Fast packet transmission to a receiver with an adaptive antenna array
Bibliographic record
Abstract
We consider fast packet transmission in a multi-user system to a receiver with an adaptive antenna array. A packet length of 53 bytes is assumed and the preamble length is usually 5 bytes. Each packet is taken to be from a different user and thus the array weights for beamforming must be determined from the preamble for each unique packet. The array weights are determined using the direct matrix inversion algorithm and are held fixed over the data portion of the packet to provide a nearly uniform error distribution to each data bit in the packet. Both Gaussian noise and fading channels are considered. The major findings are as follows: (a) for a fixed preamble length, there are an optimum number of array elements to provide the minimum bit-error-probability, (b) performance losses due to adaptive operation are less on fading channels than on Gaussian noise channels and (c) on fading channels the performance can improve with an increase in fading bandwidth.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".