Pub Date : 2024-08-20DOI: 10.1109/OJCOMS.2024.3445990
Guillem Femenias;Felip Riera-Palou
Cell-free massive MIMO (CF-mMIMO) emerges as a pivotal technology in the landscape of beyond-5G and 6G wireless networks, addressing the ever-increasing demand for seamless connectivity and unprecedented data throughput. This paper undertakes a comprehensive exploration of scalable usercentric (UC) CF-mMIMO systems, focusing on critical aspects of downlink (DL) channel state information (CSI) acquisition and its intricate interactions with both distributed and centralized precoding strategies. The paper delves into the crucial role of DL CSI acquisition, particularly in scenarios of weak channel hardening arising from sparse subsets of access points (APs) serving specific mobile stations (MS) in UC strategies, and transmission over spatially correlated multiple keyhole Ricean fading channels. The main contributions of this research work include in-depth analyses of different detection schemes under varying precoding scenarios, offering valuable insights for practical deployment. The pivotal role of DL CSI acquisition in optimizing the performance of UC CF-mMIMO networks is fully assessed, dismissing the use of DL pilot-based detection approaches and advocating for either centralized precoding architectures with statistical CSI-based decoding strategies at the MSs or distributed precoding schemes with DL blind channel estimation-based decoders at the MSs.
{"title":"Unveiling New Frontiers of Downlink Training in User-Centric Cell-Free Massive MIMO","authors":"Guillem Femenias;Felip Riera-Palou","doi":"10.1109/OJCOMS.2024.3445990","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3445990","url":null,"abstract":"Cell-free massive MIMO (CF-mMIMO) emerges as a pivotal technology in the landscape of beyond-5G and 6G wireless networks, addressing the ever-increasing demand for seamless connectivity and unprecedented data throughput. This paper undertakes a comprehensive exploration of scalable usercentric (UC) CF-mMIMO systems, focusing on critical aspects of downlink (DL) channel state information (CSI) acquisition and its intricate interactions with both distributed and centralized precoding strategies. The paper delves into the crucial role of DL CSI acquisition, particularly in scenarios of weak channel hardening arising from sparse subsets of access points (APs) serving specific mobile stations (MS) in UC strategies, and transmission over spatially correlated multiple keyhole Ricean fading channels. The main contributions of this research work include in-depth analyses of different detection schemes under varying precoding scenarios, offering valuable insights for practical deployment. The pivotal role of DL CSI acquisition in optimizing the performance of UC CF-mMIMO networks is fully assessed, dismissing the use of DL pilot-based detection approaches and advocating for either centralized precoding architectures with statistical CSI-based decoding strategies at the MSs or distributed precoding schemes with DL blind channel estimation-based decoders at the MSs.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117892","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}
The objective of our work is to provide an in-depth analysis and compilation of device-level strategies for enhancing the energy efficiency of Machine-Type Communication (MTC). The necessity for such strategies stems from the growing demand for sustainable and energy-efficient communication systems in various industries. We begin by presenting a comprehensive background on MTC, detailing its essential characteristics, the architecture of machine-type devices (MTDs), and their diverse applications. Next, we explore a range of energy-efficient techniques designed to optimize key subsystems of MTDs. These subsystems include the radio for communication efficiency, processing power for computational efficiency, and sensing subsystems for data acquisition efficiency. Each technique is evaluated for its potential impact on overall energy consumption and the trade-offs and limitations associated with these techniques are also assessed. In concluding, the paper highlights potential future research directions in this domain, outlining the ongoing need for innovative solutions to meet the escalating demands of energy efficiency in MTC.
{"title":"Device-Level Energy Efficient Strategies in Machine Type Communications: Power, Processing, Sensing, and RF Perspectives","authors":"Unalido Ntabeni;Bokamoso Basutli;Hirley Alves;Joseph Chuma","doi":"10.1109/OJCOMS.2024.3443920","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3443920","url":null,"abstract":"The objective of our work is to provide an in-depth analysis and compilation of device-level strategies for enhancing the energy efficiency of Machine-Type Communication (MTC). The necessity for such strategies stems from the growing demand for sustainable and energy-efficient communication systems in various industries. We begin by presenting a comprehensive background on MTC, detailing its essential characteristics, the architecture of machine-type devices (MTDs), and their diverse applications. Next, we explore a range of energy-efficient techniques designed to optimize key subsystems of MTDs. These subsystems include the radio for communication efficiency, processing power for computational efficiency, and sensing subsystems for data acquisition efficiency. Each technique is evaluated for its potential impact on overall energy consumption and the trade-offs and limitations associated with these techniques are also assessed. In concluding, the paper highlights potential future research directions in this domain, outlining the ongoing need for innovative solutions to meet the escalating demands of energy efficiency in MTC.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084494","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}
Pub Date : 2024-08-14DOI: 10.1109/OJCOMS.2024.3444719
Mohamed A. ElMossallamy;Miao Pan;Riku Jäntti;Karim G. Seddik;Geoffrey Ye Li;Zhu Han
In this paper, we investigate frequency shift keying (FSK) over ambient orthogonal frequency division multiplexed (OFDM) signals. By cycling through a sequence of antenna loads providing different phase shifts at the tag, we are able to unidirectionally shift the ambient OFDM spectrum either up or down in frequency to disjoint subsets of the subcarriers allowing the implementation of FSK. We exploit the guard bands and the orthogonality of the OFDM subcarriers to avoid both direct-link and adjacent channel interference. Different from energy detection based techniques that suffer from asymmetric error probabilities and rely on signal-to-noise ratio (SNR) dependent detection thresholds, the proposed scheme has symmetric error probabilities and allows simple detection without the need for a threshold. We present both binary and four-ary schemes, and analyze the error performance of the optimal noncoherent detectors. For the binary scheme, we obtain exact expressions for the average probability of error, while for the four-ary scheme, a union bound is used to characterize the error performance. Single and multi-antenna receivers are considered, and their performance is analyzed. Finally, we present simulation results to corroborate our analysis and investigate the effects of multiple system parameters. The results show that the proposed scheme outperforms the baseline energy detection based schemes available in the literature in various scenarios by up to 5 dB.
{"title":"Noncoherent Frequency-Shift Keying for Ambient Backscatter Over OFDM Signals","authors":"Mohamed A. ElMossallamy;Miao Pan;Riku Jäntti;Karim G. Seddik;Geoffrey Ye Li;Zhu Han","doi":"10.1109/OJCOMS.2024.3444719","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3444719","url":null,"abstract":"In this paper, we investigate frequency shift keying (FSK) over ambient orthogonal frequency division multiplexed (OFDM) signals. By cycling through a sequence of antenna loads providing different phase shifts at the tag, we are able to unidirectionally shift the ambient OFDM spectrum either up or down in frequency to disjoint subsets of the subcarriers allowing the implementation of FSK. We exploit the guard bands and the orthogonality of the OFDM subcarriers to avoid both direct-link and adjacent channel interference. Different from energy detection based techniques that suffer from asymmetric error probabilities and rely on signal-to-noise ratio (SNR) dependent detection thresholds, the proposed scheme has symmetric error probabilities and allows simple detection without the need for a threshold. We present both binary and four-ary schemes, and analyze the error performance of the optimal noncoherent detectors. For the binary scheme, we obtain exact expressions for the average probability of error, while for the four-ary scheme, a union bound is used to characterize the error performance. Single and multi-antenna receivers are considered, and their performance is analyzed. Finally, we present simulation results to corroborate our analysis and investigate the effects of multiple system parameters. The results show that the proposed scheme outperforms the baseline energy detection based schemes available in the literature in various scenarios by up to 5 dB.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117891","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}
Pub Date : 2024-08-14DOI: 10.1109/OJCOMS.2024.3443514
Annisa Sarah;Gianfranco Nencioni;Md Muhidul Islam Khan
Multi-access Edge Computing (MEC) allows a mobile user to access a service on a computing device called MEC Host (MEH), enabling lower latency by running the service closer to the users. When the user moves away from the serving MEH, the latency increases, which may cause a disruption of the user experience and of the service continuity. Moreover, the serving MEH may also fail, making the service unavailable. We propose a solution to a service migration problem that maximizes the MEC service availability by jointly deciding (i) migration timing and (ii) target MEH based on latency constraint, resource constraint, and availability status of a MEH. We solve the problem by using Deep Reinforcement Learning (DRL). The experiment shows that our proposed solution can successfully maintain a high service availability (more than 94%) in the presence of different failure probabilities, while another algorithm gives unstable service availability.
{"title":"DRL-Based Availability-Aware Migration of a MEC Service","authors":"Annisa Sarah;Gianfranco Nencioni;Md Muhidul Islam Khan","doi":"10.1109/OJCOMS.2024.3443514","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3443514","url":null,"abstract":"Multi-access Edge Computing (MEC) allows a mobile user to access a service on a computing device called MEC Host (MEH), enabling lower latency by running the service closer to the users. When the user moves away from the serving MEH, the latency increases, which may cause a disruption of the user experience and of the service continuity. Moreover, the serving MEH may also fail, making the service unavailable. We propose a solution to a service migration problem that maximizes the MEC service availability by jointly deciding (i) migration timing and (ii) target MEH based on latency constraint, resource constraint, and availability status of a MEH. We solve the problem by using Deep Reinforcement Learning (DRL). The experiment shows that our proposed solution can successfully maintain a high service availability (more than 94%) in the presence of different failure probabilities, while another algorithm gives unstable service availability.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636784","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084523","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}
Pub Date : 2024-08-13DOI: 10.1109/OJCOMS.2024.3442855
Osama A. Khashan;Nour M. Khafajah;Waleed Alomoush;Mohammad Alshinwan;Emad Alomari
Wireless multimedia sensor networks (WMSNs) have gained considerable attention across various applications due to their capabilities for real-time multimedia data collection, efficient monitoring, and flexible deployment. Despite advancements, challenges persist in ensuring security, optimizing efficiency, and minimizing energy consumption due to the open remote medium, large volumes of multimedia, and inherent resource constraints in WMSNs. This paper introduces an innovative energy-efficient protection model for WMSNs, leveraging advanced deep learning techniques. The model utilizes a lightweight Tiny YOLO-v7 framework to dynamically identify objects within captured images, thereby reducing the need to transmit superfluous data. Moreover, the model combines the lightweight Speck cipher for the encryption of detected objects with a scrambling method that permutes and shuffles all image pixels. An effective key management scheme is also integrated to secure communication and image exchange among nodes within the network. By restricting encryption and transmission to sensitive images containing foreign objects, the proposed model significantly reduces operational overhead. The experimental results showcase the effectiveness of the proposed model in reducing node power consumption by approximately 49% compared to conventional methods, which encrypt and transmit all generated images. Furthermore, the model demonstrates a significant 50% improvement in extending network lifetime compared to related encryption solutions. The security analysis substantiates the model’s resistance against diverse attacks, ensuring compliance with the stringent security requirements of WMSNs. Furthermore, the model exhibits strong potential for real-time applications in dynamic monitoring and secure environments.
{"title":"Smart Energy-Efficient Encryption for Wireless Multimedia Sensor Networks Using Deep Learning","authors":"Osama A. Khashan;Nour M. Khafajah;Waleed Alomoush;Mohammad Alshinwan;Emad Alomari","doi":"10.1109/OJCOMS.2024.3442855","DOIUrl":"10.1109/OJCOMS.2024.3442855","url":null,"abstract":"Wireless multimedia sensor networks (WMSNs) have gained considerable attention across various applications due to their capabilities for real-time multimedia data collection, efficient monitoring, and flexible deployment. Despite advancements, challenges persist in ensuring security, optimizing efficiency, and minimizing energy consumption due to the open remote medium, large volumes of multimedia, and inherent resource constraints in WMSNs. This paper introduces an innovative energy-efficient protection model for WMSNs, leveraging advanced deep learning techniques. The model utilizes a lightweight Tiny YOLO-v7 framework to dynamically identify objects within captured images, thereby reducing the need to transmit superfluous data. Moreover, the model combines the lightweight Speck cipher for the encryption of detected objects with a scrambling method that permutes and shuffles all image pixels. An effective key management scheme is also integrated to secure communication and image exchange among nodes within the network. By restricting encryption and transmission to sensitive images containing foreign objects, the proposed model significantly reduces operational overhead. The experimental results showcase the effectiveness of the proposed model in reducing node power consumption by approximately 49% compared to conventional methods, which encrypt and transmit all generated images. Furthermore, the model demonstrates a significant 50% improvement in extending network lifetime compared to related encryption solutions. The security analysis substantiates the model’s resistance against diverse attacks, ensuring compliance with the stringent security requirements of WMSNs. Furthermore, the model exhibits strong potential for real-time applications in dynamic monitoring and secure environments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10634885","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209476","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}
Pub Date : 2024-08-12DOI: 10.1109/OJCOMS.2024.3442709
Atefeh Termehchi;Tingnan Bao;Aisha Syed;William Sean Kennedy;Melike Erol-Kantarci
Deep reinforcement learning (DRL) has been a key machine learning technique in many 5G and 6G applications. DRL agents learn optimal (or sub-optimal) policies by interacting with the environment. However, this process often involves numerous uninformative and repetitive message transmissions between the DRL agent and its environment. In this paper, we address the problem of reducing interactions between the DRL agent and the environment, called goal-oriented DRL. Meanwhile, Terahertz (THz) bands and unmanned aerial vehicles (UAVs) are considered two of the main enablers of 6G. Therefore, we investigate the goal-oriented DRL problem in a THz-enabled UAV-aided network. We formulate it as an optimization problem with the goals of i) reducing interactions between the UAV (DRL agent) and IoT devices (environment), ii) maximizing the number of served IoT devices, and iii) ensuring fairness. The constraints include the movement characteristics of IoT devices, the maximum speed limitation of the UAV, the QoS requirements of the served IoT devices, and the limited uplink coverage of the THz-enabled UAV. This problem is a mixed-integer nonlinear programming optimization problem and is NP-hard. To address this problem, we employ the decoupling optimization method and an approach inspired by the self-triggered method from control engineering. Specifically, the problem is divided into two sub-problems; Then, we propose using supervised learning as a teacher for DRL to reduce the interactions. Our simulation results show that the goal-oriented DRL approach outperforms conventional methods by reducing interactions and maintaining good performance in terms of the number of served IoT devices and fairness.
{"title":"Goal-Oriented Reinforcement Learning in THz-Enabled UAV-Aided Network Using Supervised Learning","authors":"Atefeh Termehchi;Tingnan Bao;Aisha Syed;William Sean Kennedy;Melike Erol-Kantarci","doi":"10.1109/OJCOMS.2024.3442709","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3442709","url":null,"abstract":"Deep reinforcement learning (DRL) has been a key machine learning technique in many 5G and 6G applications. DRL agents learn optimal (or sub-optimal) policies by interacting with the environment. However, this process often involves numerous uninformative and repetitive message transmissions between the DRL agent and its environment. In this paper, we address the problem of reducing interactions between the DRL agent and the environment, called goal-oriented DRL. Meanwhile, Terahertz (THz) bands and unmanned aerial vehicles (UAVs) are considered two of the main enablers of 6G. Therefore, we investigate the goal-oriented DRL problem in a THz-enabled UAV-aided network. We formulate it as an optimization problem with the goals of i) reducing interactions between the UAV (DRL agent) and IoT devices (environment), ii) maximizing the number of served IoT devices, and iii) ensuring fairness. The constraints include the movement characteristics of IoT devices, the maximum speed limitation of the UAV, the QoS requirements of the served IoT devices, and the limited uplink coverage of the THz-enabled UAV. This problem is a mixed-integer nonlinear programming optimization problem and is NP-hard. To address this problem, we employ the decoupling optimization method and an approach inspired by the self-triggered method from control engineering. Specifically, the problem is divided into two sub-problems; Then, we propose using supervised learning as a teacher for DRL to reduce the interactions. Our simulation results show that the goal-oriented DRL approach outperforms conventional methods by reducing interactions and maintaining good performance in terms of the number of served IoT devices and fairness.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10634216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142058611","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}
In the last few years, location-aware services and network management have driven the demand for user location estimation in mobile networks. Nevertheless, the location obtained from user terminals is not usually accessible to mobile operators. In addition, available cell Key Performance Indicators (KPI) vary highly from network to network, and only a few of them are always enabled widely. Currently prevalent Machine Learning (ML) based solutions have achieved high precision, but they are bound to a trained scenario, restricting their application to new areas. We propose a method for creating scenario-agnostic prediction models which solves these problems through applying feature engineering, over a small set of easily obtainable KPIs, applicable for any ML method. Finally, the performance of the proposed method is demonstrated using real network datasets.
在过去几年中，位置感知服务和网络管理推动了移动网络对用户位置估算的需求。然而，移动运营商通常无法获取从用户终端获得的位置信息。此外，不同网络中可用的小区关键性能指标（KPI）差异很大，而且只有少数指标总是被广泛启用。目前流行的基于机器学习（ML）的解决方案已经达到了很高的精度，但它们受限于训练有素的场景，限制了它们在新领域的应用。我们提出了一种创建与场景无关的预测模型的方法，该方法通过应用特征工程来解决这些问题，它涵盖了一小部分易于获取的关键绩效指标，适用于任何 ML 方法。最后，我们使用真实网络数据集展示了所提方法的性能。
{"title":"Scenario-Agnostic Localization System for Cellular Network Based on Feature Engineering","authors":"Hao Qiang Luo-Chen;Emil J. Khatib;Deepak Sethi;Eduardo Cruz;Asier Arostegui;Raúl Martín;Raquel Barco Moreno","doi":"10.1109/OJCOMS.2024.3440186","DOIUrl":"10.1109/OJCOMS.2024.3440186","url":null,"abstract":"In the last few years, location-aware services and network management have driven the demand for user location estimation in mobile networks. Nevertheless, the location obtained from user terminals is not usually accessible to mobile operators. In addition, available cell Key Performance Indicators (KPI) vary highly from network to network, and only a few of them are always enabled widely. Currently prevalent Machine Learning (ML) based solutions have achieved high precision, but they are bound to a trained scenario, restricting their application to new areas. We propose a method for creating scenario-agnostic prediction models which solves these problems through applying feature engineering, over a small set of easily obtainable KPIs, applicable for any ML method. Finally, the performance of the proposed method is demonstrated using real network datasets.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10628101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942601","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}
Reconfigurable metasurface, also known as Reconfigurable Intelligent Surfaces (RIS), with its flexible beamforming, low-cost, and easy industrial deployment characteristics, presents many interesting solutions in wireless application scenarios. This paper presents a sophisticated reconfigurable metasurface architecture that introduces an advanced concept of flexible full-array space-time wavefront manipulation with enhanced dynamic capabilities. The practical 2-bit phase-shifting unit cell on the RIS is distinguished by its ability to maintain four stable phase states, each with 90° differences, and features an insertion loss of less than 0.6 dB across a bandwidth of 200 MHz. All reconfigurable unit cells are equipped with meticulously designed control circuits, governed by an intelligent core composed of multiple Micro-Controller Units (MCUs), enabling rapid control response across the entire RIS array. Owing to the capability of each unit cell on the metasurface to independently switch states, the entire RIS is not limited to controlling general beams with specific directional patterns but also generates beams with more complex structures, including multi-focus 3D spot beams and vortex beams. This development substantially broadens its applicability across various industrial wireless transmission scenarios. Moreover, by leveraging the rapid-respond space-time coding and the full-array independent programmability of the RIS prototyping operating at 10.7 GHz, we have demonstrated that: 1) The implementation of 3D spot beams scanning facilitates dynamic beam tracking and real-time communication under the indoor near-field scenario; 2) The rapid wavefront rotation of vortex beams enables precise modulation of signals within the Doppler domain, showcasing an innovative approach to wireless signal manipulation; 3) The beam steering experiments for blocking users under outdoor far-field scenarios, verifying the beamforming capability of the RIS board.
{"title":"2-Bit RIS Prototyping Enhancing Rapid-Response Space-Time Wavefront Manipulation for Wireless Communication: Experimental Studies","authors":"Yufei Zhao;Yuan Feng;Afkar Mohamed Ismail;Ziyue Wang;Yong Liang Guan;Yongxin Guo;Chau Yuen","doi":"10.1109/OJCOMS.2024.3439558","DOIUrl":"10.1109/OJCOMS.2024.3439558","url":null,"abstract":"Reconfigurable metasurface, also known as Reconfigurable Intelligent Surfaces (RIS), with its flexible beamforming, low-cost, and easy industrial deployment characteristics, presents many interesting solutions in wireless application scenarios. This paper presents a sophisticated reconfigurable metasurface architecture that introduces an advanced concept of flexible full-array space-time wavefront manipulation with enhanced dynamic capabilities. The practical 2-bit phase-shifting unit cell on the RIS is distinguished by its ability to maintain four stable phase states, each with 90° differences, and features an insertion loss of less than 0.6 dB across a bandwidth of 200 MHz. All reconfigurable unit cells are equipped with meticulously designed control circuits, governed by an intelligent core composed of multiple Micro-Controller Units (MCUs), enabling rapid control response across the entire RIS array. Owing to the capability of each unit cell on the metasurface to independently switch states, the entire RIS is not limited to controlling general beams with specific directional patterns but also generates beams with more complex structures, including multi-focus 3D spot beams and vortex beams. This development substantially broadens its applicability across various industrial wireless transmission scenarios. Moreover, by leveraging the rapid-respond space-time coding and the full-array independent programmability of the RIS prototyping operating at 10.7 GHz, we have demonstrated that: 1) The implementation of 3D spot beams scanning facilitates dynamic beam tracking and real-time communication under the indoor near-field scenario; 2) The rapid wavefront rotation of vortex beams enables precise modulation of signals within the Doppler domain, showcasing an innovative approach to wireless signal manipulation; 3) The beam steering experiments for blocking users under outdoor far-field scenarios, verifying the beamforming capability of the RIS board.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10623806","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942602","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}
Pub Date : 2024-08-05DOI: 10.1109/OJCOMS.2024.3438571
Mohammed A. Alshorbaji;Ahmed Q. Lawey;Syed Ali Raza Zaidi
Nano-networks are envisioned to allow several nanoscale devices to transmit and receive information. One form of such networks is electromagnetic nano-networks working within the THz band. However, high overall path loss and molecular noise experienced in the THz band, as well as limited energy storage capabilities, restrict the communication range of nano-nodes and impact network efficiency. Therefore, optimizing the nano-network resources is necessary. In this paper, we present an optimization framework employing mixed-integer linear programming (MILP) to determine the most energy-efficient routing, bandwidth, and sub-band allocation for each nano-node in an electromagnetic nano-network operating within the THz band. Our model was tested for two different scenarios related to the priority of energy saving. We also compare our proposed optimal bandwidth, routing, and sub-band allocation against less complex designs where sub-bands with fixed bandwidth are employed in nano-nodes. Furthermore, we investigate the impact of nano-node’s processing and sensing units on the overall network energy consumption and the associated optimal bandwidth allocation and routing strategy. Given the considered parameters and the model’s assumptions, the results show that using the optimal multi-hops paths with higher bandwidth allocation for the considered sub-bands can be more energy efficient than sending the traffic using a single hop and lower bandwidths, especially when the transmission power dominates in the nano-network. On the other hand, when the processing and sensing unit’s energy consumption is dominant, then single hop schemes with lower bandwidth allocation result in the minimum network energy consumption. Finally, we discuss the limitations of the proposed energy-efficient strategies and point toward possible future research directions to which the model can be adapted.
{"title":"Joint Optimization of Routing, Bandwidth, and Sub-Band Allocation in Energy-Efficient THz Nano-Networks","authors":"Mohammed A. Alshorbaji;Ahmed Q. Lawey;Syed Ali Raza Zaidi","doi":"10.1109/OJCOMS.2024.3438571","DOIUrl":"10.1109/OJCOMS.2024.3438571","url":null,"abstract":"Nano-networks are envisioned to allow several nanoscale devices to transmit and receive information. One form of such networks is electromagnetic nano-networks working within the THz band. However, high overall path loss and molecular noise experienced in the THz band, as well as limited energy storage capabilities, restrict the communication range of nano-nodes and impact network efficiency. Therefore, optimizing the nano-network resources is necessary. In this paper, we present an optimization framework employing mixed-integer linear programming (MILP) to determine the most energy-efficient routing, bandwidth, and sub-band allocation for each nano-node in an electromagnetic nano-network operating within the THz band. Our model was tested for two different scenarios related to the priority of energy saving. We also compare our proposed optimal bandwidth, routing, and sub-band allocation against less complex designs where sub-bands with fixed bandwidth are employed in nano-nodes. Furthermore, we investigate the impact of nano-node’s processing and sensing units on the overall network energy consumption and the associated optimal bandwidth allocation and routing strategy. Given the considered parameters and the model’s assumptions, the results show that using the optimal multi-hops paths with higher bandwidth allocation for the considered sub-bands can be more energy efficient than sending the traffic using a single hop and lower bandwidths, especially when the transmission power dominates in the nano-network. On the other hand, when the processing and sensing unit’s energy consumption is dominant, then single hop schemes with lower bandwidth allocation result in the minimum network energy consumption. Finally, we discuss the limitations of the proposed energy-efficient strategies and point toward possible future research directions to which the model can be adapted.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10623326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942603","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}
Pub Date : 2024-08-05DOI: 10.1109/OJCOMS.2024.3438264
Asadullah Tariq;Mohamed Adel Serhani;Farag M. Sallabi;Ezedin S. Barka;Tariq Qayyum;Heba M. Khater;Khaled A. Shuaib
Federated Learning (FL) emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL and its application in various areas increased, addressing trustworthiness issues in its various aspects became crucial. In this survey, we provided a comprehensive overview of the state-of-the-art research on Trustworthy FL, exploring existing solutions and key foundations relevant to Trustworthiness in FL. There has been significant growth in the literature on trustworthy centralized Machine Learning (ML) and Deep Learning (DL). However, there is still a need for more focused efforts toward identifying trustworthiness pillars and evaluation metrics in FL. In this paper, we proposed a taxonomy encompassing five main classifications for Trustworthy FL, including Interpretability and Explainability, Transparency, Privacy and Robustness, Fairness, and Accountability. Each category represents a dimension of trust and is further broken down into different sub-categories. Moreover, we addressed trustworthiness in a Decentralized FL (DFL) setting. Communication efficiency is essential for ensuring Trustworthy FL. This paper also highlights the significance of communication efficiency within various Trustworthy FL pillars and investigates existing research on communication-efficient techniques across these pillars. Our survey comprehensively addresses trustworthiness challenges across all aspects within the Trustworthy FL settings. We also proposed a comprehensive architecture for Trustworthy FL, detailing the fundamental principles underlying the concept, and provided an in-depth analysis of trust assessment mechanisms. In conclusion, we identified key research challenges related to every aspect of Trustworthy FL and suggested future research directions. This comprehensive survey served as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.
{"title":"Trustworthy Federated Learning: A Comprehensive Review, Architecture, Key Challenges, and Future Research Prospects","authors":"Asadullah Tariq;Mohamed Adel Serhani;Farag M. Sallabi;Ezedin S. Barka;Tariq Qayyum;Heba M. Khater;Khaled A. Shuaib","doi":"10.1109/OJCOMS.2024.3438264","DOIUrl":"10.1109/OJCOMS.2024.3438264","url":null,"abstract":"Federated Learning (FL) emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL and its application in various areas increased, addressing trustworthiness issues in its various aspects became crucial. In this survey, we provided a comprehensive overview of the state-of-the-art research on Trustworthy FL, exploring existing solutions and key foundations relevant to Trustworthiness in FL. There has been significant growth in the literature on trustworthy centralized Machine Learning (ML) and Deep Learning (DL). However, there is still a need for more focused efforts toward identifying trustworthiness pillars and evaluation metrics in FL. In this paper, we proposed a taxonomy encompassing five main classifications for Trustworthy FL, including Interpretability and Explainability, Transparency, Privacy and Robustness, Fairness, and Accountability. Each category represents a dimension of trust and is further broken down into different sub-categories. Moreover, we addressed trustworthiness in a Decentralized FL (DFL) setting. Communication efficiency is essential for ensuring Trustworthy FL. This paper also highlights the significance of communication efficiency within various Trustworthy FL pillars and investigates existing research on communication-efficient techniques across these pillars. Our survey comprehensively addresses trustworthiness challenges across all aspects within the Trustworthy FL settings. We also proposed a comprehensive architecture for Trustworthy FL, detailing the fundamental principles underlying the concept, and provided an in-depth analysis of trust assessment mechanisms. In conclusion, we identified key research challenges related to every aspect of Trustworthy FL and suggested future research directions. This comprehensive survey served as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10623386","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942605","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}
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