Pub Date : 2024-03-01DOI: 10.12720/jcm.19.3.161-167
Khaled M. Gharaibeh
—Passive Intermodulation (PIM) distortion which results from passive components such as antennas, connecters, etc. poses significant challenges in wireless communications by limiting cell coverage and data rates. In Carrier Aggregated (CA) Long-Term Evolution (LTE) system, PIM is manifested as self-interreference when intermodulation products of the transmitted signal leak to the receiver. The primary goal of this paper is to develop a new behavioral model for passive nonlinearities enabling PIM distortion in LTE systems to be predicted. The analysis employs a Threshold-Decomposition-based Piecewise Linear (TD-PWL) model to represent a passive nonlinearity and predict PIM in CA-LTE system. Simulation results show that the proposed model accurately predicts PIM and highlight its superior numerical stability and accuracy over polynomial-based models. These results position the PWL model as a promising choice in the design of PIM cancellation schemes.
{"title":"A Piecewise Linear Model for Passive Intermodulation Distortion","authors":"Khaled M. Gharaibeh","doi":"10.12720/jcm.19.3.161-167","DOIUrl":"https://doi.org/10.12720/jcm.19.3.161-167","url":null,"abstract":"—Passive Intermodulation (PIM) distortion which results from passive components such as antennas, connecters, etc. poses significant challenges in wireless communications by limiting cell coverage and data rates. In Carrier Aggregated (CA) Long-Term Evolution (LTE) system, PIM is manifested as self-interreference when intermodulation products of the transmitted signal leak to the receiver. The primary goal of this paper is to develop a new behavioral model for passive nonlinearities enabling PIM distortion in LTE systems to be predicted. The analysis employs a Threshold-Decomposition-based Piecewise Linear (TD-PWL) model to represent a passive nonlinearity and predict PIM in CA-LTE system. Simulation results show that the proposed model accurately predicts PIM and highlight its superior numerical stability and accuracy over polynomial-based models. These results position the PWL model as a promising choice in the design of PIM cancellation schemes.","PeriodicalId":53518,"journal":{"name":"Journal of Communications","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140404226","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 : 2024-03-01DOI: 10.12720/jcm.19.3.143-151
Mahmoud M. Qasaymeh, Ali A. Alqatawneh, Ahmad F. Aljaafreh
—Frequency Hopping (FH) spread spectrum system is extensively used in military and civilian fields due to its robustness against interference and efficiency in confronting radio jamming. Channel estimation is a crucial part of the FH system. However, signal processing-based channel estimation methods have some constraints, such as high computational complexity, sensitivity to noise level, and excessive overhead. To alleviate these issues, we propose a Machine Learning (ML) model for precisely estimating Narrow Band (NB) multipath fading channel parameters for a Slow Frequency Hopping (SFH) spread spectrum system. In the proposed model, we employed a Neural Network (NN) with three layers consisting of an input layer that interprets the signal's fundamental patterns, a hidden layer to extract the correlation found in the time scene, and an output layer that utilizes a linear activation function to provide the flexibility required to address the dynamic relationship between channel gain and time delay. Without prior experience, leveraging a synthetic dataset rich in complex temporal variations and channel gain nuances, the NN architecture, characterized by multiple dense layers, effectively captures complex temporal relationships. Following rigorous training and validation utilizing the Mean-Square Error (MSE) loss function, the model significantly reduced loss, emphasizing its proficiency for an accurate delay and gain estimation. A computer simulation comparison between the performance of the proposed model and previous classical models was included in this paper. Based on simulation results, the proposed ML-based estimator model significantly outperforms many classical subspace-based methods in terms of MSE, the performance improvement appears over several Signal-to-Noise Ratios (SNR). Furthermore, the proposed model provided a reasonable tradeoff between complexity and performance.
-跳频（FH）扩频系统具有抗干扰能力强、抗无线电干扰效率高的特点，因此被广泛应用于军事和民用领域。信道估计是跳频系统的重要组成部分。然而，基于信号处理的信道估计方法存在一些限制，如计算复杂度高、对噪声水平敏感、开销过大等。为了缓解这些问题，我们提出了一种机器学习（ML）模型，用于精确估计慢跳频（SFH）扩频系统的窄带（NB）多径衰落信道参数。在所提出的模型中，我们采用了一个由三层组成的神经网络（NN），其中输入层解释信号的基本模式，隐藏层提取时间场景中发现的相关性，输出层利用线性激活函数提供处理信道增益和时间延迟之间动态关系所需的灵活性。在没有先验经验的情况下，利用富含复杂时间变化和信道增益细微差别的合成数据集，以多个密集层为特征的 NN 架构能有效捕捉复杂的时间关系。在利用均方误差（MSE）损失函数进行严格的训练和验证后，该模型显著降低了损失，强调了其在准确估计延迟和增益方面的能力。本文通过计算机仿真比较了所提模型与以往经典模型的性能。根据仿真结果，所提出的基于 ML 的估计模型在 MSE 方面明显优于许多经典的基于子空间的方法，其性能的提高体现在多个信噪比（SNR）上。此外，所提出的模型在复杂性和性能之间做出了合理的权衡。
{"title":"Channel Estimation Methods for Frequency Hopping System Based on Machine Learning","authors":"Mahmoud M. Qasaymeh, Ali A. Alqatawneh, Ahmad F. Aljaafreh","doi":"10.12720/jcm.19.3.143-151","DOIUrl":"https://doi.org/10.12720/jcm.19.3.143-151","url":null,"abstract":"—Frequency Hopping (FH) spread spectrum system is extensively used in military and civilian fields due to its robustness against interference and efficiency in confronting radio jamming. Channel estimation is a crucial part of the FH system. However, signal processing-based channel estimation methods have some constraints, such as high computational complexity, sensitivity to noise level, and excessive overhead. To alleviate these issues, we propose a Machine Learning (ML) model for precisely estimating Narrow Band (NB) multipath fading channel parameters for a Slow Frequency Hopping (SFH) spread spectrum system. In the proposed model, we employed a Neural Network (NN) with three layers consisting of an input layer that interprets the signal's fundamental patterns, a hidden layer to extract the correlation found in the time scene, and an output layer that utilizes a linear activation function to provide the flexibility required to address the dynamic relationship between channel gain and time delay. Without prior experience, leveraging a synthetic dataset rich in complex temporal variations and channel gain nuances, the NN architecture, characterized by multiple dense layers, effectively captures complex temporal relationships. Following rigorous training and validation utilizing the Mean-Square Error (MSE) loss function, the model significantly reduced loss, emphasizing its proficiency for an accurate delay and gain estimation. A computer simulation comparison between the performance of the proposed model and previous classical models was included in this paper. Based on simulation results, the proposed ML-based estimator model significantly outperforms many classical subspace-based methods in terms of MSE, the performance improvement appears over several Signal-to-Noise Ratios (SNR). Furthermore, the proposed model provided a reasonable tradeoff between complexity and performance.","PeriodicalId":53518,"journal":{"name":"Journal of Communications","volume":"49 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140407258","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 : 2024-02-01DOI: 10.12720/jcm.19.2.119-126
Mohamed Najmus Saqhib, Lakshmikanth S.
—Wireless Sensor Networks (WSNs) play a vital role in Internet of Things (IoT) technology by facilitating data collection and transmission through small wireless sensors. Path loss, influenced by environmental factors, significantly impacts WSN performance, affecting communication range and sensor reliability. This emphasizes the importance of considering path loss in WSN design and optimization. The proposed work aims to evaluate a sink-led decentralized routing system designed to enhance network longevity and minimize energy consumption under various propagation loss models. The methodology employs an energy-aware model to select initiator nodes, creating multiple paths and reducing redundancy. For improved quality of service, the system picks a forward relay node based on factors like remaining energy, the quality of the radio link between adjacent nodes, and proximity to the sink node. A fuzzy logic-based decision-making process is used to identify the most optimal path among the multitude of possible pathways. The research seeks to demonstrate the impact of path loss on crucial network metrics, such as end-to-end delay, hop count, energy usage, and the number of active nodes in a WSN topology. Simulations provide a comprehensive understanding of the impact of path loss on key network metrics. Computational outcomes, derived from Received Signal Strength Indicator (RSSI) values for near-surface wave propagation, showcase that the Energy Aware Data Centric Query Driven Receiver initiated (EADQR) protocol excels in scenarios characterized by substantial environmental clutter, as represented by the clutter factor and HATA suburban models. The energy-aware strategy mitigates path loss and energy depletion, thereby prolonging the operational lifespan of the network.
-无线传感器网络（WSN）通过小型无线传感器促进数据收集和传输，在物联网（IoT）技术中发挥着重要作用。受环境因素影响，路径损耗会严重影响 WSN 性能，影响通信范围和传感器可靠性。这就强调了在 WSN 设计和优化中考虑路径损耗的重要性。所提出的工作旨在评估一个以汇为主导的分散式路由系统，该系统旨在提高网络寿命，并在各种传播损耗模型下最大限度地降低能耗。该方法采用能量感知模型来选择启动节点，创建多条路径并减少冗余。为了提高服务质量，该系统根据剩余能量、相邻节点之间无线链路的质量以及与汇节点的距离等因素选择前向中继节点。系统采用基于模糊逻辑的决策过程，在众多可能路径中找出最优路径。研究试图证明路径损耗对关键网络指标的影响，如 WSN 拓扑中的端到端延迟、跳数、能量使用和活动节点数。通过模拟可以全面了解路径损耗对关键网络指标的影响。根据近表面波传播的接收信号强度指示器（RSSI）值得出的计算结果表明，能量感知数据中心查询驱动接收器启动（EADQR）协议在以大量环境杂波为特征的场景中表现出色，这些环境杂波由杂波因素和 HATA 郊区模型表示。能量感知策略可减轻路径损耗和能量消耗，从而延长网络的运行寿命。
{"title":"Performance Evaluation of EADQR Across Various Path Loss Models Through Propagation Analysis","authors":"Mohamed Najmus Saqhib, Lakshmikanth S.","doi":"10.12720/jcm.19.2.119-126","DOIUrl":"https://doi.org/10.12720/jcm.19.2.119-126","url":null,"abstract":"—Wireless Sensor Networks (WSNs) play a vital role in Internet of Things (IoT) technology by facilitating data collection and transmission through small wireless sensors. Path loss, influenced by environmental factors, significantly impacts WSN performance, affecting communication range and sensor reliability. This emphasizes the importance of considering path loss in WSN design and optimization. The proposed work aims to evaluate a sink-led decentralized routing system designed to enhance network longevity and minimize energy consumption under various propagation loss models. The methodology employs an energy-aware model to select initiator nodes, creating multiple paths and reducing redundancy. For improved quality of service, the system picks a forward relay node based on factors like remaining energy, the quality of the radio link between adjacent nodes, and proximity to the sink node. A fuzzy logic-based decision-making process is used to identify the most optimal path among the multitude of possible pathways. The research seeks to demonstrate the impact of path loss on crucial network metrics, such as end-to-end delay, hop count, energy usage, and the number of active nodes in a WSN topology. Simulations provide a comprehensive understanding of the impact of path loss on key network metrics. Computational outcomes, derived from Received Signal Strength Indicator (RSSI) values for near-surface wave propagation, showcase that the Energy Aware Data Centric Query Driven Receiver initiated (EADQR) protocol excels in scenarios characterized by substantial environmental clutter, as represented by the clutter factor and HATA suburban models. The energy-aware strategy mitigates path loss and energy depletion, thereby prolonging the operational lifespan of the network.","PeriodicalId":53518,"journal":{"name":"Journal of Communications","volume":"45 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140463703","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}
Eman Zakaria, Ashraf Y. Hassan, H. EL Hennawy, Abdelhady M. Abdelhady
.
.
{"title":"Spectral-Efficient and Power-Efficient MIMOOFDM System with Time Diversity for Flat Fading Channel with Arbitrary Doppler Frequency Shift","authors":"Eman Zakaria, Ashraf Y. Hassan, H. EL Hennawy, Abdelhady M. Abdelhady","doi":"10.12720/jcm.19.2.53-64","DOIUrl":"https://doi.org/10.12720/jcm.19.2.53-64","url":null,"abstract":".","PeriodicalId":53518,"journal":{"name":"Journal of Communications","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140465428","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}
—Advances in low-power networking have shown remarkable evolution for the Internet of Things. LoRa technology promises low power consumption and long-range connectivity while maintaining sufficient throughput. However, in environments with a higher density of nodes, there is a high potential for packet collisions, compromising the reliability of the technology. This is a direct consequence of using an Aloha-based protocol to access the channel. This article proposes a Carrier Sense Multiple Access (CSMA) protocol called FT-CSMA, a new collision avoidance technique based on a hybrid of CSMA in IEEE 802.15.4 and CSMA in IEEE 802.11. The design of this protocol aims to provide an acceptable trade-off between the performance parameters of LoRa-based networks. Energy consumption, packet delivery ratio, and delay are interdependent; improving one will affect the others. FT-CSMA outperforms other methods in terms of Quality of Service and energy efficiency, with a 2% reduction in energy consumption and a 5% increase in packet delivery ratio.
{"title":"FT-CSMA: A Fine-Tuned CSMA Protocol for LoRa-Based Networks","authors":"Chaib Mostefa, Tahar Abbes Mounir, Allali Mahamed Abdelmadjid, Abdelouahab Nouar","doi":"10.12720/jcm.19.2.65-77","DOIUrl":"https://doi.org/10.12720/jcm.19.2.65-77","url":null,"abstract":"—Advances in low-power networking have shown remarkable evolution for the Internet of Things. LoRa technology promises low power consumption and long-range connectivity while maintaining sufficient throughput. However, in environments with a higher density of nodes, there is a high potential for packet collisions, compromising the reliability of the technology. This is a direct consequence of using an Aloha-based protocol to access the channel. This article proposes a Carrier Sense Multiple Access (CSMA) protocol called FT-CSMA, a new collision avoidance technique based on a hybrid of CSMA in IEEE 802.15.4 and CSMA in IEEE 802.11. The design of this protocol aims to provide an acceptable trade-off between the performance parameters of LoRa-based networks. Energy consumption, packet delivery ratio, and delay are interdependent; improving one will affect the others. FT-CSMA outperforms other methods in terms of Quality of Service and energy efficiency, with a 2% reduction in energy consumption and a 5% increase in packet delivery ratio.","PeriodicalId":53518,"journal":{"name":"Journal of Communications","volume":"64 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140464095","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 : 2024-02-01DOI: 10.12720/jcm.19.2.107-118
Kayode A. Olaniyi, Reolyn Heymann, Theo G. Swart
—The design of optimal channel codes with computationally efficient Forward Error Correction (FEC) codes remains an open research problem. In this paper, we explore optimal channel codes with computationally efficient FEC codes, focusing on turbo and Low-Density Parity-Check (LDPC) codes as near-capacity approaching solutions. We highlight the significance of accurate channel estimation in reliable communication technology design. We further note that the stringent requirements of contemporary communication systems have pushed conventional FEC codes to their limits. To address this, we advocate for a paradigm shift towards emerging Machine Learning (ML) applications in communication. Our review highlights ML's potential to solve current channel coding and estimation challenges by replacing traditional communication algorithms with adaptable deep neural network architectures. This approach provides competitive performance, flexibility, reduced complexity and latency, heralding the era of ML-based communication applications as the future of end-to-end efficient communication systems.
-带有计算效率高的前向纠错（FEC）码的最佳信道编码设计仍是一个未决研究课题。在本文中，我们探讨了带有计算效率高的前向纠错（FEC）码的最佳信道编码，重点是作为接近容量解决方案的涡轮编码和低密度奇偶校验（LDPC）编码。我们强调了准确信道估计在可靠通信技术设计中的重要性。我们进一步指出，当代通信系统的严格要求已将传统的 FEC 编码推向极限。为解决这一问题，我们提倡向新兴的机器学习（ML）通信应用模式转变。我们的综述强调了 ML 在解决当前信道编码和估计挑战方面的潜力，即用可适应的深度神经网络架构取代传统通信算法。这种方法提供了极具竞争力的性能、灵活性、更低的复杂性和延迟，预示着基于 ML 的通信应用将成为端到端高效通信系统的未来。
{"title":"Machine Learning for Channel Coding: A Paradigm Shift from FEC Codes","authors":"Kayode A. Olaniyi, Reolyn Heymann, Theo G. Swart","doi":"10.12720/jcm.19.2.107-118","DOIUrl":"https://doi.org/10.12720/jcm.19.2.107-118","url":null,"abstract":"—The design of optimal channel codes with computationally efficient Forward Error Correction (FEC) codes remains an open research problem. In this paper, we explore optimal channel codes with computationally efficient FEC codes, focusing on turbo and Low-Density Parity-Check (LDPC) codes as near-capacity approaching solutions. We highlight the significance of accurate channel estimation in reliable communication technology design. We further note that the stringent requirements of contemporary communication systems have pushed conventional FEC codes to their limits. To address this, we advocate for a paradigm shift towards emerging Machine Learning (ML) applications in communication. Our review highlights ML's potential to solve current channel coding and estimation challenges by replacing traditional communication algorithms with adaptable deep neural network architectures. This approach provides competitive performance, flexibility, reduced complexity and latency, heralding the era of ML-based communication applications as the future of end-to-end efficient communication systems.","PeriodicalId":53518,"journal":{"name":"Journal of Communications","volume":"131 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140465249","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}
Asma A. Alqudah, Khaled F. Hayajneh, Hasan A. Aldiabat, Hazim M. Shakhatreh
—This paper presents a comprehensive analysis of the utilization of rate-matched 5G New Radio (NR) Low-Density Parity Check (LDPC) codes for decoding faster-than-Nyquist signaled data symbols in an Additive White Gaussian Noise (AWGN) channel. The rate-matching techniques employing message-bit and parity-bit puncturing are thoroughly explained, showcasing the applications of various rates during the coding process. Moreover, in conjunction with the aforementioned rate-matching and puncturing methods, a puncturing scheme is employed on the first two columns of the parity-check matrix derived from the 5G NR LDPC base graphs. The performance of the 5G NR LDPC code, integrated as the outer decoder in turbo equalization for faster-than-Nyquist signaled data symbols, is thoroughly investigated. The achieved results are compared with the performance of convolutional codes implemented in the same system setup and operating at identical code rates. By analyzing the employment of rate-matched 5G NR LDPC codes, this paper provides valuable insights into their efficacy for decoding faster-than-Nyquist signaled data symbols in the AWGN channel. The comparison with convolutional codes offers a benchmark for performance evaluation, facilitating a deeper understanding of the benefits and trade-offs associated with each coding scheme.
-本文全面分析了在加性白高斯噪声（AWGN）信道中利用速率匹配的 5G 新无线电（NR）低密度奇偶校验（LDPC）码对快于奈奎斯特的信号数据符号进行解码的情况。采用信息位和奇偶校验位穿刺的速率匹配技术得到了详尽的解释，展示了编码过程中各种速率的应用。此外，结合上述速率匹配和穿刺方法，对从 5G NR LDPC 基图导出的奇偶校验矩阵的前两列采用了穿刺方案。对 5G NR LDPC 码的性能进行了深入研究，并将其作为外层解码器集成到快于奈奎斯特信号数据符号的涡轮均衡中。研究结果与在相同系统设置和相同码率下实施的卷积码的性能进行了比较。通过分析速率匹配的 5G NR LDPC 码的应用，本文就其在 AWGN 信道中解码快于奈奎斯特信号数据符号的功效提供了有价值的见解。与卷积码的比较为性能评估提供了一个基准，有助于更深入地了解与每种编码方案相关的优势和权衡。
{"title":"Efficient Generation of Puncturing-Assisted Rate-Matched 5G New Radio LDPC Codes for Faster-Than-Nyquist Signaling","authors":"Asma A. Alqudah, Khaled F. Hayajneh, Hasan A. Aldiabat, Hazim M. Shakhatreh","doi":"10.12720/jcm.19.2.90-98","DOIUrl":"https://doi.org/10.12720/jcm.19.2.90-98","url":null,"abstract":"—This paper presents a comprehensive analysis of the utilization of rate-matched 5G New Radio (NR) Low-Density Parity Check (LDPC) codes for decoding faster-than-Nyquist signaled data symbols in an Additive White Gaussian Noise (AWGN) channel. The rate-matching techniques employing message-bit and parity-bit puncturing are thoroughly explained, showcasing the applications of various rates during the coding process. Moreover, in conjunction with the aforementioned rate-matching and puncturing methods, a puncturing scheme is employed on the first two columns of the parity-check matrix derived from the 5G NR LDPC base graphs. The performance of the 5G NR LDPC code, integrated as the outer decoder in turbo equalization for faster-than-Nyquist signaled data symbols, is thoroughly investigated. The achieved results are compared with the performance of convolutional codes implemented in the same system setup and operating at identical code rates. By analyzing the employment of rate-matched 5G NR LDPC codes, this paper provides valuable insights into their efficacy for decoding faster-than-Nyquist signaled data symbols in the AWGN channel. The comparison with convolutional codes offers a benchmark for performance evaluation, facilitating a deeper understanding of the benefits and trade-offs associated with each coding scheme.","PeriodicalId":53518,"journal":{"name":"Journal of Communications","volume":"41 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140464442","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 : 2024-02-01DOI: 10.12720/jcm.19.2.99-106
Andrea Stomaci, D. Marabissi, Lorenzo Mucchi
—The aim of this study is to assess the effectiveness of Physical Layer Authentication (PLA) in securing IoT nodes. Specifically, we present a PLA framework based on wireless fingerprinting, where the legitimated node is distinguished from potential attackers by exploiting the unique wireless channel features. To achieve this objective, we employ various machine learning approaches for anomaly detection, making use of a wide range of channel attributes in time-varying conditions. In particular, four different Machine Learning (ML) strategies in their one class version have been considered and compared: decision-tree, kernel-based, clustering and nearest neighbors. Our study highlights advantages and disadvantages of each method, considering parameters optimization, training requirements and time complexity. Results show that the use of multiple-attribute allows to achieve accurate detection performance. In particular, our results reveal that the kernel-based solution is the one that achieves best results in terms of accuracy, but the nearest neighbor’s solution has very similar performance with a significant advantage in terms of complexity and no need for training, making it more suitable for time-varying contexts, and a promising choice for securing IoT nodes through PLA based on wireless fingerprinting. The other two alternatives have somewhat lower performance but low complexity. This research contributes valuable insights into enhancing IoT security through PLA techniques.
{"title":"Comparison of Machine Learning Approaches Based on Multiple Channel Attributes for Authentication andSpoofing Detection at the Physical Layer","authors":"Andrea Stomaci, D. Marabissi, Lorenzo Mucchi","doi":"10.12720/jcm.19.2.99-106","DOIUrl":"https://doi.org/10.12720/jcm.19.2.99-106","url":null,"abstract":"—The aim of this study is to assess the effectiveness of Physical Layer Authentication (PLA) in securing IoT nodes. Specifically, we present a PLA framework based on wireless fingerprinting, where the legitimated node is distinguished from potential attackers by exploiting the unique wireless channel features. To achieve this objective, we employ various machine learning approaches for anomaly detection, making use of a wide range of channel attributes in time-varying conditions. In particular, four different Machine Learning (ML) strategies in their one class version have been considered and compared: decision-tree, kernel-based, clustering and nearest neighbors. Our study highlights advantages and disadvantages of each method, considering parameters optimization, training requirements and time complexity. Results show that the use of multiple-attribute allows to achieve accurate detection performance. In particular, our results reveal that the kernel-based solution is the one that achieves best results in terms of accuracy, but the nearest neighbor’s solution has very similar performance with a significant advantage in terms of complexity and no need for training, making it more suitable for time-varying contexts, and a promising choice for securing IoT nodes through PLA based on wireless fingerprinting. The other two alternatives have somewhat lower performance but low complexity. This research contributes valuable insights into enhancing IoT security through PLA techniques.","PeriodicalId":53518,"journal":{"name":"Journal of Communications","volume":"46 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140464404","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}
D. Nataraj, K. Chitambara Rao, K. S. Chakradhar, G. Vinutna Ujwala, B. Sadasiva Rao, Y. S. V. Raman
{"title":"A Wide Band Antenna for both S-Band and CBand Satellite Communication Applications","authors":"D. Nataraj, K. Chitambara Rao, K. S. Chakradhar, G. Vinutna Ujwala, B. Sadasiva Rao, Y. S. V. Raman","doi":"10.12720/jcm.19.1.28-36","DOIUrl":"https://doi.org/10.12720/jcm.19.1.28-36","url":null,"abstract":"","PeriodicalId":53518,"journal":{"name":"Journal of Communications","volume":"36 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140517971","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}
Mohammed Lamnaour, Moundir Raiss, Yasser Mesmoudi, Yasser El Khamlichi, A. Tahiri, A. Touhafi
{"title":"A Semantic-Based Middleware for Supporting Heterogeneity and Context-Awareness Within IoT Applications","authors":"Mohammed Lamnaour, Moundir Raiss, Yasser Mesmoudi, Yasser El Khamlichi, A. Tahiri, A. Touhafi","doi":"10.12720/jcm.19.1.19-27","DOIUrl":"https://doi.org/10.12720/jcm.19.1.19-27","url":null,"abstract":"","PeriodicalId":53518,"journal":{"name":"Journal of Communications","volume":"104 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140521649","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}
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