Layered Division Multiplexing Enabled Broadcast Broadband Convergence in 5G: Theory, Simulations, and Scenarios
Bibliographic record
Abstract
The vision of the future 5G - Multicast Broadcast Services (5G-MBS) is to achieve full convergence of broadcast and broadband services by providing these services on the same infrastructure and dynamically switching between them without impacting user experiences. By incorporating Layered Division Multiplexing (LDM) into the new 5G-MBS system and performing proper antenna precoding, the network can transmit a 2-layered signal where the higher-power Core Layer (CL) transmits a Single Frequency Network (SFN) broadcast signal, and the lower-power Enhanced Layer (EL) is used for broadband services. To evaluate the performance of the 2-layered network, a 5G system-level simulator is created and configured according to the 3GPP self-evaluation scenarios to compare against the 3GPP calibration results. The resulting Signal to Interference & Noise Ratio (SINR) Cumulative Distribution Function (CDF) curves fall within the tolerance margin of 1~2 dB from the 3GPP calibration average. Full simulations of the 2-layered network show for an urban scenario with Inter-Site Distance (ISD) of less than 1 km, the network can provide up to three 4K video broadcast services in the CL while supporting a near full broadband network in the EL. For further ISDs up to 3 km, the network can sustain video broadcast service at 1080p while supporting a partial broadband network. For a rural scenario, at the reference ISD of 1732 m, the CL can support three 4k video broadcast services while the EL performance matches a standalone broadband network. Finally, for further ISD of up to 5 km, the CL can support one 1080p and one 720p video broadcast service, and for ISD up to 10 km, the network can provide one broadcast service at 720p in the CL, all while providing a full broadband network in the EL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".