IEEE Open Journal of the Communications Society最新文献

{"title":"Anomaly Detection for Mitigating xApp and E2 Interface Threats in O-RAN Near-RT RIC","authors":"Cheng-Feng Hung;Chi-Heng Tseng;Shin-Ming Cheng","doi":"10.1109/OJCOMS.2025.3546760","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3546760","url":null,"abstract":"As 5G networks advance, the Open Radio Access Network (O-RAN) is crucial in enabling openness and fostering collaboration across the telecom industry. O-RAN enhances flexibility, scalability, and interoperability through open interfaces, reducing dependence on a single vendor and promoting interoperability among vendors and solutions. The Near-Real-Time Radio Intelligent Controller (Near-RT RIC) is crucial for optimizing network resources and improving user experience. However, the openness of O-RAN also introduces security challenges, particularly from third-party developed xApps and E2 nodes that may exploit vulnerabilities to launch attacks. This paper proposes an anomaly traffic detector to protect the Near-RT RIC from threats on the E2 interface. The anomaly traffic detector verifies the legality of signaling through an internal state machine analysis module and checks packet fields through a conformance check module while monitoring network traffic in real time to detect and mitigate Denial of Service attacks. Additionally, we designed a fuzzer to simulate random attacks, testing the capability of the anomaly traffic detector. The anomaly traffic detector not only successfully passes the test cases highlighted in the O-RAN Security Test Specifications, effectively detecting unauthorized traffic and signaling, but also identifies real-world vulnerability exploits, including CVE-2023-40997, CVE-2023-40998, CVE-2023-41627, and CVE-2023-41628, thereby significantly enhancing the security of the Near-RT RIC.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"1682-1694"},"PeriodicalIF":6.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10907911","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cybersecurity Challenges in Smart Grid Systems: Current and Emerging Attacks, Opportunities, and Recommendations","authors":"Sanaz Amanlou;Mohammad Kamrul Hasan;Umi Asma’ Mokhtar;Khalid Mahmood Malik;Shayla Islam;Sheroz Khan;Muhammad Attique Khan;Muhammad Asghar Khan","doi":"10.1109/OJCOMS.2025.3545153","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3545153","url":null,"abstract":"Traditional power grids are transforming into Smart Grids (SGs) to address challenges such as unidirectional information flow, increasing energy demand, energy waste, and issues related to availability and security. This evolution is facilitated by the Internet of Things (IoT), and its integration with existing power grids is expected to yield future SGs that are more efficient and reliable. However, the reliable and continuous operation of SGs is threatened by cyberattacks due to the added advancements and IoT technologies. These threats pose a significant risk to the entire grid, making security a paramount concern in adopting SG technology. This paper provides a comprehensive survey of the cybersecurity challenges IoT devices face in SGs. It begins by outlining the architecture of SGs and the applications of IoT in the SGS. Then, the security challenges, objectives, and requirements of the SGs are discussed. Subsequently, the paper classifies and evaluates cyber-attacks based on the principles of Confidentiality, Integrity, and Availability (CIA) and the emerging sophisticated attacks. The paper then presents current security solutions, secure protocols and standards that counter each type of cyber-attack. It also discusses emerging solutions and modern technologies, such as artificial intelligence, blockchain, and Software-Defined Networking (SDN), that can tackle emerging cyber-attacks and enhance the robustness of SGs. Finally, the paper provides recommendations for further research based on the current literature and the findings of this research.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"1965-1997"},"PeriodicalIF":6.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10902093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maximizing Mutual Information of Radar via Optimal Training-Based On–Off Transmissions","authors":"Hien Quang Ta;Anh Dong Vuong;Trang Tien Nguyen;Hoon Oh","doi":"10.1109/OJCOMS.2025.3544767","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3544767","url":null,"abstract":"The paper considers the joint radar and training-based on-off communication system, where the on-off threshold and training is optimized to maximize the mutual information of radar while ensuring communication quality. The closed-form expressions of communication throughput under finite blocklength and radar’s mutual information are derived. The results show that the training and on-off transmission help improve the radar’s mutual information remarkably. The results also show that the improvement of radar’s mutual information is particularly pronounced at low communication power regimes, where a high on-off threshold can maximize average throughput while allowing ample time for radar transmissions.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"1794-1803"},"PeriodicalIF":6.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904189","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Resource Allocation and UAV Deployment in STAR-RIS and UAV-Relay Assisted Public Safety Networks for Video Transmission","authors":"Naveed Khan;Ayaz Ahmad;Abdulmalik Alwarafy;Munam Ali Shah;Abderrahmane Lakas;Muhammad Moazam Azeem","doi":"10.1109/OJCOMS.2025.3544440","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3544440","url":null,"abstract":"Reliable and flexible emergency communication presents significant challenges for search and rescue operations during disasters, particularly when base stations become non-functional. This research explores the integration of Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS), a novel technology that enhances signal propagation by simultaneously transmitting and reflecting wireless signals, with Unmanned Aerial Vehicles (UAV) to enhance public safety communication networks in such scenarios. STAR-RIS and UAVs offer versatile deployment options for establishing connections between affected users on the ground(AUGs) and uploading video services from the emergency-affected area to near by two emergency ground base stations. In this research, our focus is on UAV and STAR-RIS relay based video streaming Safety Communication Network(VS-PSCN). This network consists of AUGs, an observation UAV(O-UAV), STAR-RIS equipped relay UAV, and two emergency ground base stations represented by EBS-R and EBS-T. The O-UAV collects video streaming data from the AUGs, while STAR-RIS-UAV independently and simultaneously reflects and transmits the incident video streaming signal to the nearby emergency and rescue ground base stations over a fading channel. The primary aim of our suggested research is to augment the average video streaming utility(AVSU) of the AUGs efficiently. We achieve this by optimizing the locations of the O-UAV and STAR-RIS relay UAV, alongside distributing transmit power and bandwidth among the AUGs. This optimization is subject to several constraints. The formulated optimization problem is non-convex, posing a challenge in solving it. To tackle this obstacle, we introduce an iterative algorithm that effectively utilizes successive convex approximation techniques (SCA) and block coordinate descent (BCD) method. Simulation outcomes are presented, demonstrating that our proposed method offers promising improvements in terms of maximum AVSU for all AUGs compared to the benchmark schemes. Specifically, the average percentage improvement across all benchmarks is approximately 45%, underscoring the effectiveness of our approach.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"1804-1820"},"PeriodicalIF":6.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10897812","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secured Wireless Communications Using Multiple Active and Passive Intelligent Reflecting Surfaces","authors":"Apichart Nutchanat;Kampol Woradit;Paskorn Champrasert","doi":"10.1109/OJCOMS.2025.3544870","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3544870","url":null,"abstract":"This paper presents a novel approach to secure wireless communications by integrating multiple Active and Passive Intelligent Reflecting Surfaces (MAMP-IRSs). We propose a multi-hop beam routing mechanism that leverages active and passive IRSs to establish a secure communication link between a base station and a user to minimize the risk of eavesdropping. Our methodology involves optimizing the beamforming vectors at the base station and the reflection coefficients of the IRSs to maximize the secrecy rate against potential eavesdroppers. This optimization problem exhibits non-convexity, necessitating a computationally Full Search of the solution space. Therefore, we propose two heuristic algorithms: i) Skewed Dijkstra’s Algorithm and ii) Myopic Algorithm. Skewed Dijkstra’s Algorithm utilizes path decomposition and graph theory to identify optimal multi-reflection paths. The Myopic Algorithm looks up only adjacent nodes with a single hop. Numerical simulations demonstrate that Skewed Dijkstra’s Algorithm typically gives a secrecy rate close to the Full Search algorithm, and the Myopic Algorithm gives a narrower performance gap when the number of elements at each passive IRS becomes large.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"1763-1779"},"PeriodicalIF":6.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899850","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Compressed Sensing for Terahertz Ultra-Massive MIMO Channel Estimation","authors":"Ganghui Lin;Mikail Erdem;Mohamed-Slim Alouini","doi":"10.1109/OJCOMS.2025.3544871","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3544871","url":null,"abstract":"Envisioned as a pivotal technology for sixth-generation (6G) and beyond, Terahertz (THz) band communications can potentially satisfy the increasing demand for ultra-high-speed wireless links. While ultra-massive multiple-input multiple-output (UM-MIMO) is promising in counteracting the exceptionally high path loss at THz frequency, the channel estimation (CE) of this extensive antenna system introduces significant challenges. In this paper, we propose a deep compressed sensing (DCS) framework based on generative neural networks for THz CE. The proposed estimator generates realistic THz channel samples to avoid complex channel modeling for THz UM-MIMO systems, especially in the near field. More importantly, the estimator is optimized for fast channel inference. Our results show significant superiority over the baseline generative adversarial network (GAN) estimator and traditional estimators. Compared to conventional estimators, our model achieves at least 8 dB lower normalized mean squared error (NMSE). Against GAN estimator, our model achieves around 3 dB lower NMSE at 0 dB SNR with one order of magnitude lower computation complexity. Moreover, our model achieves lower training overhead compared to GAN with empirically 4 times faster training convergence.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"1747-1762"},"PeriodicalIF":6.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899780","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disaggregated and Distributed Near-Real-Time RIC for Large Scale User-Centric RAN","authors":"Amr Amrallah;Yu Tsukamoto;Takahide Murakami;Akio Ikami;Hiroyuki Shinbo;Yoshiaki Amano","doi":"10.1109/OJCOMS.2025.3543879","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3543879","url":null,"abstract":"The evolution toward user-centric radio access networks (RANs) necessitates innovative management strategies to handle growing network demands efficiently. This study presents a novel framework for the disaggregated and distributed deployment of near-real-time RAN intelligent controllers (near-RT RICs) tailored for large-scale user-centric networks. By leveraging a mixed-integer nonlinear programming (MINLP) approach, we aim to optimize both control latency and deployment costs, which are critical considerations in next-generation network management. Our proposed divide-and-conquer (D&C) and heuristic-greedy (HG)-based algorithms significantly reduce computational complexity while maintaining near-optimal performance. Simulation results indicated that the distributed framework achieved a 78% reduction in control latency with only 7% increase in deployment cost compared with the traditional centralized approach. Additionally, it achieved 99% reduction in runtime performance with an objective function value that was just 2% lower than the optimal value. These findings confirm the framework’s scalability and effectiveness, validating its suitability for operational telecom environments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"1591-1609"},"PeriodicalIF":6.3,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10896698","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scalable AP Clustering With Deep Reinforcement Learning for Cell-Free Massive MIMO","authors":"Yu Tsukamoto;Akio Ikami;Takahide Murakami;Amr Amrallah;Hiroyuki Shinbo;Yoshiaki Amano","doi":"10.1109/OJCOMS.2025.3543681","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3543681","url":null,"abstract":"Cell-free massive MIMO (CF-mMIMO) is a promising approach for future mobile networks, utilizing centralized MIMO processing for densely distributed access points (APs). In CF-mMIMO, to reduce the computational load for signal processing while meeting throughput demands, user equipment (UEs) are served by a selected number of APs. A significant challenge is AP clustering for each UE, particularly in dynamic environments with moving UEs. One approach for optimizing the AP cluster involves deep reinforcement learning (DRL). However, with numerous UEs and APs, the computational load of DRL increases due to the larger model size and higher inference frequency. To address this, we propose an AP clustering method using distributed DRL. The model focuses on determining the AP cluster for every single UE to prevent model size expansion. The per-user models act as distributed actors, enabling parallel inference. Furthermore, to suppress inference frequency, multiple UEs with low mobility are assigned to the same actor, minimizing the number of parallel actors required without compromising throughput. Numerical simulation shows that our proposed method achieves efficient AP clustering that satisfies throughput requirements with reduced computational load in DRL, even in large-scale environments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"1552-1567"},"PeriodicalIF":6.3,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10892255","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Efficient Network-Based QoE Assessment Framework for Multimedia Networks Using a Machine Learning Approach","authors":"Parsa Hassani Shariat Panahi;Amir Hossein Jalilvand;Abolfazl Diyanat","doi":"10.1109/OJCOMS.2025.3543750","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3543750","url":null,"abstract":"The Internet is integral to modern life, influencing communication, business, and lifestyles worldwide. As dependence on Internet services grows, so does the demand for high-quality service delivery. Service providers must uphold high standards of quality of service and Quality of Experience (QoE) to ensure user satisfaction. QoE, a key metric for multimedia services, reflects user satisfaction with service quality. However, measuring QoE is challenging due to its subjective nature and the complexities associated with real-time feedback.This paper presents an open-source framework for assessing QoE in multimedia networks using only key network parameters. By eliminating the need for video-specific data, this framework simplifies the traditional ITU standard for QoE assessment, achieving high accuracy in predicting Mean Opinion Scores (MOS). The framework leverages Machine Learning (ML) to model the relationship between network parameters and QoE, providing a scalable and efficient solution for real-time QoE evaluation in multimedia networks.By focusing exclusively on network metrics (e.g., delay, jitter, and packet loss), it eliminates the need for video-specific parameters to calculate MOS. Addressing the limitations of existing QoE models, the framework integrates real-time data collection, ML predictions, and adherence to international standards. This reduced-parameter approach achieves approximately 97% of the prediction accuracy of the full ITU P.1203 implementation while significantly lowering data requirements and computational demands. By enabling ITU-T P.1203 MOS score calculation without video-specific data, the framework offers a faster, scalable solution adaptable to diverse real-time multimedia services.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"1653-1669"},"PeriodicalIF":6.3,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10892313","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reduced Complexity Initial Synchronization for 5G NR Multibeam LEO-Based Non-Terrestrial Networks","authors":"Ashish Kumar Meshram;Sumit Kumar;Jorge Querol;Stefano Andrenacci;Symeon Chatzinotas","doi":"10.1109/OJCOMS.2025.3543625","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3543625","url":null,"abstract":"This paper presents a computationally efficient technique to mitigate the overall Carrier Frequency Offset (CFO) impairment introduced by the User Equipment (UE) crystal oscillator imperfection and the Doppler effect introduced by the satellite and UE movement. We assume steerable multibeam regenerative Low Earth Orbit (LEO) satellite-based 5G New Radio (NR) Non-Terrestrial Networks (NTN) under trajectory uncertainty and beam-pointing errors. The UE and the satellite do not require any prior information on the satellite ephemeris or UE location to achieve initial synchronization. The novelty of our method lies in exploiting the instantaneous satellite state vector for Doppler Pre-Compensation (DPC) to each beam of the LEO satellite relative to its Beam Center (BC). The UE then performs post-compensation to address the residual frequency offset by employing aggregated 5G New Radio (NR) Primary Synchronization Signal (PSS), followed by PSS detection, offering two-fold search space reduction during initial synchronization. We provide practical design parameters for the proposed algorithm to ensure that UE can efficiently perform initial synchronization by evaluating approximate bounds on the spot beam radius along with the No-Benefit Region (NBR) bounds of DPC. We conducted extensive simulations to assess the performance of the S- and Ka-bands under the NTN channel model and validated it with the corresponding analytical expressions conditioned on satellite trajectory uncertainty and beam-pointing error. Our method ensures that the probability of PSS detection remains above 90% while maintaining a false alarm rate of 1% at a Signal-to-Noise Ratio (SNR) as low as −6 (dB) if the standard deviation of the satellite trajectory and beam-pointing errors are within 5 (km) and 0.05°, respectively.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"1528-1551"},"PeriodicalIF":6.3,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10892320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583112","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}