IEEE Open Journal of the Communications Society最新文献

{"title":"SimProx: A Similarity-Based Aggregation in Federated Learning With Client Weight Optimization","authors":"Ayoub El-Niss;Ahmad Alzu’Bi;Abdelrahman Abuarqoub;Mohammad Hammoudeh;Ammar Muthanna","doi":"10.1109/OJCOMS.2024.3513816","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3513816","url":null,"abstract":"Federated Learning (FL) enables decentralized training of machine learning models across multiple clients, preserving data privacy by aggregating locally trained models without sharing raw data. Traditional aggregation methods, such as Federated Averaging (FedAvg), often assume uniform client contributions, leading to suboptimal global models in heterogeneous data environments. This article introduces SimProx, a novel FL approach for aggregation that addresses heterogeneity in data through three key improvements. First, SimProx employs a composite similarity-based weighting mechanism, integrating cosine and Gaussian similarity measures to dynamically optimize client contributions. Then, it incorporates a proximal term in the client weighting scheme, using gradient norms to prioritize updates closer to the global optimum, thereby enhancing model convergence and robustness. Finally, a dynamic parameter learning technique is introduced, which adapts the balance between similarity measures based on data heterogeneity, refining the aggregation process. Extensive experiments on standard benchmarking datasets and real-world multimodal data demonstrate that SimProx significantly outperforms traditional methods like FedAvg in terms of accuracy. SimProx offers a scalable and effective solution for decentralized deep learning in diverse and heterogeneous environments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7806-7817"},"PeriodicalIF":6.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786254","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859243","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":"Manifold-Based Optimizations for RIS-Aided Massive MIMO Systems","authors":"Wilson De Souza Junior;David William Marques Guerra;José Carlos Marinello Filho;Taufik Abrão;Ekram Hossain","doi":"10.1109/OJCOMS.2024.3512662","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3512662","url":null,"abstract":"Manifold optimization (MO) is a powerful mathematical framework that can be applied to optimize functions over complex geometric structures, which is particularly useful in advanced wireless communication systems, such as reconfigurable intelligent surface (RIS)-aided massive MIMO (mMIMO) and extra-large scale massive MIMO (XL-MIMO) systems. MO provides a structured approach to tackling complex optimization problems. By leveraging the geometric properties of the manifold, more efficient and effective solutions can be found compared to conventional optimization methods. This paper provides a tutorial on MO technique and provides some applications of MO in the context of wireless communications systems. In particular, to corroborate the effectiveness of MO methodology, we explore five application examples in RIS-aided mMIMO system, focusing on fairness, energy efficiency (EE) maximization, intra-cell pilot reuse interference mitigation, and grant-free (GF) random access (RA).","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7913-7940"},"PeriodicalIF":6.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10783776","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858957","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":"Federated Learning for 6G Networks: Navigating Privacy Benefits and Challenges","authors":"Chamara Sandeepa;Engin Zeydan;Tharaka Samarasinghe;Madhusanka Liyanage","doi":"10.1109/OJCOMS.2024.3513832","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3513832","url":null,"abstract":"The upcoming Sixth Generation (6G) networks aim for fully automated, intelligent network functionalities and services. Therefore, Machine Learning (ML) is essential for these networks. Given stringent privacy regulations, future network architectures should use privacy-preserved ML for their applications and services. Federated Learning (FL) is expected to play an important role as a popular approach for distributed ML, as it protects privacy by design. However, many practical challenges exist before FL can be fully utilized as a key technology for these future networks. We consider the vision of a 6G layered architecture to evaluate the applicability of FL-based distributed intelligence. In this paper, we highlight the benefits of using FL for 6G and the main challenges and issues involved. We also discuss the existing solutions and the possible future directions that should be taken toward more robust and trustworthy FL for future networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"90-129"},"PeriodicalIF":6.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786352","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938104","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":"Millimeter-Wave Massive Analog Relay MU-MIMO With Blocking-Empowered User Scheduling Toward 6G","authors":"Suwen Ke;Gia Khanh Tran;Zongdian Li;Kei Sakaguchi","doi":"10.1109/OJCOMS.2024.3514176","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3514176","url":null,"abstract":"The utilization of millimeter wave (mmWave) in 5G represents a milestone in cellular networks, which unprecedentedly enhances communication capacity. Nevertheless, mobile users are hard to satisfy with the short coverage and vulnerability to blockage of current mmWave services, which also undermines industrial confidence in mmWave promotion. To solve this dilemma, in 6G, relay technologies, such as analog repeaters and reconfigurable intelligent surfaces (RIS), need to be effectively used to enhance the coverage and capacity of existing mmWave stations. In this paper, we introduce a mmWave massive relay system, consisting of massive analog relay stations to construct artificial channels for mobile users. To enhance the performance of mmWave multi-user multiple-input and multiple-output (MU-MIMO), we propose a user scheduling method that actively exploits the blocking environment to reduce interference and optimize the system capacity. Numerical analysis and simulations are conducted to validate the proposed system and methods.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"1-12"},"PeriodicalIF":6.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10787018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938174","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":"Actor–Critic Reinforcement Learning for Throughput-Optimized Power Allocation in Energy Harvesting NOMA Relay-Assisted Networks","authors":"Faeik T. Al Rabee;Ala'Eddin Masadeh;Sharief Abdel-Razeq;Haythem Bany Salameh","doi":"10.1109/OJCOMS.2024.3514785","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3514785","url":null,"abstract":"In fifth-generation (5G) and beyond (B5G) communication systems, the growing number of connected devices and the increased traffic on the network lead to substantial energy consumption, which requires energy-efficient and high-speed communication solutions. Integrating non-orthogonal multiple access (NOMA), energy harvesting (EH), and millimeter wave (mmWave) technologies has emerged as a powerful approach for achieving massive connectivity and energy-efficient communication paradigms. NOMA-based relay-assisted mmWave networks offer high directivity and enhanced data throughput. However, their design faces significant challenges, such as blockage, limited range, Line-of-Sight (LOS) constraints, and uncertainties in channel gain. Integrating EH and NOMA brings design constraints, namely the uncertainty and dynamic nature of EH sources, that complicate energy management and NOMA’s power multiplexing challenges in optimizing power allocation. These factors require optimizing power and resources to ensure seamless connectivity and energy efficiency. Traditional optimization methods face challenges due to uncertainties in channel gains, EH, and blockages. Although reinforcement learning (RL) is typically used to manage uncertain environments, conventional RL algorithms cannot handle such environments with infinite state and action spaces. To address these challenges, this paper proposes a novel power-allocation framework that integrates an EH-capable source node, a relay, and multiple power-domain NOMA-based users. The proposed framework has two phases. During the first phase, the energy-harvesting source communicates with the relay to maximize the data rate while learning an optimal power allocation policy using an actor-critic approach. This method adapts to the uncertain EH process and varying channel conditions while addressing the limitations associated with infinite state and action spaces inherent in traditional RL for optimal power allocation. The second phase consists of a NOMA-based power allocation mechanism that assigns different powers to the users, such that the data received at the relay are transmitted to its designated users. As it turns out, this problem is non-convex. Hence, we use the sequential convex approximation method to solve this problem. Simulation results demonstrate that the proposed framework significantly outperforms traditional power allocation frameworks in data rate maximization and energy efficiency.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7941-7953"},"PeriodicalIF":6.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10787238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875078","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":"HOODIE: Hybrid Computation Offloading via Distributed Deep Reinforcement Learning in Delay-Aware Cloud-Edge Continuum","authors":"Anastasios E. Giannopoulos;Ilias Paralikas;Sotirios T. Spantideas;Panagiotis Trakadas","doi":"10.1109/OJCOMS.2024.3514456","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3514456","url":null,"abstract":"Cloud-Edge Computing Continuum (CEC) system, where edge and cloud nodes are seamlessly connected, is dedicated to handle substantial computational loads offloaded by end-users. These tasks can suffer from delays or be dropped entirely when deadlines are missed, particularly under fluctuating network conditions and resource limitations. The CEC is coupled with the need for hybrid task offloading, where the task placement decisions concern whether the tasks are processed locally, offloaded vertically to the cloud, or horizontally to interconnected edge servers. In this paper, we present a distributed hybrid task offloading scheme (HOODIE) designed to jointly optimize the tasks latency and drop rate, under dynamic CEC traffic. HOODIE employs a model-free deep reinforcement learning (DRL) framework, where distributed DRL agents at each edge server autonomously determine offloading decisions without global task distribution awareness. To further enhance the system pro-activity and learning stability, we incorporate techniques such as Long Short-term Memory (LSTM), Dueling deep Q-networks (DQN), and double-DQN. Extensive simulation results demonstrate that HOODIE effectively reduces task drop rates and average task processing delays, outperforming several baseline methods under changing CEC settings and dynamic conditions.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7818-7841"},"PeriodicalIF":6.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10787015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858956","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":"Analysis With Deep Learning of Robust UAV-Mounted Active IRS NOMA Networks With Imperfections","authors":"Chandan Kumar Singh;Deepak Kumar;Janne J. Lehtomäki;Zaheer Khan;Matti Latva-Aho;Prabhat K. Upadhyay","doi":"10.1109/OJCOMS.2024.3510887","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3510887","url":null,"abstract":"This paper introduces a robust cooperative network where an active intelligent reflecting surface (A-IRS) mounted on an unmanned aerial vehicle (UAV) is employed in order to significantly enhance the air-to-ground communications. By utilizing advanced maneuver control and intelligent reflection, the network optimizes wireless channels, substantially improving spectrum efficiency through a non-orthogonal multiple access (NOMA) scheme. We consider non-ideal system imperfections, such as co-channel interference, hardware impairments, and imperfect successive interference cancellation. We derive the expressions for users’ outage probability (OP), ergodic capacity, and system throughput in both delay-limited and delay-tolerant modes under Nakagami fading channels, reflecting realistic channel variations. Additionally, we present an asymptotic OP analysis to gain useful insights into the high signal-to-noise ratio regime and diversity order, which are useful in optimizing network parameters for maximal reliability. Our study advances complex optimization problems for deep neural network (DNN) hyperparameters, power allocation, and UAV positioning, which are crucial for the dynamic aerial communication environment. We also introduce a new method to evaluate the robustness of our system, the analysis reveals that the system performs well with fewer IRS elements, optimizing the balance between energy efficiency and outage performance. Given the significant complexity of the proposed system model, directly deriving closed-form expressions for the OP and the ergodic sum capacity is a challenge. We develop a DNN framework that predicts OP and ergodic sum capacity in real-time scenarios to overcome this issue. Extensive simulations validate the derived expressions and demonstrate that a UAV-mounted A-IRS NOMA network outperforms both passive IRS NOMA setups and traditional relaying methods. These results affirm notable enhancements in reliability and performance, establishing the network’s superiority in modern wireless communication scenarios and underscoring its potential to enhance both service quality and economic viability in deploying advanced communication infrastructures.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7878-7899"},"PeriodicalIF":6.3,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10777057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858736","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":"Statistical Characterization of the Delay and Performance Analysis in 3-D Relay Networks","authors":"Hadi Hashemi;Maryam Olyaee;Beatriz Soret;M. Carmen Aguayo-Torres;Stefano Buzzi","doi":"10.1109/OJCOMS.2024.3510882","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3510882","url":null,"abstract":"This article investigates a multi-tier 3D network that models a hybrid space-air-ground communication system and two main relay scenarios. In the first scenario, the aerial tier, consisting of entities such as Unmanned Aerial Vehicles (UAVs) or aircrafts, uses the space layer, represented by satellites, to connect to ground User Equipment (UE) devices or ground stations. In the second scenario, the space layer cooperates with satellites in a lower orbit or with aerial devices to communicate with ground-based devices. In both scenarios, it is assumed that the device acting as a relay is distributed throughout its tier according to a Poisson Point Process (PPP), and randomly selected from the coverage area that depends on the position of the source and destination devices. For both scenarios, the paper calculates the distribution function for the link distance, which permits to obtain the average delay of signal propagation. Further, in the paper the outage capacity and ergodic rate expressions for the amplify and forward (AF) and decode and forward (DF) relaying strategies under the shadowed-Rician fading channel model are derived. Also, the asymptotic behavior in the high-SNR regime is analyzed. These novel analytical expressions provide insights of the fundamental performance limits of integrated terrestrial and non-terrestrial wireless networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7787-7805"},"PeriodicalIF":6.3,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10777037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825798","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":"Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder Network","authors":"Jiahao Shan;Donghong Cai;Fang Fang;Zahid Khan;Pingzhi Fan","doi":"10.1109/OJCOMS.2024.3511951","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3511951","url":null,"abstract":"Anomaly detection of multivariate time series (MTS) is crucial in industrial intelligent systems. To address the challenges of absence of anomaly labels, fast inference time, multi-source and multi-modality in anomaly detection, researchers have primarily investigated unsupervised reconstruction-driven methods. However, the existing reconstruction-driven methods mainly focus on minimizing reconstruction errors while neglecting the importance of training methods that increase errors between normal and abnormal classes. Furthermore, accurately constructing the feature space of normal and abnormal classes during the reconstruction process remains a challenge. In this paper, we propose an innovative model, namely the confidence adversarial autoencoder (CAAE). The proposed CAAE combines a confidence network, based on window credibility judgment, with an autoencoder to provide credibility support for anomaly detection. We further introduce fake labels to provide the confidence network with a discriminative knowledge for identifying reconstructed data. Additionally, we implement the confidence adversarial training method to generate fake labels to construct an adversarial loss aiming to expand the decision boundary of anomaly scores. Extensive experimental results on publicly available time series datasets are provided to demonstrate the efficiency of our proposed CAAE. It reveals that excellent generalization ability and superior average performance are achieved on different datasets compared with the state-of-the-art methods.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7752-7766"},"PeriodicalIF":6.3,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10778252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825884","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":"Environment Semantic Communication: Enabling Distributed Sensing Aided Networks","authors":"Shoaib Imran;Gouranga Charan;Ahmed Alkhateeb","doi":"10.1109/OJCOMS.2024.3509453","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3509453","url":null,"abstract":"Millimeter-wave (mmWave) and terahertz (THz) communication systems require large antenna arrays and use narrow directive beams to ensure sufficient receive signal power. However, selecting the optimal beams for these large antenna arrays incurs a significant beam training overhead, making it challenging to support applications involving high mobility. In recent years, machine learning (ML) solutions have shown promising results in reducing the beam training overhead by utilizing various sensing modalities such as GPS position and RGB images. However, the existing approaches are mainly limited to scenarios with only a single object of interest present in the wireless environment and focus only on co-located sensing, where all the sensors are installed at the communication terminal. This brings key challenges such as the limited sensing coverage compared to the coverage of the communication system and the difficulty in handling non-line-of-sight scenarios. To overcome these limitations, our paper proposes the deployment of multiple distributed sensing nodes, each equipped with an RGB camera. These nodes focus on extracting environmental semantics from the captured RGB images. The semantic data, rather than the raw images, are then transmitted to the basestation. This strategy significantly alleviates the overhead associated with the data storage and transmission of the raw images. Furthermore, semantic communication enhances the system’s adaptability and responsiveness to dynamic environments, allowing for prioritization and transmission of contextually relevant information. Experimental results on the DeepSense 6G dataset demonstrate the effectiveness of the proposed solution in reducing the sensing data transmission overhead while accurately predicting the optimal beams in realistic communication environments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7767-7786"},"PeriodicalIF":6.3,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10776766","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859241","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}