19,151 research outputs found
Non-linear minimum variance estimation for discrete-time multi-channel systems
A nonlinear operator approach to estimation in discrete-time systems is described. It involves inferential estimation of a signal which enters a communications channel involving both nonlinearities and transport delays. The measurements are assumed to be corrupted by a colored noise signal which is correlated with the signal to be estimated. The system model may also include a communications channel involving either static or dynamic nonlinearities. The signal channel is represented in a very general nonlinear operator form. The algorithm is relatively simple to derive and to implement
Cancellation of Power Amplifier Induced Nonlinear Self-Interference in Full-Duplex Transceivers
Recently, full-duplex (FD) communications with simultaneous transmission and
reception on the same channel has been proposed. The FD receiver, however,
suffers from inevitable self-interference (SI) from the much more powerful
transmit signal. Analogue radio-frequency (RF) and baseband, as well as digital
baseband, cancellation techniques have been proposed for suppressing the SI,
but so far most of the studies have failed to take into account the inherent
nonlinearities of the transmitter and receiver front-ends. To fill this gap,
this article proposes a novel digital nonlinear interference cancellation
technique to mitigate the power amplifier (PA) induced nonlinear SI in a FD
transceiver. The technique is based on modeling the nonlinear SI channel, which
is comprised of the nonlinear PA, the linear multipath SI channel, and the RF
SI canceller, with a parallel Hammerstein nonlinearity. Stemming from the
modeling, and appropriate parameter estimation, the known transmit data is then
processed with the developed nonlinear parallel Hammerstein structure and
suppressed from the receiver path at digital baseband. The results illustrate
that with a given IIP3 figure for the PA, the proposed technique enables higher
transmit power to be used compared to existing linear SI cancellation methods.
Alternatively, for a given maximum transmit power level, a lower-quality PA
(i.e., lower IIP3) can be used.Comment: To appear in proceedings of the 2013 Asilomar Conference on Signals,
Systems & Computer
Finite-horizon estimation of randomly occurring faults for a class of nonlinear time-varying systems
This paper is concerned with the finite-horizon estimation problem of randomly occurring faults for a class of nonlinear systems whose parameters are all time-varying. The faults are assumed to occur in a random way governed by two sets of Bernoulli distributed white sequences. The stochastic nonlinearities entering the system are described by statistical means that can cover several classes of well-studied nonlinearities. The aim of the problem is to estimate the random faults, over a finite horizon, such that the influence from the exogenous disturbances onto the estimation errors is attenuated at the given level quantified by an Hâ-norm in the mean square sense. By using the completing squares method and stochastic analysis techniques, necessary and sufficient conditions are established for the existence of the desired finite-horizon Hâ fault estimator whose parameters are then obtained by solving coupled backward recursive Riccati difference equations (RDEs). A simulation example is utilized to illustrate the effectiveness of the proposed fault estimation method
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Symmetric complex-valued RBF receiver for multiple-antenna aided wireless systems
A nonlinear beamforming assisted detector is proposed for multiple-antenna-aided wireless systems employing complex-valued quadrature phase shift-keying modulation. By exploiting the inherent symmetry of the optimal Bayesian detection solution, a novel complex-valued symmetric radial basis function (SRBF)-network-based detector is developed, which is capable of approaching the optimal Bayesian performance using channel-impaired training data. In the uplink case, adaptive nonlinear beamforming can be efficiently implemented by estimating the systemâs channel matrix based on the least squares channel estimate. Adaptive implementation of nonlinear beamforming in the downlink case by contrast is much more challenging, and we adopt a cluster-variationenhanced clustering algorithm to directly identify the SRBF center vectors required for realizing the optimal Bayesian detector. A simulation example is included to demonstrate the achievable performance improvement by the proposed adaptive nonlinear beamforming solution over the theoretical linear minimum bit error rate beamforming benchmark
- âŠ