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"A Multilayer Framework for Online Metric Learning" by Wenbin Li, Yanfang Liu et al.

Online metric learning (OML) has been widely applied in classification and retrieval. It can automatically learn a suitable metric from data by restricting similar instances to be separated from dissimilar instances with a given margin. However, the existing OML algorithms have limited performance in real-world classifications, especially, when data distributions are complex. To this end, this article proposes a multilayer framework for OML to capture the nonlinear similarities among instances. Different from the traditional OML, which can only learn one metric space, the proposed multilayer OML (MLOML) takes an OML algorithm as a metric layer and learns multiple hierarchical metric spaces, where each metric layer follows a nonlinear layer for the complicated data distribution. Moreover, the forward propagation (FP) strategy and backward propagation (BP) strategy are employed to train the hierarchical metric layers. To build a metric layer of the proposed MLOML, a new Mahalanobis-based ....

Data Models , Xtraterrestrial Measurements , Etric Layer , Onhomogeneous Media , Nline Metric Learning Oml , Assive Aggressive Pa Strategy , Software Algorithms ,

"Structured DNN Based Receiver for Millimeter-Wave MIMO with Nonlinear " by Dawei Gao, Qinghua Guo et al.

This work deals with the combined effect of nonlinear distortions and inter-channel interference in millimeter wave multi-input multi-output (MIMO) communications. Deep neural networks (DNNs) can be used to handle the effect, but they often require a large number of pilot symbols, hindering their applications. With the aim of online training using a relatively small number of pilot symbols, we design a deep neural network (DNN) architecture carefully, which consists of a fully connected linear hidden layer and a non-fully connected nonlinear hidden layer. The linear hidden layer is used to deal with the co-channel interference and the nonlinear hidden layer is used to handle the nonlinear distortions. Moreover, the parameters of the DNN are properly tied to reduce the number of independent parameters. With such a DNN, the receiver is much efficient in terms of training overhead and symbol error rate performance, compared to conventional (DNN-based) techniques. Simulation results demons ....

Artificial Neural Networks , Millimeter Wave , Imo Communication , Neural Networks , Onlinear Distortion , Power Amplifier , Transmitting Antennas ,

"Adaptive Extreme Learning Machine-Based Nonlinearity Mitigation for LE" by Dawei Gao, Qinghua Guo et al.


Abstract
This work concerns the receiver design for light emitting diode (LED) communications, where the LED nonlinearity can severely degrade the system performance. The LED nonlinearity makes high speed LED communications more challenging when it is time-varying (e.g., due to temperature drifting) and/or combined with time-varying channels (due to the relative movement between the transmitter and the receiver). In this work, we use adaptive neural network techniques to address this issue. We first propose a new adaptive extreme learning machine (AELM) with a variable forgetting factor for adaptive learning in dynamic scenarios. Then, an AELM based (turbo) receiver is designed to handle the time-varying LED nonlinearity and memory effects jointly. It is demonstrated that the proposed AELM based receiver can efficiently mitigate the dynamic nonlinearity and memory effects, and outperform the state-of-the-art adaptive techniques significantly. ....

Adaptive Post Distortion , Extreme Learning Machine , Seed Communications , Neural Networks ,