基于硬件损伤和非完美CSI的IRS辅助NOMA网络鲁棒传输算法

为提升非正交多址接入(NOMA,non-orthogonal multiple access)网络的鲁棒性并减少其能量消耗,考虑了收发器的硬件损伤(HWI,hardware impairment)和非完美的信道状态信息(CSI,channel state information),提出了智能反射面(IRS,intelligent reflecting surface)辅助的NOMA网络传输功率最小化算法。考虑了用户服务质量(QoS,quality of service)约束、串行干扰消除约束以及IRS的反射相移约束,基于HWI和非完美CSI建立了基站主动波束成形和IRS被动波束成形联合优化问题模型。为求解该非凸优化问题,首先利用线性近似和S-Procedure方法对QoS约束进行转换,然后将优化问题分解为两个子问题,并且利用逐次凸逼近(SCA,successive convex approximation)方法求解主动波束成形子问题,利用惩罚凸凹过程算法求解被动波束成形子问题,最后利用交替优化将子问题交替迭代得到最终解。仿真结果表明,在传输功率方面,所提算法比正交多址鲁棒算法降低了17.05%;在系统鲁棒性方面,所提算法相较于HWI鲁棒算法和CSI鲁棒算法,分别提升了20.69%和31.14%。

Imperfect Channel State Information of AF and DF Energy Harvesting Cooperative Networks

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.

Wireless Physical Layer Security with Imperfect Channel State Information: A Survey

Physical layer security is an emerging technique for improving wireless communication security, which is widely regarded as a complement to cryptographic technologies. To design physical layer security techniques for practical scenarios, uncertainty and imperfections in the channel knowledge need to be taken into account. This paper is a survey of recent research on physical layer security that considers imperfect channel state information （CSI） at communication nodes. We first give an overview of the main information-theoretic measures of secrecy performance with imperfect CSI. Then, we describe several signal processing enhancements in secure transmission designs. These enhancements include secure on-off transmission, beamforming with artificial noise, and secure communication assisted by relay nodes or in cognitive radio systems. Recent studies of physical layer security in large-scale decentralized wireless networks are also summarized. Finally, open problems for on-going and future research are discussed.

一种融合5G CSI和地磁的集成学习定位方法

针对深度学习算法在多传感器融合定位中容易出现的局部收敛、异质融合性能不佳等问题,本文提出了一种集成双向长短期记忆网络(BiLSTM)和注意力机制的多输入卷积神经网络(CNN)的室内定位算法。该算法首先对5G信道状态信息(CSI)和地磁数据分别进行预处理;然后各自基于独立的分支网络进行离线训练,同时提取指纹数据的空间特征和时序特征,追加注意力机制层;最后在全连接层实现了异质传感器数据的融合定位。在会议室和教学楼大厅的试验结果表明,平均定位误差分别为0.95和1.84 m,相比误差反向传播网络(BPNN)分别提高了48.9%和42.7%,定位精度和系统稳定性均大幅提升。

一种基于ESP32-CSI的粮食水分检测方法

为实现粮食水分的低成本快速准确测量,将小型化的信道状态信息(channel state information,CSI)采集设备用于粮食水分检测,采用随机森林和主成分分析两种特征选择算法对CSI的振幅指标进行特征子载波提取,基于选择的特征子载波对10种粮食水分进行分类,考虑到之后其移动化场景中的应用受限于功耗以及算力,选取结构较为简洁、运算速度较快、算力要求不高的宽度学习系统(broad learning system,BLS)应用于CSI数据的处理,同时与传统的卷积神经网络(convolutional neural network,CNN)在精确度和训练时间两个方面进行对比,最后动态地增加宽度学习系统的增强节点.试验结果表明:主成分分析(principal component analysis,PCA)算法最大限度地消除了CSI数据中的冗余信息,BLS相较于卷积神经网络不仅获得了更快的速度而且在准确率方面也优于CNN算法,因此PCA-BLS组合获得了最佳的分类效果;增加增强节点的数量后,训练时间虽然有所延长,但在一定程度上提高了识别准确率.

RSSI和PC-CSI加权融合的指纹定位方法

针对基于RSSI和CSI的指纹定位技术易受环境干扰、定位精度较低的问题,提出了一种基于RSSI指纹和相位修正信道状态信息(phase correct based channel state information,PC-CSI)指纹的加权融合指纹定位技术。基于PC-CSI的指纹定位在传统基于CSI幅值的指纹定位基础上增加相位信息对定位结果进行修正,之后对RSSI指纹和PC-CSI指纹的定位结果加权重定位。实验结果表明,提出的加权融合指纹定位算法与基于CSI的主动定位算法相比,平均定位误差(mean position error,MPE)降低了36.2%,能满足室内定位需求。

RIS辅助MIMO NOMA系统中利用统计CSI的下行传输方法

针对可重构智能反射表面(RIS)辅助多输入多输出(MIMO)非正交多址接入(NOMA)下行传输系统,该文提出利用统计信道状态信息(CSI)的基站发送协方差矩阵与RIS相移矩阵设计方法。首先,在莱斯空间相关信道假设下,利用大维随机矩阵理论,推导了RIS辅助MIMO NOMA系统遍历和速率的确定性表达式;然后,在弱用户速率约束与发送功率受限的条件下,通过最大化确定性大系统近似和速率,利用统计CSI,分别设计了强、弱用户的次优发送协方差矩阵和RIS的相移矩阵。仿真结果表明,所推导的近似表达式具有很好的近似效果,所设计的发送协方差矩阵和相移矩阵能显著提升系统的和速率。

基于CSI实例标准化的域泛化人体动作识别模型

为了实现完全不依赖目标域数据的Wi-Fi跨域人体动作感知,提出了一种基于CSI实例标准化的域泛化人体动作识别模型INDG-Fi。INDG-Fi使用实例标准化去除CSI特征表示的领域信息,接着构建共享特征提取的动作分类器和域分类器,并通过动作偏向学习和对抗性的域学习,将编码层提取的特征偏向人体动作引起的信号特征,同时远离领域信号影响。为了让模型关注受人体动作影响更显著的子载波信号,在编码层中加入子载波注意力模块。实现结果表明,所提INDG-Fi在不可见的用户和位置的感知性能分别为97.99%和92.73%,能够实现鲁棒的跨域感知。

非完美CSI下的全双工中继网络的能效谱效均衡

为优化非完美信道状态信息下的解码转发全双工中继网络的能效和谱效,提出了一种基于该网络模型的能效谱效均衡策略。通过构建能量效率和频谱效率的折中优化函数,将一个非凸的多目标优化问题转换为一个凸的单目标优化问题,利用求导法和拉格朗日乘子法求解在不同折中因子下的最优中继发射功率。仿真结果表明,可以通过改变折中因子来优化系统的能效和谱效值,获得最优能效和谱效的性能折中。

基于Transformer大规模MIMO的CSI反馈网络

针对频分双工大规模多输入多输出系统中信道状态信息反馈方法反馈精度低的问题,提出了一种名为CDTransformer的基于Transformer和卷积分解(convolutional decomposition,CD)的CSI反馈网络。CDTransformer将卷积分解融入改进的Transformer结构网络中,在不增加计算复杂度的情况下提升网络性能,并通过对网络的全连接层进行二值化实现轻量级部署。针对用户端功率有限的情况,提出了一种MixedTransformer网络模型。其中编码器采用计算成本较低、结构简单的单层卷积神经网络,而解码器则采用与CDTransformer模型相同的结构。CDTransformer模型融合了Transformer结构和卷积分解,提高了CSI反馈精度并实现轻量级部署。此外,引入了MixedTransformer模型,结合了CDTransformer和卷积神经网络的优点,以在功率有限情况下提供更好的性能。结果显示,相比于CsiTransformer网络模型,CDTransformer网络模型在归一化均方误差和余弦相似度方面分别提高了37.7%和0.2%。

A Survey of Wi-Fi Sensing Techniques with Channel State Information

A review of signal processing algorithms employing Wi-Fi signals for positioning and recognition of human activities is presented.The principles of how channel state information(CSI)is used and how the Wi-Fi sensing systems operate are reviewed.It provides a brief introduction to the algorithms that perform signal processing,feature extraction and recognitions,including location,activity recognition,physiological signal detection and personal identification.Challenges and future trends of Wi-Fi sensing are also discussed in the end.

基于CSI相位采样密度的室内定位技术研究

基于信道状态信息(CSI)的室内定位技术已经被广泛应用于各种场合,在CSI相位指纹定位方案中,采样点密度大小与定位效果好坏紧密相关。由于数据采集工作量的关系,以往学者们多关注低采样密度场景或通过模拟仿真分析采样密度问题,导致难以找到定位误差最小的采样密度。本文设置不同的采样密度,将数据预处理后的CSI相位信息作为指纹,匹配WKNN算法,分析探究了不同采样点密度对于定位精度的影响。实验结果表明,在4 m×4 m的环境中,采样间隔设置为0.4 m,采样密度为7.6个/m2时,定位精度最高,平均误差为0.34 m,同时兼顾采样工作量,具有较高性价比。

基于统计CSI的空中智能反射面辅助大规模MIMO系统传输优化方案

智能反射面(IRS,intelligent reflecting surface)被认为是下一代移动通信的核心技术,其在增强网络覆盖、频谱效率、能源效率和部署成本方面具有显著优势。空中智能反射面(AIRS,aerial intelligent reflecting surface)结合了空中平台的高移动性和智能反射面提供的优质链路特性,可以有效解决复杂通信场景中基站与用户之间易受障碍物阻挡的问题,从而增强网络覆盖。针对AIRS辅助的多用户大规模多输入多输出(MIMO,multiple input multiple output)系统,研究了基站波束成形、AIRS部署位置与相移设计的联合优化问题。在已知统计信道状态信息(CSI,channel state information)的条件下,提出了一种基于块坐标下降(BCD,block coordinate descent)的系统遍历和速率优化方案。首先,建立了一个联合优化基站端波束成形、AIRS的部署位置与AIRS相移的复杂优化模型。其次,通过BCD算法,将非凸优化问题解耦成3个易于处理的子问题。最后,分别采用拉格朗日乘子法、松弛变量法和RMSProp梯度下降算法求解子问题。仿真结果表明,所提出的优化方案有效地提高了系统的遍历和速率,并且具有较好的收敛性。

Outage performance of cognitive multisource multidestination relay networks with imperfect channel state information and interference from primary transmitter

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.

Network coding with diversity and outdated channel state information

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.

Device-Free Through-the-Wall Activity Recognition Using Bi-Directional Long Short-Term Memory and WiFi Channel State Information

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.

基于特征融合的大规模MIMO系统CSI反馈

信道状态信息(Channel State Information,CSI)反馈是大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统的一个关键问题。大规模MIMO系统中基站天线数量巨大,CSI反馈出现了反馈开销大、反馈精度低等问题。为了降低反馈开销,提高反馈精度,采用深度学习方法,提出了一种基于特征融合的CSI反馈网络(Feature Fusion Net,FFNet)。利用基于注意力机制的特征融合在编码器中融合不同尺度的CSI特征,并在解码器中使用多通道多分辨率卷积网络以及通道重排,从而高精度地重建压缩后的CSI。仿真结果表明,与几种经典的深度学习CSI反馈方法相比,在室内和室外信道条件下,均具有更高的反馈精度。

A joint optimization scheme of resource allocation in downlink NOMA with statistical channel state information

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.

差分编码辅助的机器学习CSI反馈方法

在大规模多输入多输出(massive Multiple Input Multiple Output,mMIMO)系统中,信道状态信息(Channel State Information,CSI)反馈严重占用上行带宽资源。为此,提出差分编码辅助的机器学习CSI反馈,探索节省上行带宽资源,减小用户设备(User Equipment,UE)能量开销的途径。在UE端,通过差分编码方式反馈CSI,避免UE端发送用于上行信道估计的导引,从而减小上行带宽资源占用,并节省UE能量开销。在基站(Base Station,BS)端,首先对接收信号进行差分译码,随后构建轻量化神经网络恢复下行CSI。仿真结果表明,相对于上行导引信道估计的CSI反馈,所研究的差分编码辅助的机器学习CSI反馈方法在节省上行带宽资源、减小UE能量消耗的同时,提高了下行CSI的重构精度。

DFF-EDR:An Indoor Fingerprint Location Technology Using Dynamic Fusion Features of Channel State Information and Improved Edit Distance on Real Sequence

Positioning technology based on wireless network signals in indoor environments has developed rapidly in recent years as the demand for locationbased services continues to increase.Channel state information(CSI)can be used as location feature information in fingerprint-based positioning systems because it can reflect the characteristics of the signal on multiple subcarriers.However,the random noise contained in the raw CSI information increases the likelihood of confusion when matching fingerprint data.In this paper,the Dynamic Fusion Feature(DFF)is proposed as a new fingerprint formation method to remove the noise and improve the feature resolution of the system,which combines the pre-processed amplitude and phase data.Then,the improved edit distance on real sequence(IEDR)is used as a similarity metric for fingerprint matching.Based on the above studies,we propose a new indoor fingerprint positioning method,named DFF-EDR,for improving positioning performance.During the experimental stage,data were collected and analyzed in two typical indoor environments.The results show that the proposed localization method in this paper effectively improves the feature resolution of the system in terms of both fingerprint features and similarity measures,has good anti-noise capability,and effectively reduces the localization errors.