A Spectral and Energy Efficient Noise Variance and SNR Estimator for DMH OFDM-IM Systems
Bibliographic record
Abstract
The classical orthogonal frequency division multiplexing (OFDM) systems gained significant new dimensions with the introduction of index modulation (IM) schemes. However, reduced data rates are the drawback in IM-based systems when implemented using higher-order modulation techniques. Hence, to improve the data rate, we proposed a new OFDM-IM system by varying the inactive subcarriers in in-phase and quadrature-phase in every subblock, namely, a dual-mode homogenous OFDM-IM (DMH OFDM-IM) system. Furthermore, we introduce a novel noise power and signal-to-noise ratio (SNR) estimation algorithm for the proposed system, which operates over a Nakagami-m fading channel. The proposed estimation algorithm makes use of nulled subcarriers available in every subblock of the proposed system to estimate noise power. The introduced estimator is both spectral and energy efficient as it uses inactive subcarriers that carry no energy. Simulation results emphasize that the developed estimator achieves lower noise power and estimates the SNR at an ideal value in contrast to the existing estimators of OFDM system. Moreover, differential noise power (DNP) is determined for the proposed system (DMH OFDM-IM) to track channel variations effectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".