A novel ToA location algorithm using LoS range estimation for NLoS environments

One of the main problems facing accurate location in wireless communication systems is nonline-of-sight (NLoS) propagation. In this paper, we propose a novel location technique that estimates the true, or line-of-sight (LoS), ranges based on NLoS range measurements. This approach utilizes a constrained nonlinear optimization approach, when range measurements are available from three base stations (BSs) only. Bounds on the NLoS error and the relationship between the true ranges are extracted from the geometry of the cell layout and the measured range circles to serve as constraints. Simulations studying the performance of the algorithm for different NLoS error distributions and BS configurations show that the location accuracy is significantly improved over traditional algorithms, even under highly NLoS conditions.     

Mobile Localization Under Non-Line-of-Sight Conditions Using Scattering Information

A novel mobile localization algorithm under non-line-of-sight (NLoS) conditions involving at most two base stations (BS) is presented. By using such a small number of BSs, the proposed technique avoids problems related to weak mobile hearability and excessive network messaging overload. Based on a scattering model, the NLoS hybrid approximate maximum likelihood (NLoS HAML) approach presented herein estimates the distances between the mobile station (MS) and the scatterers through the measured hybrid time-of-arrival/angle-of-arrival (ToA/AoA) statistics. Then, it matches the calculated distances to a known scatterers’ distribution in a maximum likelihood (ML) sense. Numerical results prove that the proposed algorithm outperforms previous methods by providing an accuracy enhancement that reaches 60%. It is also shown that the performance enhancement provided by the proposed algorithm is maximized when the adequate scatterers distribution is adopted.     

基于双向传播算子的互质面阵二维波达方向估计

针对谱峰搜索的二维波达方向估计中现有算法复杂度高,精度受搜索间隔影响较大的问题,给出了一种双向传播算子的互质面阵二维波达方向估计算法,实现了俯仰角和方位角的低复杂、高精度、无模糊联合估计.该方法首先将互质阵列引入到二维波达方向估计中,构造互质平面阵模型,然后采用两次旋转不变传播算子方法计算出不同阵列流型方向上的旋转因子矩阵,根据旋转因子矩阵解算出目标信号的俯仰角和方位角,同时利用互质理论消除了稀疏阵列角度估计的不确定性,证明了互质阵列模型下采用双向传播算子方法进行俯仰角和方位角估计的无模糊性.对算法的复杂度进行理论分析,并给出了平面阵列角度估计的克拉美罗界推导.理论分析与仿真结果表明,算法不需要进行角度匹配和谱峰搜索,在相同条件下的均方根误差性能优于均匀平面阵的多重信号分类算法,并且以较低的复杂度无模糊的达到了高维网格搜索的精度.     

动态扰动辅助的串行抵消双比特翻转Polar译码算法

针对串行抵消翻转译码算法（Successive Cancellation Flip,SCF）受限于单比特翻转而性能提升有限问题,提出了一种双比特翻转译码算法（Successive Cancellation Flip with 2 Bits,SCF2）。针对SCP算法扰动方差初始值固定的问题,设计了一种扰动方差可随码长和码率变化的改进SCP算法。在此基础上,结合翻转和扰动机制,提出了一种动态扰动辅助的串行抵消双比特翻转（Dynamic Perturbation-Aided SCF2,DPA-SCF2）译码算法,并对其译码复杂度和性能进行了分析。仿真结果显示,相比于列表长度为4的循环冗余校验辅助串行抵消列表（Cyclic Redundancy Check Aided Successive Cancellation List,CA-SCL）译码算法,所提算法最大可获得约0.5 dB的性能增益。     

小波分析法解决无线定位跟踪问题

无线定位中,信号的非视距传播（NLoS）很大程度上决定了移动台的定位精度,而小波分析理论在信号处理中有较为明显的优点,提出了一种在蜂窝网络中非视距环境下对移动台（MS）的定位及跟踪算法。利用小波变换对信号的分解和重构,实现了对TDOA测量值误差的修正,再利用经典Chan算法对移动台位置进行估计,配合相应的距离门限值对移动台的位置进行跟踪定位。仿真结果表明,该算法能够有效地实现对移动台位置的静态定位和动态跟踪,并明显优于同等环境下经典算法的仿真结果,有效提高了定位精度。     

High‐rate uncoded space‐time labelling diversity with low‐complexity detection

Uncoded space‐time labelling diversity (USTLD) is a recent scheme that improved the error performance compared to conventional multiple‐input, multiple‐output systems. Thus far, USTLD has suffered from limited achievable data rates, as the original model uses only two transmit antennas. This motivates for the work in this paper, where the USTLD model is extended to allow for any desired number of transmit antennas. An analytical bound for the average bit error probability of this high‐rate USTLD (HR‐USTLD) system is derived. This expression is verified using the results of Monte Carlo simulations, which show a tight fit in the high signal‐to‐noise ratio region. The increased data rates associated with larger transmit antenna arrays in HR‐USTLD systems come at the cost of increased detection complexity. Therefore, this paper studies the application of low‐complexity detection algorithms based on the popular QR decomposition technique and proposes a new algorithm specifically designed for HR‐USTLD systems. Analysis of this algorithm in terms of accuracy and computational complexity is also provided and benchmarked against maximum‐likelihood detection (MLD). It is shown that the proposed algorithm achieves near‐MLD accuracy, while reducing complexity by 79.75% and 92.53% for the respective 4 × 4 16QAM and 4 × 5 16PSK HR‐USTLD systems investigated.     

基于快速贝叶斯匹配追踪的激光大气信道估计

信道估计是指接收机获知信道状态信息的方法和过程。信道估计的准确度决定了接收机的工作性能,所以均衡之前,必须先进行信道估计。目前,激光光学传输信道估计成为多输入多输出正交频分复用的自由空间光通信系统的关键技术。传统的压缩感知方法作为一种信道估计的有效方法,具有恢复和重构原始信号的能力,但在计算复杂度上付出了一定的代价。快速贝叶斯匹配追踪算法克服了现有方法重构精度低和复杂度高缺点。通过先验模型选择和近似的最小均方误差的参数向量的估计,快速贝叶斯匹配追踪算法提供了估计信道冲激响应的一种有效方式。仿真结果表明,与传统的基于压缩感知的方法相比,该方法能显著提高系统的性能。     

基于变换域APSO的任意阵列宽带DOA估计算法

为了提高宽带信号来波方向（Direction-of-arrival, DOA）估计精度并降低计算复杂度, 本文结合已知的发射信号波形, 提出了一种基于变换域加速粒子群最优化（Accelerated Particle Swarm Optimization, APSO）的宽带DOA估计算法, 该算法适用于任意阵列和低采样率情况。首先对阵列接收数据进行匹配滤波以及傅里叶变换处理,其次根据频域宽带阵列数据模型,利用确定性极大似然（Deterministic Maximum Likelihood, DML）准则构建宽带DOA估计的空间谱函数,然后采用变换域APSO算法对空间谱函数进行最大值搜索,搜索结果即为DOA估计值。该算法无需DOA预估计,不依赖空间谱函数的梯度信息,计算复杂度低。仿真实验表明,所提算法具有高估计精度和低计算复杂度,在信噪比为20?dB时,DOA估计均方根误差为0.02°。      

MIMO系统中的一种码距分配矩阵构造方案

针对多输入多输出(MIMO)系统采用奇异值分解(SVD)预编码时最优码距分配矩阵的构造无闭式解且搜索复杂度高的问题,基于变形的Hadamard矩阵,以接收端成对错误概率最小化为准则,给出了一种易求解且为闭式构造的码距分配方案。同时,给出了与之相应的功率分配方案,进一步提高了系统的性能。仿真结果表明,该算法性能与现有方法近似,但复杂度降低。当推广到具有发射相关性的MIMO系统时,仍具有较好的性能。     

OFDM系统基于二维核回归算法的衰落信道估计

提出了一种低复杂度的基于二维核回归平滑的时间频率双衰落信道估计算法.该方法首先采用LS算法估计导频处的信道增益并利用分段线性插值方法得到数据子载波处的信道增益,然后分别利用两个级联的一维核回归平滑器在时域和频域对信道矩阵进行二维平滑,以消除由信道噪声、ICI和插值误差等引入的估计误差.对插值和平滑分别进行优化处理,因此具有计算复杂度低、性能优良等优点.数值仿真表明:该算法性能较LS算法和1维DFT算法有较大的改进;与1维LMMSE算法相比性能相差很小,在低信噪比时甚至超过了后者.