The growth of the number of connected devices and network densification is driving an increasing demand for radio network resources, particularly Radio Frequency (RF) spectrum. Given the dynamic and complex nature of contemporary wireless environments, characterized by a wide variety of devices and multiple RATs, spectrum sensing is envisioned to become a building component of future 6G, including as a components within O-RAN or digital twins. However, the current SotA research for RAT classification predominantly revolves around supervised Convolutional Neural Network (CNN)- based approach that require extensive labeled dataset. Due to this, it is unclear how existing models behave in environments for which training data is unavailable thus leaving open questions regarding their generalization capabilities. In this paper, we propose a new spectrum sensing workflow in which the model training does not require any prior knowledge of the RATs transmitting in that area (i.e., no labelled data) and the class assignment can be easily done through manual mapping. Furthermore, we adaptat a SSL deep clustering architecture capable of autonomously extracting spectrum features from raw 1D Fast Fourier Transform (FFT) data. We evaluate the proposed architecture on three real-world datasets from three European cities, in the 868 MHz, 2.4 GHz and 5.9 GHz bands containing over 10 RATs and show that the developed model achieves superior performance by up to 35 percentage points with 22% fewer trainable parameters and 50% less floating-point operations per second (FLOPS) compared to an SotA AE-based reference architecture.
{"title":"Spectrum Sensing With Deep Clustering: Label-Free Radio Access Technology Recognition","authors":"Ljupcho Milosheski;Mihael Mohorčič;Carolina Fortuna","doi":"10.1109/OJCOMS.2024.3436601","DOIUrl":"10.1109/OJCOMS.2024.3436601","url":null,"abstract":"The growth of the number of connected devices and network densification is driving an increasing demand for radio network resources, particularly Radio Frequency (RF) spectrum. Given the dynamic and complex nature of contemporary wireless environments, characterized by a wide variety of devices and multiple RATs, spectrum sensing is envisioned to become a building component of future 6G, including as a components within O-RAN or digital twins. However, the current SotA research for RAT classification predominantly revolves around supervised Convolutional Neural Network (CNN)- based approach that require extensive labeled dataset. Due to this, it is unclear how existing models behave in environments for which training data is unavailable thus leaving open questions regarding their generalization capabilities. In this paper, we propose a new spectrum sensing workflow in which the model training does not require any prior knowledge of the RATs transmitting in that area (i.e., no labelled data) and the class assignment can be easily done through manual mapping. Furthermore, we adaptat a SSL deep clustering architecture capable of autonomously extracting spectrum features from raw 1D Fast Fourier Transform (FFT) data. We evaluate the proposed architecture on three real-world datasets from three European cities, in the 868 MHz, 2.4 GHz and 5.9 GHz bands containing over 10 RATs and show that the developed model achieves superior performance by up to 35 percentage points with 22% fewer trainable parameters and 50% less floating-point operations per second (FLOPS) compared to an SotA AE-based reference architecture.","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=10623390","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942604","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 : 2024-08-02DOI: 10.1109/OJCOMS.2024.3437357
Wenqian Tang;Ronghua Liu;Qiang Li;Zian Meng;Ashish Pandharipande;Xiaohu Ge
In this paper, a semantic-assisted hybrid non-orthogonal multiple access (NOMA) is investigated, in which a secondary far user (F-user) wishes to transmit to a common access point simultaneously with a primary near user (N-user) in the same frequency band. In order to take advantages of both semantic communication (SemCom) and traditional bit communication (BitCom), a SemCom/BitCom mode switching is proposed at the F-user, by adapting to the varying fading states of the channel. On this basis, in order to optimize the performance of F-user while providing adequate protection to the primary N-user, an optimization problem of maximizing the ergodic semantic rate of F-user is formulated, subject to an instantaneous bit rate constraint of N-user. This problem is non-convex and requires a joint design on the SemCom/BitCom mode switching and power adaptation over different fading states. To provide insights, it is first proved that the formulated problem satisfies the time-sharing condition, indicating that there exists a dual problem with zero duality gap to the original problem. A Lagrangian Sub-Gradient Algorithm is then proposed to solve the dual problem. Simulation results demonstrate that in the proposed semantic-assisted hybrid NOMA, significant performance improvement can be achieved for the F-user while meeting the instantaneous bit rate requirement of N-user. Furthermore, by adapting to the varying fading states, the proposed joint SemCom/BitCom mode switching and power adaptation scheme outperforms both BitCom and SemCom.
本文研究了一种语义辅助混合非正交多址接入（NOMA），在这种接入中，次远用户（F-user）希望与主近用户（N-user）在同一频段同时向一个共同接入点传输信号。为了同时利用语义通信（SemCom）和传统比特通信（BitCom）的优势，建议在 F 用户处进行 SemCom/BitCom 模式切换，以适应信道的不同衰减状态。在此基础上，为了优化 F 用户的性能，同时为主要 N 用户提供足够的保护，提出了一个优化问题，即在 N 用户瞬时比特率约束条件下，最大化 F 用户的遍历语义速率。这个问题是非凸的，需要在不同衰减状态下对 SemCom/BitCom 模式切换和功率适应进行联合设计。为了提供深入见解，首先证明了所提出的问题满足分时条件，表明存在一个与原始问题具有零对偶差距的对偶问题。然后提出了一种拉格朗日子梯度算法来求解对偶问题。仿真结果表明，在所提出的语义辅助混合 NOMA 中，在满足 N 用户瞬时比特率要求的同时，还能显著提高 F 用户的性能。此外，通过适应不同的衰减状态，所提出的 SemCom/BitCom 联合模式切换和功率适应方案的性能优于 BitCom 和 SemCom。
{"title":"Joint Optimization on Mode Switching and Power Adaptation in Semantic-Assisted Hybrid NOMA","authors":"Wenqian Tang;Ronghua Liu;Qiang Li;Zian Meng;Ashish Pandharipande;Xiaohu Ge","doi":"10.1109/OJCOMS.2024.3437357","DOIUrl":"10.1109/OJCOMS.2024.3437357","url":null,"abstract":"In this paper, a semantic-assisted hybrid non-orthogonal multiple access (NOMA) is investigated, in which a secondary far user (F-user) wishes to transmit to a common access point simultaneously with a primary near user (N-user) in the same frequency band. In order to take advantages of both semantic communication (SemCom) and traditional bit communication (BitCom), a SemCom/BitCom mode switching is proposed at the F-user, by adapting to the varying fading states of the channel. On this basis, in order to optimize the performance of F-user while providing adequate protection to the primary N-user, an optimization problem of maximizing the ergodic semantic rate of F-user is formulated, subject to an instantaneous bit rate constraint of N-user. This problem is non-convex and requires a joint design on the SemCom/BitCom mode switching and power adaptation over different fading states. To provide insights, it is first proved that the formulated problem satisfies the time-sharing condition, indicating that there exists a dual problem with zero duality gap to the original problem. A Lagrangian Sub-Gradient Algorithm is then proposed to solve the dual problem. Simulation results demonstrate that in the proposed semantic-assisted hybrid NOMA, significant performance improvement can be achieved for the F-user while meeting the instantaneous bit rate requirement of N-user. Furthermore, by adapting to the varying fading states, the proposed joint SemCom/BitCom mode switching and power adaptation scheme outperforms both BitCom and SemCom.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10620678","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883207","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}
This paper considers an OFDM-based wireless cellular network operating at millimeter waves and studies the problem of estimating the position and orientation of a mobile station (MS) upon relying on the pilot symbols emitted by the serving base station (BS), which are received via a direct link and an indirect link provided by a reconfigurable intelligent surface (RIS). To counterbalance the multiplicative pathloss in the indirect link, an active RIS is employed, which is able to reflect and amplify the incident signal. Upon introducing a convenient signal model, which accounts for the additional noise generated by the reflective amplifiers in the active RIS, we derive the ML estimators of the MS position and orientation and the corresponding Cramér Rao Lower Bounds under three levels of system cognition at the MS concerning the BS beamforming matrix, the RIS response, and the channel amplitudes on the direct and indirect links. In two cases, we also derive a suboptimal estimator with a reduced implementation complexity. Finally, we provide a numerical analysis to show the merits of the proposed estimators, assess the achievable gains granted by the use of an active RIS (as compared to a passive one), and investigate the impact of the main system parameters, including the BS-RIS distance and the amplification gain at the RIS.
本文考虑了基于 OFDM 的毫米波无线蜂窝网络，研究了依靠服务基站（BS）发射的先导符号估计移动站（MS）位置和方向的问题，这些先导符号通过可重构智能表面（RIS）提供的直接链路和间接链路接收。为了抵消间接链路中的乘法路径损耗，采用了主动式 RIS，它能够反射和放大入射信号。在引入一个方便的信号模型（该模型考虑了主动 RIS 中反射放大器产生的额外噪声）后，我们推导出了 MS 位置和方向的 ML 估计器，以及在 MS 对 BS 波束成形矩阵、RIS 响应以及直接和间接链路上的信道振幅的三个系统认知水平下相应的 Cramér Rao 下界。在两种情况下，我们还推导出了一个次优估计器，其实现复杂度有所降低。最后，我们通过数值分析展示了所提估计器的优点，评估了使用主动 RIS（与被动 RIS 相比）可实现的增益，并研究了主要系统参数（包括 BS-RIS 距离和 RIS 放大增益）的影响。
{"title":"Estimation of the User Position and Orientation in mmWave Cellular Networks Aided by an Active RIS","authors":"Georgios Mylonopoulos;Luca Venturino;Stefano Buzzi;Carmen D’Andrea","doi":"10.1109/OJCOMS.2024.3437661","DOIUrl":"10.1109/OJCOMS.2024.3437661","url":null,"abstract":"This paper considers an OFDM-based wireless cellular network operating at millimeter waves and studies the problem of estimating the position and orientation of a mobile station (MS) upon relying on the pilot symbols emitted by the serving base station (BS), which are received via a direct link and an indirect link provided by a reconfigurable intelligent surface (RIS). To counterbalance the multiplicative pathloss in the indirect link, an active RIS is employed, which is able to reflect and amplify the incident signal. Upon introducing a convenient signal model, which accounts for the additional noise generated by the reflective amplifiers in the active RIS, we derive the ML estimators of the MS position and orientation and the corresponding Cramér Rao Lower Bounds under three levels of system cognition at the MS concerning the BS beamforming matrix, the RIS response, and the channel amplitudes on the direct and indirect links. In two cases, we also derive a suboptimal estimator with a reduced implementation complexity. Finally, we provide a numerical analysis to show the merits of the proposed estimators, assess the achievable gains granted by the use of an active RIS (as compared to a passive one), and investigate the impact of the main system parameters, including the BS-RIS distance and the amplification gain at the RIS.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10621044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883203","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 : 2024-08-02DOI: 10.1109/OJCOMS.2024.3437681
Andra Blaga;Federico Campolo;Maurizio Rea;Xavier Costa-Pérez
Every year millions of lives are lost in emergency situations. Localizing missing people in the shortest possible timeframe is the most effective tool to reduce such a death toll. However, this is challenging when the victims are unable to communicate by themselves or located in large and difficult-toreach areas. Current technological approaches for victim localization are often rendered inoperable under obstacles or low visibility conditions, or due to the lack of cellular networking infrastructure. Toward addressing these issues, we present 3DSAR+, a pioneering single-drone three-dimensional (3D) cellular search-and-rescue (SAR) solution leveraging 5G-new radio (NR) technology. 3DSAR+ system introduces dynamic autonomous 3D UAV trajectories in diverse and challenging environments, offering a robust tool for first responders in SAR missions. The main novelty of the proposed approach lies in advanced distance and angle estimation combined with machine learning (ML) algorithms for position prediction and correction. The approach is able to estimate victims’ locations through their mobile phones without requiring extra equipment and improves localization accuracy by an order of magnitude compared to baseline solutions.
{"title":"3DSAR+: A Single-Drone 3D Cellular Search and Rescue Solution Leveraging 5G-NR","authors":"Andra Blaga;Federico Campolo;Maurizio Rea;Xavier Costa-Pérez","doi":"10.1109/OJCOMS.2024.3437681","DOIUrl":"10.1109/OJCOMS.2024.3437681","url":null,"abstract":"Every year millions of lives are lost in emergency situations. Localizing missing people in the shortest possible timeframe is the most effective tool to reduce such a death toll. However, this is challenging when the victims are unable to communicate by themselves or located in large and difficult-toreach areas. Current technological approaches for victim localization are often rendered inoperable under obstacles or low visibility conditions, or due to the lack of cellular networking infrastructure. Toward addressing these issues, we present 3DSAR+, a pioneering single-drone three-dimensional (3D) cellular search-and-rescue (SAR) solution leveraging 5G-new radio (NR) technology. 3DSAR+ system introduces dynamic autonomous 3D UAV trajectories in diverse and challenging environments, offering a robust tool for first responders in SAR missions. The main novelty of the proposed approach lies in advanced distance and angle estimation combined with machine learning (ML) algorithms for position prediction and correction. The approach is able to estimate victims’ locations through their mobile phones without requiring extra equipment and improves localization accuracy by an order of magnitude compared to baseline solutions.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10621056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883204","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 : 2024-08-02DOI: 10.1109/OJCOMS.2024.3437458
T. D. Dheerajlal;Amit Kumar Dutta
Terahertz (THz) band communication, operating in the range of $(0.1-10)times 10^{12}$ Hz, holds immense potential for fulfilling the ever-increasing demand for ultra-high data rate wireless communication. However, the unfavorable channel characteristics of THz communication pose a significant challenge in realizing this promising technology. In this paper, we present a novel solution to this challenge by proposing a wideband THz hybrid multiple-input multiple-output (MIMO) system design that leverages the ultra-wide bandwidth and the high spectral efficiency of MIMO schemes. We address the frequency-dependent nature of the THz channel by using a filter-bank-based channel model and a multi-bank orthogonal frequency division multiplexing (MB-OFDM) waveform design, which slices the wideband channel into smaller frequency-independent subbands. Specifically, we illustrate two different THz hybrid MIMO structures, one with a single precoder and combiner in the high sampling domain and another with a band-wise precoder design in the low sampling domain. We have adopted sub-optimal methods that alternatively optimize the beamformers. We evaluate the performance of our proposed MIMO schemes using numerical simulations that validate their feasibility and effectiveness in achieving communication in ultra-wideband frequency-dependent THz channels under various parameters and subband width assumptions. The results also indicate that the proposed MB-OFDM waveform design reduces the peak-to-average power ratio (PAPR) of the spatial streams compared to conventional OFDM.
太赫兹（THz）频段通信的工作频率范围为 10^{12}$ Hz 的 (0.1-10)times 美元，在满足日益增长的超高数据速率无线通信需求方面潜力巨大。然而，太赫兹通信的不利信道特性给实现这一前景广阔的技术带来了巨大挑战。在本文中，我们针对这一挑战提出了一种新颖的解决方案，即利用 MIMO 方案的超宽带宽和高频谱效率设计一种宽带 THz 混合多输入多输出 (MIMO) 系统。我们采用基于滤波器组的信道模型和多组正交频分复用（MB-OFDM）波形设计，将宽带信道切分为频率无关的较小子带，从而解决了太赫兹信道的频率依赖性问题。具体而言，我们展示了两种不同的太赫兹混合多输入多输出结构，一种是在高采样域采用单个前置编码器和合路器，另一种是在低采样域采用带状前置编码器设计。我们采用了次优方法，交替优化波束成形器。我们通过数值模拟评估了我们提出的 MIMO 方案的性能，验证了这些方案在各种参数和子带宽假设条件下实现超宽带频率相关太赫兹信道通信的可行性和有效性。结果还表明，与传统的 OFDM 相比，拟议的 MB-OFDM 波形设计降低了空间流的峰均功率比 (PAPR)。
{"title":"Wideband MIMO THz System Design With MB-OFDM Waveform and Hybrid Beamforming","authors":"T. D. Dheerajlal;Amit Kumar Dutta","doi":"10.1109/OJCOMS.2024.3437458","DOIUrl":"10.1109/OJCOMS.2024.3437458","url":null,"abstract":"Terahertz (THz) band communication, operating in the range of \u0000<inline-formula> <tex-math>$(0.1-10)times 10^{12}$ </tex-math></inline-formula>\u0000 Hz, holds immense potential for fulfilling the ever-increasing demand for ultra-high data rate wireless communication. However, the unfavorable channel characteristics of THz communication pose a significant challenge in realizing this promising technology. In this paper, we present a novel solution to this challenge by proposing a wideband THz hybrid multiple-input multiple-output (MIMO) system design that leverages the ultra-wide bandwidth and the high spectral efficiency of MIMO schemes. We address the frequency-dependent nature of the THz channel by using a filter-bank-based channel model and a multi-bank orthogonal frequency division multiplexing (MB-OFDM) waveform design, which slices the wideband channel into smaller frequency-independent subbands. Specifically, we illustrate two different THz hybrid MIMO structures, one with a single precoder and combiner in the high sampling domain and another with a band-wise precoder design in the low sampling domain. We have adopted sub-optimal methods that alternatively optimize the beamformers. We evaluate the performance of our proposed MIMO schemes using numerical simulations that validate their feasibility and effectiveness in achieving communication in ultra-wideband frequency-dependent THz channels under various parameters and subband width assumptions. The results also indicate that the proposed MB-OFDM waveform design reduces the peak-to-average power ratio (PAPR) of the spatial streams compared to conventional OFDM.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10621033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883205","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 : 2024-08-01DOI: 10.1109/OJCOMS.2024.3436640
Shilian Zheng;Shurun Chen;Tao Chen;Zhuang Yang;Zhijin Zhao;Xiaoniu Yang
The signal-to-noise ratio (SNR) is an important metric for measuring signal quality and its estimation has received widespread attention in various application scenarios. In this paper, we propose an SNR estimation framework based on deep learning classification. Power spectrum input is proposed to reduce the computational complexity. We also propose an SNR estimation method based on deep learning regression to overcome the inevitable estimation error problem of classification-based methods in dealing with signals with SNR not within the training label set. We conduct a large number of simulation experiments considering various scenarios. Results show that the proposed methods have better estimation accuracy than two existing deep learning-based SNR estimation methods in different noises and multipath channels. Furthermore, the proposed methods only need to be trained under one modulation signals to adapt to SNR estimation of other modulation signals, with superior transfer performance. Finally, the method using the average periodogram as input has stronger adaptability in few-shot scenario and requires lower computational complexity compared to the method with in-phase and quadrature (IQ) input.
{"title":"Deep Learning-Based SNR Estimation","authors":"Shilian Zheng;Shurun Chen;Tao Chen;Zhuang Yang;Zhijin Zhao;Xiaoniu Yang","doi":"10.1109/OJCOMS.2024.3436640","DOIUrl":"10.1109/OJCOMS.2024.3436640","url":null,"abstract":"The signal-to-noise ratio (SNR) is an important metric for measuring signal quality and its estimation has received widespread attention in various application scenarios. In this paper, we propose an SNR estimation framework based on deep learning classification. Power spectrum input is proposed to reduce the computational complexity. We also propose an SNR estimation method based on deep learning regression to overcome the inevitable estimation error problem of classification-based methods in dealing with signals with SNR not within the training label set. We conduct a large number of simulation experiments considering various scenarios. Results show that the proposed methods have better estimation accuracy than two existing deep learning-based SNR estimation methods in different noises and multipath channels. Furthermore, the proposed methods only need to be trained under one modulation signals to adapt to SNR estimation of other modulation signals, with superior transfer performance. Finally, the method using the average periodogram as input has stronger adaptability in few-shot scenario and requires lower computational complexity compared to the method with in-phase and quadrature (IQ) input.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10620237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883206","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 : 2024-07-29DOI: 10.1109/OJCOMS.2024.3435704
A. Abdelaziz Salem;Mahmoud H. Ismail;Ahmed S. Ibrahim
This paper addresses the unexplored challenge of achieving secure communication in a full-duplex (FD) cognitive radio (CR) system employing a reconfigurable intelligent surface (RIS) where a passive eavesdropper (Eve) is equipped with multiple antennas. The secondary base station (SBS), operating in full-duplex mode, serves as an uplink (UL) communication provider for secondary users (SUs) while actively launching a jamming signal against Eve to degrade its downlink (DL) interception capability, thus aiding the primary network. This study faces several technical challenges. First, complex interference management arises as the FD-SBS must manage interference between UL communications and active jamming signals to ensure both secure communication and minimal interference to legitimate users. Additionally, optimizing multi-dimensional beamforming across the primary base station (PBS) DL, the FD-SBS jamming, the passive RIS, and the UL communication power imposes substantial complexities due to conflicting objectives and constraints. To this end, we propose a coordinated beamforming approach, which maximizes the minimum secrecy rate with a minimum target rate for the UL SUs, a maximum DL PBS transmission power, jamming power limits and RIS unit modulus constraints. To address the problem non-convexity, it is decomposed into four sub-problems, which are solved via employing semidefinite programming (SDP) and successive convex approximation (SCA) based alternating optimization. Simulation results show the effectiveness of RIS phase shift optimization to enhance secrecy performance, how much jamming power is needed to keep balance between the secrecy performance and the UL communication service, as well as the effectiveness of the proposed solution against various benchmarks.
本文探讨了在采用可重构智能表面 (RIS) 的全双工 (FD) 认知无线电 (CR) 系统中实现安全通信这一尚未探索的难题，在该系统中，被动窃听者 (Eve) 配备了多个天线。在全双工模式下运行的辅助基站（SBS）充当辅助用户（SU）的上行链路（UL）通信提供者，同时主动向夏娃发射干扰信号，削弱其下行链路（DL）拦截能力，从而为主网络提供帮助。这项研究面临多项技术挑战。首先是复杂的干扰管理，因为 FD-SBS 必须管理 UL 通信和主动干扰信号之间的干扰，以确保安全通信和对合法用户的最小干扰。此外，由于目标和限制相互冲突，优化主基站 (PBS) DL、FD-SBS 干扰、无源 RIS 和 UL 通信功率之间的多维波束成形带来了巨大的复杂性。为此，我们提出了一种协调波束成形方法，在 UL SU 的最小目标速率、最大 DL PBS 发射功率、干扰功率限制和 RIS 单位模数约束下，最大化最小保密速率。为解决非凸问题，该方法将问题分解为四个子问题，并采用基于半定量编程（SDP）和连续凸近似（SCA）的交替优化方法加以解决。仿真结果表明了 RIS 相移优化对提高保密性能的有效性、需要多大的干扰功率才能保持保密性能和 UL 通信服务之间的平衡，以及所提解决方案在各种基准测试中的有效性。
{"title":"Securing Full-Duplex Cognitive Radio With Reconfigurable Intelligent Surfaces via Coordinated Beamforming and Power Control","authors":"A. Abdelaziz Salem;Mahmoud H. Ismail;Ahmed S. Ibrahim","doi":"10.1109/OJCOMS.2024.3435704","DOIUrl":"10.1109/OJCOMS.2024.3435704","url":null,"abstract":"This paper addresses the unexplored challenge of achieving secure communication in a full-duplex (FD) cognitive radio (CR) system employing a reconfigurable intelligent surface (RIS) where a passive eavesdropper (Eve) is equipped with multiple antennas. The secondary base station (SBS), operating in full-duplex mode, serves as an uplink (UL) communication provider for secondary users (SUs) while actively launching a jamming signal against Eve to degrade its downlink (DL) interception capability, thus aiding the primary network. This study faces several technical challenges. First, complex interference management arises as the FD-SBS must manage interference between UL communications and active jamming signals to ensure both secure communication and minimal interference to legitimate users. Additionally, optimizing multi-dimensional beamforming across the primary base station (PBS) DL, the FD-SBS jamming, the passive RIS, and the UL communication power imposes substantial complexities due to conflicting objectives and constraints. To this end, we propose a coordinated beamforming approach, which maximizes the minimum secrecy rate with a minimum target rate for the UL SUs, a maximum DL PBS transmission power, jamming power limits and RIS unit modulus constraints. To address the problem non-convexity, it is decomposed into four sub-problems, which are solved via employing semidefinite programming (SDP) and successive convex approximation (SCA) based alternating optimization. Simulation results show the effectiveness of RIS phase shift optimization to enhance secrecy performance, how much jamming power is needed to keep balance between the secrecy performance and the UL communication service, as well as the effectiveness of the proposed solution against various benchmarks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870421","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 : 2024-07-29DOI: 10.1109/OJCOMS.2024.3435389
Le Sun;Zhimeng Zhang;Ghulam Muhammad
The continuous development of virtual reality technology allows the metaverse to create more immersive and highly interactive experiences for users. Metaverse users upload personal information through virtual reality devices, causing data security and communication security issues. Moreover, the diversity of data sources within the metaverse exacerbates issues of data heterogeneity. To address these issues, we propose a generative learning-based federated learning algorithm to secure and process heterogeneous data from users in the metaverse, called FedCPD. It consists of three main modules: a privacy protection module for data security, a correction module to correct the bias of the classifier, and an aggregation module to improve model performance. To protect the data security of metaverse users, we design a privacy-preserving method based on conditional Generative Adversarial Networks (cGAN) in the privacy protection module. The method replaces the feature extractor with a generator in cGAN to engage in server-side aggregation to avoid data exposure. The correction module is proposed to enhance the classifier’s ability to classify unknown data by using the constructed pseudo dataset for classification model training. To alleviate the negative impact of data heterogeneity on the global model, the aggregation module utilizes local discrepancy-based aggregation weights for server-side aggregation. It assigns higher aggregation weights to the client models that perform better than other models. Extensive experiments on multiple datasets show that FedCPD exhibits the highest classification accuracy compared to existing algorithms, demonstrating its effectiveness in processing heterogeneous data.
{"title":"FedCPD: A Federated Learning Algorithm for Processing and Securing Distributed Heterogeneous Data in the Metaverse","authors":"Le Sun;Zhimeng Zhang;Ghulam Muhammad","doi":"10.1109/OJCOMS.2024.3435389","DOIUrl":"10.1109/OJCOMS.2024.3435389","url":null,"abstract":"The continuous development of virtual reality technology allows the metaverse to create more immersive and highly interactive experiences for users. Metaverse users upload personal information through virtual reality devices, causing data security and communication security issues. Moreover, the diversity of data sources within the metaverse exacerbates issues of data heterogeneity. To address these issues, we propose a generative learning-based federated learning algorithm to secure and process heterogeneous data from users in the metaverse, called FedCPD. It consists of three main modules: a privacy protection module for data security, a correction module to correct the bias of the classifier, and an aggregation module to improve model performance. To protect the data security of metaverse users, we design a privacy-preserving method based on conditional Generative Adversarial Networks (cGAN) in the privacy protection module. The method replaces the feature extractor with a generator in cGAN to engage in server-side aggregation to avoid data exposure. The correction module is proposed to enhance the classifier’s ability to classify unknown data by using the constructed pseudo dataset for classification model training. To alleviate the negative impact of data heterogeneity on the global model, the aggregation module utilizes local discrepancy-based aggregation weights for server-side aggregation. It assigns higher aggregation weights to the client models that perform better than other models. Extensive experiments on multiple datasets show that FedCPD exhibits the highest classification accuracy compared to existing algorithms, demonstrating its effectiveness in processing heterogeneous data.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614242","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870422","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 : 2024-07-29DOI: 10.1109/ojcoms.2024.3435870
Reza Ghasemi Alavicheh, S. Mohammad Razavizadeh, Halim Yanikomeroglu
{"title":"Integrated Access and Backhaul (IAB) in Low Altitude Platforms","authors":"Reza Ghasemi Alavicheh, S. Mohammad Razavizadeh, Halim Yanikomeroglu","doi":"10.1109/ojcoms.2024.3435870","DOIUrl":"https://doi.org/10.1109/ojcoms.2024.3435870","url":null,"abstract":"","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1109/OJCOMS.2024.3433561
Johannes M. Eckhardt;Tobias Doeker;Thomas Kürner
The aim of channel models is the effective prediction of wireless channels in applicationspecific environments in order to successfully design wireless communication systems. For THz communications, deterministic ray-optical channel predictions are very common. This paper shows inherent limitations of ray-tracing based models due to the accuracy of three-dimensional models of the environment in the context of THz communications and presents a new hybrid channel model approach that combines ray-optical channel predictions with an analytic path gain model extracted from measurements. The model is applied for switchable, point-to-point, wireless inter-rack links at THz frequencies in a data center that make the data center network reconfigurable at runtime. The channel parameters derived by the novel hybrid approach show promising results compared with channel parameters extracted from conducted reference measurements with an overall path gain error below 5 dB for residual multipath components.
{"title":"Hybrid Channel Model for Low Terahertz Links in a Data Center","authors":"Johannes M. Eckhardt;Tobias Doeker;Thomas Kürner","doi":"10.1109/OJCOMS.2024.3433561","DOIUrl":"10.1109/OJCOMS.2024.3433561","url":null,"abstract":"The aim of channel models is the effective prediction of wireless channels in applicationspecific environments in order to successfully design wireless communication systems. For THz communications, deterministic ray-optical channel predictions are very common. This paper shows inherent limitations of ray-tracing based models due to the accuracy of three-dimensional models of the environment in the context of THz communications and presents a new hybrid channel model approach that combines ray-optical channel predictions with an analytic path gain model extracted from measurements. The model is applied for switchable, point-to-point, wireless inter-rack links at THz frequencies in a data center that make the data center network reconfigurable at runtime. The channel parameters derived by the novel hybrid approach show promising results compared with channel parameters extracted from conducted reference measurements with an overall path gain error below 5 dB for residual multipath components.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10609428","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141770889","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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