Physical-Layer Key Generation Based on Multipath Channel Diversity Using Dynamic Metasurface Antennas

Physical layer key generation(PKG)technology leverages the reciprocal channel randomness to generate the shared secret keys.The low secret key capacity of the existing PKG schemes is due to the reduction in degree-of-freedom from multipath fading channels to multipath combined channels.To improve the wireless key generation rate,we propose a multipath channel diversity-based PKG scheme.Assisted by dynamic metasurface antennas(DMA),a two-stage multipath channel parameter estimation algorithm is proposed to efficiently realize super-resolution multipath parameter estimation.The proposed algorithm first estimates the angle of arrival(AOA)based on the reconfigurable radiation pattern of DMA,and then utilizes the results to design the training beamforming and receive beamforming to improve the estimation accuracy of the path gain.After multipath separation and parameter estimation,multi-dimensional independent path gains are utilized for generating secret keys.Finally,we analyze the security and complexity of the proposed scheme and give an upper bound on the secret key capacity in the high signal-to-noise ratio(SNR)region.The simulation results demonstrate that the proposed scheme can greatly improve the secret key capacity compared with the existing schemes.

FIXED-POINT BLIND ADAPTIVE MULTIUSER DETECTION ALGORITHM FOR IR UWB SYSTEMS IN MULTIPATH CHANNEL

Using the hypothesis that data transmitted by different users are statistically independent of each other,this paper proposes a fixed-point blind adaptive multiuser detection algorithm for Time-Hopping (TH) Impulse Radio (IR) Ultra Wide Band (UWB) systems in multipath channel,which is based on Independent Component Analysis (ICA) idea. The proposed algorithm employs maximizing negentropy criterion to separate the data packets of different users. Then the user characteristic se-quences are utilized to identify the data packet order of the desired user. This algorithm only needs the desired user’s characteristic sequence instead of channel information,power information and time-hoping code of any user. Due to using hypothesis of statistical independence among users,the proposed algorithm has the outstanding Bit Error Rate (BER) performance and the excellent ability of near-far resistance. Simulation results demonstrate that this algorithm has the performance close to that of Maximum-Likelihood (ML) algorithm and is a suboptimum blind adaptive multiuser detection algorithm of excellent near-far resistance and low complexity.

EFFECT OF MULTIPATH CHANNEL MODELS TO THE RECOVERY ALGORITHMS ON COMPRESSED SENSING IN UWB CHANNEL ESTIMATION

Multipath arrivals in an Ultra-WideBand (UWB) channel have a long time intervals between clusters and rays where the signal takes on zero or negligible values. It is precisely the signal sparsity of the impulse response of the UWB channel that is exploited in this work aiming at UWB channel estimation based on Compressed Sensing (CS). However, these multipath arrivals mainly depend on the channel environments that generate different sparse levels (low-sparse or high-sparse) of the UWB channels. According to this basis, we have analyzed the two most basic recovery algorithms, one based on linear programming Basis Pursuit (BP), another using greedy method Orthogonal Matching Pursuit (OMP), and chosen the best recovery algorithm which are suitable to the sparse level for each type of channel environment. Besides, the results of this work is an open topic for further research aimed at creating a optimal algorithm specially for application of CS based UWB systems.

Compressed sensing based channel estimation used in non-sample-spaced multipath channels of OFDM system

By virtue of an increase in spectral efficiency by reducing the transmitted pilot tones, the compressed sensing （CS） has been widely applied to pilot-aided sparse channel estimation in orthogonal frequency division multiplexing （OFDM） systems. The researches usually assume that the channel is strictly sparse and formulate the channel estimation as a standard compressed sensing problem. However, such strictly sparse assumption does not hold true in non-sample-spaced multiple channels. The authors in this article proposed a new method of compressed sensing based channel estimation in which an over-complete dictionary with a finer delay grid is applied to construct a sparse representation of the non-sample-spaced multipath channels. With the proposed, the channel estimation was formulated as the model-based CS problem and a modified model-based compressed sampling matching pursuit （CoSaMP） algorithm was applied to reconstruct the discrete-time channel impulse response （CIR）. Simulation indicates that the new method proposed here outperforms the traditional standard CS-based methods in terms of mean square error （MSE） and bit error rate （BER）.

BDS B1I multipath channel statistical model comparison between static and dynamic scenarios in dense urban canyon environment

Global Navigation Satellite System(GNSS)multipath channel models are fundamental and critical for signal simulation and receiver performance evaluation.They also aid the designing of suitable multipath error mitigation algorithms when the properties of multipath channel are available.However,there is insufficient existing research on BeiDou Navigation Satellite System(BDS)signal multipath channel models.In this study,multipath channel statistical models are established on the basis of extensive datasets of the BDS B1I signal.A multipath parameter estimation algorithm is designed to extract information of multipath rays from the intermediate frequency data.The delay,power loss,Doppler fading frequency,and lifetime distribution models for static and dynamic vehicle platforms are established and compared,and the effects of the satellite orbit type and platform speed on the models are analyzed.The results reveal the detailed distribution and variation characteristics of the multipath parameters and are valuable for the development of accurate urban navigation systems.

Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems

In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.

A MATCHED FILTER BANK BASED DOA ESTIMATION FOR ASYNCHRONOUS MULTIPATH CDMA CHANNELS

In this paper, a Direction Of Arrival (DOA) estimation algorithm is proposed for multiuser signals through uplink asynchronous multipath Code Division Multiple Access (CDMA) channels. The algorithm is based directly on the correlation matrices of matched filter bank outputs of desired user’s multipath signals and it does not require that the elements of base station antenna array outnumber the multipath signals, which is necessary for the conventional sub-space based direction-finding algorithm. Simulation results show that the proposed algorithm estimates the DOA of multipath signals effectively and acceptably. The proposed algorithm has the prominent advantages of low complexity, simpleness and practicality, which make it much more suitable for practical application.

Response of adaptive matched filter to multipath channel

Response of adaptive matched filter, also called adaptive correlator, to multipath channel is discussed in this paper. It has been proved that the new type processor can better match with multipath chan -nel. The results of experiment carried out on lake and in laboratory are presented. It shows that the processor has good detecting performance in time domain.

Multipath Fading Channel Simulation for Mobile Satellite Communication System

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PERFORMANCE ANALYSIS OF CHANNEL ESTIMATION FOR LDPC-CODED OFDM SYSTEM IN MULTIPATH FADING CHANNEL

In this paper, the channel estimation techniques for Orthogonal Frequency Division Multiplexing （OFDM） systems based on pilot arrangement are studied and we apply Low Density Parity Check （LDPC） codes to the system of IEEE 802.16a with OFDM modulation. First investigated is the influence of channel cstimation schemes on LDPC-code based OFDM system in static and multipath fading channels. According to the different propagation environments in 802.16a system, a dynamic channel estimation scheme is proposed. A good irregular LDPC code is designed with code rate of 1/2 and code length of 1200. Simulation results show that the performance of LDPC coded OFDM system proposed in this paper is better than that of the convolution Turbo coded OFDM system proposed in IEEE standard 802.16a.

Optimal Spacing Design for Pilots in OFDM Systems over Multipath Fading Channels

In wireless orthogonal frequency division multiplexing (OFDM) systems, the time-varying channel is often estimated by algorithms based on pilot symbols. Such an estimator, however, requires statistical prior knowledge that is not easily obtained. Therefore, the pilot tones have to be close enough to fulfill the sampling theorem. In this case the statistical knowledge of the channel is not required to reconstruct correctly the channel impulse response (CIR). This paper explores the optimal placement and number of pilot symbols, we investigate optimal training sequences in OFDM systems and we analyze the number of pilot symbols required to fulfill the sampling theorem. Using a general model for a multipath slowly fading channel, the approach is based on the LS as a criterion of channel estimation while the channel interpolation is done using the piecewise-constant interpolation compromising between complexity and performance. Simulation results demonstrate the good performance of our approach.

Performance Prediction of Packet Mobile Communication through Wireless Multipath Fading Channel Modeling

This paper presents a software simulator applicable to multipath fading channels in urban environments of mobile communication networks. The simulator is constructed by a two-state Markov model and several statistical models for simulating the characterizations of different environments. A core idea of the simulator is to construct a Rice distribution-based multipath fading module produced by a modified Gans Doppler power spectrum, and in combination with a Markov model to predict the time-dependent characteristics of packet in different radio circumstances. It can simply predict the packet performance of the future channel and evaluate the relations between the radio channel and the modulation schemes, error control protocols and channel coding. Simulation results demonstrate that it is a reliable and efficient method.

基于可微分架构搜索的多载波信号自动调制识别

针对城市多径信道下缺乏多载波信号通用数据集,以及传统信号特征与网络模型难以有效识别低信噪比下失真信号调制类型的问题,提出一种基于可微分架构搜索的多载波信号自动调制识别算法。首先,产生了常见OFDM、FBMC与OTFS多载波信号经过典型城市多径信道的接收信号数据集,选取对调制参数不敏感的信号时频图作为特征向量来训练神经网络;其次,采用可微分架构搜索方法自动搜索最佳网络结构,避免了网络结构设计的反复验证工作;最后,在特征学习过程中引入联合注意力机制,将失真信号特征进行空间转换以降低多径干扰影响,同时计算特征图各通道信息权重并排序,以提升相关特征图通道的分类效果。仿真结果表明,所提算法不仅能提升在城市多径信道环境下尤其是低信噪比时的识别正确率,而且对调制参数变化和小样本场景具有更好的鲁棒性。

一种基于动态门限与LMS算法相结合的多径干扰抑制算法

在船舰行驶过程中,信号的传输在岛屿反射与海面散射影响下易产生多径效应,使船舰的无线地空数据接收系统受到影响。为提高接收机的接收性能,首先完成了多径信道的建模,搭建了三径信道模型,并在此模型下,将动态门限法与最小均方(LMS)算法进行改进结合,并用此算法完成了信号处理的研究与仿真。仿真结果表明,所提算法优化方案可以提高在此种情况下信号接收系统的准确性。

LDPC编码辅助的判决反馈信道估计与均衡方法

信道频率选择性衰落较强的环境下,传统正交频分复用非盲信道估计与均衡方法的系统误码性能较差,且需要在信号中增加导频符号的插入,降低系统吞吐量。针对上述问题,提出一种低密度奇偶校验(Low-Density Parity-Check,LDPC)编码辅助的判决反馈信道估计与均衡方法。将LDPC编码增益引入判决反馈回路中,以提升收敛性,在插入极少导频符号的条件下达到渐进非盲信道估计与均衡方法的性能。另外提出一种基于条件最大似然原则的信道估计改进算法,以恒虚警原则检测有效抽头信号以提高信道估计精度,并且能够与判决反馈信道估计与均衡方法相结合,进一步提升算法性能。仿真结果表明,在仿真所选用的信道环境下,LDPC编码辅助的判决反馈信道估计与均衡方法相比传统判决反馈方法具有11~13 dB的误码性能提升,具有优秀的系统误码性能和鲁棒性。

An enhanced iterative joint channel estimation and symbol detection algorithm for OFDM systems

For orthogonal frequency division multiplexing (OFDM) wireless communication, the system throughput and data rate are usually limited by pilots, especially in a high mobility environment. In this paper, an enhanced iterative joint channel estimation and symbol detection algorithm is proposed to enhance the system throughput and data rate. With lower pilot power, the proposed scheme increases system throughput firstly, and then the channel estimation and symbol detection proceed iteratively within one OFDM symbol to improve the BER performance. In the proposed algorithm, the original channel estimate of each OFDM symbol is based on the channel estimate of the previous OFDM symbol, thus the variation of the mobile channel is traced efficiently, so the number of pilots in the time domain can be reduced greatly. Besides reducing the system overhead, the proposed algorithm is also shown by simulation to give much better BER performance than the conventional iterative algorithm does.

深海远程单阵元水声通信的逐多径簇联合均衡方法

利用深海远程水声信道的稀疏多径簇具有簇间长时延、簇内结构简单的特性,提出一种用于深海远程单阵元水声通信的低复杂度逐多径簇联合均衡方法。首先,在数据帧中插入无保护间隔的m序列进行稀疏多径簇的检测和同步;然后,对选择的多径簇信号逐个进行基于比例快速后验误差技术的低复杂度均衡,并逐个对均衡器的输出根据对应多径簇的时延进行对齐;最后,将时延对齐的均衡器输出进行最大比合并,从而以并–串转换方式将逐个多径簇软解调信息合并后输入到译码器。相对于常规均衡方法,所提方法将对整体信道多径的均衡分解为对具有简单结构多径簇的逐个均衡,获得多径簇分集增益的同时结合Polar信道编码进一步改善解调性能。数值仿真和深海实验结果表明,所提方法的输出信噪比和误比特率性能明显优于单阵元及双阵元常规均衡,在30 km的通信距离中实现了通信速率为4000 bit/s的单阵元无误码通信,可有效应用于深海远程单阵元水声通信。

一种深海多途相干信道下的高分辨DOA估计方法

针对基于平面波声场模型的深海DOA(Direction of Arrival,DOA)估计存在误差的问题,从射线理论出发,建立了多途信道下深海DOA估计的阵列信号模型,推导出了深海多径声线传播时间和相邻阵元声线传播时延差的表征方法,运用解相干DOA估计算法,提高深海DOA估计性能,通过仿真验证了算法的有效性。研究表明,深海DOA估计问题实为多维参数优化问题;充分考虑海洋声场的声传播特性,可以从根本上解决深海DOA估计的误差问题。

基于RIS多径随机叠加的物理层密钥生成方案

在静态场景下,多径相位和幅度变化缓慢,基于无线信道特征的物理层密钥速率低,难以满足高速数据业务下的通信安全需求。为此,提出了一种基于RIS多径随机叠加的物理层密钥生成方案。首先,收发双方利用RIS天线的实时可控和方向图的捷变特性,通过空间多径的功率谱特性分离多径;然后,在信道相干时间内多次改变空间多径的功率分配,构造等效快变信道,提高密钥源的随机性;最后,通过量化、协商、隐私放大等操作生成一致的安全密钥。仿真结果表明,与现有基于RIS天线的物理层密钥生成方案相比,所提方案一方面通过发掘信道的多径特征提高了信源熵;另一方面通过多径上的功率分配提高了信源的随机性,为密钥速率带来了一定的增益。

典型水声环境的浅海声信道多径特性研究

声纳的通信能力与水文条件密切相关,同样一部声纳在不同的海洋环境中具有不同的通信性能。针对船只经常活动的浅海区域,重点介绍3种典型的声速梯度,并利用仿真手段分析浅海水声传播机理,建立不同水文条件下的浅海多径信道。