Bibliographic record
Abstract
Wireless communication has grown exponentially in the last few decades.Hence, the demand for more throughput and diverse communication standards (such as GSM, Bluetooth, WiFi, LTE), have increased over this time.Software-defined radio (SDR), which aims to be easily programmable, is a good candidate to meet current market demands.However, a number of technical challenges (e.g., dynamic range, wide band, sampling frequency, number of bits of the ADCs and power consumption, etc.) need to be addressed to make SDR a viable solution.This is in addition to the challenges presented by the architecture proposed by [1] -[8].The design simplicity together with wideband characteristics of Multi-Port receiver structures provides a RF architecture that can solve many of the current SDR challenges.Multi-Port receivers use diodes as power detectors and in this work, contrasted to other results published in the literature, we not only provide a controlled continuous bias to the diodes, we also propose a novel blind algorithm that reduces the Error Vector Magnitude (EVM) by adaptively controlling the diode bias point.Another key feature of optimum diode bias control is the Local Oscillator (LO) power requirements decrease.Results presented show that a LO power variation of more than 10dB produces no EVM degradation.We also developed a novel methodology for estimating the initial diode bias voltage for the optimizer.Although we simulated the methodology for four different Schottky diodes from different vendors (Win Corp, HP, Hitachi and Siemens), the process can be applied to other diodes.The initial value is located at the maximum of the second derivative of the I-V curve.We also investigated how memory effects affect the performance of the Multi-Port receivers.The results obtained in this investigation allow us to simplify the representation of the diode to a finite power series.We also introduced a technique to mitigate high-order nonlinearities in the Multi-Port receivers.To verify the results of this research we used a Simulink model that emulates the radio frequency (RF) and digital baseband sections of a Six-Port receiver.had the pleasure of working with a great number of professors and colleagues whose contributions to my research and the creation of the thesis deserved special mention.It is a pleasure to convey my gratitude to them all in this humble acknowledgment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".