Simple Estimation Scheme Research Articles

Learning-Based Channel Estimation and Phase Noise Compensation in Doubly-Selective Channels

In this letter, we propose a learning based channel estimation scheme for orthogonal frequency division multiplexing (OFDM) systems in the presence of phase noise in doubly-selective fading channels. Two-dimensional (2D) convolutional neural networks (CNNs) are employed for effective training and tracking of channel variation in both frequency as well as time domain. The proposed network learns and estimates the channel coefficients in the entire time-frequency (TF) grid based on pilots sparsely populated in the TF grid. In order to make the network robust to phase noise (PN) impairment, a novel training scheme where the training data is rotated by random phases before being fed to the network is employed. Further, using the estimated channel coefficients, a simple and effective PN estimation and compensation scheme is devised. Numerical results demonstrate that the proposed network and PN compensation scheme achieve robust OFDM performance in the presence of phase noise.

Temperature estimation of a pair of trapped ions

We apply estimation theory to a system formed by two interacting trapped ions. By using the Fisher matrix formalism, we introduce a simple scheme for estimation of the temperature of the longitudinal vibrational modes of the ions. We use the ions interaction to effectively infer the temperature of the individual ions, by optimising the interaction time evolution and by measuring only over one of the ions. We also investigate the effect of a non-thermal reservoir over the inference approach. The non-classicality of one of the ions vibrational modes, introduced due to a squeezed thermal reservoir, does not directly affect the inference of the individual temperatures, although allowing the modes to be entangled. To check actual experimental conditions, we analyze the temperature inference under heating due to surface-electrode noise.

Approximate Message Passing With Parameter Estimation for Heavily Quantized Measurements

Designing efficient sparse recovery algorithms that could handle noisy quantized measurements is important in a variety of applications -- from radar to source localization, spectrum sensing and wireless networking. We take advantage of the approximate message passing (AMP) framework to achieve this goal given its high computational efficiency and state-of-the-art performance. In AMP, the signal of interest is assumed to follow certain prior distribution with unknown parameters. Previous works focused on finding the parameters that maximize the measurement likelihood via expectation maximization -- an increasingly difficult problem to solve in cases involving complicated probability models. In this paper, we treat the parameters as unknown variables and compute their posteriors via AMP. The parameters and signal of interest can then be jointly recovered. Compared to previous methods, the proposed approach leads to a simple and elegant parameter estimation scheme, allowing us to directly work with 1-bit quantization noise model. We then further extend our approach to general multi-bit quantization noise model. Experimental results show that the proposed framework provides significant improvement over state-of-the-art methods across a wide range of sparsity and noise levels.

Charge transfer in the vertical structures formed by two-dimensional layers

Electronic structures of superlattices and encapsulated layers are obtained by Green's function method. The simple scheme for estimation of interlayer charge transfer is proposed. As an example, layers of graphene and graphene-like compounds are considered. Keywords: 2D structure, superlattice encapsulated layer

An Approach to Empirical Mapping of Incoherent Internal Tides With Altimetry Data

AbstractMuch of the internal tide (IT) is coherent with tidal forcing, but interactions with time‐varying ocean flows result in an incoherent component of IT (IIT), which will complicate interpretation of data from the upcoming Surface Water Ocean Topography (SWOT) mission. Here, we demonstrate a simple scheme for estimation of these IIT. We use a short‐time harmonic analysis of outputs of a wind‐ and tidally forced ocean model to generate an ensemble of IT variations in an ocean patch. Principal Components Analysis is used to define the dominant spatial patterns, and combined with sinusoidal time variations at tidal frequencies to define spatio‐temporal basis functions that are used to fit sparse altimetry data available in short time windows. The scheme is illustrated using a 1‐yr Hybrid Coordinate Ocean Model (HYCOM) run off the Amazon Shelf. Tests with synthetic (SWOT) and real (nadir altimetry) data demonstrate that a significant fraction of IIT can be recovered with this procedure.

Principal component analysis of hybrid functional and vector data.

We propose a practical principal component analysis (PCA) framework that provides a nonparametric means of simultaneously reducing the dimensions of and modeling functional and vector (multivariate) data. We first introduce a Hilbert space that combines functional and vector objects as a single hybrid object. The framework, termed a PCA of hybrid functional and vector data (HFV-PCA), is then based on the eigen-decomposition of a covariance operator that captures simultaneous variations of functional and vector data in the new space. This approach leads to interpretable principal components that have the same structure as each observation and a single set of scores that serves well as a low-dimensional proxy for hybrid functional and vector data. To support practical application of HFV-PCA, the explicit relationship between the hybrid PC decomposition and the functional and vector PC decompositions is established, leading to a simple and robust estimation scheme where components of HFV-PCA are calculated using the components estimated from the existing functional and classical PCA methods. This estimation strategy allows flexible incorporation of sparse and irregular functional data as well as multivariate functional data. We derive the consistency results and asymptotic convergence rates for the proposed estimators. We demonstrate the efficacy of the method through simulations and analysis of renal imaging data.

Переход заряда в вертикальных структурах, образованных двумерными слоями

Electronic structures of superlattices and encapsulated layers are obtained by Green’s function method. The simple scheme for estimation of interlayer charge transfer is proposed. As an example, layers of graphene and graphene-like compounds are considered.

A Simple Power Estimation With Triple Phase-Shift Control for the Output Parallel DAB DC–DC Converters in Power Electronic Traction Transformer for Railway Locomotive Application

Aimed at the input-independence and output-parallel dual-active-bridge (DAB) dc–dc converters applied to power electronic traction transformer (PETT), this paper proposes a simple power estimation scheme with triple phase-shift control (PES-TPS) to achieve the transmission power balance, dynamic performance improvement, and efficiency optimization, simultaneously. Through executing the current stress optimization (CSO) and introducing the power estimation based on operational parameters, the efficiency of converters can be further improved and the power balance among DAB cells can be achieved over the whole power range without adding extra sensors to sample the output/inductor current of each cell. In addition, the dynamic performance of the adopted converters can be improved by compensating power loss online. Especially, dynamic response of the output voltage can be enhanced under input voltage and load fluctuation conditions. Finally, a comprehensive experimental comparison of CSO with dual phase shift (CSO-DPS), CSO with TPS (CSO-TPS), and the proposed PES-TPS control is conducted in the DAB converters experimental prototype with three cells. The experimental results have confirmed the excellent performance of the proposed PES-TPS control and the correctness of the theoretical analysis.

An information congestion control scheme in the Internet of Vehicles: A bargaining game approach

In VANETs (Vehicular Ad Hoc Networks), the information exchange plays an important role for context awareness. However, at intersections, a channel congestion is likely occurring if no control scheme is applied for the exchange especially when the vehicle density is high. To solve this problem, a congestion control scheme is proposed based on the non-cooperative bargaining game. First, vehicles approaching intersections are divided into clusters. By introducing a simple channel-load estimation scheme, our bargaining game is presented by which the cluster leaders could negotiate an optimal combination of the transmitting power and packet generation rate for their members. When the Nash Equilibrium of our game is reached, the utility of each player will be maximized without making the channel congested. Numerical results show that our scheme could alleviate the possible packets collisions thus reducing the average experienced delay and increasing the packets delivery ratio on the receiver side.

Post-FEC performance evaluation of coherent QPSK system with an enhanced pilot-aided CPE scheme

In this paper, a simple and robust pilot-aided carrier phase estimation scheme with a modified phase unwrapping is proposed. The principle of the proposed phase unwrapping is shown with a step-by-step procedure. Further, we test the post-FEC BER performance for the pilot-aided carrier phase estimation scheme with the traditional and proposed phase unwrapping. For the proposed phase unwrapping, a cycle slip rate reduction of about 10 times is achieved than the case using the traditional phase unwrapping through simulations in the presence of Wiener phase noise. Finally, we present a 2.2 Eb/N0 gain and post-FEC error-free performance with only 0.78 % pilot overhead in a 32-Gbaud QPSK system using soft-decision forward error correction.

Two-Stage Supervised Learning-Based Method to Detect Screams and Cries in Urban Environments

Smartphones can enable monitoring signs of distress as a human goes about his daily routine. Motivated by this possibility of 24x7 distress detection, we investigate detection of screaming and crying in urban environments, which we categorize into the contexts of indoors (home and office), outdoors, human conversation, large human gatherings, machinery, and audio from multimedia devices. Prior works are often restricted to specific environments or controlled settings. We propose a novel two-stage supervised learning based method, with tunable decision parameters for each stage, to achieve a desired true distress (scream and cry) detection rate (DR) and false alarm rate (FAR). We observe that the choice of the parameters is a function of the signal-to-noise ratio (SNR) of the distress signal, which is the ratio of the power of the distress signal to the power of the context audio. In the absence of SNR information, we show that a simple SNR estimation scheme performs well. Alternately, we show how the decision parameters can be selected based on the context estimated by the method. We show the results of testing the proposal over hundred hours of audio data recorded by the smartphones of ten volunteers as they went about their daily routines. Achieved performance is exemplified by a DR of 93.16% and a FAR of 4.76% at a SNR of 20 dB. The corresponding values for a SNR of 10 dB are 84.13% and 4.77%. Finally, we compare with, which also deals with audio event detection in noisy environments.

Leak Detection, Size Estimation and Localization in Pipe Flows

We design a leak detection system consisting of an adaptive observer based on a set of two coupled one dimensional first order hyperbolic partial differential equations governing the flow dynamics. It is assumed that measurements are only available at the inlet and outlet of the pipe. Convergence properties are provided showing that the leak size estimation problem can be tackled independently from how the leak is distributed along the pipe. This is key to obtaining a simple estimation scheme for the leak location. Simulations are provided to demonstrate the ability of the system to detect, quantify and locate leaks.

Interference mitigation and D2D parameter estimation for distributed‐control D2D underlay systems

AbstractAlthough centralised‐control schemes for device‐to‐device (D2D) underlay communications can produce satisfactory results by efficiently managing the interference between cellular and D2D transmissions, they can become prohibitively complicated and may require a large amount of feedbacks from devices, because it is important to handle the inter‐cell interference as well as the intra‐cell interference. Such a burden can be alleviated by a distributed‐control mechanism, in which a base station performs the resource management of its cellular devices and each D2D device selects its own resource without a small‐scale control of the cellular network. This paper addresses interference issues in distributed‐control D2D underlay systems and presents an interference mitigation technique based on frequency spreading, in order to meet the target outage probability. This paper also proposes a simple estimation scheme, which can be used for the mode selection and the determination of D2D transmission parameters. Copyright © 2015 John Wiley & Sons, Ltd.

Channel Hardening-Exploiting Message Passing (CHEMP) Receiver in Large-Scale MIMO Systems

In this paper, we propose a multiple-input multiple-output (MIMO) receiver algorithm that exploits channel hardening that occurs in large MIMO channels. Channel hardening refers to the phenomenon where the off-diagonal terms of the H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</sup> H matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix H increases. Specifically, we propose a message passing detection (MPD) algorithm which works with the real-valued matched filtered received vector (whose signal term becomes H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> Hx, where x is the transmitted vector), and uses a Gaussian approximation on the off-diagonal terms of the H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> H matrix. We also propose a simple estimation scheme which directly obtains an estimate of H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> H (instead of an estimate of H), which is used as an effective channel estimate in the MPD algorithm. We refer to this receiver as the channel hardening-exploiting message passing (CHEMP) receiver. The proposed CHEMP receiver achieves very good performance in large-scale MIMO systems (e.g., in systems with 16 to 128 uplink users and 128 base station antennas). For the considered large MIMO settings, the complexity of the proposed MPD algorithm is almost the same as or less than that of the minimum mean square error (MMSE) detection. This is because the MPD algorithm does not need a matrix inversion. It also achieves a significantly better performance compared to MMSE and other message passing detection algorithms using MMSE estimate of H. Further, we design optimized irregular low density parity check (LDPC) codes specific to the considered large MIMO channel and the CHEMP receiver through EXIT chart matching. The LDPC codes thus obtained achieve improved coded bit error rate performance compared to off-the-shelf irregular LDPC codes.

Real-Time Optical OFDM Long-Reach PON System Over 100 km SSMF Using a Directly Modulated DFB Laser

A real-time base-band orthogonal frequency division multiplexing (OFDM) transceiver with a simple symbol synchronization and channel estimation scheme is implemented on only one off-the-shelf commercial field programmable gate array (FPGA). The real-time optical OFDM long-reach passive optical network (PON) system with direct detection over 100 km standard single-mode fiber (SSMF) employing a cost-effective directly modulated distributed feedback (DFB) laser has been successfully demonstrated experimentally. The experimental results show that the simplified symbol synchronization with low complexity and high accuracy is effective for long-reach optical OFDM transmission systems. After 100 km SSMF transmission, the power penalty at a bit-error rate of 1e-3 is only about 1.1 dB.

An Inter Symbol Interference Estimation and Cancellation Scheme for Asynchronous Distributed Differential Space-Time Code

In cooperative communication systems, as poor synchronization of different relays may induce inter symbol interference (ISI) and lead to substantial performance degradation, it is necessary to cancel ISI. In this letter, a simple ISI estimation and cancellation scheme is proposed for asynchronous distributed differential space-time code, where the destination node does not need to know the channel state information (CSI) from the source node to different relays or the CSI from the relays to the destination node. Simulation results show that the proposed scheme can cancel ISI effectively and obtain good bit error rate performance.

A reversible flux linkage model of the switched reluctance motor with rotor position computable

PurposeThe purpose of this paper is to present a reversible flux linkage model of the switched reluctance motor so that the rotor position can be computed analytically.Design/methodology/approachThe presented flux linkage model uses a Fermi function like equation with four coefficients to fit flux linkage characteristics. In this work, the coefficients are calculated from flux‐current‐angle data using numerical curve fitting with the least squares method. A rotor position model is then derived by inverting the flux linkage model. With a simple estimation scheme based on the rotor position model, the rotor position is estimated continuously. Simulation and experiments are then performed to verify the proposed method.FindingsThe presented flux linkage model agrees well with flux linkage characteristics. The average absolute relative error (AARE) of the model varies between 0.3 per cent and 5.3 per cent. With the derived rotor position model, rotor position can be estimated conveniently for either steady or dynamic operations.Practical implicationsThe simulation and experimental results indicate that the presented model is an eligible candidate for applications such as rotor position estimation, performance simulation and other model based controls.Originality/valueUnlike previously reported methods, the presented flux linkage model is reversible so that a rotor position model can be derived and the rotor position can be computed analytically.

A channel estimation algorithm for optical orthogonal frequency division multiplexing systems

A simple channel estimation (CE) scheme, which is pilot-aided with just a little number of pilots inserted in the first half part of the subcarriers, is proposed and simulated for resisting the fiber dispersion in optical orthogonal frequency division multiplexing (OFDM) systems. The simulation results verify that the receiver sensitivity is improved by 2–3 dBm for bit error rate (BER) of 10−3 with the proposed CE algorithm than that with other kinds of CE algorithms based on linear square principle. The good constellation performance for a 40 Gbit/s transmission system can be also obtained by the proposed scheme.

A simple method for estimating the entropy of neural activity

The number of possible activity patterns in a population of neurons grows exponentially with the size of the population. Typical experiments explore only a tiny fraction of the large space of possible activity patterns in the case of populations with more than 10 or 20 neurons. It is thus impossible, in this undersampled regime, to estimate the probabilities with which most of the activity patterns occur. As a result, the corresponding entropy—which is a measure of the computational power of the neural population—cannot be estimated directly. We propose a simple scheme for estimating the entropy in the undersampled regime, which bounds its value from both below and above. The lower bound is the usual ‘naive’ entropy of the experimental frequencies. The upper bound results from a hybrid approximation of the entropy which makes use of the naive estimate, a maximum entropy fit, and a coverage adjustment. We apply our simple scheme to artificial data, in order to check their accuracy; we also compare its performance to those of several previously defined entropy estimators. We then apply it to actual measurements of neural activity in populations with up to 100 cells. Finally, we discuss the similarities and differences between the proposed simple estimation scheme and various earlier methods.

TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

Abstract. We present a detailed description of TOPAZ4, the latest version of TOPAZ – a coupled ocean-sea ice data assimilation system for the North Atlantic Ocean and Arctic. It is the only operational, large-scale ocean data assimilation system that uses the ensemble Kalman filter. This means that TOPAZ features a time-evolving, state-dependent estimate of the state error covariance. Based on results from the pilot MyOcean reanalysis for 2003–2008, we demonstrate that TOPAZ4 produces a realistic estimate of the ocean circulation in the North Atlantic and the sea-ice variability in the Arctic. We find that the ensemble spread for temperature and sea-level remains fairly constant throughout the reanalysis demonstrating that the data assimilation system is robust to ensemble collapse. Moreover, the ensemble spread for ice concentration is well correlated with the actual errors. This indicates that the ensemble statistics provide reliable state-dependent error estimates – a feature that is unique to ensemble-based data assimilation systems. We demonstrate that the quality of the reanalysis changes when different sea surface temperature products are assimilated, or when in-situ profiles below the ice in the Arctic Ocean are assimilated. We find that data assimilation improves the match to independent observations compared to a free model. Improvements are particularly noticeable for ice thickness, salinity in the Arctic, and temperature in the Fram Strait, but not for transport estimates or underwater temperature. At the same time, the pilot reanalysis has revealed several flaws in the system that have degraded its performance. Finally, we show that a simple bias estimation scheme can effectively detect the seasonal or constant bias in temperature and sea-level.