一种改进并行两箱预失真方法

针对现有椭圆球面波调制信号预失真方法算法复杂度高、预失真器工作效率较低的问题,结合椭圆球面波调制信号的幅值特点和功率放大器对信号非线性失真特性,引入分段处理的思想,提出了一种改进并行两箱预失真方法.通过设置阈值门限,对调制信号进行分段处理,仅对信号中大幅值分量进行处理,算法复杂度低,更易于工程实现.理论分析和仿真结果表明,当阈值门限为0.5时,与并行两箱预失真方法相比,所提预失真方法算法复杂度降低为原算法10%以下;当误比特率为10-5时,相对于未经放大器失真的调制信号,所需信噪比仅增加约0.02 dB.     

一种低复杂度的椭圆球面波调制信号预失真方法

针对现有椭圆球面波调制信号预失真方法算法复杂度高、工作效率低的问题,引入信号并行处理思想,提出了一种低复杂度的预失真方法,并完成了系统模型构建。该方法采用2个并行预失真模块,分别对调制信号的解析信号表达式中实部和虚部进行预失真处理,能够有效降低算法复杂度。仿真结果表明,与现有预失真方法相比,该方法的时间复杂度更低,能够有效提高算法收敛速度和预失真器工作效率;能够有效改善调制信号的功率谱,补偿带外失真约23 d B;能够有效补偿调制信号带内失真,当误码率为10-5时,与原调制信号相比,性能差距缩小至0.9 d B。     

基于IPLS的记忆功放数字预失真技术

针对现有方法普遍存在的预失真算法效率低、难以有效抑制记忆功放的互调失真等缺点,在传统预失真技术的基础上,提出了一种基于内点最小二乘(IPLS,Interior Point Least Squares)法的数字预失真技术。该方法利用内点最小二乘思想来解决预失真问题,避免了传统RLS算法中对其自相关矩阵的求逆运算,提高了数值的稳定性,降低了运算的复杂度,有效提高了运算的收敛速度和收敛精度。计算机仿真分析表明,该算法对互调失真的抑制有着非常好的效果。     

用于无线快衰落信道的快速自适应估计新算法

提出了一种用于无线快衰落信道的快速自适应估计新算法--拆分基展开递推最小二乘法.该算法通过利用多径信道之间相互独立的特性,减少了传统基展开递推最小二乘法在相关运算过程中的冗余.实验结果表明,提出的新算法与传统的基展开递推最小二乘法比较,在得到同样信道估计精确度的条件下,算法的复杂度降低了约80%.     

宽带OFDM功放自适应数字预失真算法的研究与实现

针对宽带OFDM功放的线性化问题,本文提出了一种基于训练序列的递推最小二乘算法和最小均方算法的组合算法.并将其应用到基于多项式的数字预失真系统中以实现自适应数字预失真滤波器系数的估计和更新.本文首先介绍了整个数字预失真系统的组成架构;然后是自适应数字预失真算法的实现,使用MATLAB软件对其算法进行仿真验证;最后还组建了实验系统,进行了ACLR测试实验.仿真结果和测试结果均表明基于自适应数字顶失真算法的宽带线性化功放具有良好的性能,OFDM功放输出的线性度改善6dB.     

基于改进的稀疏最小二乘双子支撑向量回归的数字预失真技术

为了补偿大容量卫星通信射频前端的功率放大器的非线性,传统的数字预失真(DPD)模型需要更多的系数和更高的阶次,严重影响预失真前馈路径的资源消耗。为了解决这一问题,该文提出一种基于改进的稀疏最小二乘双子支撑向量回归(ISLSTSVR)的低复杂度DPD方法。首先通过构建原空间的决策函数解决最小二乘双子支撑向量回归(LSTSVR)模型解不稀疏的问题;同时引用截断最小二乘损失函数增加模型的鲁棒性;然后采用Nystrom逼近方法得到核矩阵的低秩近似,进一步采用Cholesky分解降低核矩阵的运算复杂度;最后由低秩的核矩阵求得模型稀疏解。实验选用基于单管氮化镓(GaN)器件的宽带AB类功率放大器,以40 MHz的32QAM信号进行激励。预失真实验表明,该方法能在保证模型精度的情况下大幅减少DPD模型系数和计算复杂度,为星载射频前端的预失真技术提供了有效的系数降维思路和方法。     

一种高效的用于RF功率放大器线性化的自适应预失真结构

分析了当前文献中主要的几种自适应预失真结构,发现这些自适应预失真结构均不利于高效最小二乘算法的直接应用,从而限制了预失真技术的自适应性能.提出了一种新的自适应预失真结构,可直接使用高效的最小二乘算法对预失真器进行自适应更新.仿真结果表明利用此结构可快速、高效地实现非线性RF功率放大器的线性化.     

OFDM系统自适应数字预失真研究

研究了正交频分复用（OFDM）传输系统中高功率放大器（HPA）的自适应预失真方法。针对OFDM信号的高峰平比特性及HPA带来的非线性失真,提出一种基于训练序列的最小均方误差（LMS）算法和递归最小二乘（RLS）算法的组合算法,将其应用到基于记忆多项式模型的数字预失真系统中。用MATLAB构建一个基于该自适应算法的预失真系统。仿真结果表明：该算法能有效的改善放大器的非线性特性。     

一种新的用于Hammerstein预失真器的自适应结构

针对目前的自适应预失真结构不利于高效的最小二乘算法直接对Hammerstein预失真器参数进行更新的问题,该文提出了一种新的自适应预失真结构。应用该结构可以得到Hammerstein预失真器中两个子系统的误差,因此可使用高效的最小二乘算法直接对Hammerstein预失真器进行自适应更新,避免了结构误差以及子系统误差不精确对预失真器性能的影响。仿真结果表明：该文提出的自适应结构可使Hammerstein预失真器快速高效地补偿带记忆效应功率放大器的非线性失真。     

功放预失真中自适应算法研究

数字基带预失真技术是补偿功放非线性的最有效方法之一,而自适应算法的选择很大程度上影响着系统的预失真性能。其中,最小均方误差(LMS)算法和递归最小二乘法(RLS)是最常用的自适应收敛算法,LMS算法计算复杂度较低,但LMS算法收敛速度慢,易陷入局部最优且精度不高,RLS收敛性能较好,但计算复杂度高。在综合分析2种算法优缺点的基础上,提出了一种LMS和RLS算法的组合算法,仿真结果表明,组合算法的预失真性能很好,而且算法复杂度较低。     

Wireless Communications Transmitter Performance Enhancement Using Advanced Signal Processing Algorithms Running in a Hybrid DSP/FPGA Platform

This paper deals with digital base band signal processing algorithms, which are seen as enabling technologies for software-enabled radios, that are intended for the correction of the analog front end. In particular, this paper focuses on the design, optimization and testability of predistortion functions suitable for the linearization of narrowband and wideband transmitters developed with a hybrid DSP/FPGA platform. To select the best algorithm for the identification of the predistortion function, singular value decomposition, recursive least squares (RLS), and QR-RLS algorithms are implemented on the same digital signal processor; and, the computation complexity, time, accuracy and the required resources are studied. The hardware implementation of the predistortion function is then carefully performed, in order to meet the real time execution requirements.     

一种抗飞行器衰落的快速均衡算法

对流层散射通信信道为时变多径信道,当飞行器飞越散射通信链路会导致飞行器衰落。针对飞行器衰落,提出了一种收敛速度快、跟踪能力强、数值稳定性高、复杂度低的快速自适应均衡算法——基于选择更新的累积误差递归最小二乘自适应均衡算法。根据指数加权最小二乘准则,推导出累积误差递归最小二乘算法,依据共轭斜量算法提出抽头系数选择更新准则。均衡算法的复杂度分析和仿真实验表明提出的快速自适应均衡算法不仅复杂度低,而且有效地提高了均衡器克服信道时间衰落的能力。     

Adaptive predistortion of lasers using multivariable feedback algorithm

An adaptive predistortion technique for direct modulated lasers is proposed and experimentally demonstrated. The predistortion calibration is through multivariable feedback control and does not require digital signal processing or storing data in memories. Therefore, this technique suggests a potential analogue implementation with less circuit complexity. A design example is implemented using standard CMOS technology. Experimental results confirm the validity and the robustness of the feedback algorithm. Measured results show about 5-15-dB reduction of IM3 and HD2 distortion over more than 300-MHz bandwidth.     

Automatic ocular artifact removal from EEG data using a hybrid CAE-RLS approach

Traditional methods for removing ocular artifacts (OAs) from electroencephalography (EEG) signals often involve a large number of EEG electrodes or require electrooculogram (EOG) as the reference, these constraints make subjects uncomfortable during the acquisition process and increase the complexity of brain-computer interfaces (BCI). To address these limitations, a method combining a convolutional autoencoder (CAE) and a recursive least squares (RLS) adaptive filter is proposed. The proposed method consists of offline and online stages. In the offline stage, the peak and local mean of the four-channel EOG signals are automatically extracted to obtain the CAE model. Once the model is trained, the EOG channels are no longer needed. In the online stage, by using the CAE model to identify the OAs from a single-channel raw EEG signal, the identified OAs and the given raw EEG signal are used as the reference and input for an RLS adaptive filter. Experiments show that the root mean square error (RMSE) of the CAE-RLS algorithm and independent component analysis (ICA) are 1.253 3 and 1.254 6 respectively, and the power spectral density (PSD) curve for the CAE-RLS is similar to the original EEG signal. These experimental results indicate that by using only a couple of EEG channels, the proposed method can effectively remove OAs without parallel EOG records and accurately reconstruct the EEG signal. In addition, the processing time of the CAE-RLS is shorter than that of ICA, so the CAE-RLS algorithm is very suitable for BCI system.     

A set of algorithms linking NLMS and block RLS algorithms

This paper describes a set of block processing algorithms which contains as extremal cases the normalized least mean squares (NLMS) and the block recursive least squares (BRLS) algorithms. All these algorithms use small block lengths, thus allowing easy implementation and small input-output delay. It is shown that these algorithms require a lower number of arithmetic operations than the classical least mean squares (LMS) algorithm, while converging much faster. A precise evaluation of the arithmetic complexity is provided, and the adaptive behavior of the algorithm is analyzed. Simulations illustrate that the tracking characteristics of the new algorithm are also improved compared to those of the NLMS algorithm. The conclusions of the theoretical analysis are checked by simulations, illustrating that, even in the case where noise is added to the reference signal, the proposed algorithm allows altogether a faster convergence and a lower residual error than the NLMS algorithm. Finally, a sample-by-sample version of this algorithm is outlined, which is the link between the NLMS and recursive least squares (RLS) algorithms     

Adaptive set-membership identification in O(m)time for linear-in-parameters models

Some fundamental contributions to the theory and applicability of optimal bounding ellipsoid (OBE) algorithms for signal processing are described. All reported OBE algorithms are placed in a general framework that demonstrates the relationship between the set-membership principles and least square error identification. Within this framework, flexible measures for adding explicit adaptation capability are formulated and demonstrated through simulation. Computational complexity analysis of OBE algorithms reveals that they are of O(m²) complexity per data sample with m the number of parameters identified. Two very different approaches are described for rendering a specific OBE algorithm, the set-membership weighted recursive least squares algorithm, of O(m) complexity. The first approach involves an algorithmic solution in which a suboptimal test for innovation is employed. The performance is demonstrated through simulation. The second method is an architectural approach in which complexity is reduced through parallel competition     

Finite word-length effects in recursive least squares algorithms with application to adaptive equalization

In this paper we provide a summary of recent and new results on finite word length effects in recursive least squares adaptive algorithms. We define the numerical accuracy and numerical stability of adaptive recursive least squares algorithms and show that these two properties are related to each other, but are not equivalent. The numerical stability of adaptive recursive least squares algorithms is analyzed theoretically and the numerical accuracy with finite word length is investigated by computer simulation. It is shown that the conventional recursive least squares algorithm gives poor numerical accuracy when a short word length is used. A new form of a recursive least squares lattice algorithm is presented which is more robust to round-off errors compared to the conventional form. Optimum scaling of recursive least squares algorithms for fixedpoint implementation is also considered.     

Adaptive MIMO decision feedback equalization for receivers with time-varying channels

In an attempt to reduce the computational complexity of vertical Bell Labs layered space time (V-BLAST) processing with time-varying channels, an efficient adaptive receiver is developed based on the generalized decision feedback equalizer (GDFE) architecture. The proposed receiver updates the filter weight vectors and detection order using a recursive least squares (RLS)-based time- and order-update algorithm. The convergence of the algorithm is examined by analysis and simulation, and it is shown that the proposed adaptive technique is considerably simpler to implement than a V-BLAST processor with channel tracking, yet the performances are almost comparable.