Neural Networks for Multiuser Detection of Signals in DS/CDMA Systems

Multiple access interference and near-far effect cause the performance of the conventional single user detector in DS/CDMA systems to degrade. Due to the high complexity of the optimum multiuser detector, suboptimal multiuser detectors with less complexity and reasonable performance have received considerable attention. In this paper, we analyse the performance of the multilayer perceptron backpropagation neural network as a multiuser detector of CDMA signals in AWGN and multipath fading channels. Our results show significant improvement over previous research. We compare neural network performance with the other detectors, and apply different neural networks and criteria, such as decision-based, fuzzy decision, discriminative learning, minimum classification, and cross entropy neural networks, and compare their performance. We propose a modified decision-based network which significantly improves the performance.     

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。     

基于改进卡尔曼滤波算法的盲多用户检测器

基于盲检测器的新典范表示形式,提出了一种多径信道下CDMA系统的盲多用户检测器。该检测器使用了一种改进的自适应卡尔曼滤波算法,它在进行状态滤波的同时,在线估计时变的未知噪声统计特性,提高了算法的滤波精度,确保了算法收敛于期望用户。仿真表明,对比原有的随机梯度（LMS）算法,该算法的收敛、跟踪性能更好,且误码率减小了18.7%。     

Support vector machine multiuser receiver for DS-CDMA signals inmultipath channels

The problem of constructing an adaptive multiuser detector (MUD) is considered for direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. The emerging learning technique, called support vector machines (SVM), is proposed as a method of obtaining a nonlinear MUD from a relatively small training data block. Computer simulation is used to study this SVM MUD, and the results show that it can closely match the performance of the optimal Bayesian one-shot detector. Comparisons with an adaptive radial basis function (RBF) MUD trained by an unsupervised clustering algorithm are discussed.     

基于粒子群算法的盲多用户检测器

在信道参数未知的多径环境下,盲多用户检测算法性能存在诸如收敛速度慢和估计精度低等问题。将粒子群算法运用到基于恒模算法的盲多用户检测中。仿真结果显示,粒子群算法能够更精确地估计出信道的参数,且其收敛速度非常快,在估计出信道参数之后再进行盲多用户检测,检测性能优良。     

Low-complexity approximate iterative LMMSE detection for large-scale MIMO systems

This paper deals with iterative detection for uplink large-scale MIMO systems. The well-known iterative linear minimum mean squared error (LMMSE) detector requires quadratic complexity (per symbol per iteration) with the number of antennas, which may be a concern in large-scale MIMO. In this work, we develop approximate iterative LMMSE detectors based on transformed system models where the transformation matrices are obtained through channel matrix decompositions. It is shown that, with quasi-linear complexity (per symbol per iteration), the proposed detectors can achieve almost the same performance as the conventional LMMSE detector. It is worth mentioning that the linear transformations are also useful to reduce the complexity of downlink precoding, so the relevant computational complexity can be shared by both uplink and downlink.     

基于免疫粒子群算法的多用户检测技术研究

将免疫系统的免疫机制引入到粒子群优化算法的设计中,模拟免疫系统、群集智能和神经网络的信息处理机制,提出了免疫粒子群优化算法。这种免疫粒子群算法结合了粒子群的近似全局优化能力和由Hopfield神经网络构成的免疫系统的快速信息处理机制,加快了算法的收敛速度,并提高了粒子群算法的全局收敛能力。然后利用此算法对CDMA系统的多用户检测性能改进问题进行实验研究,证明了本文的方法有较快的收敛速度,并且无论是抗多址干扰能力还是抗远近效应能力都优于传统方法和一些应用优化算法的多用户检测器。     

Ant colony input parameters optimization for multiuser detection in DS/CDMA systems

In this work a simple and efficient methodology for tuning the input parameters applied to the ant colony optimization multiuser detection (ACO-MuD) in direct sequence code division multiple access (DS-CDMA) is proposed. The motivation in using a heuristic approach is due to the nature of the NP complexity posed by the wireless multiuser detection optimization problem. The challenge is to obtain suitable data detection performance in solving the associated hard complexity problem in a polynomial time. Previous results indicated that the application of heuristic search algorithms in several wireless optimization problems have been achieved excellent performance-complexity tradeoffs. Regarding different system operation and channels scenarios, a complete input parameters optimization procedure for the ACO-MuD is provided herein, which represents the major contribution of this work. The performance of the ACO-MuD is analyzed via Monte-Carlo simulations. Simulation results show that, after convergence, the performance reached by the ACO-MuD is much better than the conventional detector, and somewhat close to the single user bound (SuB). Flat Rayleigh channels is initially considered, but the input parameter optimization methodology is straightforward applied to selective fading channels scenarios, as well as to joint time-spatial wireless channels diversities.     

A novel low complexity data demodulation algorithm for pulse position modulation

In this paper, a low complexity data demodulation algorithm is proposed that requires time of arrival information of the received signal exclusively. As an application example, the algorithm is applied to an ultra-wideband impulse radio communication system with pulse position modulation. The algorithm is insensitive to a common time delay for all pulses, that means, it does not require an accurate synchronization between the transmitter and the receiver. For the performance estimation, only a symbol synchronization is assumed, i.e., that there is a priori knowledge which pulse marks the beginning of a received data symbol. The performance of the proposed algorithm is evaluated for straightforward time of arrivals estimators, such as a maximum detector or a threshold detector. It is shown that the proposed algorithm outperforms a least squares algorithm in all considered scenarios. In particular, an increased robustness against additive white Gaussian noise, impulse like noise, and multiuser interference is demonstrated as well as an improved performance for multipath propagation channels.     

Hysteretic HNN MAI-Cancellation for CDMA Signals in Multipath Fading Channels

Multiple-access interference cancellation using hysteretic Hopfield neural network receiver for direct sequence code-division multiple access (DS-CDMA) in multipath fading channels is investigated. It has been shown that by applying the phenomenon of “hysteresis” to the Hopfield neural network (HNN) detector, performance of this detector may be enhanced in all near-far situations for different number of multipath rays. Introducing the concept of Hysteresis into HNN has made this suboptimum CDMA detector even closer to the optimum multiuser CDMA detector. As shown by simulation results, the bit-error rate performance achieved by the Hysteretic Hopfield Neural Network detector outperforms the classical HNN detector with a good margin and is promising.     

基于卡尔曼滤波的线性共轭半盲多用户检测

考虑实际的多载波CDMA（MC-CDMA）上行链路系统,深入研究了基于卡尔曼滤波的盲自适应多用户检测和线性共轭多用户检测,提出MC-CDMA系统下一种基于自适应卡尔曼滤波的线性共轭半盲多用户检测算法。该算法利用小区内所有用户的扩频码和伪自相关矩阵的信息设计了一种基于平均输出能量（MOE）准则的线性共轭半盲检测器,结合修正的PASTd算法和卡尔曼滤波算法自适应得到MOE权向量。仿真实验验证了所提算法的有效性,与卡尔曼滤波盲自适应算法相比,提出的算法有更好的输出信干比和误码率性能,并保持较低的复杂度。     

新的基于仿射投影算法的盲多用户检测器*

多径衰落信道下的盲自适应多用户检测算法的运算复杂度通常都比较大,将基于仿射投影算法的盲多用户检测器与RAKE分集技术相结合,提出了一种盲自适应接收算法。该接收算法平衡了收敛速度和计算复杂度之间的矛盾,具有相对较好的收敛性能及较小的运算复杂度。通过模拟实验比较了几种算法的收敛、跟踪及误码性能,结果表明该方法具有明显的整体优势。     

基于子空间跟踪的修正MMSE多用户检测算法

在研究基于子空间跟踪的最小均方误差（MMSE）多用户检测器（MUD）的基础上，为解决原算法因引入特征值估计误差而导致检测性能下降的问题，设计了一种改进的算法——修正的MMSE多用户检测器．采用正交性能优良的OPAST算法跟踪子空间，提出一种基于OPAST的修正MMSE多用户检测算法．仿真结果显示,与基于OPAST的MMSE MUD相比，基于OPAST的修正MMSE MUD算法收敛速度快，输出信干比和误码率性能逼近SVD MUD算法，并且计算复杂度较低．     

An efficient initial ranging algorithm for WiMAX (802.16e) OFDMA

Ranging is one of the most important processes in the mobile WiMAX standard. Power adjustment, timing offset estimation, and synchronization between a base station (BS) and all users within a cell are done through the ranging process referred to as initial ranging. In this paper, we discuss the details of initial ranging together with some of the proposed algorithms, as well as a novel algorithm to carry out a successful ranging process. Performance curves and computational complexity comparisons are presented. The system performance is evaluated for both additive white Gaussian noise (AWGN) channels and practical multipath fading channels with multiuser interference. It is shown that the proposed algorithm offers a reduced complexity ranging method that can be employed in practical WiMAX-based BSs.     

Multiuser detection based on modified PSO algorithm for synchronous CDMA systems

The computational complexity of the optimum maximum-likelihood detector does not allow its utility for multiuser detection (MUD) in code-division multiple-access (CDMA) systems. In this paper, a novel particle swarm optimization (PSO) algorithm is suggested to carry out MUD for synchronous CDMA systems. This work considers two new aspects, namely an adaptive velocity updating mechanism based on Newton method and a dynamic inertia weight into the standard PSO. These mechanisms can provide more diversity to help avoiding premature convergence and significantly improve the bit error rate performance. Several computer simulation results demonstrate that the proposed modified PSO detector significantly outperforms the decorrelating detector, the linear minimum mean square error detector, and the standard PSO-based detector.     

Detection of nonlinear FM signals via forward–backward cost-reference particle filter

Many applications require the detection of unknown nonlinear frequency modulated (FM) signals in noise. In this paper, a nonlinear FM signal in one time interval is approximated by linear FM (LFM) segments in successive subintervals. Each LFM segment is parameterized by a 2-dimensional (2D) state vector and its evolution from a subinterval to the next one is modeled as a dynamic system of unknown statistics with linear state transition equations and nonlinear measurement equations. A forward–backward cost-reference particle filter (FB-CRPF) is proposed to estimate the state sequence. From the estimated state sequence, the generalized likelihood ratio test (GLRT) statistic and the total variation (TV) statistic are computed for signal detection. In the 2D feature plane of the GLRT versus TV, the decision region of the null hypothesis at a given false alarm rate is determined by the 2D convexhull learning algorithm from noise-only training samples. Two kinds of simulated signals are used to test the proposed detector and results show that the proposed detector attains better performance than the two existing detectors.     

基于改进子空间的盲MMSE多用户检测算法

算法收敛性与计算复杂度之间的矛盾是目前基于子空间的多用户检测技术难以投入实际应用的主要问题。将内部模型法则(IMP)与PAST算法相结合,提出了一种改进的子空间跟踪算法——newPAST-IMP算法,并应用于盲MMSE检测器中。该方法收敛速度快,跟踪性能好且计算复杂度较低。仿真表明,该检测器具有优异的抗多址、多径干扰以及抗远近效应的能力。     

凸约束最小误码率线性多用户检测

论文研究了DS-CDMA系统线性多用户检测器的误码率代价函数,证明了最小误码率线性多用户检测器等价于一个约束最优化问题。然后提出了凸约束最小误码率线性多用户检测器,并给出了一个相应的牛顿型迭代算法。仿真结果表明,凸约束最小误码率线性多用户检测器具有良好的性能,而且该算法收敛速度快,稳定性好,易于工程实现。     

基于信号子空间的MMSE盲多用户检测*

修正的最小均方误差(MMSE)多用户检测器解决了传统MMSE因引入特征值估计误差而导致检测性能下降的问题。基于修正MMSE多用户检测器,采用正交性能良好的改进PASTd算法跟踪子空间,提出一种基于改进PASTd的修正MMSE多用户检测算法,改进后的MMSE盲多用户检测误码率低,收敛速度快。仿真结果验证了该算法的有效性和可行性。     

基于神经网络量子算法的多用户检测器

利用遗传量子算法和Hopfield神经网络,提出了一种融合两种算法优点的神经网络量子算法,并将其应用到CDMA通信系统的多用户检测问题中。所提算法把神经网络嵌入到遗传量子算法的每一代中,可进一步提高量子种群的适应度函数值。通过混合神经网络到GQA中,还可加快GQA的收敛速度进而减少算法的计算复杂度。另外,GQA所提供的良好初值改善了HNN的性能,嵌入的HNN也提高了GQA的性能。仿真结果证明了该方法的抗多址干扰能力和抗远近效应能力都优于传统检测器和一些应用智能算法的多用户检测器。