IEEE Open Journal of the Communications Society最新文献

{"title":"Cross-Layer Integrated Sensing and Communication: A Joint Industrial and Academic Perspective","authors":"Henk Wymeersch;Nuutti Tervo;Stefan Wänstedt;Sharief Saleh;Joerg Ahlendorf;Ozgur Akgul;Vasileios Tsekenis;Sokratis Barmpounakis;Liping Bai;Martin Beale;Rafael Berkvens;Nabeel Nisar Bhat;Hui Chen;Shrayan Das;Claude Desset;Antonio de la Oliva;Prajnamaya Dass;Jeroen Famaey;Hamed Farhadi;Gerhard P. Fettweis;Yu Ge;Hao Guo;Rreze Halili;Katsuyuki Haneda;Abdur Rahman Mohamed Ismail;Akshay Jain;Sylvaine Kerboeuf;Musa Furkan Keskin;Emad Ibrahim;Bilal Khan;Siddhartha Kumar;Stefan Köpsell;Apostolos Kousaridas;Pekka Kyösti;Simon Lindberg;Mohammad Hossein Moghaddam;Ahmad Nimr;Victor Pettersson;Aarno Pärssinen;Basuki Priyanto;Athanasios Stavridis;Tommy Svensson;Sonika Ujjwal","doi":"10.1109/OJCOMS.2025.3595459","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3595459","url":null,"abstract":"Integrated sensing and communication (ISAC) enables radio systems to simultaneously sense and communicate with their environment. This paper, developed within the Hexa-X-II project funded by the European Union, presents a comprehensive cross-layer vision for ISAC in 6G networks, integrating insights from physical-layer design, hardware architectures, AI-driven intelligence, and protocol-level innovations. We begin by revisiting the foundational principles of ISAC, highlighting synergies and trade-offs between sensing and communication across different integration levels. Enabling technologies (such as multiband operation, massive and distributed MIMO, non-terrestrial networks, reconfigurable intelligent surfaces, and machine learning) are analyzed in conjunction with hardware considerations including waveform design, synchronization, and full-duplex operation. To bridge implementation and system-level evaluation, we introduce a quantitative cross-layer framework linking design parameters to key performance and value indicators. By synthesizing perspectives from both academia and industry, this paper outlines how deeply integrated ISAC can transform 6G into a programmable and context-aware platform supporting applications from reliable wireless access to autonomous mobility and digital twinning.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"6966-7015"},"PeriodicalIF":6.3,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11112522","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027964","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":"Simultaneous RIS Adjustment and Transmission Based on Markov Chain Monte Carlo and Simulated Annealing","authors":"Ilya Burtakov;Arseny Poyda;Aleksey Kureev;Evgeny Khorov","doi":"10.1109/OJCOMS.2025.3595217","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3595217","url":null,"abstract":"Reconfigurable Intelligent Surface (RIS) is a promising solution for enhancing the coverage and capacity of current and future wireless systems. To fully exploit its potential, RIS requires a fast adjustment algorithm, which determines the optimal reflection phase for each RIS Unit Cell (UC) and provides rapid and reliable data transmission. Existing statistical RIS adjustment algorithms typically rely on extensive channel measurements obtained through uniform random sampling of RIS configurations. Namely, each UC phase is selected with equal probability independently of the other UC phases. However, these random configurations result in low RIS channel gains during the adjustment process, which hinders data transmission. This paper addresses this issue and proposes a statistical RIS adjustment algorithm using Markov Chain Monte Carlo and Simulated Annealing (MCMC-SA). MCMC-SA chooses RIS configurations during adjustment according to the transition probabilities of a Markov chain. This feature allows MCMC-SA to improve the system capacity and simultaneously harvest performance gains during RIS adjustment. MCMC-SA can be used in large RISs with hundreds of elements and its complexity does not depend on the number of possible phase shifts on each UC. Also, it exhibits flexible parameter variability, which allows it to be robust to noise. Numerical results demonstrate that MCMC-SA significantly outperforms existing statistical RIS adjustment algorithms in signal-to-noise ratio (SNR) during the adjustment.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"6506-6516"},"PeriodicalIF":6.3,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11111700","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904828","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 KLJN-Based Thermal Noise Modulation Scheme With Enhanced Reliability for Low-Power IoT Communication","authors":"Mohammed A. Salem;Hossam M. Kasem;Roayat I. Abdelfatah;Mohamed Youssef El-Ganiny;Radwa A. Roshdy","doi":"10.1109/OJCOMS.2025.3595087","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3595087","url":null,"abstract":"Kirchhoff’s Law Johnson Noise (KLJN)-based thermal noise modulation (Ther-Mod) represents a viable answer for secure Internet of Things (IoT) communication at ultra-low power levels. However, the conventional symmetric KLJN systems are not reliable, unless a large number of noise samples are employed, giving rise to a high latency and a low effective bitrate. We propose P-Ther-Mod, a new asymmetric KLJN-based modulation scheme, which utilizes a four-resistor structure in order to improve the bit error rate (BER) without imposing additional noise samples per bit. Theoretical bit error probability (BEP) expressions are developed for both wired and wireless IoT channels where the additive white Gaussian noise (AWGN) and Rayleigh fading models are considered. Simulation results demonstrate that the proposed P-Ther-Mod technique reduces the noise samples by up to 35% (e.g., reaching BER <inline-formula> <tex-math>${=} 10{^{-}5 }$ </tex-math></inline-formula> at N <inline-formula> <tex-math>${=}35$ </tex-math></inline-formula> compared to BER <inline-formula> <tex-math>${=}10^{-5}$ </tex-math></inline-formula> at N <inline-formula> <tex-math>${=}50$ </tex-math></inline-formula> in existing methods), and achieves BER <inline-formula> <tex-math>$approx ~10^{-8}$ </tex-math></inline-formula> at N = 200, improving previous approaches by 5 orders of magnitude. The asymmetric approach allows balancing higher bit rates by decreasing oversampling of noise. Furthermore, optimizing the detection threshold parameter (delta representing signal-to-noise ratio (SNR) in detection) enhances robustness and enables 7 dB savings in the SNR even under the fading channel. These developments render P-Ther-Mod a reliable, secure and scalable approach for IoT deployments with a vulnerable environment to interference, such as industrial sensor networks and wearable devices.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"6336-6351"},"PeriodicalIF":6.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11107228","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888626","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 Reinforcement Learning-Based Joint Trajectory Design and Resource Allocation for Secure and Energy-Efficient UAV Networks","authors":"Abdulmalik Alwarafy;Suhib Bani Melhem;Rand Abou Chahine;Batool Said;Maitha Alharethi;Latifa Almazrouei;Sara Alblooshi","doi":"10.1109/OJCOMS.2025.3594373","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3594373","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) have been extensively used recently for wireless networks. However, such networks encounter several challenges that remain unsolved. In this paper, we address the issue of joint optimization of trajectory design and resource allocation in UAV-based wireless networks in the presence of eavesdroppers. We first formulate an optimization problem with the objective to maximize a utility function defined in terms of secrecy rate, energy utilization efficiency, and interference. Due to the high dimensionality and non-convex nature of the formulated problem, we propose a Proximal Policy Optimization (PPO)-based Deep Reinforcement Learning (DRL) algorithm to solve the problem and learn the environment. Our proposed PPO algorithm solves the problem by jointly controlling the 3D position of UAVs, power, and energy harvesting. Simulation results demonstrate the efficiency of the proposed algorithm in solving the problem, learning the environment dynamics, and its superiority over some existing conventional and DRL-based methods.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"6491-6505"},"PeriodicalIF":6.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11106477","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904725","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":"Design of SIMO M-QAM System for Ultrasonic Communications on Shallow Waters","authors":"Unai Fernández-Plazaola;Francisco J. Cañete;Eduardo Martos-Naya;Luis Díez","doi":"10.1109/OJCOMS.2025.3594489","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3594489","url":null,"abstract":"In this work, we present a transmission system that uses single-carrier modulation M-ary symbols quadrature amplitude modulation (M-QAM) for underwater acoustic communications (UAC) in the ultrasonic band. It exploits the diversity that four hydrophones provide at reception in a single-input multiple-output (SIMO) <inline-formula> <tex-math>$1times 4$ </tex-math></inline-formula> configuration and also includes, for each stream, a phase-locked loop (PLL) for synchronization and a turbo-equalization subsystem, with fractionally-spaced equalizer (FSE) and decision feedback equalizer (DFE) filters, adaptive by means of least mean squares (LMS) algorithm, both of which are driven by an low-density parity check (LDPC) decoder decisions. The paper describes the system design and analyze its performances on trials carried out on shallow waters of the Mediterranean sea. Achievable data rates exceeding 200 kb/s between links of transmission distances longer than 200 m are obtained with a satisfactory low bit-error rate (BER).","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"6323-6335"},"PeriodicalIF":6.3,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11105458","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891096","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":"QoS-Aware Link Adaptation for Beyond 5G Networks: A Deep Reinforcement Learning Approach","authors":"Ali Parsa;Neda Moghim;Sachin Shetty","doi":"10.1109/OJCOMS.2025.3593836","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3593836","url":null,"abstract":"Modern wireless communication systems face increasingly complex challenges due to rapidly changing channel conditions and the growing diversity of application-specific Quality of Service (QoS) requirements. Traditional link adaptation mechanisms primarily aim to maximize throughput and often lack the flexibility to support emerging applications, such as Extended Reality (XR) and Virtual Reality (VR), which demand simultaneous guarantees for high data rates, ultra low latency, and high reliability. These stringent and multidimensional QoS needs call for more intelligent and adaptive solutions. In this paper, we propose QDRLLA (QoS-aware Deep Reinforcement Learning-based Link Adaptation), a novel framework that employs deep reinforcement learning to dynamically adjust key link parameters, including modulation and coding schemes, transmission power, and subcarrier spacing, based on the QoS requirements of each application. QDRLLA learns from the environment and past observations to make informed decisions that go beyond conventional heuristic-based methods. Through extensive simulations, we demonstrate that QDRLLA significantly improves compliance with QoS targets across a range of network conditions and application types. It also improves energy efficiency by avoiding unnecessary retransmissions and optimizing resource usage. These results underscore the effectiveness of QDRLLA in supporting the complex service requirements of next-generation wireless networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"6368-6382"},"PeriodicalIF":6.3,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11104833","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891267","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":"Edge Server Placement and Task Allocation for Maximum Delay Reduction","authors":"Koki Shibata;Sumiko Miyata","doi":"10.1109/OJCOMS.2025.3593641","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3593641","url":null,"abstract":"When edge computing is deployed for delay-sensitive applications such as autonomous driving systems and online gaming, it is important to reduce the maximum delay because real-time performance for all users must be ensured from Quality-of-Service (QoS) perspective. The primary delays in edge computing include network delay during data transmission and waiting time at the edge server. Since the waiting time at edge servers depends on server utilization, an increase in utilization bias leads to an increase in maximum delay. If a user is extremely far from the edge server, the network delay for that user will also increase. Conventional edge computing methods focus on reducing the average propagation delay of user-processing requests (tasks). However, these methods increase the utilization variance of each edge server, thus increasing the maximum delay. In this paper, we propose a method for determining both edge server placement and task allocation to reduce the maximum delay. Our method uses a genetic algorithm to optimize server utilization and the distance between users and servers. The maximum delay has been successfully reduced compared with that using conventional methods by simultaneously optimizing the server utilization and distance between users and servers.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"6207-6217"},"PeriodicalIF":6.3,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11099545","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852695","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":"Fact-Checking 5G Security: Bridging the Gap Between Expectations and Reality","authors":"Oscar Lasierra;Norbert Ludant;Gines Garcia-Aviles;Esteban Municio;Guevara Noubir;Antonio Skarmeta;Xavier Costa-Pérez","doi":"10.1109/OJCOMS.2025.3593140","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3593140","url":null,"abstract":"5G cellular systems are currently being deployed worldwide delivering the promised unprecedented levels of throughput and latency to hundreds of millions of users. At such scale and reach, security is crucial. Consequently, the 5G standard includes a new series of features to improve the security of its predecessors (i.e., 3G and 4G). In this work, we evaluate the security of currently deployed 5G commercial networks in Europe and North America. Specifically, by collecting 5G signaling traffic in the wild in several cities in Spain, Germany, France, Canada, and the USA, we i) fact-check which 5G security enhancements are implemented in current deployments, ii) provide a rich overview of the implementation status of each 5G security feature in a selection of 5G commercial networks in Europe and North America and compare it with previous results in China, iii) analyze the implications of optional features not being deployed, and iv) discuss on the still remaining 4G-inherited vulnerabilities. Our findings indicate that the rollout of 5G security features in the analyzed commercial networks is still a work in progress. On the one hand, several networks continue to rely on 4G for their core network operations, which hinders the deployment of new security features (e.g., SUCI) and, on the other hand, fully-fledged 5G deployments lack mandatory security measures such as GUTI reallocation after paging. Moreover, we find that some operators fail to provide proper temporary identifier randomization, in both 4G and 5G networks. Some of the obtained results are aligned with results previously reported from China <xref>[1]</xref> and keep the European and North American studied networks vulnerable to some 4G attacks, during their migration period from 4G to 5G. Conversely, studied networks deployed in North America exhibit stronger adherence to 5G security standards, with near-complete compliance observed, in contrast to deployments in China and Europe, where comparatively lower compliance levels have been observed.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"6242-6257"},"PeriodicalIF":6.3,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11098478","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891004","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":"Discrete Phase Shift IRS-Assisted Energy Harvesting in Cognitive Radio Networks With Spectrum Sensing","authors":"Lilian Chiru Kawala;Guoquan Li;Mihertie Habtamu Demeke;Junzhou Xiong;Hao Xiong;Hang Hu","doi":"10.1109/OJCOMS.2025.3592936","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3592936","url":null,"abstract":"The rapid growth in wireless device usage has intensified the demand for spectrum resources, leading to inefficiencies in traditional resource allocation methods. Cognitive Radio Networks (CRNs) address this by enabling secondary users (SUs) to access licensed spectrum bands of primary users (PUs) without compromising their Quality of Service (QoS). However, CRNs face challenges such as limited battery life and potential interference with PUs. Energy Harvesting (EH) techniques, particularly RF-based EH, offer a solution by powering CRN terminals, thereby enhancing spectrum utilization efficiency. Simultaneously, Intelligent Reflecting Surfaces (IRSs) have emerged as a powerful technology to enhance wireless propagation environments and support RF-based EH in CRNs. Despite this potential, most existing IRS-assisted CRN frameworks assume ideal continuous phase shifts, an impractical assumption given hardware limitations that permit only discrete phase levels, leading to quantization errors and increased design complexity. In this paper, we establish a unified system model for IRS-assisted Multiple Input Single Output (MISO) EH-CRNs that formulates an optimization problem to maximize SU throughput under practical constraints, including discrete IRS phase shifts, beamforming design, false alarm control, energy causality, and SU Quality of Service (QoS) requirements. To solve the non-convex problem, we develop a quantization-aware alternating optimization algorithm that decomposes the problem into interrelated subproblems for detection probability maximization, false alarm minimization, energy harvesting optimization, and SU throughput enhancement. Advanced techniques such as semidefinite relaxation (SDR), Successive Convex Approximation (SCA), and Nearest Point Search with Penalty (NPSP) are utilized to address practical implementation constraints. Simulation results demonstrate the superior performance of the proposed framework and the novel resource allocation algorithm based on alternating optimization. These results highlight the transformative potential of IRS with discrete phase shifts in enhancing EH-CRN efficiency, particularly in improving energy harvesting and SU throughput under practical constraints.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"6551-6565"},"PeriodicalIF":6.3,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11098521","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904743","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":"STARMAP: Spaceborne Target Acquisition Radar With Meta-RL Assisted Placement","authors":"Alireza Famili;Shihua Sun;Tolga Atalay;Angelos Stavrou","doi":"10.1109/OJCOMS.2025.3593088","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3593088","url":null,"abstract":"The urgent requirement to monitor and identify unmanned aerial systems (UASs) within restricted airspace has become increasingly critical. Traditional methods fail to detect low-observable (LO) UASs effectively, thus presenting considerable threats in defense and civil sectors. While radar systems are traditionally favored for their robust detection capabilities, the standard active radar configurations—where transmitters are collocated with receivers—face numerous operational challenges. A more promising solution is adopting passive radar technology, which leverages ambient environmental signals, thereby obviating the need for proprietary transmitters. In this vein, we introduce STARMAP framework, a cutting-edge methodology employing spaceborne illuminators. This approach is particularly effective for extensive range operations such as the surveillance of missiles and fighter jets at high altitudes. Despite the benefits, the major challenges include measuring distance and pinpointing targets, exacerbated by the unknown positions of transmitters. STARMAP overcomes these limitations by integrating time difference of arrival (TDOA) methods with bistatic Doppler shift evaluations. A crucial yet often neglected aspect in passive radar systems is the influence of receiver spatial configuration on localization precision. STARMAP underscores the necessity to optimize the arrangement of receivers to minimize errors induced by unfavorable geometries, a task complicated by its NP-hard nature. To address this, we have developed an advanced meta-reinforcement learning (meta-RL) algorithm, enhancing a double deep Q-network (DDQN) to optimize receiver placement. Through rigorous testing across various scenarios and dimensions, our findings demonstrate that STARMAP substantially improves localization accuracy by reducing geometry-induced errors compared to traditional placement strategies.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"6218-6241"},"PeriodicalIF":6.3,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11098477","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853424","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}