Cell search evaluation: A step towards the next generation LTE-MTC systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main features for future Machine Type Communication (MTC) cellular networks enable low-power, low-cost, narrow band, and extended coverage system. To support these features, new challenges for the system design have emerged. For instance, the device shall be able to setup a call at a signal to noise ratio (SNR) of -15dB in the extended coverage mode with only one receive antenna and almost no frequency diversity. For these reasons, we present an evaluation to the conventional cell search and initial synchronization algorithms subject to these new hard requirements. The performance of most of the algorithms can be enhanced by utilizing time averaging on the account of increasing the processing time. By simulating exact LTE-MTC system, the performance of various algorithms is obtained with the expected time budget to meet LTE-MTC specifications, if applicable.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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