Average SER Analysis for Layered Division Multiplexing System with Index Modulation
Why this work is in the frame
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Bibliographic record
Abstract
A novel Layered Division Multiplexing (LDM) With Index Modulation (LDM-IM) system is proposed in this paper. It employs the Index Modulation (OFDM-IM) technology to enhance the transmission performance of the original LDM system by transmitting extra bits through the Orthogonal Frequency Division Multiplexing (OFDM) subcarriers indices. The proposed system is based on a two-layer, Upper Layer (UL) and Lower Layer (LL), LDM system that serves two independent data services for at least two User Equipment(s) (UE) simultaneously. Besides this, by exploiting the Index Modulation (IM), each UE can receive the extra bits by decoding the subcarriers activation patterns. To map the extra bits to the subcarriers, a simple random codebook is designed in the proposed system based on the concept of OFDM-IM. To proof the availability and reliability of the proposed system, two metrics are chosen to evaluate the system performance, the average Symbol Error Rate (SER) and the transmission rate. In this paper, the architecture of the proposed system is introduced firstly. After that, the average SER of it is analyzed and verified by the Monte Carlo simulation. Finally, the transmission rate of the proposed system and the original LDM system is compared and evaluated.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
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 | 0.001 | 0.000 |
| 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 | 0.001 |
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