MC-CDMA系统中的自适应半盲多用户检测算法

研究多载波CDMA(MC-CDMA)上行半盲多用户检测技术,对基于Chebyshev逼近算法实现的最小输出能量(MOE)盲多用户检测进行改进,提出MC-CDMA系统下一种基于子空间跟踪和Chebyshev逼近的自适应半盲多用户检测算法。该算法基于MOE线性检测器原理,充分利用小区内所有用户的扩频码,设计了一种基于MOE准则的半盲检测器,很好地消除了多址干扰。为了减少计算复杂度,将修正的PASTd算法应用于Chebyshev逼近算法估计MOE半盲检测器的最优权向量。该算法计算复杂度低,具有较好的抗多址干扰性能和检测性能。仿真结果验证了该算法的可行性和优越性。     

一种频率域相关性分布式扩散最小均方算法

以均方误差为代价函数的最小均方（LMS）自适应滤波算法具有结构简单、易于实现、计算复杂度低、稳定性好等优点,然而在对未知系统的脉冲响应进行估计时,传统的分布式扩散最小均方（DLMS）算法易受到噪声的干扰,从而降低估计精度。针对该问题,提出一种频率域相关性分布式扩散最小均方（FCDLMS）算法。利用不相关信号的相关函数值趋近于零的性质,在DLMS算法基础上分别将输入信号的自相关函数以及输入和期望信号的互相关函数作为新的观测数据,消除噪声干扰,从而给出相关性DLMS （CDLMS）算法,并将算法扩展至频率域,在频率域中使用乘法运算而非卷积运算来更新抽头系数,减少计算复杂度。实验结果表明,与传统DLMS算法相比,频率域相关性分布式扩散最小均方算法在噪声环境下对分布式自适应网络中的未知系统脉冲响应具有更好的估计结果,算法性能更优,同时也能较好地适应多抽头数、多节点数、强噪声的复杂环境。     

球形译码算法在信号空间分集系统中的应用研究

针对信号空间分集系统中最大似然检测算法复杂度高的问题,选用球形译码算法作为信号空间分集系统接收机检测算法,可以使接收机在接近最大似然算法检测性能的同时,降低接收机检测复杂度。为进一步降低检测复杂度,通过采用最小均方误差算法减小噪声对接收信号的干扰,来降低因搜索噪声点而产生的复杂度,并利用衰减因子k加快搜索半径收缩来达到进一步降低球形译码算法检测复杂度的目的。仿真结果表明,在信号空间分集系统中引入球形译码算法可以降低接收机检测复杂度,并且改进后的球形译码算法检测时间在低信噪比情况下约为传统球形译码算法的12%—33%。因此,球形译码算法可以有效地降低信号空间分集系统接收机检测复杂度。     

非平稳信号的递推最小二乘盲分离

针对非平稳信号盲分离问题提出了一种基于递推最小二乘(RLS)算法的非平稳信号盲分离新方法.首先引入遗忘因子对常规代价函数进行指数加权修正,得到一种新的具有递归结构的代价函数;然后利用RLS算法最小化代价函数,推导最优分离矩阵的自适应更新算法,逐步实现信号分离.该算法避免了最小二乘类算法关于学习速率选择困难的缺点,具有收...     

模糊步长的RAKE接收机仿真研究

研究无线通信中多路径传播优化信号质量问题,针对传统的RAKE接收机需要通过增加分支来提高系统性能,提出了一种模糊步长LMS算法的自适应RAKE接收机,采用对抽头延迟线的权值进行自适应调整的方法可以有效地合并多径信号,克服多径干扰,并且消除信号间的干扰和噪声.对LMS算法的步长进行了改进,提出一种新的模糊步长的方法,以便提高LMS算法的收敛速度和降低稳态误差.在MATLAB上进行仿真.仿真结果表明,模糊步长的RAKE接收机性能优于传统的RAKE接收机性能.     

基于WRELAX的小运算量GNSS多径干扰抑制算法

针对全球卫星导航系统（Global navigation satellite system,GNSS）中的多径干扰抑制问题,本文提出了一种有效的小运算量算法。该算法将多径抑制问题转换为时延估计问题,利用基于信号分离理论的Weighted RELAXation （WRELAX）算法求解非线性最小二乘代价函数。考虑到GNSS信号相关函数的特点,通过对接收数据与参考信号的相关函数及参考信号的自相关函数进行加窗截取,取出相关函数信息量较大的主峰值及其附近的范围,从而减小了数据长度,从根本上减小了WRELAX进行参数估计时的运算量,为工程实现提供了可能。最后通过理论分析和仿真实验验证了本文算法在降低运算量的同时能够保证性能不会受到较大损失,尤其对实际中多径干扰与直达信号相比较弱时,本文算法可获得与现有算法相当的性能。     

一种低复杂度高性能的MIMO系统自适应检测算法

如何克服发射信号的重叠和码间干扰是MIMO系统信号检测技术面临的关键问题。信号检测算法的性能优劣是影响MIMO技术能否真正适于实际应用的关键因素。结合MLD算法高性能和MMSE-SIC算法低复杂的优点,对Hybrid算法进行了改进,提出了一种基于信道最大/最小特征值的自适应混合检测算法。该算法重新定义了自适应系数,并通过信道矩阵特征值的特性,自适应控制三种子混合算法检测数据流时的百分比,以达到更高的检测效率。仿真结果表明：无论信道在何种复杂环境下,该算法具有与MLD算法几乎相同的误码性能,计算复杂度也有很大的改善。     

煤矿井下电磁无线随钻测量信号滤波研究

针对采用自适应滤波最小均方算法和递推最小二乘算法提取煤矿井下电磁无线随钻测量(EMMWD)信号存在收敛速度慢与运算时间长等问题,提出了一种稳定快速横向递归最小二乘(SFT-RLS)算法,用于自适应滤除井下非稳态电气干扰与工频及其2倍频、3倍频干扰,实时提取微弱EM-MWD信号。该算法基于RLS算法,利用4个并行滤波器结构可降低运算时间,并利用加权最小二乘误差进行反馈以提高稳定性。仿真试验结果表明,SFT-RLS算法可实现对采样率为1kHz的EM-MWD信号进行自适应滤波和自适应陷波,平均每次迭代运行时间小于156.98μs,实现了快速稳定收敛运算与实时自适应滤波;SFTRLS算法自适应滤波可抑制信号与干扰比为-115dB的非稳态电气干扰;SFT-RLS算法自适应陷波可实时滤除工频及其2倍频、3倍频干扰,有效提取频率为6.25Hz的时间脉冲位置调制的EM-MWD信号波形,为正确解码EM-MWD信号提供了可靠数据。     

结合最小均方误差的改进球形译码检测算法

在多输入多输出(MIMO)系统的信号检测算法中,球形译码算法的检测性能最接近最大似然算法,但传统球形译码算法运算复杂度较高。为降低球形译码算法复杂度,提出一种新型的球形译码检测算法。新算法由改进的快速球形译码算法与最小均方误差算法相结合而成。改进的快速球形译码算法通过在球形半径收缩时乘上一个常量参数来提高半径收缩速度,减少算法搜索的信号点数,从而达到降低复杂度的目的。最小均方误差算法则能够通过减小噪声对接收信号的干扰来降低因搜索噪声点而产生的复杂度。将最小均方误差算法的信道矩阵应用在改进的快速球形译码算法中,将两种算法有效地结合,能够进一步降低算法复杂度。仿真结果表明,当信噪比(SNR)低于10 dB时,新算法相比于原始球形译码算法,检测性能平均提高了9%左右。     

一种高效的QMC检测算法

基于垂直分层空时码的MIMO-OFDM系统提出一种高效的QMC检测算法,该算法对信道矩阵进行一次排序QR分解,对最先检测的信号层采用ML-OSIC算法,用M算法检测中间的信号层,逐层增加保留值M以提高算法有效性,利用串行干扰消除检测余下的信号层。与QRD-M算法相比,QMC检测算法能降低计算复杂度。仿真结果表明,该算法以更低的计算复杂度获得更接近最大似然检测的性能,取得性能与复杂度之间的折中更理想。     

基于图像分割的实时空间错误隐藏算法

基于模板的空间错误隐藏算法计算复杂度较高。为此,提出一种基于图像分割的实时空间错误隐藏算法。将分割后错误块边界的同类纹理作为参考模板,在对应的纹理区域中寻找最佳匹配块,以实现错误隐藏。实验结果表明,与传统算法相比,该算法能获得较好的隐藏效果,且计算量较低。     

改进的RS码时域译码算法

在实际通信中,产生重量较轻错误图样的概率大于产生重量较重错误图样的概率。该文提出一种改进的RS码时域译码算法,在传统时域译码算法的基础上,对存在0个错误和1个错误的错误图样进行特殊处理,降低其计算复杂度。该算法最多可比传统算法减少约一半的计算量。     

空间调制系统下改进的QRD-M检测算法

空间调制（SM）系统中性能最优的最大似然（ML）检测算法复杂度很高,用基于信道矩阵QR分解的M算法（QRD-M）可以降低复杂度,但传统QRD-M算法检测时,每层都保留固定的M个节点,仍会造成额外的计算量。针对传统QRD-M算法中存在的问题,提出一种低复杂度的动态M值QRD-M检测算法——LC-QRD-dM。LC-QRD-dM算法利用设计的阈值与累积分支度量值进行比较,每层自适应地选择不超过M的保留节点数,相对于传统QRD-M算法以牺牲少量性能为代价大大降低了复杂度。接着又针对该改进算法在信道衰落较深时会产生较大误码率的问题,进一步提出一种基于信道状态的动态M值QRD-M检测算法——CS-QRD-dM。CS-QRD-dM利用LC-QRD-dM的原理,在低信噪比（SNR）时,每层根据阈值选择不小于M的保留节点数;在高信噪比时,每层则选择不超过M的保留节点数。理论分析和仿真结果表明：相比传统QRD-M,CS-QRD-dM在低信噪比时有约1.3 dB的信噪比增益（误码率为10^-2）,以增加少量复杂度为代价,显著地改善了检测性能;在高信噪比时,其检测性能及复杂度与LC-QRD-dM相同。     

Structure identification of generalized adaptive neuro-fuzzy inference systems

This paper presents a method to identify the structure of generalized adaptive neuro-fuzzy inference systems (GANFISs). The structure of GANFIS consists of a number of generalized radial basis function (GRBF) units. The radial basis functions are irregularly distributed in the form of hyper-patches in the input-output space. The minimum number of GRBF units is selected based on a heuristic using the fuzzy curve. For structure identification, a new criterion called structure identification criterion (SIC) is proposed. SIC deals with a trade off between performance and computational complexity of the GANFIS model. The computational complexity of gradient descent learning is formulated based on simulation study. Three methods of initialization of GANFIS, viz., fuzzy curve, fuzzy C-means in x/spl times/y space and modified mountain clustering have been compared in terms of cluster validity measure, Akaike's information criterion (AIC) and the proposed SIC.     

计算资源受限的视频编码多模式决策

由于视频编码技术趋向于采用越来越复杂的分块模式,多模式决策技术也随之成为一种非常重要的编码技术.多模式决策的优劣不仅会大幅度地影响视频编码的计算消耗,而且也对编码性能的高低起到关键的作用.为使多模式决策在计算能力相差悬殊的平台上都能获得优化的率失真性能,给出一种计算复杂度自适应的优化多模式决策算法.首先,利用视频序列中不同宏块模式间的时空相关性,预测这些宏块多模式决策后的拉格朗日代价和计算复杂度的斜率(Lagrangian cost and complexity slope,简称J-C slope).J-C slope越大,说明在该宏块上的模式决策消耗每单位的计算资源可以获取的率失真收益越多.在计算资源有限的情况下,多模式决策应该按照J-C slope的大小顺序执行,也就是性价比优先的顺序,以便保证计算资源优先分配给率失真收益大的宏块.另外,还通过建立J-C slope阈值与实际计算复杂度的关系模型,设计了一种根据给定计算约束自适应调整计算复杂度的算法.根据实验结果,该方法不仅可以准确地控制多模式决策的计算复杂度,而且还能在不同的计算约束下获得优化的率失真性能.     

Efficient link scheduling with joint power control and successive interference cancellation in wireless networks无线网络中联合功率控制和串行干扰消除的高效链路调度算法

Existing works have addressed the interference mitigation by any two of the three approaches: link scheduling, power control, and successive interference cancellation (SIC). In this paper, we integrate the above approaches to further improve the spectral efficiency of the wireless networks and consider the max-min fairness to guarantee the transmission demand of the worst-case link. We formulate the link scheduling with joint power control and SIC (PCSIC) problem as a mixed-integer non-linear programming (MINLP), which has been proven to be NP-complete. Consequently, we propose an iterative algorithm to tackle the problem by decomposing it into a series of linear subproblems, and then the analysis shows that the algorithm has high complexity in the worst case. In order to reduce the computational complexity, we have further devised a two-stage algorithm with polynomial-time complexity. Numerical results show the performance improvements of our proposed algorithms in terms of the network throughput and power consumption compared with the link scheduling scheme only with SIC.     

Simplified MMSE Detectors for Turbo Receiver in BICM MIMO Systems

In this article, two methods adopting simplified minimum mean square error (MMSE) filter with soft parallel interference cancellation (SPIC) are discussed for turbo receivers in bit interleaved coded modulation (BICM) multiple-input multiple-output (MIMO) systems. The proposed methods are utilized in the non-first iterative process of turbo receiver to suppress residual interference and noise. By modeling the components of residual interference after SPIC plus the noise as uncorrelated Gaussian random variables, the matrix inverse for weighting vector of conventional MMSE becomes unnecessary. Thus the complexity can be greatly reduced with only slight performance deterioration. By introducing optimal ordering to SPIC, performance gap between simplified MMSE and conventional MMSE further narrows. Monte Carlo simulation results confirm that the proposed algorithms can achieve almost the same performance as the conventional MMSE SPIC in various MIMO configurations, but with much lower computational complexity.     

一种非视距环境下的目标定位算法

针对机器人、无人机和其他智能系统的位置信息,研究了非视距(non line of sight, NLOS)环境中基于到达时间(time of arrival,TOA)测距的目标定位问题。在建模过程中,通过引入平衡参数来抑制NLOS误差对定位精度的影响,并成功将定位问题的形式与一个广义信赖域子问题(generalized trust region subproblem,GTRS)框架进行耦合。与其他凸优化算法不同的是,本文没有联合估计目标节点的位置和平衡参数,而是采用了一种迭代求精的思想,算法可以用二分法高速有效地进行求解。 所提算法与已有的算法相比,不需要任何关于NLOS路径的信息。此外,与大多数现有算法不同,所提算法的计算复杂度低,能够满足实时定位的需求。仿真结果表明:该算法具有稳定的NLOS误差抑制能力,在定位性能和算法复杂度之间有着很好的权衡。     

Simplified algorithms for rate-distortion optimization in high efficiency video coding

HEVC is the latest coding standard to improve the coding efficiency by a factor of two over the previous H.264/AVC standard at the cost of the increased complexity of computation rate-distortion optimization (RDO) is one of the computationally demanding operations in HEVC and makes it difficult to process the HEVC compression in real time with a reasonable computing power. This paper aims to present various simplified RDO algorithms with the evaluation of their RD performance and computational complexity. The algorithms for the simplified estimation of the sum of squared error (SSE) and context-adaptive binary arithmetic coding (CABAC) proposed for H.264/AVC are reviewed and then they are applied to the simplification of HEVC RDO. By modifying the previous algorithm for H.264/AVC, a new simplified RDO algorithm is proposed for modifying the previous algorithm for H.264/AVC to be optimized for the hierarchical coding structure of HEVC. Further simplification is attempted to avoid the transforms operations in RDO. The effectiveness of the existing H.264/AVC algorithms as well as the proposed algorithms targeted for HEVC is evaluated and the trade-off relationship between the RD performance and computational complexity is presented for various simplification algorithms. Experimental results show that reasonable combinations of RDO algorithms reduce the computation by 80–85% at the sacrifice of the BD-BR by 3.46–5.93% for low-delay configuration.     

Symbol Detection Based on Back Tracking Search Algorithm in MIMO-NOMA Systems

One of the most important methods used to cope with multipath fading effects, which cause the symbol to be received incorrectly in wireless communication systems, is the use of multiple transceiver antenna structures. By combining the multi-input multi-output (MIMO) antenna structure with non-orthogonal multiple access (NOMA), which is a new multiplexing method, the fading effects of the channels are not only reduced but also high data rate transmission is ensured. However, when the maximum likelihood (ML) algorithm that has high performance on coherent detection, is used as a symbol detector in MIMO NOMA systems, the computational complexity of the system increases due to higher-order constellations and antenna sizes. As a result, the implementation of this algorithm will be impractical. In this study, the backtracking search algorithm (BSA) is proposed to reduce the computational complexity of the symbol detection and have a good bit error performance for MIMO-NOMA systems. To emphasize the efficiency of the proposed algorithm, simulations have been made for the system with various antenna sizes. As can be seen from the obtained results, a considerable reduction in complexity has occurred using BSA compared to the ML algorithm, also the bit error performance of the system is increased compared to other algorithms.