遗传算法在多用户检测中的应用研究

本文针对CDMA系统中多用户检测的组合优化问题,提出一种结合遗传算法和Hopfield神经网络的检测方法。该方法首先由遗传算法给神经网络提供一个初始解,神经网络在此基础上再进行局部寻优。研究表明：这种方法具有平方的计算复杂度,优于Hopfield神经网络检测方法、以及单独采用遗传算法的检测方法,对远近问题不敏感,具有良好的误码率性能和抗多址干扰性能。     

基于SMO算法的多用户检测技术

由于支持向量机的出色的学习性能，它已成为继神经网络之后新的研究热点，并在很多领域得到了成功的应用。本文提出了一种基于支持向量机多用户检测器，并采用顺序最小优化（SMO）算法构建了多用户检测器。计算机仿真的结果表明，该检测器的抗误码性能和抗远近效应性能都优于传统的多用户检测器。     

基于神经网络的智能用户检测技术在CDMA中的应用

多用户检测技术是第3代移动通信系统IMT－2000中的一项关键技术。求解最优多用户检测器的目标函的最小值问题可以转化为求解Hopfield神经网络的能量函数的最小值问题。对两种基于神经网络的智能多户检测器进行性能分析和计算机仿真,通过与最佳多用户检测器和传统检测器的比较,证实了这两种神经网络检测器都能较好地实现多用户检测的功能,因此,神经网络多用户检测技术是一种切实可行的方案。     

一种神经网络多用户检测器

本文提出采用Ｈｏｐｆｉｅｌｄ神经网络实现ＣＤＭＡ多用户通信系统中多用户信号的检测。利用基于检测序列最大后验概率最佳多用户检测器的似然函数与Ｈｏｐｆｉｅｌｄ神经网络的能量函数的对应关系,构造一种离散Ｈｏｐｆｉｅｌｄ神经网络多用户检测器。研究表明,这种多用户检测器具有优良的性能,其计算复杂度低于最佳多用户检测器、抑制多址干扰和克服远近效应能力又大大优于传统检测器。     

DS-CDMA系统的一种进化规划多用户检测算法

针对码分多址接入（CDMA）系统中最优多用户检测的指数计算复杂度问题,结合CDMA通信的实际特点,利用进化规划免去了交叉操作因而计算复杂度小的特点,提出了一种基于进化规划的多用户检测问题的优化处理方法。实验结果表明本方法可获得接近最佳检测的性能,但计算复杂度降低。     

一种基于蚁群算法的CDMA多用户检测方法

提出一种基于蚁群优化算法(ant colony optimization algorithm)的CDMA多用户检测(MUD)的方法。该方法在基本蚁群算法的基础上，应用一种新的相遇和搜索分区的策略，来解决最佳多用户检测的组合优化问题，可提高搜索的质量和效率，通过分析以及仿真表明，该方法具有多项式的计算复杂度，并可以得到较好的误码率性能，为寻求新的多用户检测方法提供了思路。     

一种基于禁忌搜索的多用户检测方法

本文提出一种实现码分多址(ＣＤＭＡ)系统上多用户检测(ＭＵＤ)的禁忌搜索(ｔａｂｕｓｅａｒｃｈ)的方法。 该方法利用传统检测方法的输出作为初始解,直接应用禁忌搜索算法来解决最佳多用户检测的非线性优化组合问题。通过分析以及对同步和异步情况的仿真表明,该方法简单易于实现,具有多项式的计算复杂度,对远近问题不敏感,并且能够得到与最佳检测方法(ＯＤ)非常接近的误码率性能和抗多址干扰性能．     

基于并行遗传算法的CDMA优化多用户检测方法

多用户检测技术是第三代移动通信系统CDMA中的一项关键技术。在多用户检测中求解最佳矢量问题可以转化为在遗传算法中求解具有最高适应度函数的问题。本文提出了一种基于并行遗传算法的CDMA多用户检测器,并与最佳多用户检测和传统检测器进行比较,实验结果表明本方法可获得接近最佳检测的性能。由于采用并行遗传算法,这种多用户检测器更易于实时应用和硬件实现。     

基于Hopfield神经网络的DS—CDMA多用户检测

从新的角度研究ＤＳ－ＣＤＭＡ（直接序列码分多址）系统中的多用户检测，将多用户检测的优化问题映射为Ｈｏｐｆｉｅｌｄ神经网络（ＨＮＮ）“能量”函数的最小化问题，利用连续ＨＮＮ固有的快速下降特性，实现了坟对ＣＤＭＡ（码分多址）系统的多用户检测。与现有各种方案比较，具有运算量小、抗远近效应强、实时性好等优点。     

直接序列码分多址通信系统中最佳多用户检测器的一种实现方法

本文叙述了通过构造一种复合神经网络实现直接序列码分多址通信系统中实时最佳多用户检测器的方法。这种最佳多用户检测的实时性来自复合神经网络运算结构的并行性，复合神经网络由一个前向单层网络和一个竞争网络构成，其中前向单层网络进行似然函数计算，而竞争网络实现选择最大项操作。     

Performance of multiuser detectors based on EM-like method for ultra-wideband communications systems in multipath fading channel

An adaptive multiuser detector (MUD) is proposed for direct-sequence ultra-wideband (DS-UWB) multiple access communication systems to suppress both multiple access interference (MAI) and inter-symbol interference (ISI). In this contribution, considering the MUD from a combination viewpoint, we proposed a MUD based on electromagnetism-like (EM) method, which applied the concept of EM search to Hopfield neural network (EMHNN) for solving optimization problems. We analyze the performance of the EMHNN MUD in multipath fading channel, and compare it with the optimum detector and several suboptimum schemes such as conventional, decorrelator detector (DD), minimum-mean-squared error (MMSE) and HNN MUD. Simulation results will demonstrate that the proposed EMHNN MUD, which alleviates the detrimental effects of the MAI problem, can significantly improve the system performance.     

Neural Network Implementation of the Optimum CDMA Multi-user Detector

lintroductionCDMA,owingtoitshighcapacitypotentialandotherdesirablefeatures,suchasanti-mutipath,fadingandantljammingcapabilities,isaimatprovidingamuchhighercapacitythanthepreviousgenerationsofmobilecommunicationsystems.ThistechniquepermitSalltheuserin...     

A Neutral-Type Delayed Projection Neural Network for Solving Nonlinear Variational Inequalities

A neutral-type delayed projection neural network is proposed to deal with nonlinear variational inequalities. Compared with the existing delayed neural networks for linear variational inequalities, the proposed approach apparently has the larger application domain. By the theory of functional differential equation, a delay-dependent sufficient stability condition is derived. This stability condition is easily checked, and can guarantee that the proposed neural network is convergent to the solution of nonlinear variational inequality problem exponentially, which improves the existing stability criteria for the neutral-type delayed neural network. Moreover, many related problems, such as the projection equation and optimization problems, can also be dealt with by the proposed method. Finally, simulation examples are given to illustrate the satisfactory performance of the proposed method.      

BER analysis of the optimum multiuser detection with channel mismatch in MC-CDMA systems

In this paper, we analyze the bit-error-rate (BER) performance of the optimum multiuser detection (MUD) with channel mismatch in multicarrier code-division-multiple-access (MC-CDMA) systems. The BER performance of the optimum MUD without channel mismatch in MC-CDMA systems has been recently derived using the replica method. However, it is left unjustified, since the replica method is not a rigorous approach. In addition, it is NP-hard to implement an optimum MUD algorithm. To justify the BER performance and to make the optimum MUD feasible, based on Pearl's belief propagation (BP) scheme, we put together a low-complexity iterative MUD algorithm for MC-CDMA systems. Furthermore, channel mismatch is introduced into the BP-based MUD algorithm to make the scenario general. With channel mismatch, the analytical results of the BP-based MUD algorithm conform perfectly to, and the simulation results of the BP-based MUD algorithm conform very closely to the BER performance of the optimum MUD derived using the replica method, which is a nontrivial extension of the existing replica approach mentioned above. Without channel mismatch, the problem becomes a special case of our contribution.     

Complex-valued neural networks with adaptive spline activationfunction for digital-radio-links nonlinear equalization

In this paper, a new complex-valued neural network based on adaptive activation functions is proposed. By varying the control points of a pair of Catmull-Rom cubic splines, which are used as an adaptable activation function, this new kind of neural network can be implemented as a very simple structure that is able to improve the generalization capabilities using few training samples. Due to its low architectural complexity (low overhead with respect to a simple FIR filter), this network can be used to cope with several nonlinear DSP problems at a high symbol rate. In particular, this work addresses the problem of nonlinear channel equalization. In fact, although several authors have already recognized the usefulness of a neural network as a channel equalizer, one problem has not yet been addressed: the high complexity and the very long data sequence needed to train the network. Several experimental results using a realistic channel model are reported that prove the effectiveness of the proposed network on equalizing a digital satellite radio link in the presence of noise, nonlinearities, and intersymbol interference (ISI)     

A Neural Network Approach for Signal Detection in Digital Communications

In this paper, we propose a new approach for signal detection in wireless digital communications based on the neural network with transient chaos and time-varying gain (NNTCTG), and give a concrete model of the signal detector after appropriate transformations and mappings. It is well known that the problem of the maximum likelihood signal detection can be described as a complex optimization problem that has so many local optima that conventional Hopfield-type neural networks fail to solve. By refraining from the serious local optima problem of Hopfield-type neural networks, the NNTCTG makes use of the time-varying parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks only with point attractors, so that it can be expected to have much ability to search for globally optimal or near-optimal solutions. After going through a transiently inverse-bifurcation process, the NNTCTG can approach the global optimum or the neighborhood of global optimum of our problem. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for the signal detection in digital communications.     

Neural network-based multiuser detection for SDMA–OFDM system over IEEE 802.11n indoor wireless local area network channel models

Space division multiple access – orthogonal frequency division multiplexing-based wireless communication has the potential to offer high-spectral efficiency, system performance and capacity. This article proposes an efficient blind multiuser detection (MUD) scheme using artificial neural network models such as the radial basis function. The proposed MUD technique is consistently outperforming the existing minimum mean square error and minimum bit error rate (MBER) MUDs with the performance close to the optimal maximum likelihood (ML) detector. Besides that, the computational complexity of the proposed one is comparatively lower than both the MBER and ML detectors. Further, it can also outperform MBER MUD in the overload scenario, where the number of users is more than that of the number of receiving antennas simulation-based study showing BER performance and complexity are carried out to prove the efficiency of the proposed techniques. This analysis is carried through the IEEE 802.11n standard channel models, which are designed for indoor wireless local area network applications of bandwidth up to 100?MHz at frequencies 2 and 5?GHz.     

Multiuser Detection in SDMA–OFDM Wireless Communication System Using Complex Multilayer Perceptron Neural Network

The space division multiple access–orthogonal frequency division multiplexing (SDMA–OFDM) wireless system has become very popular owing high spectral efficiency and high load capability. The optimal maximum likelihood multiuser detection (MUD) technique suffers from high computational complexity. On the other hand the linear minimum mean square error (MMSE) MUD techniques yields poor performance and also fails to detect users in overload scenario, where the number of users are more than that of number of receiving antennas. By contrast, the differential evolution algorithm (DEA) aided minimum symbol error rate (MSER) MUD can sustain in overload scenario as it can directly minimizes probability of error rather than mean square error. However, all these classical techniques are still complex as these do channel estimation and multiuser detection sequentially. In this paper, complex multi layer perceptron (CMLP) neural network model is suggested for MUD in SDMA–OFDM system as it do both channel approximation and MUD simultaneously. Simulation results prove that the CMLP aided MUD performs better than the MMSE and MSER techniques in terms of enhanced bit error rate performance with low computational complexity.     

Performance analysis of multi‐stage interference cancellation for ultra‐wide band multiple‐access communication systems

In this contribution, a novel particle swarm optimization (PSO)‐based multi‐user detector (MUD) aided time‐hopping ultra‐wide band (TH‐UWB) system has been investigated in the multi‐path channel model. In this approach, the PSO‐based MUD employs the output of the Rake receiver as its initial value to search for the best solution which results in a formulated optimization mechanism. By taking advantage of the heuristic values and the collective intelligence of PSO technique, the proposed detector offers almost the same bit error rate (BER) performance as the full‐search‐based optimum MUD does, while greatly reducing the potentially computational complexity. Simulation results have been provided to examine the evolutionary behavior and the detection performance of the proposed PSO‐based MUD in both the additive white Gaussian noise and the multi‐path fading channel. Copyright © 2011 John Wiley & Sons, Ltd.