User equipment geolocation depended on long-term evolution signal-level measurements and timing advance

A new approach is described for investigating the accuracy of positioning active long-term evolution (LTE) users. The explored approach is a network-based method and depends on signal level measurements as well as the coverage of the serving cell. In a two-dimensional coordinate system, the algorithm simultaneously applies LTE measured data with a combination of a basic prediction model to locate the mobile device’s user. Furthermore, we introduce a unique method that combines timing advance (TA) and the measured signal level to narrow the search region and improve accuracy. The developed method is assessed by comparing the predicted results from the proposed algorithm with satellite measurements from the global positioning system (GPS) in various scenarios calculated via the number of cells that user equipment concurrently reports. This work separates seven different cases starting from a single reported cell to five reported cells from up to 3 sites. For analysis, the root mean square error (RMSE) is computed to obtain the validation for the proposed approach. The study case demonstrates location accuracy based on the numbers of registered cells with the mean RMSE improved using TA to approximately 70-191 m for the range of scenarios

NSCAT high-resolution surface wind measurements in Typhoon Violet

NASA scatterometer (NSCAT) measurements of the western Pacific Supertyphoon Violet are presented for revolutions 478 and 485 that occurred in September 1996. A tropical cyclone planetary boundary layer numerical, model, which uses conventional meteorological and geostationary cloud data, is used to estimate the winds at 10-m elevation in the cyclone. These model winds are then compared with the winds inferred from the NSCAT backscatter data by means of a novel approach that allows a wind speed to be recovered from each individual backscatter cell. This spatial adaptive (wind vector) retrieval algorithm employs several unique steps. The backscatter values are first regrouped in terms of closest neighbors in sets of four. The maximum likelihood estimates of speed and direction are then used to obtain speeds and directions for each group. Since the cyclonic flow around the tropical cyclone is known, NSCAT wind direction alias selection is easily accomplished. The selected wind directions are then used to convert each individual backscatter value to a wind speed. The results are compared to the winds obtained from the tropical cyclone boundary layer model. The NSCAT project baseline geophysical model function, NSCAT 1, was found to yield wind speeds that were systematically too low, even after editing for suspected rain areas of the cyclone. A new geophysical model function was developed using conventional NSCAT data and airborne Ku band scatterometer measurements in an Atlantic hurricane. This new model uses the neural network method and yields substantially better agreement with the winds obtained from the boundary layer model according to the statistical tests that were used

Interference cancellation by time adjustable sampling

The problem of improving the frequency response of linear arrays is addressed in this thesis. The improvement is measured in the level of rejection of the undesired sources. A new, simple, and practical method is suggested. It is based on adjusting the sampling time in selected channels. Interference cancellation for finite bandwidth signals is accomplished only at, or close to, the carrier frequency. Wideband cancellation requires additional hardware and computational complexity. Conventional way of the wideband interference cancellation utilizes tapped delay line adaptive filters in each channel. The Time Adjustable Sampling (TAS) method achieves a part of the tapped delay line effect by adjusting the sampling time in some selected array channels. Signal beamforming is combined with TAS. The beamforming is done using complex weights. The combination of the complex weights and TAS provides improvement over the usage of the complex weights only. Hardware and computational requirements in implementing the TAS method are significantly lower than for the tapped delay line structure of the same dimension. Different configurations are investigated. The comparison with a conventional method is given. It was found that TAS significantly improves wideband linear prediction. A computer simulation confirmed results obtained by the model analysis

Relative calibration of scatterometer antennas using land targets

The NASA scatterometer (NSCAT) antennas are cross-calibrated using a simple method. The method forces all antennas to the reference value of the average σ0 from all beams. The corrections are calculated as differences between the measurements and the reference σ0(θ) response. The magnitude of the corrections clearly shows the necessity of an on-orbit calibration. The corrected σ0 data set is input to wind retrieval algorithms. The advantage of the method is its simplicity and relatively fast convergence compared to other methods (ground station, ocean measurements)

Scatterometer-Retrieved Hurricane Wind Direction Ambiguity Removal Using Spiral Dealias

A simple method aimed at improving ambiguity removal during wind retrievals in tropical cyclones is investigated. The method uses the a priori knowledge of the general wind circulation about the center of rotation to produce a spiral `de-alias\u27 of directions in multiple wind solutions. Technique is illustrated using observations from the SeaWinds instrument on board the QuickScat satellite

Tropical Cyclone Geophysical Model Function For Ocean Surface Wind Retrievals From Nasa Scatterometer Measurements At High Wind Speeds

A hybrid model function, the Tropical Cyclone Geophysical Model Function (TCGMF) that was derived from the NSCAT1b model function and airborne KUSCAT scatterometer measurements during tropical cyclones, is described. At higher speeds, TCGMF assigns lower backscatter values to obtain a given wind vector, thus compared to other model functions, its use results in higher winds. Use of TCGFM for wind retrieval from the NASA Scatterometer measurements during several high-wind events gave good results

Machine-Learning-Based Uplink Throughput Prediction from Physical Layer Measurements

The uplink (UL) throughput prediction is indispensable for a sustainable and reliable cellular network due to the enormous amounts of mobile data used by interconnecting devices, cloud services, and social media. Therefore, network service providers implement highly complex mobile network systems with a large number of parameters and feature add-ons. In addition to the increased complexity, old-fashioned methods have become insufficient for network management, requiring an autonomous calibration to minimize utilization of the system parameter and the processing time. Many machine learning algorithms utilize the Long-Term Evolution (LTE) parameters for channel throughput prediction, mainly in favor of downlink (DL). However, these algorithms have not achieved the desired results because UL traffic prediction has become more critical due to the channel asymmetry in favor of DL throughput closing rapidly. The environment (urban, suburban, rural areas) affect should also be taken into account to improve the accuracy of the machine learning algorithm. Thus, in this research, we propose a machine learning-based UL data rate prediction solution by comparing several machine learning algorithms for three locations (Houston, Texas, Melbourne, Florida, and Batman, Turkey) and determine the best accuracy among all. We first performed an extensive LTE data collection in proposed locations and determined the LTE lower layer parameters correlated with UL throughput. The selected LTE parameters, which are highly correlated with UL throughput (RSRP, RSRQ, and SNR), are trained in five different learning algorithms for estimating UL data rates. The results show that decision tree and k-nearest neighbor algorithms outperform the other algorithms at throughput estimation. The prediction accuracy with the R2 determination coefficient of 92%, 85%, and 69% is obtained from Melbourne, Florida, Batman, Turkey, and Houston, Texas, respectively

Validation Of Quikscat Radiometric Estimates Of Rain Rate

The SeaWinds scatterometer on the QuikScat satellite simultaneously measures the active (scattering) and passive (emission) microwave characteristics of the earth\u27s surface. This paper describes the use of the QuikScat Radiometer (QRad) brightness temperature measurements to infer rain rate over the oceans. A discussion of the rain rate algorithm is presented, and comparisons are made with near-simultaneous integrated rain rate measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). These results demonstrate the potential for using QRad rain index to infer moderate to high rain rates over the ocean

Nasa Scatterometer Measurements Of Ocean Surface Winds In Tropical Cyclones

The NASA Scatterometer (NSCAT) has obtained realistic measurements of ocean surface winds in hurricanes and typhoons using an improved wind retrieval algorithm. This Spatial Adaptive Retrieval Algorithm (SARA) retrieves wind speeds at each scatterometer backscatter measurement location using an estimate of the wind direction from conventional NSCAT wind vector processing or using directions from a tropical cyclone surface wind numerical model. Also, SARA uses an improved geophysical model function, that has been especially adapted for high wind speeds, to relate the ocean\u27s normalized radar cross section, sigma-O, and the ocean surface wind vector