Multi-user detection for multi-rate DS/CDMA systems

Wireless cellular communication is witnessing a rapid growth in market, technology and range of services. Current and future demands for wireless communication services motivate the need for handling multi-media traffic types. In a multimedia communication system, users with different and even time-varying rates and quality of services (QoS) requirements, such as voice, image and data, must be accommodated. The use of Spread Spectrum modulation with Code Division Multiple Access (CDMA) technology is an attractive approach for economical spectrally efficient and high quality cellular and personal communication services. This dissertation explores the technologies of applying different interference cancellation techniques to multi-rate CDMA systems that serve users with different QoS. Multiple Access Interference (MAI) and multipath propagation are the major issues in wireless communication systems. It is also true for multi-rate CDMA systems. Multi-user detection has been shown to be effective in combating the near-far problem and providing superior performance over conventional detection method. In this dissertation, we combine both linear minimum mean squared error (LMMSE) detector, nonlinear decision feedback detector, with other signal processing techniques, such as array processing and multipath combining, to create effective near-far resistant detectors for multi-rate CDMA systems. Firstly, we propose MMSE receivers for synchronous multi-rate CDMA system and compare the performance with the corresponding multi-rate decorrelating detectors. The multi-rate decorrelating detector is optimally near-far resistant and easy to implement. The proposed linear MMSE multi-rate receiver can be adaptively implemented only with the knowledge of the desired user. Due to the fact that MMSE detector offers best trade-off between the MAI cancellation and noise variance enhancement, it is shown that multi-rate MMSE receiver can offer better performance than the multi-rate decorrelator when the interfering users\u27 Signal to Noise Ratio (SNR) is relatively low comparing to the desired user\u27s SNR. Secondly, the asynchronous multi-rate CDMA system, we propose multi-rate multi-shoot decorrelating detectors and multi-rate multi-shot MMSE detectors. The performance of multi-shot detectors can be improved monotonically with increasing the number of stacked bits, but a great computational complexity is going to be introduced in order to get better performance. A debiasing method is introduced to multi-rate multi-shot linear detectors. Debiasing method optimizes multi-rate detectors based on the multi-rate multi-shot model. Debiasing multi-shot MMSE detector for multi-rate signals can offer performance than the corresponding debiasing multi-shot decorrelating detector. Thirdly, we propose linear space-time receivers for multi-rate CDMA systems. The minimum mean-squared error criteria is used. We perform a comparative study on the multi-rate receiver which uses either multipath (temporal) processing or array (spatial) processing, and the one which uses both array and multipath (space-time) processing. The space-time receiver for the multi-rate CDMA signals give us the potential of improving the capacity of multi-rate systems. The space-time processing combined with multiuser detection have the advantages of combating multipath fading through temporal processing, reducing MAI through MMSE method and provide antenna or diversity gain through spatial processing and increasing the capacity of the multi-rate CDMA systems. Lastly, the group-wise interference cancellation methods are proposed for multi-rate CDMA signals. The non-linear decision feedback detection (DFD) schemes are used in the proposed receivers. The proposed interference cancellation schemes benefit from the nature of the unequal received amplitudes for multi-rate CDMA signals. Users with same data rate are grouped together. Users with the highest data-rate are detected first. Interference between the groups is cancelled in a successive order. The results show that the group-wise MMSE DFD yields better performance than multi-rate linear MMSE detector and multi-rate decorrelating detector, especially for highly loaded CDMA systems

The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects

Understanding the behavior of stochastic gradient descent (SGD) in the context of deep neural networks has raised lots of concerns recently. Along this line, we study a general form of gradient based optimization dynamics with unbiased noise, which unifies SGD and standard Langevin dynamics. Through investigating this general optimization dynamics, we analyze the behavior of SGD on escaping from minima and its regularization effects. A novel indicator is derived to characterize the efficiency of escaping from minima through measuring the alignment of noise covariance and the curvature of loss function. Based on this indicator, two conditions are established to show which type of noise structure is superior to isotropic noise in term of escaping efficiency. We further show that the anisotropic noise in SGD satisfies the two conditions, and thus helps to escape from sharp and poor minima effectively, towards more stable and flat minima that typically generalize well. We systematically design various experiments to verify the benefits of the anisotropic noise, compared with full gradient descent plus isotropic diffusion (i.e. Langevin dynamics).Comment: ICML 2019 camera read

A DSRPCL-SVM Approach to Informative Gene Analysis

Microarray data based tumor diagnosis is a very interesting topic in bioinformatics. One of the key problems is the discovery and analysis of informative genes of a tumor. Although there are many elaborate approaches to this problem, it is still difficult to select a reasonable set of informative genes for tumor diagnosis only with microarray data. In this paper, we classify the genes expressed through microarray data into a number of clusters via the distance sensitive rival penalized competitive learning (DSRPCL) algorithm and then detect the informative gene cluster or set with the help of support vector machine (SVM). Moreover, the critical or powerful informative genes can be found through further classifications and detections on the obtained informative gene clusters. It is well demonstrated by experiments on the colon, leukemia, and breast cancer datasets that our proposed DSRPCL-SVM approach leads to a reasonable selection of informative genes for tumor diagnosis

A feature fusion method using WPD-SVD and t-SNE for gearbox fault diagnosis

The vibration signals of a gearbox always contain the dynamic operation information, which are important for the feature extraction and further work. However, the low signal-to-noise ratio and combined multi-mode faults make it difficult to extract discriminable features of gearboxes. In this study, a feature fusion method based on wavelet packet decomposition (WPD), singular value decomposition (SVD) and t-Distributed stochastic neighbor embedding (t-SNE) for gearbox fault diagnosis is proposed. First, time-frequency analysis method of WPT-SVD as well as time-domain analysis methods are utilized to extract robust feature vectors of gearboxes with different conditions. As an effective method for the visualization of high-dimensional datasets, t-SNE is then introduced to realize the dimensionality reduction of feature vectors. Finally, with the fused features, a radial basis function (RBF) neural network is trained to realize the classification of gearbox fault modes. Sufficient experiments have been implemented to validate the effectiveness and superiority of the proposed method by analyzing the vibration signals of gearboxes

PDO-eS2\text{S}^\text{2}CNNs: Partial Differential Operator Based Equivariant Spherical CNNs

Spherical signals exist in many applications, e.g., planetary data, LiDAR scans and digitalization of 3D objects, calling for models that can process spherical data effectively. It does not perform well when simply projecting spherical data into the 2D plane and then using planar convolution neural networks (CNNs), because of the distortion from projection and ineffective translation equivariance. Actually, good principles of designing spherical CNNs are avoiding distortions and converting the shift equivariance property in planar CNNs to rotation equivariance in the spherical domain. In this work, we use partial differential operators (PDOs) to design a spherical equivariant CNN, PDO-e$\text{S}^\text{2}$CNN, which is exactly rotation equivariant in the continuous domain. We then discretize PDO-e$\text{S}^\text{2}$CNNs, and analyze the equivariance error resulted from discretization. This is the first time that the equivariance error is theoretically analyzed in the spherical domain. In experiments, PDO-e$\text{S}^\text{2}$CNNs show greater parameter efficiency and outperform other spherical CNNs significantly on several tasks.Comment: Accepted by AAAI202