingly dedicated to artificial intelligence (AI) training processes. To enable large-scale model training, parallelization schemes such as distributed data parallelism (DDP) and pipeline parallelism (PP) are essential. Both techniques require extensive data transmission through optical communication links within a DC. The latency and power consumption of these links are critical factors affecting DC efficiency for AI training. Although optical links are typically engineered for near-error-free transmission, the impact of data transmission errors during AI training remains insufficiently explored. In this work, we analytically investigate the effect of communication link errors on the learning process under the DDP and PP schemes. Our analysis reveals that link errors introduce bounded noise into the model weights, allowing weight error levels to be controlled by maintaining an appropriate link bit error rate (BER). Relaxing the error-free requirement opens new opportunities for optimizing optical link performance. Specifically, we propose a novel protocol stack layer for optical links, with minimal deviation from the IEEE 802.3bs standard, to enable data transmission with reduced latency. We validate our theoretical findings through simulations by training the ChatGPT2 model, consisting of 124 million parameters. The results highlight several practical implications and confirm the theoretical relationship between link BER and bounded weight noise. For example, our simulations demonstrate that a communication link with a BER less than 1e-4 has negligible impacts on DDP performance, while the PP method requires a link BER less than 1e-5.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9748-9763"},"PeriodicalIF":6.3,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11248925","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612042","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 : 2025-11-13DOI: 10.1109/OJCOMS.2025.3632643
Mehmet Ali Aygul;Ebubekir Memisoglu;Hakan Ali Cirpan;Huseyin Arslan
Wireless sensor networks (WSNs) are resource-constrained and highly vulnerable to eavesdropping due to their broadcast nature. Traditional symmetric key cryptography provides strong security but imposes excessive computational and energy demands on sensor nodes. Physical layer–based secret key generation (SKG) offers a lightweight alternative by exploiting channel reciprocity and randomness; however, existing methods are mostly limited to two-user scenarios and lack scalability in multi-user settings. This paper introduces two scalable multi-user SKG frameworks: a sequential method and a star topology-based method. In the sequential method, users cooperatively relay random signals to derive a shared key, while the star topology leverages a central node for signal aggregation and redistribution. A comparative analysis reveals trade-offs between the proposed methods. The sequential method is more computationally efficient but sensitive to node failures, while the star topology offers lower delay at the cost of requiring a fully connected central node. Analyses and simulations confirm both methods’ effectiveness in key mismatch probability, key generation rate, and key randomness validated by the National Institute of Standards and Technology test suite.
{"title":"Multi-User Secret Key Generation for WSNs via Wireless Channels","authors":"Mehmet Ali Aygul;Ebubekir Memisoglu;Hakan Ali Cirpan;Huseyin Arslan","doi":"10.1109/OJCOMS.2025.3632643","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3632643","url":null,"abstract":"Wireless sensor networks (WSNs) are resource-constrained and highly vulnerable to eavesdropping due to their broadcast nature. Traditional symmetric key cryptography provides strong security but imposes excessive computational and energy demands on sensor nodes. Physical layer–based secret key generation (SKG) offers a lightweight alternative by exploiting channel reciprocity and randomness; however, existing methods are mostly limited to two-user scenarios and lack scalability in multi-user settings. This paper introduces two scalable multi-user SKG frameworks: a sequential method and a star topology-based method. In the sequential method, users cooperatively relay random signals to derive a shared key, while the star topology leverages a central node for signal aggregation and redistribution. A comparative analysis reveals trade-offs between the proposed methods. The sequential method is more computationally efficient but sensitive to node failures, while the star topology offers lower delay at the cost of requiring a fully connected central node. Analyses and simulations confirm both methods’ effectiveness in key mismatch probability, key generation rate, and key randomness validated by the National Institute of Standards and Technology test suite.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9654-9672"},"PeriodicalIF":6.3,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11245554","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560734","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 : 2025-11-13DOI: 10.1109/OJCOMS.2025.3632519
Tayfun Yilmaz;Haci Ilhan;Ibrahim Hokelek
Reconfigurable Intelligent Surface (RIS)-assisted communication has recently attracted significant attention for enhancing wireless performance in challenging environments, making accurate error analysis under practical hardware constraints and imperfect channel state information (CSI) conditions crucial for future multi-antenna systems. This paper presents a unified theoretical framework for the symbol error rate (SER) analysis of RIS-assisted multiple-antenna systems employing orthogonal space–time block codes (OSTBC), considering practical reflection models with amplitude-dependent and quantized phase responses under channel estimation errors (CEEs). By exploiting the Gramian structure of the cascaded channel f, we derive exact moment-generating function (MGF) expressions of the nonzero eigenvalue of $ mathbf {f}^{dagger } mathbf {f} $ for small RIS sizes. For large-scale RIS deployments, where closed-form analysis becomes intractable, we employ Saddle Point Approximation (SPA) to approximate the eigenvalue distribution. Using these results, we derive unified SER expressions using exact and SPA-based MGF formulations, applicable to arbitrary RIS sizes, phase configuration, and both identical and non-identical amplitude responses. Extensive Monte Carlo simulations confirm the accuracy of the proposed SER expressions, demonstrating very close agreement for all configurations and under imperfect channel state information (CSI) scenarios. In addition, by applying asymptotic SNR analysis on the SPA-based SER formulation, we mathematically establish that the coding gain is inversely proportional to the $N_{t}$ -th negative moment of the SPA-approximated probability density function (PDF) corresponding to the nonzero eigenvalue of the cascaded RIS–receiver Gram matrix. This insight motivates a negative moment minimization algorithm that efficiently identifies hardware-constrained RIS phase configurations, achieving near-optimal SER performance with low complexity.
{"title":"Space-Time Coded RIS-Assisted Wireless Systems With Imperfect CSI and Practical Reflection Models: Error Rate Analysis and Optimization With Saddle Point Approximation","authors":"Tayfun Yilmaz;Haci Ilhan;Ibrahim Hokelek","doi":"10.1109/OJCOMS.2025.3632519","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3632519","url":null,"abstract":"Reconfigurable Intelligent Surface (RIS)-assisted communication has recently attracted significant attention for enhancing wireless performance in challenging environments, making accurate error analysis under practical hardware constraints and imperfect channel state information (CSI) conditions crucial for future multi-antenna systems. This paper presents a unified theoretical framework for the symbol error rate (SER) analysis of RIS-assisted multiple-antenna systems employing orthogonal space–time block codes (OSTBC), considering practical reflection models with amplitude-dependent and quantized phase responses under channel estimation errors (CEEs). By exploiting the Gramian structure of the cascaded channel f, we derive exact moment-generating function (MGF) expressions of the nonzero eigenvalue of <inline-formula> <tex-math>$ mathbf {f}^{dagger } mathbf {f} $ </tex-math></inline-formula> for small RIS sizes. For large-scale RIS deployments, where closed-form analysis becomes intractable, we employ Saddle Point Approximation (SPA) to approximate the eigenvalue distribution. Using these results, we derive unified SER expressions using exact and SPA-based MGF formulations, applicable to arbitrary RIS sizes, phase configuration, and both identical and non-identical amplitude responses. Extensive Monte Carlo simulations confirm the accuracy of the proposed SER expressions, demonstrating very close agreement for all configurations and under imperfect channel state information (CSI) scenarios. In addition, by applying asymptotic SNR analysis on the SPA-based SER formulation, we mathematically establish that the coding gain is inversely proportional to the <inline-formula> <tex-math>$N_{t}$ </tex-math></inline-formula>-th negative moment of the SPA-approximated probability density function (PDF) corresponding to the nonzero eigenvalue of the cascaded RIS–receiver Gram matrix. This insight motivates a negative moment minimization algorithm that efficiently identifies hardware-constrained RIS phase configurations, achieving near-optimal SER performance with low complexity.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9547-9568"},"PeriodicalIF":6.3,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11245553","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560735","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 : 2025-11-12DOI: 10.1109/OJCOMS.2025.3632191
Sidqy I. Alnagar;Ali A. Nasir;Salam A. Zummo
This paper studies a UAV-assisted symbiotic radio (SR) system in which a reconfigurable intelligent surface (RIS) backscatters IoT data to a UAV while simultaneously assisting the primary transmission. To extend coverage without the power and noise penalties of a fully active RIS or the range limitations of a fully passive RIS, we propose a hybrid active/passive RIS that enables element-wise mode selection and per-active-element gain control. We formulate an energy-efficiency maximization problem that accounts for both amplification noise and circuit power under statistical channel state information (CSI), jointly optimizing RIS mode selection, the active-element amplification matrix, RIS phase shifts, the UAV’s 3D location, and transmit beamforming. The resulting mixed-integer, nonconvex fractional program captures tight couplings among geometry, activation, amplifier noise, and circuit power. To solve it, we develop a block coordinate descent (BCD) framework that combines Dinkelbach’s transform for the fractional objective with successive convex approximation (SCA) and a relaxation–rounding strategy for mode selection. Numerical results show consistent energy-efficiency gains over fully passive and fully active baselines (both optimized and random), highlighting the benefits of hybrid selective amplification and UAV placement in SR. We also evaluate a practical discrete-phase hybrid RIS with 4-bit resolution; despite finite-resolution quantization, it closely approaches the continuous-phase design and outperforms the fully passive and fully active baselines.
{"title":"Energy-Efficient Hybrid Active/Passive RIS-Assisted UAV-Enabled IoT Data Collection in Symbiotic Radio Systems","authors":"Sidqy I. Alnagar;Ali A. Nasir;Salam A. Zummo","doi":"10.1109/OJCOMS.2025.3632191","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3632191","url":null,"abstract":"This paper studies a UAV-assisted symbiotic radio (SR) system in which a reconfigurable intelligent surface (RIS) backscatters IoT data to a UAV while simultaneously assisting the primary transmission. To extend coverage without the power and noise penalties of a fully active RIS or the range limitations of a fully passive RIS, we propose a hybrid active/passive RIS that enables element-wise mode selection and per-active-element gain control. We formulate an energy-efficiency maximization problem that accounts for both amplification noise and circuit power under statistical channel state information (CSI), jointly optimizing RIS mode selection, the active-element amplification matrix, RIS phase shifts, the UAV’s 3D location, and transmit beamforming. The resulting mixed-integer, nonconvex fractional program captures tight couplings among geometry, activation, amplifier noise, and circuit power. To solve it, we develop a block coordinate descent (BCD) framework that combines Dinkelbach’s transform for the fractional objective with successive convex approximation (SCA) and a relaxation–rounding strategy for mode selection. Numerical results show consistent energy-efficiency gains over fully passive and fully active baselines (both optimized and random), highlighting the benefits of hybrid selective amplification and UAV placement in SR. We also evaluate a practical discrete-phase hybrid RIS with 4-bit resolution; despite finite-resolution quantization, it closely approaches the continuous-phase design and outperforms the fully passive and fully active baselines.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9714-9735"},"PeriodicalIF":6.3,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11242202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612073","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}
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