弱相关非高斯环境下基于局部最佳检测器的伪码捕获方法

该文为解决弱相关非高斯噪声环境下的伪码捕获问题,提出了一种基于局部最佳检测算法的伪码捕获方法,将伪码捕获等价为假设检验问题,将弱相关非高斯噪声建模为一阶滑动平均SS噪声模型,利用局部最佳检测算法推导出弱相关非高斯噪声环境下的伪码捕获检测统计量,在此基础上对检测统计量进行了简化,给出了其实现结构,并与传统的伪码捕获方法进行了性能仿真对比,仿真结果表明该文所提出的捕获方法在弱相关非高斯噪声环境下检测性能有较大幅度的提高,且非高斯噪声脉冲特性越明显,所设计的检测器优势越明显。     

Multiuser detection in impulsive channels

The present paper deals with the problem of data detection in direct-sequence code-division multiple-access (CDMA) systems with non-Gaussian ambient noise. This issue arises in pratical situations because many physical channels in which multiple-access communications is applied are known to be decidedly non-Gaussian, due largely to impulsive phenomena. (Such channels include urban and indoor radio channels, and underwater acoustic-modem channels). The optimum multiuser detector for the additive noise channel model is derived, and several suboptimal multiuser detectors are proposed. Moreover, for performance comparison purposes the robust multiuser detector, recently proposed in the literature, is also considered.     

Direct sequence code acquisition in multiplicative and non-Gaussian noises

A new detector for direct sequence spread spectrum code acquisition in multiplicative and non-Gaussian noises is proposed in this article. Modelling the acquisition problem as a hypothesis-testing problem, a detector is derived for multiplicative and non-Gaussian noises, based on the locally optimum detection technique. Numerical results show that the proposed detector can offer substantial performance improvement over the conventional schemes in multiplicative and non-Gaussian noises.     

乘性噪声环境下基于局部最佳检测器的伪码捕获方法

扩频系统常常工作在多径环境中,伴随着加性噪声的同时往往还存在着乘性噪声。该文提出了一种乘性噪声环境下伪码捕获方法,将伪码捕获等价为假设检验问题,利用局部最佳检测算法推导出乘性噪声环境下的伪码捕获检测统计量,文中给出了基于局部最佳检测算法的捕获结构,并与传统的平方和检测器进行了性能仿真对比,结果表明该文所提出的捕获方法在乘性噪声环境下检测性能较平方和检测器有较大幅度的提高,而在无乘性噪声的环境下检测性能只较传统的平方和检测器检测性能稍有降低。     

乘性噪声环境下基于符号秩统计量的非参伪码捕获方法

扩频系统常常工作在多径环境中,伴随着加性噪声的同时往往还存在着乘性噪声.该文提出了一种乘性噪声环境下基于符号秩统计量的非参伪码捕获方法,将伪码捕获等价为假设检验问题,利用局部最佳检测算法推导出了乘性噪声环境下基于符号秩的检测统计量,通过简化记分函数进一步给出了局部次佳秩检测器.将局部次佳秩检测器与局部最佳检测器和平方和检测器的性能进行了仿真对比,结果表明该文所提出的捕获方法在乘性噪声环境下检测性能接近于乘性噪声环境下的局部最佳检测器,而较平方和检测器则有较大幅度的提高.     

A blind detector for Rayleigh flat-fading channels with non-Gaussian interference via the particle learning algorithm

A blind particle learning detector (BPLD) is developed for signal detection in Rayleigh flat-fading channels with non-Gaussian interference. The parameters of the fading channel model and the noise model are all unknown. The impulsive noise is modeled as a mixture of Gaussian distributions, which is capable of representing a broad class of non-Gaussian noise. The particle learning algorithm is employed to simultaneously estimate signal and parameters of the fading channel model and the noise model. The delay weight method is used to improve the performance. Simulation results show that the performance of the BPLD proposed can follow closely the performance of the detector with known parameters of the fading channel model and the noise model.     

Multichannel detection for correlated non-Gaussian random processesbased on innovations

The paper addresses the problem of multichannel signal detection in additive correlated non-Gaussian noise using the innovations approach. Although this problem has been addressed extensively for the case of additive Gaussian noise, the corresponding problem for the non-Gaussian case has received limited attention. This is due to the fact that there is no unique specification for the joint probability density function (PDF) of N correlated non-Gaussian random variables. The authors overcome this problem by using the theory of spherically invariant random processes (SIRPs) and derive the innovations-based detector. It is found that the optimal estimators for obtaining the innovations, processes are linear and that the resulting detector is canonical for the class of PDFs arising from SIRPs. The authors also present a performance analysis of the innovations-based detector for the case of a K-distributed SIRP     

高斯混合噪声环境中基于分数低阶矩的频谱感知算法研究

传统的频谱感知算法因非高斯噪声的干扰,其检测性能严重退化。为抑制实际通信环境中非高斯噪声的干扰,本文提出了一种基于分数低阶矩的空闲频谱检测方法,该方法不需要主用户信号、通信信道和噪声的先验信息。本文采用高斯混合分布拟合非高斯噪声环境,根据中心极限定理及广义二项式定理推导出信道为无衰落和Nakagami衰落时基于分数低阶矩感知算法的检测概率和虚警概率。理论分析和仿真结果表明,在非高斯噪声环境中,基于分数低阶矩感知算法的检测性能明显优于传统的能量检测算法,且采用多天线技术有助于进一步提高感知性能和频率资源的利用率。      

Fuzzy decorrelating detector for non-Gaussian CDMA channel

A fuzzy decorrelating detector for CDMA non-Gaussian channels is proposed. The proposed scheme combines a linear decorrelator with a nonlinear preprocessor based on fuzzy logic and rank ordering that is incorporated to combat impulsive noise. Simulation results show that the proposed technique is very robust in combating impulsive noise.     

A new choice of penalty function for robust multiuser detection based on M-estimation

In this letter, we propose a new robust MUD, called /spl alpha/ detector, for non-Gaussian noise. We consider the Gaussian-mixture model for non-Gaussian or impulsive noise. Our technique outperforms the decorrelator and the minimax detectors in highly impulsive noise. The proposed method uses a parametric cost function, where the parameter /spl alpha/ is selected using the difference between the asymptotic variance of estimation error of the /spl alpha/ detector and that of the minimax detector.     

基于分数低阶统计量的盲多用户检测算法

多用户检测算法是抑制CDMA系统中多址干扰的重要手段,但广泛存在的非高斯信道噪声会降低以往的基于高斯噪声模型假设的算法性能.本文采用α稳定分布作为噪声模型,提出了基于分数低阶统计量的盲多用户检测算法,并对该算法进行了理论分析.仿真和分析表明,该算法具有很好的韧性,同时适用于高斯噪声和脉冲噪声环境.     

Improved Robust Techniques for Multiuser Detection in Non-Gaussian Channels

In many physical channels where multiuser detection techniques are to be applied, the ambient channel noise is known through experimental measurements to be decidedly non-Gaussian, due largely to impulsive phenomena. This is due to the impulsive nature of man-made electromagnetic interference and a great deal of natural noise. This paper presents a robust multiuser detector for combating multiple access interference and impulsive noise in code division multiple access (CDMA) communication systems. A new M-estimator is proposed for "robustifying" the detector. The approach is corroborated with simulation results to evaluate the performance of the proposed robust multiuser detector compared with that of the linear decorrelating detector, and the Huber and the Hampel M-estimator based detectors. Simulation results show that the proposed detector with significant performance gain outperforms the linear decorrelating detector, and the Huber and the Hampel M-estimator based detectors. This paper also presents an improved robust blind multiuser detection technique based on a subspace approach, which requires only the signature waveform and the timing of the desired user to demodulate that user's signal. Finally, we show that the robust multiuser detection technique and its blind adaptive version can be applied to both synchronous and asynchronous CDMA channels.     

Memoryless discrete-time signal detection in long-range dependent noise

The problem of designing optimum memoryless detectors for known signals in long-range dependent (LRD) noise is considered. In particular, under the performance criterion of asymptotic relative efficiency (ARE), optimum memoryless detection in LRD noise is investigated by exploiting the Hermite expansions. The detectors considered have the form of a nonlinearity followed by an accumulator and threshold comparator. It is shown that when the noise is LRD and Gaussian, all nonlinearities with Hermite rank one are asymptotically equivalent in terms of efficiency and are most powerful. Moreover, the optimum nonlinearities with Hermite rank greater than one are given by the corresponding Hermite polynomials. The case of non-Gaussian LRD noise that can be derived by nonlinear transformation of Gaussian noise is also considered. In this case, the globally optimum nonlinearity is very difficult to obtain in general. Instead, we proposed a suboptimal nonlinearity, which is given by a linear combination of the corresponding locally optimum detector and the inverse of the transform function generating the noise. Simulations show that the proposed detector outperforms the locally optimal detector for LRD non-Gaussian noise.     

Neural networks applications for CDMA systems in non-Gaussian multi-path channels

Non-Gaussian noise is one of the most common noise models observed in wireless channels. This type of noise has severe impact on wireless systems with multiuser detection devices. In this paper, the issue of multiuser detection in non-Gaussian noise multipath channel is addressed. We also pay a close attention to the neural network applications, and propose a new robust neural network detector for multipath impulsive channels. The maximal ratio combining (MRC) technique is adopted to combine the multipath signals. Moreover, we discuss the performance of the proposed multiuser neural network decorrelating detector (NNDD), under class A Middleton model. Furthermore, the performance of the system under power imbalance scenario is shown. We show that the proposed NNDD has magnificent effect on the system performance. The system performance is measured through the bit error rate (BER). It is shown that the proposed robust receiver reduces the impact of the impulsive noise by processing the received signal and clipping the extreme amplitudes.     

Detection of non-Gaussian signals using integrated polyspectrum

We consider the problem of detecting an unknown, random, stationary, non-Gaussian signal in Gaussian noise of unknown correlation structure. The same framework applies if one desires to determine whether the given random signal is non-Gaussian. The most commonly used method for detection of random signals is the so-called energy detector, which cannot distinguish between Gaussian and non-Gaussian signals and requires the knowledge of the noise power. Recently, the use of bispectrum and/or trispectrum of the signal has been suggested for detection of non-Gaussian signals. The higher order spectra-based detectors do not require the knowledge of the noise statistics if the noise is Gaussian. In this paper, we suggest the use of an integrated polyspectrum (bispectrum of trispectrum) to improve computational efficiency of the detectors based on polyspectrum and to possibly further enhance their detection performance. We investigate conditions under which use of the integrated polyspectrum is appropriate. The detector structure is derived, acid its performance is evaluated via simulations and comparisons with several other existing approaches     

Efficient algorithms for sequence detection in non-Gaussian noisewith intersymbol interference

Sequence detection is studied for communication channels with intersymbol interference and non-Gaussian noise using a novel adaptive receiver structure. The receiver adapts itself to the noise environment using an algorithm which employs a Gaussian mixture distribution model and the expectation maximization algorithm. Two alternate procedures are studied for sequence detection. These are a procedure based on the Viterbi algorithm and a symbol-by-symbol detection procedure. The Viterbi algorithm minimizes the probability the sequence is in error and the symbol-by-symbol detector minimizes symbol error rate, which are different     

Detection of weak signals in non-Gaussian noise

A locally optimum detector structure is derived for the detection of weak signals in non-Gaussian environments. Optimum performance is obtained by employing a zero-memory nonlinearity prior to the matched filter that would be optimum for detecting the signal were the noise Gaussian. The asymptotic detection performance of the locally optimum detector under non-Gaussian conditions is derived and compared with that for the corresponding detector optimized for operations in Gaussian noise. Numerical results for the asymptotic detection performance are shown for signal detection in noise environments of practical interest.     

Robust Detectors for Signals in Non-Gaussian Noise

The robustness of suboptimal nonlinear detectors for known discrete-time signals in non-Gaussian noise is investigated. The measure of robustness used is the degradation of asymptotic relative efficiency (compared to a linear detector) from that achieved by an optimal nonlinear detector. The first order density of non-Gaussian noise is modeled as a mixture of a small variance Gaussian background noise pdf and a large variance impulsive pdf.     

Blind detection of synchronous CDMA in non-Gaussian channels

Most blind source separation algorithms assume the channel noise to be Gaussian. This paper considers the problem of noncooperative blind detection of synchronous direct-sequence code-division multiple-access communications (no knowledge of the spreading sequences or training data) in non-Gaussian channels. Three iterative algorithms with different performance and complexity tradeoffs are proposed. Simulation results show that they significantly outperform Gaussian-optimal blind source separation algorithms in non-Gaussian channels. The Cramer-Rao lower bound for this problem is computed, and the performance of the proposed algorithms is shown to approach this bound for moderate signal-to-noise ratios.     

非高斯噪声中信号的神经网络检测

了非主斯噪声中信号的检测，采用多层感知器神经网络作为检测器。介绍了工作原理、网络结构和训练算法。计算机仿真证明在非主斯噪声条件下神经网络检测器性能优于线性最佳匹配滤波器检测器的局部最佳检测器。