How Does Channel Coding Affect the Design of Uplink SCMA Multidimensional Constellations?
Bibliographic record
Abstract
Sparse code multiple access (SCMA) is a potential non-orthogonal multiple access candidate for future wireless systems. The key performance indicators (KPIs) of uplink SCMA multidimensional constellations (MdCs) that should be considered in their design process have recently been identified in conjunction with the LTE turbo code for different channel scenarios. However, it is questionable whether the same KPIs are applicable to designing MdCs when a different error correcting code is employed. In this paper, we investigate the effect of the high-rate and low-rate 5G low density parity check (LDPC) codes on determining KPIs in designing MdCs for uplink SCMA systems under various channel scenarios. Through simulations, we show that similar results to the LTE turbo coded case occur in the presence of 5G LDPC code, with one notable exception over one specific scenario. The exception is in the performance of one MdC, which has a low number of distinct points; its performance is significantly worse than predicted by the KPIs when the low-rate 5G LDPC code is employed. This phenomenon happens due to the inherent structure of the 5G LDPC code, in which we propose a pseudorandom interleaver to rectify the problem.
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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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".