Moment-Based SNR Estimation for SIMO Wireless Communication Systems Using Arbitrary QAM
Bibliographic record
Abstract
A new method for signal-to-noise ratio (SNR) estimation is considered when multiple receiving antenna elements receive quadrature amplitude modulation (QAM) signals in complex additive white Gaussian noise (AWGN) spatially and temporally white (uncorrelated between antenna elements). In this paper, we also present the extension of other existing methods to the single input multiple output (SIMO) configuration. The procedure is non-data-aided (NDA) since it is a moment- based method and does not require, therefore, the a priori knowledge or the detection of the transmitted symbols. Monte Carlo simulations are used to estimate the normalized root mean square error (NRMSE) as a measure of performance. The new method is shown to outperform the best NDA moment-based SNR estimation methods, namely the M2M4and Gao's methods even when they are extended to the SIMO configuration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".