Pub Date : 2024-11-15DOI: 10.1016/j.phycom.2024.102541
Davide Mattera, Mario Tanda
Orthogonal frequency division multiplexing (OFDM) systems present some shortcomings such as strict requirements for synchronization and reduced spectral efficiency. Filter bank multicarrier (FBMC) systems based on quadrature amplitude modulation (QAM-FBMC) have attracted increasing attention since they are considered an effective alternative to OFDM schemes. In this paper, the bit error rate (BER) of QAM-FBMC systems with single-tap equalization, is obtained. Moreover, the asymptotic (for a large number of subcarriers) expression of the BER and an approximate expression of the asymptotic BER are derived. The numerical results show that the derived BER expressions are quite accurate.
{"title":"On the performance of QAM-FBMC systems in frequency-selective channels","authors":"Davide Mattera, Mario Tanda","doi":"10.1016/j.phycom.2024.102541","DOIUrl":"10.1016/j.phycom.2024.102541","url":null,"abstract":"<div><div>Orthogonal frequency division multiplexing (OFDM) systems present some shortcomings such as strict requirements for synchronization and reduced spectral efficiency. Filter bank multicarrier (FBMC) systems based on quadrature amplitude modulation (QAM-FBMC) have attracted increasing attention since they are considered an effective alternative to OFDM schemes. In this paper, the bit error rate (BER) of QAM-FBMC systems with single-tap equalization, is obtained. Moreover, the asymptotic (for a large number of subcarriers) expression of the BER and an approximate expression of the asymptotic BER are derived. The numerical results show that the derived BER expressions are quite accurate.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102541"},"PeriodicalIF":2.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1016/j.phycom.2024.102538
Yongli An, Shuoyang Lu, Haoran Cai, Zhanlin Ji
Some deep learning-based CSI feedback models have high computational and storage requirements, which limit their feedback efficiency on mobile devices, making them challenging to deploy on a large scale. Therefore, to address the poor feasibility of existing deep learning-based CSI feedback methods in practical deployment on user devices, a lightweight CSI feedback network suitable for mobile terminals is proposed to reduce the demand for computational and storage resources. This network enables efficient feedback on mobile devices. It leverages the design concept of a multi-resolution network to enhance feedback performance while reducing the number of parameters and computational load of the feedback network. Additionally, it employs dynamic convolution to effectively capture the contextual information of CSI. Through simulation comparison, it is found that compared with other lightweight CSI feedback networks based on deep learning, the feedback accuracy in each scenario is improved by 8.57% on average.
{"title":"A deep learning-based approach to lightweight CSI feedback","authors":"Yongli An, Shuoyang Lu, Haoran Cai, Zhanlin Ji","doi":"10.1016/j.phycom.2024.102538","DOIUrl":"10.1016/j.phycom.2024.102538","url":null,"abstract":"<div><div>Some deep learning-based CSI feedback models have high computational and storage requirements, which limit their feedback efficiency on mobile devices, making them challenging to deploy on a large scale. Therefore, to address the poor feasibility of existing deep learning-based CSI feedback methods in practical deployment on user devices, a lightweight CSI feedback network suitable for mobile terminals is proposed to reduce the demand for computational and storage resources. This network enables efficient feedback on mobile devices. It leverages the design concept of a multi-resolution network to enhance feedback performance while reducing the number of parameters and computational load of the feedback network. Additionally, it employs dynamic convolution to effectively capture the contextual information of CSI. Through simulation comparison, it is found that compared with other lightweight CSI feedback networks based on deep learning, the feedback accuracy in each scenario is improved by 8.57% on average.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102538"},"PeriodicalIF":2.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1016/j.phycom.2024.102540
Neha Payal, Devendra Singh Gurjar
This work examines the performance of the terrestrial–underwater communication system utilizing hybrid free space optics (FSO)/radio-frequency (RF) and underwater wireless optical communication (UWOC) links. Here, the base station communicates with the underwater vehicle via a decode-and-forward (DF) based relay (buoy) in two phases. In the first phase, a hybrid FSO/RF link is used to transmit signal to the buoy, where the RF link acts as an alternative link to increase the reliability of the system, and in the next phase, the buoy forwards signal to the underwater vehicle through the UWOC link. To enhance the reliability of the RF link, the buoy is deployed with multiple antennas, and it exploits a maximal ratio combining scheme on the received RF signals. The analysis takes into consideration some primary variables that influence the system’s performance, such as atmospheric turbulence, attenuation, temperature gradient, air bubbles, water salinity variations, pointing errors, and detection techniques. Closed-form expressions for the outage probability, system throughput, and average channel capacity in terms of the Meijer- and bivariate Fox- functions are derived. Simulation results are presented to validate the analytical expressions and disclose valuable findings.
{"title":"Hybrid FSO/RF and UWOC system for enabling terrestrial–underwater communication: Performance analysis","authors":"Neha Payal, Devendra Singh Gurjar","doi":"10.1016/j.phycom.2024.102540","DOIUrl":"10.1016/j.phycom.2024.102540","url":null,"abstract":"<div><div>This work examines the performance of the terrestrial–underwater communication system utilizing hybrid free space optics (FSO)/radio-frequency (RF) and underwater wireless optical communication (UWOC) links. Here, the base station communicates with the underwater vehicle via a decode-and-forward (DF) based relay (buoy) in two phases. In the first phase, a hybrid FSO/RF link is used to transmit signal to the buoy, where the RF link acts as an alternative link to increase the reliability of the system, and in the next phase, the buoy forwards signal to the underwater vehicle through the UWOC link. To enhance the reliability of the RF link, the buoy is deployed with multiple antennas, and it exploits a maximal ratio combining scheme on the received RF signals. The analysis takes into consideration some primary variables that influence the system’s performance, such as atmospheric turbulence, attenuation, temperature gradient, air bubbles, water salinity variations, pointing errors, and detection techniques. Closed-form expressions for the outage probability, system throughput, and average channel capacity in terms of the Meijer-<span><math><mi>G</mi></math></span> and bivariate Fox-<span><math><mi>H</mi></math></span> functions are derived. Simulation results are presented to validate the analytical expressions and disclose valuable findings.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102540"},"PeriodicalIF":2.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-12DOI: 10.1016/j.phycom.2024.102534
Safalata S. Sindal, Y.N. Trivedi
Deep learning has revolutionized communication systems by introducing innovative approaches to address channel impairments through end-to-end models. Autoencoders, a type of deep learning architecture, are adept at learning compact data representations. However, conventional autoencoders in end-to-end models can suffer from overfitting, which limits their effectiveness in noisy communication environments. To address this issue, we propose a Sparse Autoencoder-based (SAE) model that enforces sparsity and promotes the extraction of robust features. Despite its effectiveness, the SAE model may still lack the ability to focus on the most relevant features of the input data. To overcome this limitation, we further introduce an Attention Mechanism-based Sparse Autoencoder (ASA) model. This model integrates the feature extraction capabilities of a sparse autoencoder with an attention mechanism that selectively highlights informative features of the signal. Through simulations, we demonstrate that both proposed models significantly improve -PSK and -QAM communication system performance. When trained at 7 dB, both proposed models exhibit significant performance improvements at higher testing average SNRs. Our results show that the SAE model outperforms the conventional Maximum Likelihood Detection (MLD) model and baseline autoencoder systems but suffers from error floor issues. The SAE model suffers from an error floor at average SNRs beyond 16 dB for BPSK and 14 dB for higher-order modulation schemes. As the value of increases, the performance gap between the MLD and the proposed SAE model narrows. The ASA model, however, effectively mitigates the error floor observed in the SAE model for all values of and across all modulation schemes. This research highlights the benefits of integrating an attention mechanism with SAE, resulting in enhanced robustness and reliability in communication systems characterized by improved accuracy and reduced error rates.
深度学习引入了创新方法，通过端到端模型解决信道损伤问题，从而彻底改变了通信系统。自动编码器是深度学习架构的一种，善于学习紧凑的数据表示。然而，端到端模型中的传统自动编码器可能存在过拟合问题，从而限制了其在高噪声通信环境中的有效性。为解决这一问题，我们提出了一种基于稀疏自动编码器（SAE）的模型，该模型可加强稀疏性并促进鲁棒特征的提取。尽管 SAE 模型很有效，但它可能仍然缺乏关注输入数据中最相关特征的能力。为了克服这一局限，我们进一步引入了基于注意力机制的稀疏自动编码器（ASA）模型。该模型集成了稀疏自动编码器的特征提取能力和注意力机制，后者可选择性地突出信号的信息特征。通过仿真，我们证明这两种模型都能显著提高 M-PSK 和 M-QAM 通信系统的性能。当在 7 dB 下进行训练时，在测试平均信噪比较高的情况下，这两种建议的模型都表现出显著的性能改进。我们的结果表明，SAE 模型的性能优于传统的最大似然检测 (MLD) 模型和基线自动编码器系统，但存在误差下限问题。当 BPSK 的平均信噪比超过 16 dB 和高阶调制方案的平均信噪比超过 14 dB 时，SAE 模型会出现误差下限。随着 M 值的增加，MLD 与拟议的 SAE 模型之间的性能差距也在缩小。然而，ASA 模型能有效缓解 SAE 模型在所有 M 值和所有调制方案中观察到的误差下限。这项研究强调了将注意力机制与 SAE 相结合的好处，从而增强了通信系统的鲁棒性和可靠性，提高了准确性并降低了错误率。
{"title":"Enhancing performance of end-to-end communication system using Attention Mechanism-based Sparse Autoencoder over Rayleigh fading channel","authors":"Safalata S. Sindal, Y.N. Trivedi","doi":"10.1016/j.phycom.2024.102534","DOIUrl":"10.1016/j.phycom.2024.102534","url":null,"abstract":"<div><div>Deep learning has revolutionized communication systems by introducing innovative approaches to address channel impairments through end-to-end models. Autoencoders, a type of deep learning architecture, are adept at learning compact data representations. However, conventional autoencoders in end-to-end models can suffer from overfitting, which limits their effectiveness in noisy communication environments. To address this issue, we propose a Sparse Autoencoder-based (SAE) model that enforces sparsity and promotes the extraction of robust features. Despite its effectiveness, the SAE model may still lack the ability to focus on the most relevant features of the input data. To overcome this limitation, we further introduce an Attention Mechanism-based Sparse Autoencoder (ASA) model. This model integrates the feature extraction capabilities of a sparse autoencoder with an attention mechanism that selectively highlights informative features of the signal. Through simulations, we demonstrate that both proposed models significantly improve <span><math><mi>M</mi></math></span>-PSK and <span><math><mi>M</mi></math></span>-QAM communication system performance. When trained at 7 dB, both proposed models exhibit significant performance improvements at higher testing average SNRs. Our results show that the SAE model outperforms the conventional Maximum Likelihood Detection (MLD) model and baseline autoencoder systems but suffers from error floor issues. The SAE model suffers from an error floor at average SNRs beyond 16 dB for BPSK and 14 dB for higher-order modulation schemes. As the value of <span><math><mi>M</mi></math></span> increases, the performance gap between the MLD and the proposed SAE model narrows. The ASA model, however, effectively mitigates the error floor observed in the SAE model for all values of <span><math><mi>M</mi></math></span> and across all modulation schemes. This research highlights the benefits of integrating an attention mechanism with SAE, resulting in enhanced robustness and reliability in communication systems characterized by improved accuracy and reduced error rates.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"67 ","pages":"Article 102534"},"PeriodicalIF":2.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-09DOI: 10.1016/j.phycom.2024.102537
Xiaohui Zhang, Dongle Wang, Ling Xing, Honghai Wu
Cell-free massive multiple-input multiple-output (mMIMO) significantly improves the spectral efficiency (SE) performance compared to conventional centralized mMIMO through its distributed antenna architecture. Fractional power allocation (FPA) algorithm is widely used for scalable power control with good performance in downlink (DL) of cell-free mMIMO. In this paper, we propose modified FPA (MFPA) and generalized FPA (GFPA) strategies for centralized and distributed precoding in the DL of cell-free networks, respectively. For the former, we abandon the traditional normalization of precoding vectors and introduce three adjustment parameters, which can dynamically adjust the power allocation of the DL according to the actual channel conditions. Regarding the latter, the GFPA strategy finds effective channel factors suitable for various distributed precoding schemes and correlates them with the power allocation coefficients of each user equipment (UE), enabling power allocation to adapt to multiple precoding schemes. Analysis and simulation results demonstrate that, under the MFPA strategy, UEs with poorer channel conditions can achieve higher SE, but at the expense of other UEs with better channel conditions. Under the GFPA strategy, UEs with better channel conditions experience significant SE improvements without sacrificing UEs performance with poorer channel conditions.
{"title":"Modified fractional power allocation for downlink cell-free massive MIMO systems","authors":"Xiaohui Zhang, Dongle Wang, Ling Xing, Honghai Wu","doi":"10.1016/j.phycom.2024.102537","DOIUrl":"10.1016/j.phycom.2024.102537","url":null,"abstract":"<div><div>Cell-free massive multiple-input multiple-output (mMIMO) significantly improves the spectral efficiency (SE) performance compared to conventional centralized mMIMO through its distributed antenna architecture. Fractional power allocation (FPA) algorithm is widely used for scalable power control with good performance in downlink (DL) of cell-free mMIMO. In this paper, we propose modified FPA (MFPA) and generalized FPA (GFPA) strategies for centralized and distributed precoding in the DL of cell-free networks, respectively. For the former, we abandon the traditional normalization of precoding vectors and introduce three adjustment parameters, which can dynamically adjust the power allocation of the DL according to the actual channel conditions. Regarding the latter, the GFPA strategy finds effective channel factors suitable for various distributed precoding schemes and correlates them with the power allocation coefficients of each user equipment (UE), enabling power allocation to adapt to multiple precoding schemes. Analysis and simulation results demonstrate that, under the MFPA strategy, UEs with poorer channel conditions can achieve higher SE, but at the expense of other UEs with better channel conditions. Under the GFPA strategy, UEs with better channel conditions experience significant SE improvements without sacrificing UEs performance with poorer channel conditions.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"67 ","pages":"Article 102537"},"PeriodicalIF":2.0,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-09DOI: 10.1016/j.phycom.2024.102536
Humairah Hamid, G.R. Begh
A broad spectrum of communication and information technologies is currently being investigated for their potential applications in disaster management. A high level of situational awareness, combined with a prompt and accurate response, is essential for the preservation of life during catastrophe scenarios. This study presents a novel communication strategy employing Unmanned Aerial Vehicles (UAVs) as aerial base stations for providing connectivity to the affected area. The system takes advantage of the flexibility and quick deployment characteristics of UAVs. The main focus is to determine the optimal UAV deployment along with trajectory planning to ensure connectivity in areas where conventional base stations are inaccessible. The proposed system employs two types of UAVs: cluster UAVs which act as stationary base stations and relay UAVs acting as mobile base stations. A three-step strategy is proposed to find the suitable location of cluster UAVs, optimize their height and power, and find the optimal trajectory of the relay UAVs to maximize the percentage of users served. Gaussian Mixture Model (GMM) clustering is employed to determine the optimal horizontal location of cluster UAVs. An optimization problem is framed for finding out the optimal height and power for cluster UAVs. Heuristic-based A-star algorithm is used to find out the trajectory of the relay UAVs which can efficiently minimize the overall path length while avoiding obstacles. The simulation results confirm the effectiveness of the proposed approach and demonstrate the performance enhancement by comparing it with the benchmark schemes.
{"title":"Clustering based strategic 3D deployment and trajectory optimization of UAVs with A-star algorithm for enhanced disaster response","authors":"Humairah Hamid, G.R. Begh","doi":"10.1016/j.phycom.2024.102536","DOIUrl":"10.1016/j.phycom.2024.102536","url":null,"abstract":"<div><div>A broad spectrum of communication and information technologies is currently being investigated for their potential applications in disaster management. A high level of situational awareness, combined with a prompt and accurate response, is essential for the preservation of life during catastrophe scenarios. This study presents a novel communication strategy employing Unmanned Aerial Vehicles (UAVs) as aerial base stations for providing connectivity to the affected area. The system takes advantage of the flexibility and quick deployment characteristics of UAVs. The main focus is to determine the optimal UAV deployment along with trajectory planning to ensure connectivity in areas where conventional base stations are inaccessible. The proposed system employs two types of UAVs: cluster UAVs which act as stationary base stations and relay UAVs acting as mobile base stations. A three-step strategy is proposed to find the suitable location of cluster UAVs, optimize their height and power, and find the optimal trajectory of the relay UAVs to maximize the percentage of users served. Gaussian Mixture Model (GMM) clustering is employed to determine the optimal horizontal location of cluster UAVs. An optimization problem is framed for finding out the optimal height and power for cluster UAVs. Heuristic-based A-star algorithm is used to find out the trajectory of the relay UAVs which can efficiently minimize the overall path length while avoiding obstacles. The simulation results confirm the effectiveness of the proposed approach and demonstrate the performance enhancement by comparing it with the benchmark schemes.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"67 ","pages":"Article 102536"},"PeriodicalIF":2.0,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-08DOI: 10.1016/j.phycom.2024.102535
Yuanxin Liu , Demin Li , Xuemin Chen
In vehicle networks, accurate vehicle localization is crucial. This paper proposes a joint roadside unit (RSU) and agent vehicles cooperative localization framework based on dual-function radar-communication (DFRC) technology. It utilizes unscented Kalman filtering (UKF) to process DFRC signals and obtain vehicle status information. To improve the angle prediction accuracy of the agent vehicle, an angle fusion estimation scheme based on the maximum likelihood algorithm is proposed. Furthermore, a weighted method is introduced within the joint RSU and agent vehicle cooperative localization to enhance vehicle localization accuracy. Experimental results demonstrate that the proposed angle fusion scheme reduces angle estimation error, and the joint RSU and agent vehicle localization framework significantly improves vehicle localization accuracy.
{"title":"Joint RSU and agent vehicle cooperative localization using mmWave sensing","authors":"Yuanxin Liu , Demin Li , Xuemin Chen","doi":"10.1016/j.phycom.2024.102535","DOIUrl":"10.1016/j.phycom.2024.102535","url":null,"abstract":"<div><div>In vehicle networks, accurate vehicle localization is crucial. This paper proposes a joint roadside unit (RSU) and agent vehicles cooperative localization framework based on dual-function radar-communication (DFRC) technology. It utilizes unscented Kalman filtering (UKF) to process DFRC signals and obtain vehicle status information. To improve the angle prediction accuracy of the agent vehicle, an angle fusion estimation scheme based on the maximum likelihood algorithm is proposed. Furthermore, a weighted method is introduced within the joint RSU and agent vehicle cooperative localization to enhance vehicle localization accuracy. Experimental results demonstrate that the proposed angle fusion scheme reduces angle estimation error, and the joint RSU and agent vehicle localization framework significantly improves vehicle localization accuracy.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"67 ","pages":"Article 102535"},"PeriodicalIF":2.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-07DOI: 10.1016/j.phycom.2024.102533
Muhammad Majid Aziz, Aamir Habib, Adnan Zafar
The complexity of the radar environment increases with technological advancement, especially when considering the difficulties presented by repeating jammers. These jammers can impede radar detection, especially when they create false targets in non-line-of-sight (NLOS) situations. This study focuses on optimizing the phase shifts of Reconfigurable Intelligent Surfaces (RIS) to address the problem of NLOS between a target and radar for detection in order to address these NLOS issues. Specifically, we investigate RIS phase shift optimization using a Genetic Algorithm (GA) to address the challenges posed by repeating jammers across various dynamic scenarios. Our objective is to increase the radar system’s ability to detect actual targets in non-LOS scenarios when repeater jammers are present in the environment. According to the experimental results, this method offers a practical way to mitigate the effects of repeater jammers by improving radar detection performance in NLOS environments.
{"title":"Reconfigurable Intelligent Surfaces Assisted NLOS Radar Anti Jamming Using Deep Reinforcement Learning","authors":"Muhammad Majid Aziz, Aamir Habib, Adnan Zafar","doi":"10.1016/j.phycom.2024.102533","DOIUrl":"10.1016/j.phycom.2024.102533","url":null,"abstract":"<div><div>The complexity of the radar environment increases with technological advancement, especially when considering the difficulties presented by repeating jammers. These jammers can impede radar detection, especially when they create false targets in non-line-of-sight (NLOS) situations. This study focuses on optimizing the phase shifts of Reconfigurable Intelligent Surfaces (RIS) to address the problem of NLOS between a target and radar for detection in order to address these NLOS issues. Specifically, we investigate RIS phase shift optimization using a Genetic Algorithm (GA) to address the challenges posed by repeating jammers across various dynamic scenarios. Our objective is to increase the radar system’s ability to detect actual targets in non-LOS scenarios when repeater jammers are present in the environment. According to the experimental results, this method offers a practical way to mitigate the effects of repeater jammers by improving radar detection performance in NLOS environments.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"67 ","pages":"Article 102533"},"PeriodicalIF":2.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-04DOI: 10.1016/j.phycom.2024.102532
Zhongqiang Luo , Zaiqiang Wang
Integrated Sense of Communication (ISAC) is gradually becoming one of the core technologies in the sixth-generation mobile communication system. ISAC enhances spectrum efficiency and reduces equipment size, costs, and power consumption while minimizing interference between the two functions. The technology is introduced in terms of historical background and definition, application scenarios, current problems of ISAC technology, and the development history of ISAC technology. The latest research progress of ISAC technology is summarized in terms of waveform design, introducing sensing-centered waveform design, communication-centered waveform design, and sensing-communication joint waveform design, respectively. The ISAC waveform performance index introduces the performance aspects that need attention in waveform design to achieve improvement. It presents the application of ISAC technology in some recent emerging technologies and the current challenges and future outlook of ISAC waveform design.
{"title":"Integrated sensing and communications waveform design: Fundamentals, applications, challenges","authors":"Zhongqiang Luo , Zaiqiang Wang","doi":"10.1016/j.phycom.2024.102532","DOIUrl":"10.1016/j.phycom.2024.102532","url":null,"abstract":"<div><div>Integrated Sense of Communication (ISAC) is gradually becoming one of the core technologies in the sixth-generation mobile communication system. ISAC enhances spectrum efficiency and reduces equipment size, costs, and power consumption while minimizing interference between the two functions. The technology is introduced in terms of historical background and definition, application scenarios, current problems of ISAC technology, and the development history of ISAC technology. The latest research progress of ISAC technology is summarized in terms of waveform design, introducing sensing-centered waveform design, communication-centered waveform design, and sensing-communication joint waveform design, respectively. The ISAC waveform performance index introduces the performance aspects that need attention in waveform design to achieve improvement. It presents the application of ISAC technology in some recent emerging technologies and the current challenges and future outlook of ISAC waveform design.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"67 ","pages":"Article 102532"},"PeriodicalIF":2.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.phycom.2024.102531
Islam S. Abdelfattah , Ahmed F. Tayel , Ahmed Y. Zakariya , Ahmed H. Abd El-Malek , Sherif I. Rabia
The radio frequency spectrum became crowded because of the huge number of wireless devices, hence cognitive radio networks (CRNs) should increase the efficiency of spectrum utilization. Moreover, energy-saving techniques are becoming essential to prolong the devices’ life. Consequently, this work considers a hybrid active/passive (interweave/backscatter) symbiotic network while adopting the energy harvesting technology and applying the sensing interval concept. The sensing interval concept helps the secondary users (SUs) to transmit their data for consecutive time slots depending on the sensing result of the first time slot. This concept helps the SUs to reduce the consumed energy in sensing the channel each time slot. The hybrid transmission mode improves the spectrum utilization. The energy harvesting technology, the sensing interval concept, and the backscatter mode improve the energy efficiency. These spectrum utilization and energy efficient techniques are combined, for the first time, in one model. To deal with the mixed system state (the energy of the SUs which is fully observable state and the primary users activity which is partially observable state due to the imperfect sensing) and take the future rewards into consideration, a mixed observable Markov decision process is proposed. Moreover, we derive a closed form expression for the data outage probability of the backscatter transmission with/without spectrum sensing. The numerical results show that the proposed model prevails over the other models in the literature in terms of throughput and energy efficiency. Moreover, the interference on the primary user receiver due to applying the sensing interval concept is reduced by introducing a new penalty parameter.
由于无线设备数量庞大，无线电频谱变得拥挤不堪，因此认知无线电网络（CRN）应提高频谱利用效率。此外，节能技术对于延长设备寿命也变得至关重要。因此，本研究考虑了一种混合主动/被动（交织/反向散射）共生网络，同时采用了能量收集技术和感知间隔概念。感知间隔概念帮助次级用户（SU）根据第一个时隙的感知结果，在连续的时隙内传输数据。这一概念可帮助 SU 减少每个时隙感知信道时消耗的能量。混合传输模式提高了频谱利用率。能量收集技术、感知间隔概念和反向散射模式提高了能效。这些频谱利用率和能源效率技术首次结合在一个模型中。为了处理混合系统状态（SU 的能量是完全可观测的状态，而主用户的活动由于传感不完善而属于部分可观测的状态）并考虑到未来的回报，我们提出了一个混合可观测马尔可夫决策过程。此外，我们还推导出了有/无频谱感知的反向散射传输数据中断概率的闭合形式表达式。数值结果表明，所提出的模型在吞吐量和能效方面优于文献中的其他模型。此外，通过引入一个新的惩罚参数，减少了因应用感知间隔概念而对主用户接收器造成的干扰。
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