Channel Capacity Estimation using Free Probability Theory

In many channel measurement applications, one needs to estimate some characteristics of the channels based on a limited set of measurements. This is mainly due to the highly time varying characteristics of the channel. In this contribution, it will be shown how free probability can be used for channel capacity estimation in MIMO systems. Free probability has already been applied in various application fields such as digital communications, nuclear physics and mathematical finance, and has been shown to be an invaluable tool for describing the asymptotic behaviour of many large-dimensional systems. In particular, using the concept of free deconvolution, we provide an asymptotically (w.r.t. the number of observations) unbiased capacity estimator for MIMO channels impaired with noise called the free probability based estimator. Another estimator, called the Gaussian matrix mean based estimator, is also introduced by slightly modifying the free probability based estimator. This estimator is shown to give unbiased estimation of the moments of the channel matrix for any number of observations. Also, the estimator has this property when we extend to MIMO channels with phase off-set and frequency drift, for which no estimator has been provided so far in the literature. It is also shown that both the free probability based and the Gaussian matrix mean based estimator are asymptotically unbiased capacity estimators as the number of transmit antennas go to infinity, regardless of whether phase off-set and frequency drift are present. The limitations in the two estimators are also explained. Simulations are run to assess the performance of the estimators for a low number of antennas and samples to confirm the usefulness of the asymptotic results.Comment: Submitted to IEEE Transactions on Signal Processing. 12 pages, 9 figure

Capacity estimation of two-dimensional channels using Sequential Monte Carlo

We derive a new Sequential-Monte-Carlo-based algorithm to estimate the capacity of two-dimensional channel models. The focus is on computing the noiseless capacity of the 2-D one-infinity run-length limited constrained channel, but the underlying idea is generally applicable. The proposed algorithm is profiled against a state-of-the-art method, yielding more than an order of magnitude improvement in estimation accuracy for a given computation time

The impact of boundary conditions on CO2 capacity estimation in aquifers

The boundary conditions of an aquifer determine the extent to which fluids (including formation water and CO2) and pressure can be transferred into adjacent geological formations, either laterally or vertically. Aquifer boundaries can be faults, lithological boundaries, formation pinch-outs, salt walls, or outcrop. In many cases compliance with regulations preventing CO2 storage influencing areas outside artificial boundaries defined by non-geological criteria (international boundaries; license limits) may be necessary. A bounded aquifer is not necessarily a closed aquifer. The identification of an aquifer’s boundary conditions determines how CO2 storage capacity is estimated in the earliest screening and characterization stages. There are different static capacity estimation methods in use for closed systems and open systems. The method used has a significant impact on the final capacity estimate. The recent EU Directive (2009/31/EC) stated that where more than one storage site within a single “hydraulic unit” (bounded aquifer volume) is being considered, the characterization process should account for potential pressure interactions. The pressure interplay of multiple sites (or even the pressure footprint of just one site) is heavily influenced by boundary conditions

Gaussian Process Regression for In-situ Capacity Estimation of Lithium-ion Batteries

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data as Incremental Capacity (IC) or Differential Voltage (DV) curves. This overcomes the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells respectively. In each case, within certain voltage ranges, as little as 10 seconds of galvanostatic operation enables capacity estimates with approximately 2-3% RMSE.Comment: 12 pages, 10 figures, submitted to IEEE Transactions on Industrial Informatic

Capacity Estimation at Signalized Roundabouts

There has been an exponential increment in the number of inhabitants in our nation in most recent four decades. This increment in populace had driven expansion in activity interest and more number of mechanized vehicles, therefore prompting clog. The circuitous aides in diminishing the quantity of contention focuses at the crossing points. Signalized roundabouts invalidate the contention focuses, along these lines decreasing clog. This thusly expands the security of the travelers. The activity stream information was gathered from different urban communities of Chandigarh and Bilaspur and the information was separated from the two hour video that was recorded from these spots. The geometrical information that incorporates island measurement, section width, circling stream, path width, weaving length were gathered from the locales. This exploration paper aligns the Ackelik Model M1 to figure the limit of signalized roundabouts in view of Indian activity conditions.This paper gives an unmistakable understanding that simply adjusting the Ackelik Model does not fulfill the Indian movement condition. In this way, an additional parameter, speed, was added to fulfill the outcomes. At long last the aftereffects of Ackelik, Calibrated and the created model were looked at

New insights into pedestrian flow through bottlenecks

Capacity estimation is an important tool for the design and dimensioning of pedestrian facilities. The literature contains different procedures and specifications which show considerable differences with respect to the estimated flow values. Moreover do new experimental data indicate a stepwise growing of the capacity with the width and thus challenge the validity of the specific flow concept. To resolve these differences we have studied experimentally the unidirectional pedestrian flow through bottlenecks under laboratory conditions. The time development of quantities like individual velocities, density and individual time gaps in bottlenecks of different width is presented. The data show a linear growth of the flow with the width. The comparison of the results with experimental data of other authors indicates that the basic assumption of the capacity estimation for bottlenecks has to be revised. In contradiction with most planning guidelines our main result is, that a jam occurs even if the incoming flow does not overstep the capacity defined by the maximum of the flow according to the fundamental diagram.Comment: Traffic flow, pedestrian traffic, crowd dynamics, capacity of bottlenecks (16 pages, 8 figures); (+ 3 new figures and minor revisions

Data-Driven Methods for Robust Battery Capacity Estimation based on Electrochemical Impedance Spectroscopy

To ensure accurate battery capacity estimation over the battery life time, it is important to extract those features from battery data sets that give a good indication of battery capacity degradation. Data obtained from electrochemical impedance spectroscopy (EIS) are a promising route for detecting different aging effects. Many present methods for extracting battery aging features from EIS data are unsuitable for cells that have very different aging behaviour, which leads to low robustness in the battery capacity estimation. To improve battery capacity estimation of cells with significantly different aging behaviour, two methods for feature detection and consistency analysis are proposed for finding the high aging-correlated features in EIS data of these cells. A novel feature-consistency coefficient is proposed to assess whether the detected features are suitable for use in capacity determination. Based on the two new features that are found using this approach on a published data set of 8 battery cells with significantly inconsistent aging behavior, a capacity estimation is subsequently carried out using several advanced machine learning (ML) techniques, using Gaussian process regression (GPR) and Support vector machine (SVM) models. It appears that a third ML method based on automatic feature extraction and capacity estimation using convolution neural networks (CNNs) gives the best, most robust capacity estimation result. All methods presented in this paper significantly outperform GPR-based estimations published in the literature.</p

Topology Control in VANET and Capacity Estimation

International audienceSome safety applications using VANET exchange a large amount of data, and consequently require an important network capacity. In this paper, we focus on extended perception map applications, that use information from local and distant sensors to offer driving assistance (autonomous driving, collision warning, etc). Extended perception requires a high bandwidth that might not be available in practice in classical IEEE 802.11p ad hoc networks. Therefore, we propose an adaptive power control algorithm optimized for this particular application. We show through an analytical model and a large set of simulations that the network capacity is then significantly increased

Sensitivity Analysis of Steel Box-Section Girders.

The paper deals with the load–carrying capacity stochastic variance based sensitivity analysis of thin–walled box–section girder subjected to pure bending. The lower– and uppe-r-bound load–capacity estimation is performed. The methodology is based on the Monte-Carlo method . The exemplary results are presented in diagrams and pie charts showing the sensitivity of load–capacity to different random input variables. The analysis is focused on the variance of the yield stress of the girder material and girder’s wall thickness. Some final conclusions, concerning an efficiency of the applied models and the sensitivity analysis are derived