Bibliographic record
Abstract
The authors analyze the performance of a switched diversity system operating on nonselective Nakagami fading channels. The analysis is based on a discrete time model, i.e. one switches only at discrete instants of time t=nT, where n is an integer and T is the symbol duration. They propose the following switch-and-stay strategy. Assuming a two-branch diversity system and that antenna 1 is being used at t=(n-1)T, one switches to antenna 2 at t=nT if the local power on antenna 1 at t=nT is below a predetermined threshold value, regardless of the local power in antenna 2 at t=nT. Switching from antenna 2 to antenna 1 is done in a similar manner. They derive expressions for the average bit error rate (BER) using noncoherent frequency shift keying (NCFSK) on slow, nonselective Nakagami (1960) fading channels. Both the cases of independent and correlated diversity signals are considered. The performance depends on the switching threshold chosen. They derive closed form expressions for the optimum switching threshold which minimizes the average BER. The results for a Rayleigh fading channel are obtained and presented as a special case of the more generalized fading models.>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".