A comparative analysis of diversity combining techniques for repetitive transmissions in time spreading <scp>SCMA</scp> systems
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Bibliographic record
Abstract
Abstract Sparse Code Multiple Access (SCMA) is a recently introduced wireless communication network technology. There are various techniques in SCMA systems to increase the system's efficiency, and one of these techniques is time spreading. By adding repetitive transmission and time spreading into SCMA, it is shown in previous works that the Bit‐Error‐Rate (BER) results are improved convincingly. However, in the previous works, other diversity combining techniques have not been considered. This paper introduces a new approach to further improve the performance of repetitive transmission in SCMA systems with time spreading by adding imperialist competitive algorithm in diversity combining. Alongside, four different combining techniques; equal gain combining, maximal ratio combining, selection combining, and genetic algorithm are considered to bring comparative analysis to show the significance of the new technique. Results show that the proposed method offers up to 2.3 dB gain in terms of BER, under certain conditions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it