通信感知一体化(Integrated Sensing and Communication,ISAC)系统可以将通信感知功能有机融合,以取得更高的频谱效率和硬件利用率,但传统的大规模集中式天线阵列在平面波假设下无法提供距离维增益,且其混合波束赋形设计为非凸优化问题...通信感知一体化(Integrated Sensing and Communication,ISAC)系统可以将通信感知功能有机融合,以取得更高的频谱效率和硬件利用率,但传统的大规模集中式天线阵列在平面波假设下无法提供距离维增益,且其混合波束赋形设计为非凸优化问题,仍是具有挑战性的难题。为此,提出了一种基于子阵列的混合波束赋形设计方案,在较低的硬件复杂度下通过扩展球面波区域范围提供距离维增益,以在满足感知性能约束和发射功率预算的前提下最大化通信速率。首先提出了一种基于分式规划和最优化最小化方法的算法,将非凸优化问题转化为凸问题后迭代求解得到一个联合波束赋形矩阵;进而提出一种基于流形优化和最小二乘法的算法,迭代求解后将其分解为数字/模拟波束赋形矩阵。仿真结果表明,基于子阵列的算法相较于集中式阵列能够获得更多的距离维信息和感知自由度,通信性能提升40%,且流形优化后混合波束赋形方案能够很好地逼近联合优化的数字波束赋形方案的性能。展开更多
Integrated sensing and communication(ISAC),assisted by reconfigurable intelligent surface(RIS)has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless network.However,a significa...Integrated sensing and communication(ISAC),assisted by reconfigurable intelligent surface(RIS)has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless network.However,a significant challenge in RIS-ISAC systems is the acquisition of channel state information(CSI),largely due to co-channel interference,which hinders meeting the required reliability standards.To address this issue,a minimax-concave penalty(MCP)-based CSI refinement scheme is proposed.This approach utilizes an element-grouping strategy to jointly estimate the ISAC channel and the RIS phase shift matrix.Unlike previous methods,our scheme exploits the inherent sparsity in RIS-assisted ISAC channels to reduce training overhead,and the near-optimal solution is derived for our studied RIS-ISAC scheme.The effectiveness of the element-grouping strategy is validated through simulation experiments,demonstrating superior channel estimation results when compared to existing benchmarks.展开更多
车联网作为未来智慧交通系统中的重要组成部分,对通信和感知的要求也越来越高。通信感知一体化(Integrated Sensing and Communication,ISAC)作为车联网方向极具潜力的一项技术,因可以解决通信场景下的位置感知问题而引起业内的广泛关...车联网作为未来智慧交通系统中的重要组成部分,对通信和感知的要求也越来越高。通信感知一体化(Integrated Sensing and Communication,ISAC)作为车联网方向极具潜力的一项技术,因可以解决通信场景下的位置感知问题而引起业内的广泛关注。由于城市场景中电磁环境更加随机和不可控,使得当前传统的通信系统架构已经无法满足车-路侧单元(Vehicle-to-Infrastructure,V2I)系统中的通信与感知需求。对此,考虑在传统的V2I系统加入智能反射面(Reconfigurable Intelligent Surface,RIS),将其与ISAC技术结合,构建多径场景下新的被动感知模型。深入分析了在新的被动感知模型下,RIS辅助ISAC通信系统的抗多径性能优化。提出一种大尺寸RIS单元优化分组方法,使部分单元参与信号的反射,并将单元优化分组后的大尺寸RIS与相同单元数的小尺寸RIS进行系统性能对比。仿真结果表明,优化设计大尺寸RIS实现了高于小尺寸RIS大约1.5 bit/s的信号传输效率,在提升系统信号传输性能的同时有效地减少了信道的估计开销。展开更多
Background:Scabies is a very common skin infection in convicts.The SIMSPE Society(SocietItaliana di Medicina e Sanit`a Penitenziaria)has organized and conducted a multicentre,randomized,comparative,parallel group,in...Background:Scabies is a very common skin infection in convicts.The SIMSPE Society(SocietItaliana di Medicina e Sanit`a Penitenziaria)has organized and conducted a multicentre,randomized,comparative,parallel group,investigator-blinded trial to evaluate the efficacy and tolerability of synergized pyrethrins foam(PF)in comparison with benzyl benzoate(BB)lotion.Methods:A total of 240 convicted patients,enrolled in eight National Jail Institutions,with a clinical diagnosis of scabies,were treated with PF(n = 120)for three consecutive days or BB(n = 120)for five consecutive days.Primary study endpoints were the clinical cure rate and the local tolerability.Secondary endpoints were clinical evolution of scabietic lesions and itching intensity.Study outcomes were assessed using appropriate semiquantitative scores at baseline and after 2 and 4 weeks.A second treatment cycle was applied if after 2 weeks the patient was not judged clinically cured.Results:At week 2,a total of 75%(95 %CI:66-82%)and 71%(95%CI:62-78%)of patients showed a complete clinical cure rate in the PF and BB groups,respectively.At week 4,the percentage of totally cured patients increased up to 95%(95%CI:89-97%)and 91%(95%CI:83-94%)in the PF and BB groups,respectively(P = NS between groups).At week 4,5%in the PF group and 9%in the BB group complained of itching.Burning and irritation after treatment applications were more common in the BB group in comparison with the PF group.The tolerability score was better in the PF group in comparison with to BB group(2.9 vs.2.2;P = 0.0001).A total of 95%of patients in the PV group had a good tolerability score(i.e.= 3)in comparison with 41%in the BB group.Conclusion:Our results show that a 3-day treatment with pyrethrins thermofobic foam is at least as effective as a 5-day treatment with benzyl benzoate lotion in convicted subjects with scabies.The foam formulation is better tolerated than the benzyl benzoate lotion.展开更多
A simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)aided integrated sensing and communication(ISAC)dual-secure communication system is studied in this paper.The sensed target and le...A simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)aided integrated sensing and communication(ISAC)dual-secure communication system is studied in this paper.The sensed target and legitimate users(LUs)are situated on the opposite sides of the STAR-RIS,and the energy splitting and time switching protocols are applied in the STAR-RIS,respectively.The long-term average security rate for LUs is maximized by the joint design of the base station(BS)transmit beamforming and receive filter,along with the STAR-RIS transmitting and reflecting coefficients,under guarantying the echo signal-to-noise ratio thresholds and rate constraints for the LUs.Since the channel information changes over time,conventional convex optimization techniques cannot provide the optimal performance for the system,and result in excessively high computational complexity in the exploration of the long-term gains for the system.Taking continuity control decisions into account,the deep deterministic policy gradient and soft actor-critic algorithms based on off-policy are applied to address the complex non-convex problem.Simulation results comprehensively evaluate the performance of the proposed two reinforcement learning algorithms and demonstrate that STAR-RIS is remarkably better than the two benchmarks in the ISAC system.展开更多
Integrated sensing and communications(ISAC)is a key enabler for next-generation wireless systems,aiming to support both high-throughput communication and high-accuracy environmental sensing using shared spectrum and h...Integrated sensing and communications(ISAC)is a key enabler for next-generation wireless systems,aiming to support both high-throughput communication and high-accuracy environmental sensing using shared spectrum and hardware.Theoretical performance metrics,such as mutual information(MI),minimum mean squared error(MMSE),and Bayesian Cram´er-Rao bound(BCRB),play a key role in evaluating ISAC system performance limits.However,in practice,hardware impairments,multipath propagation,interference,and scene constraints often result in nonlinear,multimodal,and non-Gaussian distributions,making it challenging to derive these metrics analytically.Recently,there has been a growing interest in applying score-based generative models to characterize these metrics from data,although not discussed for ISAC.This paper provides a tutorial-style summary of recent advances in score-based performance evaluation,with a focus on ISAC systems.We refer to the summarized framework as scoring ISAC,which not only reflects the core methodology based on score functions but also emphasizes the goal of scoring(i.e.,evaluating)ISAC systems under realistic conditions.We present the connections between classical performance metrics and the score functions and provide the practical training techniques for learning score functions to estimate performance metrics.Proof-of-concept experiments on target detection and localization validate the accuracy of score-based performance estimators against groundtruth analytical expressions,illustrating their ability to replicate and extend traditional analyses in more complex,realistic settings.This framework demonstrates the great potential of score-based generative models in ISAC performance analysis,algorithm design,and system optimization.展开更多
Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable...Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable.Existing methods,such as map-based navigation or site-specific fingerprinting,often require intensive data collection and lack generalization capability across different buildings,thereby limiting scalability.This study proposes a cross-site,map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge.The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes,facilitating accurate localization in unseen environments.Evaluation across two validation sites demonstrates the framework’s effectiveness.In crosssite testing(Site-A),the Transformer achieved a mean localization error of 9.44 m,outperforming the Deep Neural Network(DNN)(10.76 m)and Convolutional Neural Network(CNN)(12.02 m)baselines.In a real-time deployment(Site-B)spanning three floors,the Transformer maintained an overall mean error of 9.81 m,compared with 13.45 m for DNN,12.88 m for CNN,and 53.08 m for conventional trilateration.For vertical positioning,the Transformer delivered a mean error of 4.52 m,exceeding the performance of DNN(4.59 m),CNN(4.87 m),and trilateration(>45 m).The results confirm that the Transformer-based framework generalizes across heterogeneous indoor environments without requiring site-specific calibration,providing stable,sub-12 m horizontal accuracy and reliable vertical estimation.This capability makes the framework suitable for real-time applications in smart buildings,emergency response,and autonomous systems.By utilizing multipath reflections as an informative structure rather than treating them as noise,this work advances artificial intelligence(AI)-native indoor localization as a scalable and efficient component of future 6G ISAC networks.展开更多
文摘通信感知一体化(Integrated Sensing and Communication,ISAC)系统可以将通信感知功能有机融合,以取得更高的频谱效率和硬件利用率,但传统的大规模集中式天线阵列在平面波假设下无法提供距离维增益,且其混合波束赋形设计为非凸优化问题,仍是具有挑战性的难题。为此,提出了一种基于子阵列的混合波束赋形设计方案,在较低的硬件复杂度下通过扩展球面波区域范围提供距离维增益,以在满足感知性能约束和发射功率预算的前提下最大化通信速率。首先提出了一种基于分式规划和最优化最小化方法的算法,将非凸优化问题转化为凸问题后迭代求解得到一个联合波束赋形矩阵;进而提出一种基于流形优化和最小二乘法的算法,迭代求解后将其分解为数字/模拟波束赋形矩阵。仿真结果表明,基于子阵列的算法相较于集中式阵列能够获得更多的距离维信息和感知自由度,通信性能提升40%,且流形优化后混合波束赋形方案能够很好地逼近联合优化的数字波束赋形方案的性能。
基金supported in part by the National Natural Science Foundation of China under Grant 62001171in part by the Natural Science Foundation of Guangdong Province under Grant 2024A1515011172in part by the Henan Science and Technology Research and Development Program Joint Fund under Grant 235200810049。
文摘Integrated sensing and communication(ISAC),assisted by reconfigurable intelligent surface(RIS)has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless network.However,a significant challenge in RIS-ISAC systems is the acquisition of channel state information(CSI),largely due to co-channel interference,which hinders meeting the required reliability standards.To address this issue,a minimax-concave penalty(MCP)-based CSI refinement scheme is proposed.This approach utilizes an element-grouping strategy to jointly estimate the ISAC channel and the RIS phase shift matrix.Unlike previous methods,our scheme exploits the inherent sparsity in RIS-assisted ISAC channels to reduce training overhead,and the near-optimal solution is derived for our studied RIS-ISAC scheme.The effectiveness of the element-grouping strategy is validated through simulation experiments,demonstrating superior channel estimation results when compared to existing benchmarks.
文摘Background:Scabies is a very common skin infection in convicts.The SIMSPE Society(SocietItaliana di Medicina e Sanit`a Penitenziaria)has organized and conducted a multicentre,randomized,comparative,parallel group,investigator-blinded trial to evaluate the efficacy and tolerability of synergized pyrethrins foam(PF)in comparison with benzyl benzoate(BB)lotion.Methods:A total of 240 convicted patients,enrolled in eight National Jail Institutions,with a clinical diagnosis of scabies,were treated with PF(n = 120)for three consecutive days or BB(n = 120)for five consecutive days.Primary study endpoints were the clinical cure rate and the local tolerability.Secondary endpoints were clinical evolution of scabietic lesions and itching intensity.Study outcomes were assessed using appropriate semiquantitative scores at baseline and after 2 and 4 weeks.A second treatment cycle was applied if after 2 weeks the patient was not judged clinically cured.Results:At week 2,a total of 75%(95 %CI:66-82%)and 71%(95%CI:62-78%)of patients showed a complete clinical cure rate in the PF and BB groups,respectively.At week 4,the percentage of totally cured patients increased up to 95%(95%CI:89-97%)and 91%(95%CI:83-94%)in the PF and BB groups,respectively(P = NS between groups).At week 4,5%in the PF group and 9%in the BB group complained of itching.Burning and irritation after treatment applications were more common in the BB group in comparison with the PF group.The tolerability score was better in the PF group in comparison with to BB group(2.9 vs.2.2;P = 0.0001).A total of 95%of patients in the PV group had a good tolerability score(i.e.= 3)in comparison with 41%in the BB group.Conclusion:Our results show that a 3-day treatment with pyrethrins thermofobic foam is at least as effective as a 5-day treatment with benzyl benzoate lotion in convicted subjects with scabies.The foam formulation is better tolerated than the benzyl benzoate lotion.
文摘A simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)aided integrated sensing and communication(ISAC)dual-secure communication system is studied in this paper.The sensed target and legitimate users(LUs)are situated on the opposite sides of the STAR-RIS,and the energy splitting and time switching protocols are applied in the STAR-RIS,respectively.The long-term average security rate for LUs is maximized by the joint design of the base station(BS)transmit beamforming and receive filter,along with the STAR-RIS transmitting and reflecting coefficients,under guarantying the echo signal-to-noise ratio thresholds and rate constraints for the LUs.Since the channel information changes over time,conventional convex optimization techniques cannot provide the optimal performance for the system,and result in excessively high computational complexity in the exploration of the long-term gains for the system.Taking continuity control decisions into account,the deep deterministic policy gradient and soft actor-critic algorithms based on off-policy are applied to address the complex non-convex problem.Simulation results comprehensively evaluate the performance of the proposed two reinforcement learning algorithms and demonstrate that STAR-RIS is remarkably better than the two benchmarks in the ISAC system.
基金supported in part by the General Research Fund from the Research Grants Council of Hong Kong,China,under Project 14202421,Project 14214122,Project 14202723,and Project 14207624in part by Area of Excellence Scheme grant from the Research Grants Council of Hong Kong,China,under Project number AoE/E-601/22-R+2 种基金in part by NSFC/RGC Collaborative Research Scheme from the Research Grants Council of Hong Kong,China,under Project CRS HKUST603/22 and Project CRS HKU702/24in part by the National Natural Science Foundation of China under Grant 62571087in part by Sichuan Science and Technology Program under Grant 2024ZYD0036.
文摘Integrated sensing and communications(ISAC)is a key enabler for next-generation wireless systems,aiming to support both high-throughput communication and high-accuracy environmental sensing using shared spectrum and hardware.Theoretical performance metrics,such as mutual information(MI),minimum mean squared error(MMSE),and Bayesian Cram´er-Rao bound(BCRB),play a key role in evaluating ISAC system performance limits.However,in practice,hardware impairments,multipath propagation,interference,and scene constraints often result in nonlinear,multimodal,and non-Gaussian distributions,making it challenging to derive these metrics analytically.Recently,there has been a growing interest in applying score-based generative models to characterize these metrics from data,although not discussed for ISAC.This paper provides a tutorial-style summary of recent advances in score-based performance evaluation,with a focus on ISAC systems.We refer to the summarized framework as scoring ISAC,which not only reflects the core methodology based on score functions but also emphasizes the goal of scoring(i.e.,evaluating)ISAC systems under realistic conditions.We present the connections between classical performance metrics and the score functions and provide the practical training techniques for learning score functions to estimate performance metrics.Proof-of-concept experiments on target detection and localization validate the accuracy of score-based performance estimators against groundtruth analytical expressions,illustrating their ability to replicate and extend traditional analyses in more complex,realistic settings.This framework demonstrates the great potential of score-based generative models in ISAC performance analysis,algorithm design,and system optimization.
基金funded by the Ministry of Science and Technology,Taiwan,under grant number MOST 114-2224-E-A49-002was received by En-Cheng Liou.
文摘Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable.Existing methods,such as map-based navigation or site-specific fingerprinting,often require intensive data collection and lack generalization capability across different buildings,thereby limiting scalability.This study proposes a cross-site,map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge.The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes,facilitating accurate localization in unseen environments.Evaluation across two validation sites demonstrates the framework’s effectiveness.In crosssite testing(Site-A),the Transformer achieved a mean localization error of 9.44 m,outperforming the Deep Neural Network(DNN)(10.76 m)and Convolutional Neural Network(CNN)(12.02 m)baselines.In a real-time deployment(Site-B)spanning three floors,the Transformer maintained an overall mean error of 9.81 m,compared with 13.45 m for DNN,12.88 m for CNN,and 53.08 m for conventional trilateration.For vertical positioning,the Transformer delivered a mean error of 4.52 m,exceeding the performance of DNN(4.59 m),CNN(4.87 m),and trilateration(>45 m).The results confirm that the Transformer-based framework generalizes across heterogeneous indoor environments without requiring site-specific calibration,providing stable,sub-12 m horizontal accuracy and reliable vertical estimation.This capability makes the framework suitable for real-time applications in smart buildings,emergency response,and autonomous systems.By utilizing multipath reflections as an informative structure rather than treating them as noise,this work advances artificial intelligence(AI)-native indoor localization as a scalable and efficient component of future 6G ISAC networks.
