IEEE Open Journal of the Communications Society最新文献

{"title":"An Overview of Performance Analysis and Optimization in Coexisting Satellites and Future Terrestrial Networks","authors":"Sirine Ben Ati;Hayssam Dahrouj;Mohamed-Slim Alouini","doi":"10.1109/OJCOMS.2025.3562152","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3562152","url":null,"abstract":"The existing radio frequency band is overcrowded and will soon be unable to accommodate the growing demand for greater data rate services. Consequently, considerable efforts are being undertaken to develop adaptable spectrum-sharing techniques and handle the unprecedented data traffic demands. Using satellite services to enhance terrestrial communications is emerging as a cutting-edge solution for future application-aware networks. The coexistence of space and ground communications infrastructures, however, introduces exacerbated levels of wireless interference. Hence, intelligent resource allocation strategies are essential to ensure dependable communication and high-capacity broadband access globally. To this end, this manuscript surveys the advances in optimization and performance analysis methods in coexisting satellites networks and future wireless systems. The paper first presents an overview of the existing papers related to the more general scope of space-air-ground communications. The survey then highlights optimization frameworks for coexisting satellites and sixth-generation communication systems (6G), focusing on maximizing sum rate, maximizing energy efficiency,and minimizing transmit power. Additionally, the survey presents a number of papers dealing with performance analysis, namely outage probability, ergodic capacity, average symbol error rate, and effective capacity. The paper further discusses the critical challenges imposed by such integrated systems, e.g., backhaul-access cross-interference, resource coordination, channel modeling, and algorithmic complexity. Finally, we identify several open research issues, e.g., a cognitive radio approach to the considered network, integrated backhaul-access system, integration with emerging disruptive systems, artificial intelligence, and quantum communication technologies for coexisting satellites and future terrestrial networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3834-3852"},"PeriodicalIF":6.3,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896486","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":"A Matching Game for LLM Layer Deployment in Heterogeneous Edge Networks","authors":"Benedetta Picano;Dinh Thai Hoang;Diep N. Nguyen","doi":"10.1109/OJCOMS.2025.3561605","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3561605","url":null,"abstract":"With the growing demand for computational and storage capabilities of modern learning models, performing their computation exclusively in a centralized manner has become increasingly impractical. Executing the inference of foundation models in a distributed manner presents significant challenges, particularly in optimizing both computing and communication resources. This work introduces a novel deployment scheme for large language model (LLM) layers that jointly considers computation and communication efficiency within an edge network environment to address these issues. Specifically, we resort to the matching theory to effectively orchestrate the distributed deployment of the LLM layers across the edge nodes of the networks, where nodes have varying computational capacities and communication speed. This framework is based on a two-sided game, enabling each layer to express its individual preferences for node allocation while allowing nodes to prioritize their preferred layers. This mutual selection process minimizes inference latency in the learning process and models the bubble time as game externalities, assuming a sequential pipeline execution. The algorithmic solution reaches a stable matching outcome. Performance evaluation was conducted considering both simulations and a small-scale testbed to measure the effectiveness of the proposed algorithm compared to state-of-the-art alternatives. In particular, the small-scale testbed was developed to distribute an LLM to support autonomous driving, leveraging the vision-language model paradigm. The results highlight performance improvements of up to around 10% in comparison to the Koklata game alternative.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3795-3805"},"PeriodicalIF":6.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10966456","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896342","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":"ML-Aided Dynamic BSR Periodicity Adjustment for Enhanced UL Scheduling in Cellular Systems","authors":"Nadezhda Chukhno;Salwa Saafi;Sergey Andreev","doi":"10.1109/OJCOMS.2025.3561002","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3561002","url":null,"abstract":"Contemporary research has revealed a limitation in the Uplink (UL) Buffer Status Report (BSR) scheduling procedure – its reliance on outdated information. In addition, a significant limitation in current BSR implementations lies in their inflexibility. The 3rd Generation Partnership Project (3GPP) specifications constrain BSR periodicities to certain quantized values based on Quality of Service (QoS) requirements for various applications. For instance, applications demanding low latency may require very small BSR periodicities, resulting in substantial overhead due to frequent BSR reports. This may result in the wastage of network resources in case of a low BSR periodicity setting. Alternatively, a high BSR periodicity setting may lead packets to wait more at the user buffer and thus result in higher packet latencies. To address these limitations, we propose a framework that predicts time intervals between packet arrivals and subsequently adjusts the BSR periodicity according to the predicted traffic arrivals. The simulation results demonstrate that the proposed Machine Learning (ML)-aided BSR reporting provides flexibility in BSR periodicity adapted to the intensity of traffic arrival and converges to optimal periodicity depending on the mean traffic arrival rate.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3513-3527"},"PeriodicalIF":6.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10965763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883328","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":"THz Connect: Energy-Efficient 6G Small Cells With Fuzzy Power Control and Advanced Clustering","authors":"Preksha Jain;Akhil Gupta;Nitin Rakesh;Rashmi Sharma;Manas Ranjan Pradhan","doi":"10.1109/OJCOMS.2025.3561014","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3561014","url":null,"abstract":"The increasing number of users in 6G networks requires more base stations. Mobile small cell BSs solve this but complicate the power allocation and clustering, which becomes even more challenging with a dynamic system topology as the user density increases. Computational Intelligence-based systems can support addressing this problem via the learning-based mechanism for making near-optimal decisions. In this paper, we combine mobile BS, THz communication, massive multiple input multiple outputs (mMIMO), non-orthogonal multiple access (NOMA), and device-to-device (D2D) technologies in a 6G HetNet environment. We propose a power allocation and clustering scheme to maximize the sum rate of the proposed 6G mobile small cell system. First, we propose a green 6G mobile small-cell model with the help of THz, NOMA, and D2D. Then, a novel fuzzy logic system that exploits channel gains to design optimum power allocation in NOMA is proposed. Further, we propose an advanced clustering scheme that effectively integrates user device energy, proximity, and antenna parameters to reduce interference and enhance the energy efficiency of MSBS.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3882-3891"},"PeriodicalIF":6.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10965731","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Complexity RSMA Approach for Enhanced Multi-User Decode-and-Forward Relay Systems","authors":"Ahmet Sacid Sümer;Mehmet Mert Şahin;Hüseyin Arslan","doi":"10.1109/OJCOMS.2025.3560826","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3560826","url":null,"abstract":"Rate-Splitting Multiple Access (RSMA) has emerged as a robust transmission strategy for multi-antenna wireless systems. This paper investigates the performance of RSMA in a downlink Decode-and-Forward (DF) relay network under imperfect Channel State Information (CSI) at both the transmitter and the relay. The system operates in two phases: in the first phase, the Base Station (BS) transmits signals to both BS Users (BUs) and the relay; in the second phase, the relay decodes and forwards the signals to Relay Users (RUs) located outside the BS coverage area. RSMA is employed for facilitating transmission from both the BS and the relay. To optimize the network performance, we derive a tractable lower bound for the ergodic sum-rate, which enables the power allocation coefficients of common and private streams in the RSMA structures to maximize the overall sum-rate in both phases. The simulation results demonstrate that the proposed power allocation algorithm, coupled with a low-complexity precoding design, significantly improves the sum-rate performance of DF relay RSMA networks compared to scenarios where RSMA is not utilized. Notably, RSMA outperforms Spatial Division Multiple Access (SDMA)-based benchmarks, achieving sum-rate gains of up to 81%. Furthermore, a three-user use-case scenario is examined, revealing that RSMA consistently surpasses Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA)-based benchmarks, even in the presence of imperfect channel state information (CSI) at both the transmitter and the relay.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3904-3919"},"PeriodicalIF":6.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10965750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918652","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":"Online Charger-Placement Algorithm for Sustainable Energy-Harvesting Wireless Sensor Networks","authors":"Ammar M. Gharaibeh;Osamah S. Badarneh;Mustafa K. Alshawaqfeh;Fares S. Almehmadi","doi":"10.1109/OJCOMS.2025.3560310","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3560310","url":null,"abstract":"Energy Harvesting (EH) is envisioned as one of the potential solutions for a sustainable Wireless Sensor Networks, addressing the challenges of the scarcity of energy resources. In EH, the sensors replenish their batteries from a wireless signal received from a charging station, thus prolonging the network’s lifetime. In this paper, we investigate the strategic placement of the charging stations. This paper primarily contributes by proposing an online algorithm for strategic placement of charging stations, a critical challenge when future charging requests from sensors are unknown. The problem is initially formulated as an Integer Linear Program (ILP) that minimizes a cost function related to the average charging time of the sensor nodes. It is shown analytically that the online algorithm achieves a competitive ratio of <inline-formula> <tex-math>$mathcal {O}(log (J)log (I))$ </tex-math></inline-formula>, with a probability of success of <inline-formula> <tex-math>$1 - {}frac {1}{J}$ </tex-math></inline-formula>, where J is the number of sensors, and I is the number of charging stations. Simulation results show the ILP achieves at least 40% increase in the total harvested energy while reducing the total costs by at least 12% when compared to fixed deployment of the charging stations at the center of the network, as well as certain scenarios where the online algorithm outperforms the fixed deployment in all metrics.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3502-3512"},"PeriodicalIF":6.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964260","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883438","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":"Attenuation Modeling Using Physics Guided Deep Reinforcement Learning: A Channel Estimation Use Case","authors":"P. Mithillesh Kumar;M. Supriya","doi":"10.1109/OJCOMS.2025.3560319","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3560319","url":null,"abstract":"Along the path of propagation, the radio waves are subjected to a number of losses such as attenuation, refraction, obstruction etc., which can affect the signal strength and quality. Attenuation can be caused even due to changes in environmental conditions along the path of propagation. The impact of rainfall attenuation is mathematically modelled using the recommendations from International Telecommunication Union. These real time physical losses are modelled using the approach of providing the physical losses to the neural architecture. In this work, the physical loss information is provided to the neural architecture. From the results of the simulation, it can be noted that the model has learnt the variations in the dynamic environment when exposed to environmental changes and shows scientifically consistent performance. Proximal Policy optimization algorithm has exhibited better network utility and higher training rewards in comparison to Advantage Actor Critic algorithm.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3696-3709"},"PeriodicalIF":6.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964241","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896297","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":"Future of Connectivity: A Comprehensive Review of Innovations and Challenges in 7G Smart Networks","authors":"Vinay Chamola;Mritunjay Shall Peelam;Mohsen Guizani;Dusit Niyato","doi":"10.1109/OJCOMS.2025.3560035","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3560035","url":null,"abstract":"The evolution from 1G to 6G networks has transformed global communication, progressing from basic voice calls in 1G to the immersive, AI-enabled experiences of 6G. As emerging AI-driven applications like autonomous systems, the Internet of Everything (IoE), and immersive technologies demand unprecedented capabilities, 7G networks are set to redefine connectivity by overcoming the limitations of earlier generations. This paper comprehensively reviews the innovations and challenges in 7G networks, focusing on integrating advanced AI and machine learning paradigms such as meta-learning, incremental learning, distributed intelligence, and reinforcement learning to enhance adaptability, resource allocation, and edge performance. The review also examines the role of Large Language Models (LLMs) in enabling real-time actionable intelligence and optimizing edge devices within 7G. The paper highlights the use of technologies, including blockchain for decentralized security, quantum computing for robust encryption, terahertz communication for ultra-fast data transfer, zero-energy solutions for sustainability, and generative AI for intelligent network optimization and automation. By addressing these challenges and exploring cutting-edge strategies, this paper envisions 7G networks as the foundation for a secure, intelligent, and sustainable digital future, equipped to combat emerging cyber warfare threats, enhance resilience against technological disruptions, and support innovations across smart cities, autonomous systems, healthcare, and industrial IoT.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3555-3613"},"PeriodicalIF":6.3,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10963909","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896477","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":"Device-Free Floor-Scale Human Detection With Indoor LTE Antennas","authors":"Sitian Li;Alexios Balatsoukas-Stimming;Andreas Burg","doi":"10.1109/OJCOMS.2025.3560153","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3560153","url":null,"abstract":"This paper introduces a novel method for device-free human detection by leveraging existing wireless communication signals from 4G-long-term evolution (4G-LTE) systems. By utilizing the pervasive 4G-LTE signals, our approach enhances the efficiency and coverage of human presence detection compared to WiFi signal based approaches. A previously overlooked, but crucial human presence scenario involving subtle human activities is successfully addressed and detected. Effective human presence detection relies heavily on precise feature extraction from channel estimates and careful feature selection. Through a detailed analysis and comparison of features discussed in previous work, along with the introduction of new features, we develop a machine learning-based approach to identify the most effective features for detecting human presence. Our machine learning model, trained with these selected features, is tested across different buildings and various scenarios using a commercial 4G-LTE network. The results demonstrate that our selected features significantly enhance detection accuracy and robustness, outperforming features introduced in previous literature across diverse environments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3683-3695"},"PeriodicalIF":6.3,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10963839","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896417","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":"SIM-IPS: Stacked Intelligent Metasurface-Based Indoor Positioning System","authors":"Aisha Javed;Naveed Ul Hassan;Marco Di Renzo;Chau Yuen","doi":"10.1109/OJCOMS.2025.3559946","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3559946","url":null,"abstract":"In this paper, we introduce a stacked intelligent metasurface-based indoor positioning system (SIM-IPS), which is a novel IPS developed using an SIM, a transmissive metasurface with multiple layers of independently controlled meta-atoms. These layers function analogously to the hidden layers in a deep neural network (DNN) and enable advanced beamforming in the electromagnetic (EM) wave domain, thus improving the control of the power distribution at the receiver plane. The SIM-IPS setup includes a single Wi-Fi access point (AP) dedicated to localization, which transmits all of its power through the SIM for beamforming. This approach utilizes two distinct configurations types for improved performance: zone identification, which determines the zone where the user is located, and precise localization, which uses random radiation patterns to pinpoint the exact location of the user within the identified zone. We employ the gradient descent algorithm and the alternating optimization (AO) technique to optimize the phase shifts of meta-atoms across different SIM layers for zone configuration. The resulting radiation patterns are stored in a database. During the online phase of the IPS, the AP cycles through the available configurations, and the user provides received signal strength indicator (RSSI) measurements to estimate their location. To ensure high localization accuracy, we optimize the selection of configurations and fine-tune system parameters to achieve good performance. Simulations demonstrate a substantial increase in power concentration at target locations within specific zones by incorporating additional SIM layers. This refined power distribution enables precise differentiation between the target zone and surrounding areas, significantly increasing localization accuracy. Furthermore, we present a detailed comparison of localization accuracy improvements across varying numbers of zones and SIM layers, emphasizing the critical importance of parameter optimization for enhancing the overall performance of SIM-IPS. Specifically, we achieve optimal accuracy with an 8 zone system and 5 layers, attaining a localization accuracy of 1.72 m and 1.66 m by considering continuous-valued and discrete-valued phase shifts, respectively.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3528-3542"},"PeriodicalIF":6.3,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10963905","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883486","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}