SNR Estimation for FM-DCSK System over Multipath Rayleigh Fading Channels
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
In this paper, we deal with the problem of maximum likelihood (ML) estimation of the signal-to-noise ratio (SNR) parameter for frequency modulated differential chaos shift key (FM-DCSK) system over multipath Rayleigh fading channels. The ML estimators are derived for various scenarios including data-aided (DA), non-data aided (NDA) and joint DA-NDA estimation by using both the data and pilot symbols. For comparison purposes, the Cramér- Rao lower bounds (CRLBs) for the SNR estimators are derived. The performance of the estimators is evaluated by simulations and comparing with CRLBs in terms of the mean-square-error. Simulated results show that for a large spreading factor the proposed scheme performs well over a wide SNR range in comparisons with CRLBs
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.
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 | 0.000 |
| 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) | 0.000 | 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