Using Layered-Division-Multiplexing to Achieve Enhanced Spectral Efficiency in 5G-MBMS
Bibliographic record
Abstract
3GPP is pursuing a multimedia broadcast multicast service subsystem in 5G (5G-MBMS) based on the latest LTE further evolved MBMS (feMBMS) system in Rel. 14. In this paper, Layered Division Multiplexing (LDM) is proposed as a new addition to the 5G technology toolbox for achieving significantly enhanced spectral efficiency for the 5G-MBMS to efficiently deliver mixed broadcast and unicast services. A capacity analysis is first conducted to derive the capabilities of an LDM-based 5G-MBMS system to deliver broadcast and unicast services in the different signal layers, under the conditions with severe co-channel interference (CCI). It is shown that using LDM can provide a nearly full-capacity unicast network on top of high-quality broadcast network. The broadcast capacity is mainly determined by the power allocations between the two signal layers. The unicast capacity can be maximized by deliberately operating the system in a CCI-limited mode. Simulation results are presented to demonstrate the capacity gain that can be achieved by incorporating LDM in the 5G-MBMS system. This study shows that LDM is one of the enabling technologies to close the gap between the LTE feMBMS capability and the 5G-MBMS requirements.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".