基于多项式模型的时频二维OFDM信道估计算法

本文提出了一种内插导频的时频二维信道估计算法,用于无线移动信道下的正交频分复用(OFDM)系统。该算法基于梳状内插的导频信号模型,采用多项式模型对信道进行拟合,从而能够精确的估计出信道信息。与时频二维维纳滤波以及自适应判决反馈的算法相比,该算法不需要预先知道信道相关矩阵以及信噪比等信道信息,简单易于实现,并且能够有效的抑制信道估计中的噪声干扰。仿真结果表明,在衰落环境下,该算法的信道估计最小方差(MSE)性能与最小二乘(LS)估计最小方差性能相比有显著提高。     

基于动态规划(DP)的信道模型变化检测

本文提出一种适用于快衰落信道结构的模型变化检测算法.针对信号包络幅值寻找信道模型发生突变的采样时间点,运用动态规划(DP)方法推导了快衰落信道的模型变化检测的理论表达式,构造了信道的变结构模型切换准则.理论分析和仿真实验表明结合幅值变化检测,可有效地找出模型变化采样时间点,并在这些时间点对信道进行模型切换,使估计器达到...     

WCDMA系统中结合导频与信息码的信道估计方法

研究了快衰落环境中第三代移动通信空中接口WCDMA(FDD),基于时分复用的导频信号辅助衰落信道下的信道估计。提出了同时根据导频信号及数据信号估计信道参数的信道估计方法,该方法将信道估计分为根据导频信号和数据信号两部分进行并由其加权合并完成。计算了最小均方误差下的合并系数。仿真结果表明该方法能快速跟踪信道的衰落,有效地估计出各时隙中数据段的信道参数。利用该算法,尤其在高速运动情况下,接收机的性能可得到显著的改善。     

交互多模算法在蜂窝网指纹定位技术中的应用

针对目标运动及无线信道动态衰落特性对用户位置估计波动较大的影响,利用基于卡尔曼滤波的交互多模算法的特性,并将其应用于蜂窝网指纹定位技术中.根据K近邻匹配算法估计出用户位置坐标,然后利用交互多模算法对用户位置坐标进行滤波处理,提高定位精度.通过对真实环境下数据的处理和分析,基于卡尔曼滤波的交互多模算法能有效提高定位精度.     

小波包多载波系统中的LMS信道估计

为了提高正交小波包波分复用(OWPDM)系统抗多径衰落和多普勒频移的性能,提出了一种新的半盲信道估计算法。首先利用时域滤波原理滤去信道响应中的噪声,之后利用信号在时域补零相当于频域内插的原理来获得更精确的最小均方算法(LMS)估计器迭代初值。仿真结果表明,在插入更少导频数目的情况下,新算法估计性能优于最小二乘(LS)信道估计算法,在插入相同数目导频的情况下,与传统的LMS信道估计算法相比,新算法能使系统获得更好的误码性能。     

地空通信中基于贪婪算法的SC-FDE系统稀疏信道估计

针对在地空频率选择性衰落信道中,信道冲激响应具有时域稀疏特性的情况,提出了基于贪婪算法的单载波频域均衡(SC-FDE)系统稀疏信道估计方法。Chu序列是SC-FDE中常用的导频序列,对其进行分析并证明了将Chu序列进行循环移位所构造的导频矩阵满足RIP条件,将导频矩阵作为测量矩阵,把地空信道估计问题建模为稀疏重构模型,采用贪婪算法中的OMP和Co Sa MP算法对信道进行稀疏重构,仿真验证了所得信道估计较传统最小二乘(LS)信道估计方法更加准确。在相同的训练序列长度和信道环境下,利用所得信道估计对接收信号进行最小均方误差(MMSE)均衡,蒙特卡罗仿真结果表明,所提方法与传统LS信道估计方法相比,系统误码性能提高2~3 d B。     

基于改进广义Steiner估计的MC-CDMA系统空时多用户检测

研究了频率选择性瑞利衰落信道中的同步MC-CDMA系统上行链路空时信道估计及多用户检测算法。考虑对应于子载波的衰落系数是信道冲激响应的离散傅里叶变换,通过在两个数据块之间插入训练序列(midamble)进行所有用户的联合信道估计。首先采用广义Steiner估计器(GSE)来进行阵列天线信道冲激响应的初始估计,然后提出一种简单有效的适用于均匀线阵的互相关波达方向(CCDOA)估计算法,用以改进阵列天线信道冲激响应的估计,从空间的角度降低了信道响应中的噪声。在估计出所有用户空时信道参数的基础上,构造最大比合并(MRC)、解相关检测和最小均方误差检测(MMSE)来进行信号检测。仿真结果表明基于互相关DOA估计的改进信道估计算法与广义Steiner估计器相比在系统性能上有显著的改善。     

LTE下行信道估计算法研究

近年来,正交频分复用（OFDM）技术以其在高速数据传输中具有的良好的抗多径衰落的特性以及较高的频谱利用率等优点得到了广泛的应用。在OFDM通信系统中,信号在带宽小于信道相干带宽的多个正交子载波上发射和传输,以克服多径衰落信道所带来的信号频率选择性衰落。为了补偿各个子载波上的多径衰落,这就需要一个性能良好的信道估计方案。对LTE中基于导频的下行信道估计算法进行研究,首先在导频子载波处采用LS（最小二乘法）算法估计出信道参数,然后通过内插的方法在数据子载波处内插得到信道参数。频域内插主要采用拉格朗日内插或是LMMSE（线性最小均方误差）内插;时域内插采用拉格朗日内插。     

正交频分复用系统中的频域插入导频算法

OFDM(正交频分复用)技术是第三代、第四代移动通信的关键技术之一,而移动信道是极为复杂的一种信道,信号通过无线信道时,会遭受各种衰落的影响,信道估计技术就是使得信道对输入信号的影响减至最小化的估计算法.在介绍了OFDM系统相干检测下信道估计方法的基础上,着重讨论和仿真了利用导频插入的方式对OFDM系统进行信道估计的算法.     

基于毫米波通信的混合MIMO系统的信道估计

作为5G的一项关键技术,毫米波通信要求提出专有的信道估计和预编码算法,为此,针对毫米波MIMO系统的窄带平坦衰落信道研究下行信道估计方案.由于毫米波系统的稀疏特性,将稀疏多径信道的信道估计转化为稀疏信号的重建,基于压缩感知设计适用于毫米波通信系统的信道估计方案,深入研究了正交匹配追踪算法.仿真结果显示,它可以高概率地恢复信号,与传统的最小二乘法比较,能获得更好的信道估计性能.     

Access Point Height Based Location Accuracy Characterization in LOS and OLOS Scenarios

Location estimation in a wireless local area network (WLAN) using received signal strength indication (RSSI) has gained considerable attention in recent years. In a conventional RSSI based indoor WLAN localization, mobile node position is estimated through access point (AP) placed at ceiling height. Researchers have proposed solutions for location estimation in line of sight (LOS) scenarios, by installing the AP at a fixed position. This paper demonstrates the improved location accuracy in LOS and obstructed line of sight (OLOS) scenarios by placing the AP at lower heights. The RSSI variations caused by shadow fading for changing AP heights are used to estimate the location accuracy. The localization performance is computed in terms of Cramer-Rao lower bound (CRLB) of range estimate under dynamic environments which is relatively less complex computation technique and is calibration free. Simulation results reveal that the proposed method has better performance than the multilateration with linearization for access point localization algorithm. The minimum mean localization errors are obtained by deploying the access point at 2 m height. The results also demonstrate that the indoor localization accuracy improves for higher order path loss exponent.     

Effective-SNR estimation for wireless sensor network using Kalman filter

In many Wireless Sensor Network (WSN) applications, the availability of a simple yet accurate estimation of the RF channel quality is vital. However, due to measurement noise and fading effects, it is usually estimated through probe or learning based methods, which result in high energy consumption or high overheads. We propose to make use of information redundancy among indicators provided by the IEEE 802.15.4 system to improve the estimation of the link quality. A Kalman filter based solution is used due to its ability to give an accurate estimate of the un-measurable states of a dynamic system subject to observation noise. In this paper we present an empirical study showing that an improved indicator, termed Effective-SNR, can be produced by combining Signal to Noise Ratio (SNR) and Link Quality Indicator (LQI) with minimal additional overhead. The estimation accuracy is further improved through the use of Kalman filtering techniques. Finally, experimental results demonstrate that the proposed algorithm can be implemented on resource constraints devices typical in WSNs.     

基于CC2431实现的无线传感器网络定位

无线传感器网络（WSN）中的定位技术是目前该领域研究的热点。对无线传感器网络中常用的定位技术进行了概述,着重分析了基于接收的信号强度指示（RSSI）定位技术的误差来源及减小误差的办法。重点讨论了利用CC2431内嵌的定位引擎实现的无线传感器网络定位功能及其原理,并通过实际测试验证了其定位准确度满足本应用需求,最后对该定位系统的各项性能指标进行了全面的分析总结。     

子空间算法在OFDM信道时延估计中的应用

到达时延(TOA)和到达时延差(TDOA)定位算法是无线定位中应用最广泛的2种方法,TOA测量值的准确获取将影响定位的精确度。正交频分复用(OFDM)是实现无线多媒体通信的关键技术,利用OFDM信号的导频子载波所携带的信息,参考均匀线列阵的DOA估计过程,提出将MUSIC子空间法应用到OFDM信道的时延估计上。给出了OFDM符号内导频子载波分布的相关条件,对提出算法进行仿真实验,可以看出,导频子载波个数的增加和导频子载波间隔的扩大都可以提高估计精确度。     

Manifold‐based canonical correlation analysis for wireless sensor network localization

Signal‐strength‐based location estimation in wireless sensor networks is to locate the physical positions of unknown sensors via the received signal strengths. In this field, there are few localization researches sufficiently exploiting topology structures of the network in both signal space and physical space. The goal of this paper is to first establish two effective localization models based on specific manifold (or local) structures of both signal space and physical (location) space by using our previous locality preserving canonical correlation analysis (LPCCA) model and a newly‐proposed locality correlation analysis (LCA) model, and then develop their corresponding novel location algorithms, called location estimation—LPCCA (LE—LPCCA) and location estimation—LCA (LE—LCA). Since both LPCCA and LCA relatively sufficiently take into account locality characteristics of the manifold structures in both the spaces, our localization algorithms developed from them consequently achieve better localization accuracy than other publicly available advanced algorithms. Copyright © 2011 John Wiley & Sons, Ltd.     

Robust wireless location over fading channels

This paper develops an estimation algorithm for the time and amplitude of arrival of a known transmitted sequence over a single-path fading channel. The algorithm is optimized to enhance robustness to fast channel fading and low signal-to-noise ratio (SNR) conditions, which are common in wireless location applications. The paper also presents a noise and fading bias correction technique for amplitude of arrival estimation that improves the estimation accuracy significantly. The proposed algorithm is then applied to the case of code-division multiple-access (CDMA) wireless location finding for which the paper gives simulation results that demonstrate significant estimation accuracy improvement over known algorithms.     

基于分簇传感网的RSSD/TDOA协同定位方法

针对基于能量有限的分簇传感网实现对非配合辐射源的高精确度无源定位问题,提出了一种适用于传感网的接收信号强度差fRSSD）定位方法,并在其相关性能分析基础上提出了以RSSD定位为辅、到达时间差（TDOA）定位为主的两轮协同定位方案,即以RSSD定位结果作为目标位置初估计,并根据各簇在该初估计位置处的TDOA定位CRB,选择部分预期定位精确度较高的簇参与进一步的TDOA定位,最后通过对这些簇定位结果的加权平均得到最终的目标位置估计。仿真结果表明,相比基于GDOP的簇选择标准和定位融合方法,提出的改进措施提高了定位精确度,而RSSD／TDOA协同定位方案在保证较高定位精确度的同时,有效地减轻了网络负载,延长了网络生命周期,具有较好的工程应用前景。     

Performance Analysis of Received Signal Strength Fingerprinting Based Distributed Location Estimation System for Indoor WLAN

Location Estimation has become important for many applications of indoor wireless networks. Received Signal Strength (RSS) fingerprinting methods have been widely used for location estimation. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. The accuracy and response time of estimation are critical issues in location estimation system for large sites. In this paper, we have proposed a distributed location estimation method, which divide the location estimation system into subsystems. Our method partitions the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between RSS fingerprint and their respective location in a subsystem. We have performed experimentation on two RSS dataset, which are gathered on different testbeds, and compared our results with benchmark RADAR method. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site.     

Improved Frequency Domain Channel Estimation and Equalization for MIMO Wireless Communications

This paper introduces an improved frequency domain channel estimation method based on interpolation vectors for single carrier frequency domain equalization (SC-FDE) with the multiple-input multiple-output (MIMO) scheme. The proposed algorithm is derived by employing the least squares (LS) criterion, and a specified application for the wide sense stationary uncorrelated scattering (WSSUS) Rayleigh fading channel is presented. The channel frequency domain responses estimated at two adjacent pilot blocks are used to track the time-variant channel information, which can effectively improve the accuracy of channel estimation without significantly increasing complexity. Maximum mean square error (MMSE) frequency domain equalization based on the estimated channel is employed in the receiver to recover transmitted signals. This paper also investigates a training sequence design method for multiple transmit antennas and a noise variance estimation method. Numerical simulation results show that the proposed methods can perform very well for fading channels with long multipath delay and high Doppler spread.     

Environmental-adaptive indoor radio path loss model for wireless sensor networks localization

In the received signal strength indicator (RSSI) based indoor wireless sensor networks localization system, RSSI measurements are very susceptible to multipath fading, anisotropy of antenna, low supply voltage of node and so on, which will cause the system failure to achieve a high location accuracy. This paper presents an environmental-adaptive path loss model. In the process of localization, the calibrated coefficient LSV of low supply voltage, which can be determined by monitoring the supply voltage of the sender, is used to calibrate ranging errors caused by its low supply voltage. The blind node utilizes the absolute value of RSSI to generate the phase of the corresponding receiver's location so as to determine the correction coefficient of indoor multipath fading R_i. Furthermore, in order to improve the accuracy of RSSI measurements, we also take full consideration of the effect of antenna to accurately determine the corresponding path loss model of the two communication nodes. The proposed path loss model is suitable for the majority of wireless location systems that are on the basis of RSSI-based ranging techniques. Experiment results show that the estimation accuracy and adaptability of the proposed path loss model are significantly higher than that of the traditional one.