Hybrid-Mux Signal Structure and Resource Allocation for In-Band Distribution Link and ITND Transmission in SFN Environment
Bibliographic record
Abstract
This paper investigates the multiplexing and resource allocation to improve the spectrum efficiency and reduce demultiplexing complexity and latency in the wireless in-band distribution link (IDL) and inter-tower networks and datacasting (ITND) for terrestrial broadcasting systems. A novel Hybrid-Mux technology is proposed, which combines orthogonal multiplexing (TDM/FDM), non-orthogonal multiplexing (NOM, or LDM), and hierarchical modulation (HM) with the non-uniform constellation (NUC). Hybrid-Mux technology can achieve high spectrum efficiency, low complexity, and low latency to provide versatile services of mobile/fixed broadcast services, inter-tower communication networks and datacasting, as well as the in-band distribution link to support SFN operation. Two Hybrid-Mux signal structures with 2-layer and 3-layer power-based non-orthogonal multiplexing of different broadcast and network data services are introduced. The throughput optimization of each Hybrid-Mux structure is formulated and resolved under the constraint of ITND and IDL data capacity, SNR requirement, and optimized NUC HM. Simulation results show that, by properly implementing resource allocation and constellation design, the proposed 3-layer Hybrid-Mux structures can achieve higher capacity than the simple 2-layer Hybrid-Mux structure, while the demodulation and demultiplexing complexity is not much increased. A 2-tier ITND service is proposed. It consists of a robust control and datacasting (ITCD) tier and a high data rate intertower networking (ITCN) link. This design can increase the total aggregated system data rate substantively. MIMO structures supporting ITND and IDL transmission are also proposed to further improve the spectrum efficiency.
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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 | 0.001 |
| 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".