A Hybrid LDM, TDM/FDM and Hierarchical Modulation Signal Structure for In-band Distribution Link Transmission in SFN
Bibliographic record
Abstract
This paper investigates the multiplexing technology to improve the wireless in-band distribution link (IDL) spectrum efficiency and reduce demultiplexing complexity. A Hybrid-Mux technology is proposed for high spectrum efficiency (compared to TDM/FDM), and low complexity and latency (compared to LDM). A 2-layer structured HM based on 2-layer LDM, and a 3-layer structured HM based on 3-layer LDM are introduced. The key signal structure of Hybrid-Mux and several typical cases of applications are presented, where the system complexity and capacity requirement can be satisfied. The throughput optimization is formulated and resolved, under the constraint of IDL capacity and size of constellation. Simulation results show that by using HM, Hybrid-Mux can achieve higher capacity than TDM and LDM only approach, while the demodulation and demultiplexing complexity of Hybrid-Mux is not much higher than the structure where IDL is TDM-ed with existing LDM signal, and is lower than the structure where IDL is TDM-ed with enhanced layer (EL) signal. The difference of LDM vs. HM in the design and implementation are discussed.
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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 | 0.000 | 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) | 0.001 | 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".