With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State I...With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.展开更多
In this paper, we investigate the outage performance of a dual-hop amplify-and-forward(AF) multipleinput multiple-output(MIMO) relay network, considering beamforming transmission with outdated channel state informatio...In this paper, we investigate the outage performance of a dual-hop amplify-and-forward(AF) multipleinput multiple-output(MIMO) relay network, considering beamforming transmission with outdated channel state information(CSI) and an arbitrary number of co-channel interferers at both the relay and the destination. The exact closed-form expression for the outage probability is derived, and the asymptotic behavior of the outage probability is also presented to provide further insights into the system performance. Our results show that the system suffers a diversity gain loss caused by the outdated CSI, and the interference only degrade the coding gain of the system. Moreover, increasing antennas can achieve more performance gains, however, the gains decrease as the delay of the CSI increases.展开更多
Physical-layer network coding （PNC） has the potential to significantly improve the throughput of wireless networks where the channels can be modeled as additive white Gaussian noise （AWGN） channel. As extending to...Physical-layer network coding （PNC） has the potential to significantly improve the throughput of wireless networks where the channels can be modeled as additive white Gaussian noise （AWGN） channel. As extending to mul- tiple channels, this technique requires both amplitude and phase compensation at each transmitter and will lead to inef- ficient systems yielding no diversity even with perfect channel state information （CSI）. In order to avoid these limita- tions, we apply network coding with diversity （NCD） to achieve a form of selection diversity and extend NCD to coop- erative multiple access channels in this paper. However, in practical wireless communication systems, the CSI could become outdated due to the difference between the CSI used in the relay selection and data transmission phases. Hence, the selected relay may not be the best one during data transmission phase due to the dynamic change in the wireless channels. Therefore, we first explore the relation between the present and past CSIs. Exploiting this relationship, the NCD scheme with outdated CSI is investigated based on the past CSI. To evaluate the performance of this scheme, an information-theoretic metric, namely the outage capacity, is studied under this condition.展开更多
Wireless information and powered transfer networks(WIPT) has recently been implemented in 5th generation wireless networks. In this paper, we consider half-duplex relaying system in which the energy constrained relay ...Wireless information and powered transfer networks(WIPT) has recently been implemented in 5th generation wireless networks. In this paper, we consider half-duplex relaying system in which the energy constrained relay node collects energy via radio frequency(RF) signals from the surrounding resources. Regarding energy harvesting protocol, we propose power time switching-based relaying(PTSR) architecture for both amplify-and-forward(AF) and decode-and-forward(DF). Especially, we reveal the analytical expressions of achievable throughput, ergodic capacity and energy-efficient in case of imperfect channel state information(CSI) for both AF and DF network. Through numerical analysis, we analyse the throughput performance, energy-efficient and ergodic capacity for different parameters, including power splitting ratio and energy harvesting time. Moreover, we also depict the performance comparison between AF and DF network with perfect and imperfect CSI. The results in numerical analysis reveal that the result of AF relaying network is less significant than DF relaying network in the various scenarios.展开更多
Activity recognition plays a key role in health management and security.Traditional approaches are based on vision or wearables,which only work under the line of sight(LOS)or require the targets to carry dedicated dev...Activity recognition plays a key role in health management and security.Traditional approaches are based on vision or wearables,which only work under the line of sight(LOS)or require the targets to carry dedicated devices.As human bodies and their movements have influences on WiFi propagation,this paper proposes the recognition of human activities by analyzing the channel state information(CSI)from the WiFi physical layer.The method requires only the commodity:WiFi transmitters and receivers that can operate through a wall,under LOS and non-line of sight(NLOS),while the targets are not required to carry dedicated devices.After collecting CSI,the discrete wavelet transform is applied to reduce the noise,followed by outlier detection based on the local outlier factor to extract the activity segment.Activity recognition is fulfilled by using the bi-directional long short-term memory that takes the sequential features into consideration.Experiments in through-the-wall environments achieve recognition accuracy>95%for six common activities,such as standing up,squatting down,walking,running,jumping,and falling,outperforming existing work in this field.展开更多
To reduce the performance deterioration induced by imperfect channel state information(CSI) in correlated multiple input multiple output(MIMO) downlink,the linear transmit/receive filters should be optimized to be rob...To reduce the performance deterioration induced by imperfect channel state information(CSI) in correlated multiple input multiple output(MIMO) downlink,the linear transmit/receive filters should be optimized to be robust to imperfect CSI.A sub-optimization algorithm based on minimizing sum MSE conditional on available imperfect CSI estimates subject to a per-user power constraint is proposed.The algorithm adapts the existing MMSE algorithm from uncorrelated single-user MIMO system with perfect CSI to correlated MIMO downlink with imperfect CSI.Simulation shows that the suboptimal algorithm can effectively mitigate the performance loss induced by imperfect CSI and has a good convergence performance.In addition,the effect of spatial correlation on the performance of the proposed algorithm is also simulated.展开更多
Given imperfect channel state information(CSI)and considering the interference from the primary transmitter,an underlay cognitive multisource multidestination relay network is proposed.A closed-form exact outage proba...Given imperfect channel state information(CSI)and considering the interference from the primary transmitter,an underlay cognitive multisource multidestination relay network is proposed.A closed-form exact outage probability and asymptotic outage probability are derived for the secondary system of the network.The results show that the outage probability is influenced by the source and destination number,the CSI imperfection as well as the interference from the primary transmitter,while the diversity order is independent of the CSI imperfection and the interference from the primary transmitter,yet it is equal to the minimum of the source and destination number.Moreover,extensive simulations are conducted with different system parameters to verify the theoretical analysis.展开更多
In a real communication scenario,it is very difficult to obtain the real-time channel state infor-mation(CSI)accurately,so the non-orthogonal multiple access(NOMA)system with statistical CSI has been researched.Aiming...In a real communication scenario,it is very difficult to obtain the real-time channel state infor-mation(CSI)accurately,so the non-orthogonal multiple access(NOMA)system with statistical CSI has been researched.Aiming at the problem that the maximization of system sum rate cannot be solved directly,a step-by-step resource allocation optimization scheme based on machine learning is proposed.First,in order to achieve a trade-off between the system sum rate and user fairness,the system throughput formula is derived.Then,according to the combinatorial characteristics of the system throughput maximization problem,the original optimization problem is divided into two sub-problems,that are power allocation and user grouping.Finally,genetic algorithm is introduced to solve the sub-problem of power allocation,and hungarian algorithm is introduced to solve the sub-problem of user grouping.By comparing the ergodic data rate of NOMA users with statistical CSI and perfect CSI,the effectiveness of the statistical CSI sorting is verified.Compared with the orthogonal multiple access(OMA)scheme,the NOMA scheme with the fixed user grouping scheme and the random user grouping scheme,the system throughput performance of the proposed scheme is signifi-cantly improved.展开更多
This paper considers an intelligent reflecting surface(IRS)-assisted multiple-input multiple-output(MIMO)system.To maximize the average achievable rate(AAR)under outdated channel state information(CSI),we propose a tw...This paper considers an intelligent reflecting surface(IRS)-assisted multiple-input multiple-output(MIMO)system.To maximize the average achievable rate(AAR)under outdated channel state information(CSI),we propose a twin-timescale passive beamforming(PBF)and power allocation protocol which can reduce the IRS configuration and training overhead.Specifi-cally,the short-timescale power allocation is designed with the outdated precoder and fixed PBF.A new particle swarm opti-mization(PSO)-based long-timescale PBF optimization is pro-posed,where mini-batch channel samples are utilized to update the fitness function.Finally,simulation results demonstrate the effectiveness of the proposed method.展开更多
针对卷积神经网络(convolutional neural network,CNN)计算复杂度高和内存占用大的问题,本文提出了一种基于轻量级CNN的信道状态信息(channel state information,CSI)图像指纹被动定位(LCNNLoc)方法.离线训练阶段,将幅值差矩阵和相位矩...针对卷积神经网络(convolutional neural network,CNN)计算复杂度高和内存占用大的问题,本文提出了一种基于轻量级CNN的信道状态信息(channel state information,CSI)图像指纹被动定位(LCNNLoc)方法.离线训练阶段,将幅值差矩阵和相位矩阵构造成类似于“RGB”的三通道特征图像;同时设计了一个轻量级CNN架构,利用特征图像作为该框架的输入进行训练,在训练结束时将CNN模型保存为指纹数据库.在线定位阶段,采用概率加权质心方法实现了实时的位置估计.实验结果表明,相较于传统方法,LCNNLoc不仅提升了定位精度,还降低了算法运行耗时.展开更多
为应对大规模多输入多输出(Multiple⁃input multiple⁃output,MIMO)系统中信道状态信息(Channel state information,CSI)反馈开销的日益增长,基于深度学习的CSI反馈网络(如Transformer网络)受到了广泛的关注,是一种非常有应用前景的智能...为应对大规模多输入多输出(Multiple⁃input multiple⁃output,MIMO)系统中信道状态信息(Channel state information,CSI)反馈开销的日益增长,基于深度学习的CSI反馈网络(如Transformer网络)受到了广泛的关注,是一种非常有应用前景的智能传输技术。为此,本文提出了一种基于数据聚类的CSI反馈Transformer网络的简化方法,采用基于聚类的近似矩阵乘法(Approximate matrix multiplication,AMM)技术,以降低反馈过程中Transformer网络的计算复杂度。本文主要对Transformer网络的全连接层计算(等效为矩阵乘法),应用乘积量化(Product quantization,PQ)和MADDNESS等简化方法,分析了它们对计算复杂度和系统性能的影响,并针对神经网络数据的特点进行了算法优化。仿真结果表明,在适当的参数调整下,基于MADDNESS方法的CSI反馈网络性能接近精确矩阵乘法方法,同时可大幅降低计算复杂度。展开更多
该文针对闭环多用户MIMO-OFDM系统提出一种基于线性预测的低速率CSI(Channel State Information)反馈方法。根据相关带宽将OFDM子载波划分成多个子带,移动台对每个子带的CSI作线性预测,并对预测误差进行量化编码后反馈给基站;基站使用...该文针对闭环多用户MIMO-OFDM系统提出一种基于线性预测的低速率CSI(Channel State Information)反馈方法。根据相关带宽将OFDM子载波划分成多个子带,移动台对每个子带的CSI作线性预测,并对预测误差进行量化编码后反馈给基站;基站使用相同的线性预测滤波器将反馈来的预测误差恢复成CSI,然后在每个子带上通过迫零-波束赋形实现多用户空间复用。同时,该文还在采用注水定理分配发射功率的条件下,从理论上分析了下行链路信道容量。数值仿真结果显示,每个反馈数据的实部或虚部仅用1bit量化时,本方法仍能够以较高的精度恢复CSI。与目前3GPP LTE标准所采用的基于码书的反馈方案相比,该方法能够在反馈开销相同情况下,有效地抑制同信道干扰,大幅提高系统容量。展开更多
基金supported by National Natural Science Foundation of China(NSFC)under grant U23A20310.
文摘With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.
基金the National High Technology Research and Development Program(863)of China(No.2014AA01A704)the National Natural Science Foundation of China(Nos.91438113+2 种基金6117206761371086 and 61401180)the Natural Science Fund of the Jiangsu Higher Education Institutions(No.13KJB510007)
文摘In this paper, we investigate the outage performance of a dual-hop amplify-and-forward(AF) multipleinput multiple-output(MIMO) relay network, considering beamforming transmission with outdated channel state information(CSI) and an arbitrary number of co-channel interferers at both the relay and the destination. The exact closed-form expression for the outage probability is derived, and the asymptotic behavior of the outage probability is also presented to provide further insights into the system performance. Our results show that the system suffers a diversity gain loss caused by the outdated CSI, and the interference only degrade the coding gain of the system. Moreover, increasing antennas can achieve more performance gains, however, the gains decrease as the delay of the CSI increases.
基金funded by the EPSRC of UK under Grant EP/I037423/1
文摘Physical-layer network coding （PNC） has the potential to significantly improve the throughput of wireless networks where the channels can be modeled as additive white Gaussian noise （AWGN） channel. As extending to mul- tiple channels, this technique requires both amplitude and phase compensation at each transmitter and will lead to inef- ficient systems yielding no diversity even with perfect channel state information （CSI）. In order to avoid these limita- tions, we apply network coding with diversity （NCD） to achieve a form of selection diversity and extend NCD to coop- erative multiple access channels in this paper. However, in practical wireless communication systems, the CSI could become outdated due to the difference between the CSI used in the relay selection and data transmission phases. Hence, the selected relay may not be the best one during data transmission phase due to the dynamic change in the wireless channels. Therefore, we first explore the relation between the present and past CSIs. Exploiting this relationship, the NCD scheme with outdated CSI is investigated based on the past CSI. To evaluate the performance of this scheme, an information-theoretic metric, namely the outage capacity, is studied under this condition.
文摘Wireless information and powered transfer networks(WIPT) has recently been implemented in 5th generation wireless networks. In this paper, we consider half-duplex relaying system in which the energy constrained relay node collects energy via radio frequency(RF) signals from the surrounding resources. Regarding energy harvesting protocol, we propose power time switching-based relaying(PTSR) architecture for both amplify-and-forward(AF) and decode-and-forward(DF). Especially, we reveal the analytical expressions of achievable throughput, ergodic capacity and energy-efficient in case of imperfect channel state information(CSI) for both AF and DF network. Through numerical analysis, we analyse the throughput performance, energy-efficient and ergodic capacity for different parameters, including power splitting ratio and energy harvesting time. Moreover, we also depict the performance comparison between AF and DF network with perfect and imperfect CSI. The results in numerical analysis reveal that the result of AF relaying network is less significant than DF relaying network in the various scenarios.
基金the Key Research and Development Projects of Sichuan Science and Technology Department under Grant No.2018GZ0464the UESTC-ZHIXIAOJING Joint Research Center of Smart Home under Grant No.H04W210180.
文摘Activity recognition plays a key role in health management and security.Traditional approaches are based on vision or wearables,which only work under the line of sight(LOS)or require the targets to carry dedicated devices.As human bodies and their movements have influences on WiFi propagation,this paper proposes the recognition of human activities by analyzing the channel state information(CSI)from the WiFi physical layer.The method requires only the commodity:WiFi transmitters and receivers that can operate through a wall,under LOS and non-line of sight(NLOS),while the targets are not required to carry dedicated devices.After collecting CSI,the discrete wavelet transform is applied to reduce the noise,followed by outlier detection based on the local outlier factor to extract the activity segment.Activity recognition is fulfilled by using the bi-directional long short-term memory that takes the sequential features into consideration.Experiments in through-the-wall environments achieve recognition accuracy>95%for six common activities,such as standing up,squatting down,walking,running,jumping,and falling,outperforming existing work in this field.
基金the National Natural Science Foundationof China(No.60572156)
文摘To reduce the performance deterioration induced by imperfect channel state information(CSI) in correlated multiple input multiple output(MIMO) downlink,the linear transmit/receive filters should be optimized to be robust to imperfect CSI.A sub-optimization algorithm based on minimizing sum MSE conditional on available imperfect CSI estimates subject to a per-user power constraint is proposed.The algorithm adapts the existing MMSE algorithm from uncorrelated single-user MIMO system with perfect CSI to correlated MIMO downlink with imperfect CSI.Simulation shows that the suboptimal algorithm can effectively mitigate the performance loss induced by imperfect CSI and has a good convergence performance.In addition,the effect of spatial correlation on the performance of the proposed algorithm is also simulated.
基金the National Science and Technology Major Project“TD-LTE/FDD-LTE/TDSCDMA/WCDMA/GSM Multi-mode Baseband Commercial Chip Development”(No.2013ZX03001007-004)
基金Supported by the National Natural Science Foundation of China(No.61301170,61571340)the Fundamental Research Funds for the Central Universities(No.JB150109)the 111 Project(No.B08038)
文摘Given imperfect channel state information(CSI)and considering the interference from the primary transmitter,an underlay cognitive multisource multidestination relay network is proposed.A closed-form exact outage probability and asymptotic outage probability are derived for the secondary system of the network.The results show that the outage probability is influenced by the source and destination number,the CSI imperfection as well as the interference from the primary transmitter,while the diversity order is independent of the CSI imperfection and the interference from the primary transmitter,yet it is equal to the minimum of the source and destination number.Moreover,extensive simulations are conducted with different system parameters to verify the theoretical analysis.
基金Supported by the National Natural Science Foundation of China(No.62001001).
文摘In a real communication scenario,it is very difficult to obtain the real-time channel state infor-mation(CSI)accurately,so the non-orthogonal multiple access(NOMA)system with statistical CSI has been researched.Aiming at the problem that the maximization of system sum rate cannot be solved directly,a step-by-step resource allocation optimization scheme based on machine learning is proposed.First,in order to achieve a trade-off between the system sum rate and user fairness,the system throughput formula is derived.Then,according to the combinatorial characteristics of the system throughput maximization problem,the original optimization problem is divided into two sub-problems,that are power allocation and user grouping.Finally,genetic algorithm is introduced to solve the sub-problem of power allocation,and hungarian algorithm is introduced to solve the sub-problem of user grouping.By comparing the ergodic data rate of NOMA users with statistical CSI and perfect CSI,the effectiveness of the statistical CSI sorting is verified.Compared with the orthogonal multiple access(OMA)scheme,the NOMA scheme with the fixed user grouping scheme and the random user grouping scheme,the system throughput performance of the proposed scheme is signifi-cantly improved.
基金supported by the National Natural Science Foundation of China(62271068)the Beijing Natural Science Foundation(L222046).
文摘This paper considers an intelligent reflecting surface(IRS)-assisted multiple-input multiple-output(MIMO)system.To maximize the average achievable rate(AAR)under outdated channel state information(CSI),we propose a twin-timescale passive beamforming(PBF)and power allocation protocol which can reduce the IRS configuration and training overhead.Specifi-cally,the short-timescale power allocation is designed with the outdated precoder and fixed PBF.A new particle swarm opti-mization(PSO)-based long-timescale PBF optimization is pro-posed,where mini-batch channel samples are utilized to update the fitness function.Finally,simulation results demonstrate the effectiveness of the proposed method.
文摘针对卷积神经网络(convolutional neural network,CNN)计算复杂度高和内存占用大的问题,本文提出了一种基于轻量级CNN的信道状态信息(channel state information,CSI)图像指纹被动定位(LCNNLoc)方法.离线训练阶段,将幅值差矩阵和相位矩阵构造成类似于“RGB”的三通道特征图像;同时设计了一个轻量级CNN架构,利用特征图像作为该框架的输入进行训练,在训练结束时将CNN模型保存为指纹数据库.在线定位阶段,采用概率加权质心方法实现了实时的位置估计.实验结果表明,相较于传统方法,LCNNLoc不仅提升了定位精度,还降低了算法运行耗时.
文摘该文针对闭环多用户MIMO-OFDM系统提出一种基于线性预测的低速率CSI(Channel State Information)反馈方法。根据相关带宽将OFDM子载波划分成多个子带,移动台对每个子带的CSI作线性预测,并对预测误差进行量化编码后反馈给基站;基站使用相同的线性预测滤波器将反馈来的预测误差恢复成CSI,然后在每个子带上通过迫零-波束赋形实现多用户空间复用。同时,该文还在采用注水定理分配发射功率的条件下,从理论上分析了下行链路信道容量。数值仿真结果显示,每个反馈数据的实部或虚部仅用1bit量化时,本方法仍能够以较高的精度恢复CSI。与目前3GPP LTE标准所采用的基于码书的反馈方案相比,该方法能够在反馈开销相同情况下,有效地抑制同信道干扰,大幅提高系统容量。
