This work investigates a radar-centric integrated sensing and communication (ISAC) transceiver that incorporates a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and a radar receiver equipped with a passive electronically scanned array (PESA) and a single digital channel. A periodic pulsed waveform, emitted by a feeder, illuminates the STAR-RIS, which applies space-time modulation to the redirected pulses. The spatial response of the STAR-RIS and the spatial beamformer of the radar receiver are jointly designed to illuminate desired directions on each side of the STAR-RIS, i.e., those monitored by the radar and those corresponding to communication users, while mitigating clutter returns. Time modulation enables both the embedding of communication data and the assignment of unique signatures to echoes from each side of the STAR-RIS. Two encoding schemes are proposed, enabling either simultaneous or sequential illumination of the two sides, while the corresponding radar and communication receivers are designed using a generalized information criterion. Several trade-offs between radar and communication functionalities can be achieved by adjusting the STAR-RIS and PESA array power patterns; in addition, the proposed data encoding schemes allow different data transmission and error rates, while having minimal impact on the radar performance.
{"title":"ISAC STAR-RIS Transceivers With Space-Time Coded Pulsed Signals","authors":"Hedieh Taremizadeh;Emanuele Grossi;Luca Venturino;Marco Lops","doi":"10.1109/OJCOMS.2025.3632145","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3632145","url":null,"abstract":"This work investigates a radar-centric integrated sensing and communication (ISAC) transceiver that incorporates a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and a radar receiver equipped with a passive electronically scanned array (PESA) and a single digital channel. A periodic pulsed waveform, emitted by a feeder, illuminates the STAR-RIS, which applies space-time modulation to the redirected pulses. The spatial response of the STAR-RIS and the spatial beamformer of the radar receiver are jointly designed to illuminate desired directions on each side of the STAR-RIS, i.e., those monitored by the radar and those corresponding to communication users, while mitigating clutter returns. Time modulation enables both the embedding of communication data and the assignment of unique signatures to echoes from each side of the STAR-RIS. Two encoding schemes are proposed, enabling either simultaneous or sequential illumination of the two sides, while the corresponding radar and communication receivers are designed using a generalized information criterion. Several trade-offs between radar and communication functionalities can be achieved by adjusting the STAR-RIS and PESA array power patterns; in addition, the proposed data encoding schemes allow different data transmission and error rates, while having minimal impact on the radar performance.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9569-9586"},"PeriodicalIF":6.3,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11242204","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560741","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.2025.3631799
Ohood Sabr;Georges Kaddoum;Kuljeet Kaur
Network slicing (NS) is a cornerstone technology for sixth-generation (6G) networks, enabling the support of heterogeneous services with diverse quality-of-service (QoS) requirements. However, existing radio access network (RAN) slicing schemes often rely on single-level resource allocation, limiting their adaptability to the dynamic nature of RAN and the efficient use of limited radio resources. This leads to challenges in satisfying service-level agreements (SLAs). Moreover, effective hierarchical slicing that operates under fluctuating traffic loads, and hardware impairments for multiple antenna systems remains a challenge. To address these issues, we propose a hierarchical self-optimization framework aimed at maximizing both the long-term QoS and the spectral efficiency. Specifically, the proposed framework consists of two slicing management schemes: a cooperative multiple actor-critic (CoMA2C) scheme to manage the power and bandwidth among heterogeneous slices on a large scale. Concurrently, a multi-agent deep Q-network (MADQN) scheme manages the power and beamforming for active users within each slice on a small time scale, accounting for hardware impairments, user mobility, traffic fluctuations, and channel variations. The DQN and A2C algorithms are employed in the design of the proposed schemes owing to their proven effectiveness in real-time decision-making in dynamic environments. Furthermore, a promising scheme based on rate-splitting multiple access (RSMA) is investigated for heterogeneous services. Simulation results showcase the effectiveness of our proposed framework, demonstrating its ability to satisfy SLAs for heterogeneous services while reducing network overhead and outperforming existing state-of-the-art approaches.
{"title":"HiSO-CoMA: Hierarchical Self-Optimizing Framework for O-RAN Slicing Using Cooperative Multiple Agent Deep Reinforcement Learning","authors":"Ohood Sabr;Georges Kaddoum;Kuljeet Kaur","doi":"10.1109/OJCOMS.2025.3631799","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3631799","url":null,"abstract":"Network slicing (NS) is a cornerstone technology for sixth-generation (6G) networks, enabling the support of heterogeneous services with diverse quality-of-service (QoS) requirements. However, existing radio access network (RAN) slicing schemes often rely on single-level resource allocation, limiting their adaptability to the dynamic nature of RAN and the efficient use of limited radio resources. This leads to challenges in satisfying service-level agreements (SLAs). Moreover, effective hierarchical slicing that operates under fluctuating traffic loads, and hardware impairments for multiple antenna systems remains a challenge. To address these issues, we propose a hierarchical self-optimization framework aimed at maximizing both the long-term QoS and the spectral efficiency. Specifically, the proposed framework consists of two slicing management schemes: a cooperative multiple actor-critic (CoMA2C) scheme to manage the power and bandwidth among heterogeneous slices on a large scale. Concurrently, a multi-agent deep Q-network (MADQN) scheme manages the power and beamforming for active users within each slice on a small time scale, accounting for hardware impairments, user mobility, traffic fluctuations, and channel variations. The DQN and A2C algorithms are employed in the design of the proposed schemes owing to their proven effectiveness in real-time decision-making in dynamic environments. Furthermore, a promising scheme based on rate-splitting multiple access (RSMA) is investigated for heterogeneous services. Simulation results showcase the effectiveness of our proposed framework, demonstrating its ability to satisfy SLAs for heterogeneous services while reducing network overhead and outperforming existing state-of-the-art approaches.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9632-9653"},"PeriodicalIF":6.3,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11244133","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560733","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 : 2025-11-11DOI: 10.1109/OJCOMS.2025.3631341
Muhammad Iqbal;Muhammad Ali;Tabinda Ashraf;Aryan Kaushik;Muhammad Sami Akram;Fazal Sajid;Jen-Yi Pan
Reconfigurable intelligent surfaces (RIS) have emerged as a key technology to enhance the performance of next-generation wireless networks by intelligently reconfiguring the propagation environment. In particular, simultaneously transmitting and reflecting RIS (STAR-RIS) extend this paradigm by enabling full-space coverage through concurrent reflection and transmission. This paper investigates a downlink multiple-input single-output (MISO) system assisted by a STAR-RIS and addresses the joint optimization of base station beamforming and RIS coefficients. To tackle the inherent non-convexity of this problem, we propose a reinforcement learning framework based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The proposed approach formulates the design task as a continuous control problem, allowing the agent to directly learn interference-aware policies that adapt to dynamic channel conditions. Extensive simulations validate the effectiveness of the proposed framework. The results demonstrate that the TD3-based policy achieves stable convergence and significantly improves the achievable sum rate compared to the baseline Deep Deterministic Policy Gradient (DDPG) method and the passive RIS benchmark. Performance scaling with the number of RIS elements and transmit power is clearly observed, confirming the scalability of the approach. In addition, hyperparameter sensitivity analysis highlights the importance of learning rate and decay parameter tuning for robust training. Cumulative distribution function (CDF) comparisons further show that the proposed framework enhances both average throughput and reliability across different channel realizations. The findings establish deep reinforcement learning, and TD3 in particular, as a promising tool for real-time optimization in STAR-RIS-assisted wireless systems. The proposed framework provides a flexible and scalable solution for intelligent resource allocation, paving the way for more reliable and efficient 6G communication networks.
{"title":"Twin Delayed Deep Deterministic Policy Gradient for Intelligent Optimization in STAR-RIS-Assisted Wireless Networks","authors":"Muhammad Iqbal;Muhammad Ali;Tabinda Ashraf;Aryan Kaushik;Muhammad Sami Akram;Fazal Sajid;Jen-Yi Pan","doi":"10.1109/OJCOMS.2025.3631341","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3631341","url":null,"abstract":"Reconfigurable intelligent surfaces (RIS) have emerged as a key technology to enhance the performance of next-generation wireless networks by intelligently reconfiguring the propagation environment. In particular, simultaneously transmitting and reflecting RIS (STAR-RIS) extend this paradigm by enabling full-space coverage through concurrent reflection and transmission. This paper investigates a downlink multiple-input single-output (MISO) system assisted by a STAR-RIS and addresses the joint optimization of base station beamforming and RIS coefficients. To tackle the inherent non-convexity of this problem, we propose a reinforcement learning framework based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The proposed approach formulates the design task as a continuous control problem, allowing the agent to directly learn interference-aware policies that adapt to dynamic channel conditions. Extensive simulations validate the effectiveness of the proposed framework. The results demonstrate that the TD3-based policy achieves stable convergence and significantly improves the achievable sum rate compared to the baseline Deep Deterministic Policy Gradient (DDPG) method and the passive RIS benchmark. Performance scaling with the number of RIS elements and transmit power is clearly observed, confirming the scalability of the approach. In addition, hyperparameter sensitivity analysis highlights the importance of learning rate and decay parameter tuning for robust training. Cumulative distribution function (CDF) comparisons further show that the proposed framework enhances both average throughput and reliability across different channel realizations. The findings establish deep reinforcement learning, and TD3 in particular, as a promising tool for real-time optimization in STAR-RIS-assisted wireless systems. The proposed framework provides a flexible and scalable solution for intelligent resource allocation, paving the way for more reliable and efficient 6G communication networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9696-9713"},"PeriodicalIF":6.3,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11240115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560762","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 : 2025-11-07DOI: 10.1109/OJCOMS.2025.3630471
S. Gopikrishnan;Pujitha Jonnalagadda;Maha Driss;Wadii Boulila
The rapid proliferation of the Internet of Things (IoT) has expanded the surface of cyberattacks, intensifying the need for intelligent and adaptive Intrusion Detection Systems (IDS). Conventional IDS solutions often struggle with high-dimensional feature spaces, class imbalance, and computational limitations of edge and fog devices. This paper presents EFS-IDS, an Efficient Feature-Selection-based Intrusion Detection System tailored for IoT environments. The framework addresses three significant challenges: feature redundancy, data imbalance, and model generalization through a multistage deep learning pipeline. First, an ensemble feature selection mechanism combines Information Gain, Fast Correlation-Based Filter, and Average Feature Importance to identify a compact yet highly discriminative subset of features. Second, a hybrid balancing strategy integrating SMOTE and Borderline-SMOTE mitigates skewed class distributions, improving recognition of minority attacks. Third, a cost-sensitive CNN–DNN hybrid classifier leverages convolutional layers for localized flow pattern extraction and deep dense layers for global decision modeling, optimized through a weighted cross-entropy loss. Together, these modules enhance detection accuracy, robustness, and resource efficiency across heterogeneous IoT devices. Extensive experiments on the CIC-IDS and CIC-IoT benchmark datasets show that EFS-IDS achieves up to 98% accuracy and 0.96 F1-score, outperforming state-of-the-art models in both balanced and imbalanced conditions. The proposed framework demonstrates superior adaptability, reduced false alarms, and efficient deployment across edge–fog–cloud layers, positioning EFS-IDS as a scalable and effective defense mechanism for next-generation IoT networks.
{"title":"EFS-IDS: An Enhanced Feature-Selective Intrusion Detection System for Imbalanced IoT Traffic Data","authors":"S. Gopikrishnan;Pujitha Jonnalagadda;Maha Driss;Wadii Boulila","doi":"10.1109/OJCOMS.2025.3630471","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3630471","url":null,"abstract":"The rapid proliferation of the Internet of Things (IoT) has expanded the surface of cyberattacks, intensifying the need for intelligent and adaptive Intrusion Detection Systems (IDS). Conventional IDS solutions often struggle with high-dimensional feature spaces, class imbalance, and computational limitations of edge and fog devices. This paper presents EFS-IDS, an Efficient Feature-Selection-based Intrusion Detection System tailored for IoT environments. The framework addresses three significant challenges: feature redundancy, data imbalance, and model generalization through a multistage deep learning pipeline. First, an ensemble feature selection mechanism combines Information Gain, Fast Correlation-Based Filter, and Average Feature Importance to identify a compact yet highly discriminative subset of features. Second, a hybrid balancing strategy integrating SMOTE and Borderline-SMOTE mitigates skewed class distributions, improving recognition of minority attacks. Third, a cost-sensitive CNN–DNN hybrid classifier leverages convolutional layers for localized flow pattern extraction and deep dense layers for global decision modeling, optimized through a weighted cross-entropy loss. Together, these modules enhance detection accuracy, robustness, and resource efficiency across heterogeneous IoT devices. Extensive experiments on the CIC-IDS and CIC-IoT benchmark datasets show that EFS-IDS achieves up to 98% accuracy and 0.96 F1-score, outperforming state-of-the-art models in both balanced and imbalanced conditions. The proposed framework demonstrates superior adaptability, reduced false alarms, and efficient deployment across edge–fog–cloud layers, positioning EFS-IDS as a scalable and effective defense mechanism for next-generation IoT networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9673-9695"},"PeriodicalIF":6.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11234896","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560740","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 : 2025-11-07DOI: 10.1109/OJCOMS.2025.3630551
Sahar Allahkaram;Francisco A. Monteiro;Ioannis Chatzigeorgiou
Supporting ultra-reliable and low-latency communication (URLLC) is a challenge in current wireless systems. Channel codes that generate large codewords improve reliability but necessitate the use of interleavers, which introduce undesirable latency. Only short codewords can eliminate the requirement for interleaving and reduce decoding latency. This paper suggests a coding and decoding method which, when combined with the high spectral efficiency of spatial multiplexing, can provide URLLC over a fading channel. Random linear coding and modulation are used to transmit information over a massive multiple-input multiple-output (mMIMO) uplink channel, followed by zero-forcing detection and guessing random additive noise decoding (GRAND) at a receiver. This work considers symbol-level GRAND, which is a variant of GRAND that was originally proposed for single-antenna systems employing square $M$ -ary quadrature amplitude modulation, and generalizes it to schemes that combine spatial multiplexing with any $M$ -ary modulation method. The paper studies the impact of the orthogonality defect of the underlying mMIMO lattice on symbol-level GRAND, and proposes to leverage side-information that comes from the mMIMO channel-state information and relates to the reliability of each receive antenna. Additionally, a lightweight membership test is introduced to reduce the number of error patterns that undergo full membership tests, by making use of a row in the parity-check matrix that eliminates candidate error patterns. All proposals reduce the decoding speed without compromising the decoding performance. The proposed decoder operating at the symbol level, when combined with antenna sorting and syndrome-constrained decoding, has the potential to reduce complexity by 90% when compared to bit-level GRAND in some of the tested configurations.
{"title":"Constrained Symbol-Level Noise-Guessing Decoding With Antenna Sorting for Massive MIMO","authors":"Sahar Allahkaram;Francisco A. Monteiro;Ioannis Chatzigeorgiou","doi":"10.1109/OJCOMS.2025.3630551","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3630551","url":null,"abstract":"Supporting ultra-reliable and low-latency communication (URLLC) is a challenge in current wireless systems. Channel codes that generate large codewords improve reliability but necessitate the use of interleavers, which introduce undesirable latency. Only short codewords can eliminate the requirement for interleaving and reduce decoding latency. This paper suggests a coding and decoding method which, when combined with the high spectral efficiency of spatial multiplexing, can provide URLLC over a fading channel. Random linear coding and modulation are used to transmit information over a massive multiple-input multiple-output (mMIMO) uplink channel, followed by zero-forcing detection and guessing random additive noise decoding (GRAND) at a receiver. This work considers symbol-level GRAND, which is a variant of GRAND that was originally proposed for single-antenna systems employing square <inline-formula> <tex-math>$M$ </tex-math></inline-formula>-ary quadrature amplitude modulation, and generalizes it to schemes that combine spatial multiplexing with any <inline-formula> <tex-math>$M$ </tex-math></inline-formula>-ary modulation method. The paper studies the impact of the orthogonality defect of the underlying mMIMO lattice on symbol-level GRAND, and proposes to leverage side-information that comes from the mMIMO channel-state information and relates to the reliability of each receive antenna. Additionally, a lightweight membership test is introduced to reduce the number of error patterns that undergo full membership tests, by making use of a row in the parity-check matrix that eliminates candidate error patterns. All proposals reduce the decoding speed without compromising the decoding performance. The proposed decoder operating at the symbol level, when combined with antenna sorting and syndrome-constrained decoding, has the potential to reduce complexity by 90% when compared to bit-level GRAND in some of the tested configurations.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9486-9503"},"PeriodicalIF":6.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11235519","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560737","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 : 2025-11-07DOI: 10.1109/OJCOMS.2025.3630145
Hao Y. Wang;Alexander M. Wyglinski
Future lunar missions demand reliable wireless communication infrastructure, the design of which require accurate wireless channel models tailored to the unique lunar environment. Current models, such as Analytic Propagation Approximation over Variable Terrain (APA) model, Irregular Terrain Model (ITM), and Irregular Terrain with Obstructions Model (ITWOM), do not fully capture the chaotic propagation effects of the lunar environment, as they only account for a limited set of factors, including obstructions, multipath effects, and regolith properties. The proposed Barren Irregular, Chaotic Terrain Ring Model (BICTR), inspired by Jake’s model and the Two-ray model accounts for all of these factors. It is specifically designed for the lunar environments, which are characterized as barren with no foliage, irregular with varying terrain, and chaotic with numerous obstacles that block Line of sight (LOS). In comparison to ITM and ITWOM, BICTR is on average 7.52dBm and 9.90dBm, respectively, closer to the NASA Desert Research and Technologies Studies (NASA DRATS) lunar analog measurement campaign.
{"title":"Barren, Irregular, Chaotic Terrain Ring Model for Lunar 5G Applications","authors":"Hao Y. Wang;Alexander M. Wyglinski","doi":"10.1109/OJCOMS.2025.3630145","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3630145","url":null,"abstract":"Future lunar missions demand reliable wireless communication infrastructure, the design of which require accurate wireless channel models tailored to the unique lunar environment. Current models, such as Analytic Propagation Approximation over Variable Terrain (APA) model, Irregular Terrain Model (ITM), and Irregular Terrain with Obstructions Model (ITWOM), do not fully capture the chaotic propagation effects of the lunar environment, as they only account for a limited set of factors, including obstructions, multipath effects, and regolith properties. The proposed Barren Irregular, Chaotic Terrain Ring Model (BICTR), inspired by Jake’s model and the Two-ray model accounts for all of these factors. It is specifically designed for the lunar environments, which are characterized as barren with no foliage, irregular with varying terrain, and chaotic with numerous obstacles that block Line of sight (LOS). In comparison to ITM and ITWOM, BICTR is on average 7.52dBm and 9.90dBm, respectively, closer to the NASA Desert Research and Technologies Studies (NASA DRATS) lunar analog measurement campaign.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9523-9533"},"PeriodicalIF":6.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11234915","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560739","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 work investigates the fundamental performance bound of three-dimensional (3D) target localization and velocity estimation in monostatic orthogonal frequency-division multiplexing (OFDM)-based ISAC systems equipped with uniform rectangular arrays (URAs). Using the equivalent Fisher information matrix, we derive the Cramér-Rao lower bound (CRLB) for position estimation in the 3D case and show how it can be naturally reduced to existing two-dimensional bounds when the URA becomes linear, thereby generalizing prior work. Additionally, under the practical assumption of known direction of motion, particularly relevant in applications such as road traffic monitoring with unmanned aerial vehicles (UAVs), we derive a closed-form CRLB expression for the estimation of target velocity magnitude. These CRLB expressions are then used to assess the impact of key system parameters, including subcarrier count, OFDM frame size, and array geometry, on estimation accuracy. The results provide actionable insights into UAV fleet deployment strategies, such as selecting the optimal sensing node based on spatial configuration and performance metrics. Numerical simulations validate the analytical bounds and highlight fundamental trade-offs in the design of future non-terrestrial network (NTN)-based ISAC architectures.
{"title":"Fundamental Limits of Target Parameter Estimation in OFDM-Based 3D NTN ISAC Systems","authors":"Luca Arcangeloni;Enrico Testi;Lorenzo Pucci;Andrea Giorgetti","doi":"10.1109/OJCOMS.2025.3630072","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3630072","url":null,"abstract":"This work investigates the fundamental performance bound of three-dimensional (3D) target localization and velocity estimation in monostatic orthogonal frequency-division multiplexing (OFDM)-based ISAC systems equipped with uniform rectangular arrays (URAs). Using the equivalent Fisher information matrix, we derive the Cramér-Rao lower bound (CRLB) for position estimation in the 3D case and show how it can be naturally reduced to existing two-dimensional bounds when the URA becomes linear, thereby generalizing prior work. Additionally, under the practical assumption of known direction of motion, particularly relevant in applications such as road traffic monitoring with unmanned aerial vehicles (UAVs), we derive a closed-form CRLB expression for the estimation of target velocity magnitude. These CRLB expressions are then used to assess the impact of key system parameters, including subcarrier count, OFDM frame size, and array geometry, on estimation accuracy. The results provide actionable insights into UAV fleet deployment strategies, such as selecting the optimal sensing node based on spatial configuration and performance metrics. Numerical simulations validate the analytical bounds and highlight fundamental trade-offs in the design of future non-terrestrial network (NTN)-based ISAC architectures.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9534-9546"},"PeriodicalIF":6.3,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11231051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560732","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 : 2025-11-06DOI: 10.1109/OJCOMS.2025.3630049
Luca Pallotta;Piergiuseppe Di Marco
With the evolution toward 6G wireless networks, new technologies such as reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) are considered to meet increasing demands for spectral efficiency, connectivity, and network reconfigurability. This paper investigates the uplink sum-rate optimization problem in RIS-assisted power-domain NOMA systems. We consider a scenario where a direct line-of-sight path between users and the base station (BS) is blocked, and communication is enabled exclusively via a passive RIS. The goal is to design the RIS phase shifts to maximize the achievable sum-rate under unit-modulus constraints, which leads to a challenging non-convex optimization problem with coupled variables. To address this, we propose an alternating optimization (AO) strategy, where RIS configurations are optimized for one user at a time while keeping others fixed. Each subproblem is tackled using a phase-only conjugate gradient method (CGM), adapted from adaptive array processing theory. This method preserves the phase-only constraint while iteratively maximizing the user-specific signal-to-interference-plus-noise ratio (SINR). Simulation results demonstrate that the proposed AO-CGM approach outperforms conventional strategies in terms of sum-rate and user fairness, while offering a practical and scalable solution for future 6G networks.
{"title":"Uplink Sum-Rate Optimization in RIS-Assisted NOMA Systems via Conjugate Gradient Descent Method","authors":"Luca Pallotta;Piergiuseppe Di Marco","doi":"10.1109/OJCOMS.2025.3630049","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3630049","url":null,"abstract":"With the evolution toward 6G wireless networks, new technologies such as reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) are considered to meet increasing demands for spectral efficiency, connectivity, and network reconfigurability. This paper investigates the uplink sum-rate optimization problem in RIS-assisted power-domain NOMA systems. We consider a scenario where a direct line-of-sight path between users and the base station (BS) is blocked, and communication is enabled exclusively via a passive RIS. The goal is to design the RIS phase shifts to maximize the achievable sum-rate under unit-modulus constraints, which leads to a challenging non-convex optimization problem with coupled variables. To address this, we propose an alternating optimization (AO) strategy, where RIS configurations are optimized for one user at a time while keeping others fixed. Each subproblem is tackled using a phase-only conjugate gradient method (CGM), adapted from adaptive array processing theory. This method preserves the phase-only constraint while iteratively maximizing the user-specific signal-to-interference-plus-noise ratio (SINR). Simulation results demonstrate that the proposed AO-CGM approach outperforms conventional strategies in terms of sum-rate and user fairness, while offering a practical and scalable solution for future 6G networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9764-9774"},"PeriodicalIF":6.3,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11230884","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612051","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 : 2025-11-05DOI: 10.1109/OJCOMS.2025.3629335
Muhammad Umar Masood;Ihtesham Khan;Bruno Correia;Enrico Ghillino;Paolo Bardella;Andrea Carena;Vittorio Curri
The exponential rise in bandwidth demand from cloud services, real-time streaming, and emerging AI workloads is rapidly exhausting the scalability headroom of today’s optical transport networks. Conventional advances in coherent transceivers and C-band systems are nearing saturation, underscoring the need for new capacity-scaling strategies. This paper provides a comparative analysis of three orthogonal approaches: (i) transceiver evolution, (ii) spectral expansion through multi-band transmission, and (iii) spatial scaling via space-division multiplexing (SDM). Using a statistical network assessment of the German core network, we quantify their individual and combined impacts on capacity, efficiency, and long-term scalability. Results show that transceiver upgrades alone improve per-fiber throughput by up to 66% (400G to 1.2T) but yield diminishing returns under fixed spectrum. Multi-band operation increases per-fiber capacity by more than 150% when extending from C-band to full C+L+S operation, though it requires band-specific amplification and inter-band power equalization. SDM provides nearly linear scaling, delivering up to a 56-fold increase with six parallel fibers, albeit at higher infrastructure costs. A forward-looking 10-year projection under 25% annual traffic growth reveals that C-band-only systems saturate within 4–6 years, whereas hybrid strategies combining multi-band expansion with SDM sustain multi-petabit traffic, extending scalability by more than fivefold compared to single-band operation. These findings highlight that future-ready optical infrastructures must jointly exploit transceiver efficiency, ultra-wideband photonics, and spatial parallelism to ensure sustainable long-term growth.
{"title":"A Comparative Study of Capacity Scaling Strategies for Future Optical Transport Networks","authors":"Muhammad Umar Masood;Ihtesham Khan;Bruno Correia;Enrico Ghillino;Paolo Bardella;Andrea Carena;Vittorio Curri","doi":"10.1109/OJCOMS.2025.3629335","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3629335","url":null,"abstract":"The exponential rise in bandwidth demand from cloud services, real-time streaming, and emerging AI workloads is rapidly exhausting the scalability headroom of today’s optical transport networks. Conventional advances in coherent transceivers and C-band systems are nearing saturation, underscoring the need for new capacity-scaling strategies. This paper provides a comparative analysis of three orthogonal approaches: (i) transceiver evolution, (ii) spectral expansion through multi-band transmission, and (iii) spatial scaling via space-division multiplexing (SDM). Using a statistical network assessment of the German core network, we quantify their individual and combined impacts on capacity, efficiency, and long-term scalability. Results show that transceiver upgrades alone improve per-fiber throughput by up to 66% (400G to 1.2T) but yield diminishing returns under fixed spectrum. Multi-band operation increases per-fiber capacity by more than 150% when extending from C-band to full C+L+S operation, though it requires band-specific amplification and inter-band power equalization. SDM provides nearly linear scaling, delivering up to a 56-fold increase with six parallel fibers, albeit at higher infrastructure costs. A forward-looking 10-year projection under 25% annual traffic growth reveals that C-band-only systems saturate within 4–6 years, whereas hybrid strategies combining multi-band expansion with SDM sustain multi-petabit traffic, extending scalability by more than fivefold compared to single-band operation. These findings highlight that future-ready optical infrastructures must jointly exploit transceiver efficiency, ultra-wideband photonics, and spatial parallelism to ensure sustainable long-term growth.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9435-9447"},"PeriodicalIF":6.3,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11229440","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510201","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 : 2025-11-05DOI: 10.1109/OJCOMS.2025.3629359
Aymen Khaleel;Recep Vural;Mehmet C. Ilter;Majid Gerami;Ertugrul Basar
Reconfigurable intelligent surface (RIS)-empowered communication is one of the promising physical layer enabling technologies for the upcoming sixth generation (6G) wireless networks due to their unprecedented capabilities in shaping the wireless communication environment. In this paper, we consider the unique problem of RIS identification in a mobile wireless network where multiple RISs are deployed to assist the base station (BS)-user equipment (UE) communication. Here, considering dynamic link blockages, we aim to enable the BS to perceive the UE-RIS potential associations for better resource allocation. Specifically, we first introduce a novel network-level problem where the BS aims to detect and uniquely identify RISs that are reachable by a specific UE (UE-RIS-BS link is available) in a given time slot. Next, to solve this problem, we propose a novel RIS identification and detection (RIS-ID) scheme that enables the BS to pair UEs with their corresponding reachable RISs in a given time slot. On the BS side, the proposed RIS-ID scheme can be used as an initial and essential step before optimizing each RIS to serve its nearby UE, for more efficient resource allocation. Furthermore, to assess the proposed RIS-ID scheme, we propose two performance metrics: the false and miss-detection probabilities. These probabilities are analytically derived and verified through computer simulations, revealing the effectiveness of the proposed RIS-ID scheme under different operating scenarios.
{"title":"A Practical and Simple Detection and Identification Scheme for RIS-Assisted Systems","authors":"Aymen Khaleel;Recep Vural;Mehmet C. Ilter;Majid Gerami;Ertugrul Basar","doi":"10.1109/OJCOMS.2025.3629359","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3629359","url":null,"abstract":"Reconfigurable intelligent surface (RIS)-empowered communication is one of the promising physical layer enabling technologies for the upcoming sixth generation (6G) wireless networks due to their unprecedented capabilities in shaping the wireless communication environment. In this paper, we consider the unique problem of RIS identification in a mobile wireless network where multiple RISs are deployed to assist the base station (BS)-user equipment (UE) communication. Here, considering dynamic link blockages, we aim to enable the BS to perceive the UE-RIS potential associations for better resource allocation. Specifically, we first introduce a novel network-level problem where the BS aims to detect and uniquely identify RISs that are reachable by a specific UE (UE-RIS-BS link is available) in a given time slot. Next, to solve this problem, we propose a novel RIS identification and detection (RIS-ID) scheme that enables the BS to pair UEs with their corresponding reachable RISs in a given time slot. On the BS side, the proposed RIS-ID scheme can be used as an initial and essential step before optimizing each RIS to serve its nearby UE, for more efficient resource allocation. Furthermore, to assess the proposed RIS-ID scheme, we propose two performance metrics: the false and miss-detection probabilities. These probabilities are analytically derived and verified through computer simulations, revealing the effectiveness of the proposed RIS-ID scheme under different operating scenarios.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"9619-9631"},"PeriodicalIF":6.3,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11227015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560738","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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