Web-based radio fading channel modeling, estimation and identification.
Bibliographic record
Abstract
This thesis focuses on the practical implementation issues for modeling, simulation, estimation and system identification of wireless fading channels. The state space models are introduced to describe fading channels including flat fading and frequency-selective fading channels. Without measurement data, simulation and estimation of fading channels are related to the motion of the transmitter or the receiver. The state space realizations and the Kalman filtering are derived for Rayleight fading, Ricean fading and frequency-selective fading channels. The inphase, quadrature and envelope components of the received signals are simulated and estimated through the state space realizations and the Kalman filtering. These model parameters are defined by the transfer function of a given Doppler power spectral density (DPSD) associated to several physical factors such as carried frequency, mobile moving speed, signal-to-noise ratio (SNR), an angle and an interval time. With measurement data, system identification is based on the Expectation-Maximization (EM) algorithm together with the Kalman filtering. These system parameters are computed recursively. Various identified results illustrate the processes of system identification for flat fading and frequency-selective fading channels based on the measurement data provided by Communications Research Center Canada (CRC). Measurement data could also be input by users. Finally, a web-based wireless fading channel simulation and estimation system is analyzed, designed and implemented using UML(TM) techniques and Java(TM) programming language. The web-based system can provide friendly interfaces to complete modeling, simulation, estimation and system identification of fading channels. The Web-based simulation and estimation system can be run on the following site: http://www.site.uottawa.ca/∼jzhan037/onlineSystem.html or http://www.site.uottawa.ca/∼chadcha/onlineSystem.html .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".