Bibliographic record
Abstract
The performance of preamble detection in Physical Random Access Channel (PRACH) for long term evolution (LTE) and its advanced degrades when channels become more time dispersive, such as Extended Typical Urban (ETU). However, the Base Station (BS) target received power is determined by users with time dispersive channels, in order to keep the acceptable performance for most of users in a cell. Motivated by the idea of collecting the energy of multiple paths, the optimal statistic is derived in this paper given the Power Delay Profile (PDP) of Rayleigh fading channels. An analytical framework of performance is also proposed in this paper and verified by simulation. It is shown that the proposed optimal statistic increasingly outperforms the traditional peak detecting when time-dispersion of channels increases. Considering the difficulty of achieving the accurate PDP in some scenarios, a suboptimal but robust solution is further developed. Compared with peak detecting, the suboptimal solution lowers the BS's target received power by over 1.1dB for Format 0 PRACH with both 2 and 4 receive antennas in the ETU channel, significantly reduces the user's power consumption and improves the LTE PRACH coverage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".