70,242 research outputs found
Reciprocity Calibration for Massive MIMO: Proposal, Modeling and Validation
This paper presents a mutual coupling based calibration method for
time-division-duplex massive MIMO systems, which enables downlink precoding
based on uplink channel estimates. The entire calibration procedure is carried
out solely at the base station (BS) side by sounding all BS antenna pairs. An
Expectation-Maximization (EM) algorithm is derived, which processes the
measured channels in order to estimate calibration coefficients. The EM
algorithm outperforms current state-of-the-art narrow-band calibration schemes
in a mean squared error (MSE) and sum-rate capacity sense. Like its
predecessors, the EM algorithm is general in the sense that it is not only
suitable to calibrate a co-located massive MIMO BS, but also very suitable for
calibrating multiple BSs in distributed MIMO systems.
The proposed method is validated with experimental evidence obtained from a
massive MIMO testbed. In addition, we address the estimated narrow-band
calibration coefficients as a stochastic process across frequency, and study
the subspace of this process based on measurement data. With the insights of
this study, we propose an estimator which exploits the structure of the process
in order to reduce the calibration error across frequency. A model for the
calibration error is also proposed based on the asymptotic properties of the
estimator, and is validated with measurement results.Comment: Submitted to IEEE Transactions on Wireless Communications,
21/Feb/201
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Investigating the impact of remotely sensed precipitation and hydrologic model uncertainties on the ensemble streamflow forecasting
In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies have actually implemented SDA methods using realistic input error models for precipitation. In this study we use particle filtering as a SDA method to propagate input errors through a conceptual hydrologic model and quantify the state, parameter and streamflow uncertainties. Recent progress in satellite-based precipitation observation techniques offers an attractive option for considering spatiotemporal variation of precipitation. Therefore, we use the PERSIANN-CCS precipitation product to propagate input errors through our hydrologic model. Some uncertainty scenarios are set up to incorporate and investigate the impact of the individual uncertainty sources from precipitation, parameters and also combined error sources on the hydrologic response. Also probabilistic measure are used to quantify the quality of ensemble prediction. Copyright 2006 by the American Geophysical Union
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Dual state-parameter estimation of hydrological models using ensemble Kalman filter
Hydrologic models are twofold: models for understanding physical processes and models for prediction. This study addresses the latter, which modelers use to predict, for example, streamflow at some future time given knowledge of the current state of the system and model parameters. In this respect, good estimates of the parameters and state variables are needed to enable the model to generate accurate forecasts. In this paper, a dual state-parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. A systematic approach for identification of the perturbation factors used for ensemble generation and for selection of ensemble size is discussed. The dual EnKF methodology introduces a number of novel features: (1) both model states and parameters can be estimated simultaneously; (2) the algorithm is recursive and therefore does not require storage of all past information, as is the case in the batch calibration procedures; and (3) the various sources of uncertainties can be properly addressed, including input, output, and parameter uncertainties. The applicability and usefulness of the dual EnKF approach for ensemble streamflow forecasting is demonstrated using a conceptual rainfall-runoff model. © 2004 Elsevier Ltd. All rights reserved
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Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information
Several contributions to the hydrological literature have brought into question the continued usefulness of the classical paradigm for hydrologic model calibration. With the growing popularity of sophisticated 'physically based' watershed models (e.g., landsurface hydrology and hydrochemical models) the complexity of the calibration problem has been multiplied many fold. We disagree with the seemingly widespread conviction that the model calibration problem will simply disappear with the availability of more and better field measurements. This paper suggests that the emergence of a new and more powerful model calibration paradigm must include recognition of the inherent multiobjective nature of the problem and must explicitly recognize the role of model error. The results of our preliminary studies are presented. Through an illustrative case study we show that the multiobjective approach is not only practical and relatively simple to implement but can also provide useful information about the limitations of a model
Practical Accuracy Limits of Radiation-Aware Magneto-Inductive 3D Localization
The key motivation for the low-frequency magnetic localization approach is
that magnetic near-fields are well predictable by a free-space model, which
should enable accurate localization. Yet, limited accuracy has been reported
for practical systems and it is unclear whether the inaccuracies are caused by
field distortion due to nearby conductors, unconsidered radiative propagation,
or measurement noise. Hence, we investigate the practical performance limits by
means of a calibrated magnetoinductive system which localizes an active
single-coil agent with arbitrary orientation, using 4 mW transmit power at 500
kHz. The system uses eight single-coil anchors around a 3m x 3m area in an
office room. We base the location estimation on a complex baseband model which
comprises both reactive and radiative propagation. The link coefficients, which
serve as input data for location estimation, are measured with a multiport
network analyzer while the agent is moved with a positioner device. This
establishes a reliable ground truth for calibration and evaluation. The system
achieves a median position error of 3.2 cm and a 90th percentile of 8.3 cm.
After investigating the model error we conjecture that field distortion due to
conducting building structures is the main cause of the performance bottleneck.
The results are complemented with predictions on the achievable accuracy in
more suitable circumstances using the Cram\'er-Rao lower bound.Comment: To appear at the IEEE ICC 2019 Workshops. This work has been
submitted to the IEEE for possible publication. Copyright may be transferred
without notice, after which this version may no longer be accessibl
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