IEEE Open Journal of the Communications Society最新文献

Guest Editorial Special section on Emerging Modulation Techniques Toward 6G Networks 嘉宾编辑专题：面向6G网络的新兴调制技术

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2025-06-20 DOI: 10.1109/OJCOMS.2025.3571304

Miaowen Wen;Tianqi Mao;Ertugrul Basar;Gunes Karabulut Kurt;Zhaocheng Wang;Naofal Al-Dhahir

引用次数: 0

Guest Editorial Special Section on Generative AI and Large Language Models Enhanced 6G Wireless Communication and Sensing 嘉宾编辑专题：生成人工智能和大型语言模型增强6G无线通信和传感

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2025-06-19 DOI: 10.1109/OJCOMS.2025.3571515

Jiacheng Wang;Geng Sun;Dusit Niyato;Hina Tabassum;Gabriel-Miro Muntean;Nelson Fonseca

引用次数: 0

GLIM: Generalized Detection of Low-SNR Signals Using an Iterative Feedback Model 基于迭代反馈模型的低信噪比信号广义检测

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2025-06-03 DOI: 10.1109/OJCOMS.2025.3576207

Nathaniel W. Rowe;Dola Saha

{"title":"GLIM: Generalized Detection of Low-SNR Signals Using an Iterative Feedback Model","authors":"Nathaniel W. Rowe;Dola Saha","doi":"10.1109/OJCOMS.2025.3576207","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3576207","url":null,"abstract":"Accurate detection of unknown signals in low signal-to-noise ratio environments has utility in many wireless applications, such as opportunistic spectrum sharing, signal localization, and operations in long-range scenarios. Existing methods rely largely on signal processing-based techniques that perform poorly at lower energies, or machine learning techniques that rely on well-structured, offline training data with known signal labels sufficient for model training. This is impractical in environments where labeled training data is limited or difficult to obtain, such as for the detection of unknown signals that may or may not have been previously observed. To overcome these challenges, this paper introduces a novel feedback architecture for pseudo-label generation in an online-learning paradigm to detect wireless signals without a priori signal knowledge or model pre-training. The methodology improves upon digital signal processing-based techniques in low-energy detection, and performs within 3 dB of deep learning-based models trained with known signal labels, without similar limitations. The iterative architecture exhibits generalized learning as new, unknown signals are introduced to its online detection method. It is generalized for varying waveforms, sequence lengths and timing offsets, and its practical design and implementation make it ready for adoption in realistic scenarios.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4854-4873"},"PeriodicalIF":6.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11022741","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314758","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}

引用次数: 0

Channel Estimation for Wideband Multi-RIS-Assisted mmWave Massive-MIMO OFDM System With Beam Squint Effect 具有波束斜视效应的宽带多ris辅助毫米波海量mimo OFDM系统信道估计

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2025-06-02 DOI: 10.1109/OJCOMS.2025.3575947

Thabang C. Rapudu;Olutayo O. Oyerinde

{"title":"Channel Estimation for Wideband Multi-RIS-Assisted mmWave Massive-MIMO OFDM System With Beam Squint Effect","authors":"Thabang C. Rapudu;Olutayo O. Oyerinde","doi":"10.1109/OJCOMS.2025.3575947","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3575947","url":null,"abstract":"Reconfigurable intelligent surfaces (RISs) and massive multiple-input multiple-output (massive-MIMO) systems are promising technologies for improving the energy efficiency of millimeter-wave (mmWave) communication. Furthermore, in urban areas, where there is high obscurity, multiple RISs can be deployed to circumvent blockages between communicating nodes. However, deploying both multi-RIS and massive-MIMO systems significantly increases the dimensionality of a wireless communication channel and thus, accurate channel state information (CSI) acquisition by channel estimation (CE) becomes non-trivial mainly due to the passive nature of the RISs. Additionally, existing wideband RIS-assisted CE schemes ignore the beam squint effect despite its severe CE performance degradation. Therefore, in this paper, a beam squint aware machine learning (ML)-based uplink CE scheme for wideband multi-RIS-assisted mmWave massive-MIMO orthogonal frequency division multiplexing (OFDM) system is proposed. Specifically, to reduce the beam squint effect, the bandwidth of the system is divided into subbands, and thereafter, a denoising convolutional neural network bidirectional long-short term memory (DnCNN-Bi-LSTM) scheme is proposed for cascaded uplink CE. For certain parameter settings, the proposed beam squint aware DnCNN-Bi-LSTM CE scheme achieves better normalized minimum mean squared error (NMSE) performance than the state-of-the-art beam squint aware CE methods.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4804-4817"},"PeriodicalIF":6.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11021458","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289259","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}

引用次数: 0

Erratum to “UWB and GNSS Sensor Fusion Using ML-Based Positioning Uncertainty Estimation” “基于ml的定位不确定性估计的UWB和GNSS传感器融合”的勘误

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2025-06-02 DOI: 10.1109/OJCOMS.2025.3571303

Mihkel Tommingas;Taavi Laadung;Sander Varbla;Ivo Muursepp;Muhammad Mahtab Alam

引用次数: 0

Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems 无人机- ris认知系统隐蔽通信中深度学习驱动的吞吐量最大化

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2025-04-30 DOI: 10.1109/OJCOMS.2025.3565764

Van Nhan Vo;Nguyen Quoc Long;Viet-Hung Dang;Tu Dac Ho;Hung Tran;Symeon Chatzinotas;Dinh-Hieu Tran;Surasak Sanguanpong;Chakchai So-In

{"title":"Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems","authors":"Van Nhan Vo;Nguyen Quoc Long;Viet-Hung Dang;Tu Dac Ho;Hung Tran;Symeon Chatzinotas;Dinh-Hieu Tran;Surasak Sanguanpong;Chakchai So-In","doi":"10.1109/OJCOMS.2025.3565764","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3565764","url":null,"abstract":"This paper examines a cognitive radio (CR) nonorthogonal multiple access (NOMA) system in which an unmanned aerial vehicle equipped with a reconfigurable intelligent surface (UAV-RIS) plays two roles: relaying and friendly jamming. The communication protocol has two phases. The first is an energy harvesting phase in which the UAV harvests radio frequency energy from a power beacon. In the second phase, a secondary transmitter (ST) simultaneously sends superimposed signals to secondary receivers (SRs) (a public SR and a covert SR) via NOMA with the assistance of the UAV-RIS. Then, a UAV warden and a UAV jammer launch a cooperative attack, in which the first adversary wiretaps the signals from the ST and UAV-RIS, whereas the second interferes with the SRs to force the ST to increase its transmit power. For improved secrecy, the UAV-RIS uses its harvested energy to combat the UAV warden. For this system, the secrecy performance is evaluated on the basis of the concept of covert communication. In particular, optimization algorithms are employed to maximize the covert SR throughput under outage probability and security constraints. A deep neural network model is subsequently trained to discover the relationships between the environmental parameters and optimized parameters to enable rapid adaptation to environmental conditions.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4140-4155"},"PeriodicalIF":6.3,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10980338","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073252","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}

引用次数: 0

Revolutionizing FANETs With Reinforcement Learning: Optimized Data Forwarding and Real-Time Adaptability 用强化学习革新fanet：优化数据转发和实时适应性

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2025-04-29 DOI: 10.1109/OJCOMS.2025.3565471

Yasir Ibraheem Mohammed;Rosilah Hassan;Mohammad Kamrul Hasan;Shayla Islam;Huda Saleh Abbas;Muhammad Asghar Khan;Muhammad Attique Khan

{"title":"Revolutionizing FANETs With Reinforcement Learning: Optimized Data Forwarding and Real-Time Adaptability","authors":"Yasir Ibraheem Mohammed;Rosilah Hassan;Mohammad Kamrul Hasan;Shayla Islam;Huda Saleh Abbas;Muhammad Asghar Khan;Muhammad Attique Khan","doi":"10.1109/OJCOMS.2025.3565471","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3565471","url":null,"abstract":"Uncrewed Aerial Vehicles (UAVs), commonly known as drones, have significantly advanced wireless communication frameworks by enabling the formation of Flying Ad-Hoc Networks (FANETs). FANETs facilitate autonomous collaboration among UAVs through decentralized and self-organizing communication protocols, proving especially effective in dynamic applications such as military surveillance, disaster management, and environmental monitoring. Nevertheless, traditional routing algorithms, initially developed for terrestrial networks, often fail to meet the unique challenges of FANETs, notably their high mobility and frequently changing network topologies. A framework was proposed to address these challenges; this paper formulates a multi-objective optimization problem aimed at optimizing UAV trajectories, enhancing energy efficiency, and maximizing communication range to improve overall data forwarding performance. A Reinforcement Learning (RL)-based agent is created that constantly enhances its decision-making capacity by utilizing real-time feedback and dynamically chooses best forwarding tactics. This work also combines developments in large-scale data collecting from Wireless Sensor Networks (WSNs), using mobile sinks supported by FANETs in conjunction with multi-objective optimization approaches to improve data collecting efficiency greatly. Experimental tests show that the suggested RL-based techniques outperform conventional routing protocols by properly lowering delays and raising the Packet Delivery Ratio (PDR). Moreover, simulation findings show the better scalability and adaptability of RL-enabled UAV networks, stressing its possible use in dynamic real-world situations such as disaster relief operations and environmental monitoring tasks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4295-4310"},"PeriodicalIF":6.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979975","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125455","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}

引用次数: 0

DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-Offs 基于drl的无人机覆盖洞规避路径规划：能量消耗和停机时间最小化权衡

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2025-04-28 DOI: 10.1109/OJCOMS.2025.3564837

Bahareh Jafari;Mazen Hasna;Hossein Pishro-Nik;Nizar Zorba;Tamer Khattab;Hamid Saeedi

{"title":"DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-Offs","authors":"Bahareh Jafari;Mazen Hasna;Hossein Pishro-Nik;Nizar Zorba;Tamer Khattab;Hamid Saeedi","doi":"10.1109/OJCOMS.2025.3564837","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3564837","url":null,"abstract":"Coverage holes pose critical challenges to reliability of wireless networks and their quality of service (QoS) and therefore should be avoided in the coverage design. In this paper, we address this issue through the deployment of unmanned aerial vehicles (UAVs) as mobile base stations, and we propose specific UAV path planning. A point is said to be in a coverage hole if the coverage probability for that point is below a certain threshold, e.g., 90%. This definition is more suitable for applications such as surveillance or sensor networks. In this paper, we target applications such as wireless communications for which QoS requirement allow only for short time disconnections, i.e., minimal outage time. As such, in addition to avoiding coverage holes, we should also make the outage time as small as possible. By deploying a deep reinforcement learning algorithm, we find optimal UAV paths based on the two families of trajectories: spiral and oval curves, to tackle different design considerations and constraints, in terms of QoS, energy consumption and coverage hole avoidance. We show that for a typical point on the cell, there is a trade-off between minimizing the maximum outage time length and consumed mechanical energy. Our observations indicate that such a trade-off is more pronounced for spiral trajectories compared to oval trajectories, but both of them are useful depending on the QoS and energy constraints imposed by the system.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4194-4205"},"PeriodicalIF":6.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10977832","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072920","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}

引用次数: 0

Robust and Adaptive UAVs-Based Localization Without Predefined NLoS Error Models 无预定义NLoS误差模型的鲁棒自适应无人机定位

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2025-04-25 DOI: 10.1109/OJCOMS.2025.3564497

Ruhul Amin Khalil;Junaid Bahadar Khan;Asiya Jehangir;Nasir Saeed

{"title":"Robust and Adaptive UAVs-Based Localization Without Predefined NLoS Error Models","authors":"Ruhul Amin Khalil;Junaid Bahadar Khan;Asiya Jehangir;Nasir Saeed","doi":"10.1109/OJCOMS.2025.3564497","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3564497","url":null,"abstract":"In UAV-based localization systems utilizing Time of Arrival (ToA) measurements, Non-Line-of-Sight (NLoS) conditions present a persistent challenge by introducing significant errors that degrade localization accuracy. Traditional techniques rely heavily on prior knowledge of NLoS error statistics or measurement noise characteristics. These dependencies make such methods computationally intensive and less adaptable to dynamic or large-scale scenarios. This paper presents a low-complexity localization algorithm that overcomes these limitations by eliminating the need for prior NLoS error statistics or path status information. The proposed approach dynamically identifies and excludes ToA measurements affected by severe NLoS errors while refining localization accuracy through iterative updates. A two-stage Robust Regression Algorithm (RRA) is employed, combined with an adaptive UAV selection strategy, ensuring both computational efficiency and precise positioning. Theoretical convergence analysis verifies the algorithm’s robustness in selecting reliable UAVs and estimating the accurate position of the target. Simulation results show the algorithm’s superior performance compared to state-of-the-art methods, achieving higher accuracy and efficiency even under severe NLoS conditions. The proposed method’s adaptability, scalability, and robustness make it a valuable solution for accurate localization in complex and dynamic environments, including 5G ultra-dense networks and UAV-based deployments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4051-4062"},"PeriodicalIF":6.3,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10977048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073253","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}

引用次数: 0

Two-Timescale Cross-Layer Design for URLLC Over Parallel Fading Channels With Imperfect CSI 不完全CSI下并行衰落信道URLLC的双时间尺度跨层设计

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2025-04-25 DOI: 10.1109/OJCOMS.2025.3564296

Hongsen Peng;Meixia Tao

{"title":"Two-Timescale Cross-Layer Design for URLLC Over Parallel Fading Channels With Imperfect CSI","authors":"Hongsen Peng;Meixia Tao","doi":"10.1109/OJCOMS.2025.3564296","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3564296","url":null,"abstract":"This paper investigates the cross-layer design for point-to-point ultra-reliable low latency communication (URLLC) over parallel fading sub-channels by jointly considering channel estimation and adaptive data transmission. The model includes a stochastic traffic arrival process and the transmissions are done in the finite blocklength (FBL) regime with imperfect channel state information (CSI). Specifically, we formulate a two-timescale total average power minimization problem under reliability, latency, and peak power constraints. In the large timescale, the pilot length and pilot power are optimized while in the small timescale, the data transmit power and decoding error probability are optimized according to the estimated channel coefficients and queueing information. As a starting step in our small timescale solution, we train a deep reinforcement learning (DRL) agent employing the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to allocate the data transmit power on each sub-channel and determine the decoding error probability to satisfy the URLLC constraints in an ideal environment with perfect instantaneous CSI. Then we utilize a water-filling framework to accommodate the trained TD3 network for the environment with imperfect CSI. Based on the small timescale optimization method, we adopt the ternary search algorithm to optimize the pilot length and pilot power through Monte Carlo evaluations in the large timescale. Simulation results are provided to reveal the impact of the reliability, latency and the number of sub-channels. Furthermore, the trained network is demonstrated to be robust towards different traffic arrival models, as well as variations of the average arrival rate.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4126-4139"},"PeriodicalIF":6.3,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976658","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073250","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}

引用次数: 0