Performance Insights Into Neural Estimation in mm-Wave Systems
Why this work is in the frame
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Bibliographic record
Abstract
This work seeks to uncover the black-box nature of the Neural Networks (NNs) solutions in wireless systems. Specifically, this work examines direction and range estimation in millimetre-wave (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$mm$</tex-math></inline-formula>-wave) systems, particularly under real-world conditions influenced by the transceivers' hardware impairments (HWIs). As a case study, we focus on the Extreme Learning Machine (ELM) algorithm, widely used in the literature as a low-complexity and efficient NN-based solution for mitigating HWIs effects. To address the challenges associated with the hidden nature of NNs, the well-known metric, Mean Squared Error (MSE), of the ELM solution is derived and studied analytically. Simulation results validate the accuracy of the derived MSE expression, demonstrating the effectiveness of this approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it