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.

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.

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.

Capacity of a Class of Dirty-Tape Gaussian Orthogonal Relay Channel with State Information at the Source and Relay

A Gaussian channel with additive interference that is causally known to the transmitter is called a Dirty-Tape Channel(DTC).In this paper,we consider a state-dependent dirty-tape Gaussian relay channel with orthogonal channels from the source to the relay and from the source and relay to the destination.The orthogonal channels are corrupted by two independent additive interferences causally known to both the source and relay.The lower and upper bounds of the channel capacity are established.The lower bound is obtained by employing superposition coding at the source,Partial Decode-and-Forward(PDF)relaying at the relay,and a strategy similar to that used by Shannon at the source and relay.The explicit capacity is characterised when the power of the relay is sufficiently large.Finally,several numerical examples are provided to illustrate the impact of additive interferences and the role of the relay in information transmission and in removing the interference.

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.

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.

Robust MIMO Precoding for Cognitive Multiuser Relay Networks with Imperfect Channel State Information

In this paper, a novel robust precoder with imperfect channel state information(CSI)is proposed for multi-input multi-output(MIMO)cognitive multiuser networks equipped with relays. In the proposed model, the secondary users(SUs)are allowed to share the spectrum with the primary users(PUs)when the interference temperature(IT)is below a specific threshold. The transmitting strategy of relays is amplify-and-forward(AF), and the CSI error is characterized in terms of spherical uncertainty region. A minmax problem for the transmit power of the relays is considered when the mean square error(MSE)of SUs and the IT of PU meet their corresponding thresholds, and it is transformed into a semi-definite programming(SDP)problem to search for the solution. Numerical simulations demonstrate the effectiveness of the proposed precoder.

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.

Adaptive Energy-Efficient Power Allocation for DAS with Imperfect Channel State Information and Antenna Selection

Considering that perfect channel state information(CSI) is difficult to obtain in practice,energy efficiency(EE) for distributed antenna systems(DAS) based on imperfect CSI and antennas selection is investigated in Rayleigh fading channel.A novel EE that is defined as the average transmission rate divided by the total consumed power is introduced.In accordance with this definition,an adaptive power allocation(PA) scheme for DAS is proposed to maximize the EE under the maximum transmit power constraint.The solution of PA in the constrained EE optimization does exist and is unique.A practical iterative algorithm with Newton method is presented to obtain the solution of PA.The proposed scheme includes the one under perfect CSI as a special case,and it only needs large scale and statistical information.As a result,the scheme has low overhead and good robustness.The theoretical EE is also derived for performance evaluation,and simulation result shows the validity of the theoretical analysis.Moreover,EE can be enhanced by decreasing the estimation error and/or path loss exponents.

A novel quantum information hiding protocol based on entanglement swapping of high-level Bell states

Using entanglement swapping of high-level Bell states, we first derive a covert layer between the secret message and the possible output results of the entanglement swapping between any two generalized Bell states, and then propose a novel high-efficiency quantum information hiding protocol based on the covert layer. In the proposed scheme, a covert channel can be built up under the cover of a high-level quantum secure direct communication （QSDC） channel for securely transmitting secret messages without consuming any auxiliary quantum state or any extra communication resource. It is shown that this protocol not only has a high embedding efficiency but also achieves a good imperceptibility as well as a high security.

An Efficient CSI Feedback Scheme for Dual-Polarized MIMO Systems Using Layered Multi-Paths Information

Massive MIMO is one of tile enabling technologies tbr beyond 4G and 5G systems due to its ability to provide beamforming gain and reduce interference Dual-polarized antenna is widely adopted to accommodate a large number of antenna elements in limited space. However, current CSI（channel state information） feedback schemes developed in LTE for conventional MIMO systems are not efficient enough for massive MIMO systems since the overhead increases almost linearly with the number of antenna. Moreover, the codebook for massive MIMO will be huge and difficult to design with the LTE methodology. This paper proposes a novel CSI feedback scheme named layered Multi-paths Information based CSI Feedback （LMPIF）, which can achieve higher spectrum efficiency for dual-polarized antenna system with low feedback overhead. The MIMO channel is decomposed into long term components （multipath directions and amplitudes） and short term components （multipath phases）. The relationship between the two components and the optimal precoder is derived in closed form. To reduce the overhead, different granularities in feedback time have been applied for the long term components and short term components Link and system level simulation results prove that LMPIF can improve performance considerably with low CSI feedback overhead.

非理想CSI下IRS辅助MISO保密速率最大化方法

基于智能反射面辅助多输入、单输出的安全无线通信系统,提出一种最大化系统保密速率为目标联合主被动波束形成算法.考虑非理想信道状态信息下基站发射波束成形向量和无源智能反射面相移矩阵联合优化设计问题.为解决非凸分式规划问题,通过Charnes-Cooper变换引入两个辅助变量,将单项分式问题转化为差的形式,同时采用一种交替迭代优化联合半正定松弛方法,得到易于求解的凸问题.仿真实验结果表明,该算法相比传统算法,有效提升了系统的安全性,保密性能提升10%~30%,且在一定信道状态信息误差下保密速率下降不明显,具有较强的鲁棒性.

基于CSI商联合AOA的增强型指纹定位方法

针对当前指纹定位技术需要基于多个接入点(AP)被动定位,导致单AP室内场景使用受限的问题,提出一种信道状态信息(CSI)商联合到达角(AOA)的室内指纹定位方法:指出技术关键是利用单链路上的多维信号参数来构建指纹模型;利用多进多出(MIMO)系统空间分集,构建CSI商以获得更稳健的CSI指纹信号,并结合多信号分类(MUSIC)算法原理设计一种多载波AOA指纹表示方法,该方法与原始CSI指纹相比具有区分性;然后为了解决单AP的AOA指纹对称性问题,将CSI商与AOA指纹相结合,得到新的指纹,通过机器学习的方法进行目标位置匹配。实验结果表明,本方法在空教室和实验室环境下的定位准确率分别达到98.96%和97.08%,平均定位误差分别为0.46 m和0.68 m,具有较高的定位性能。

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%。

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.

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。

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。

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。