Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) is a promising multiple access scheme for Fifth Generation (5G) cellular networks as it provides a high spectral efficiency to meet the demands of new wireless applications, such as Internet-of-Things (IoT). Low density spreading (LDS) is a code-domain NOMA technique that spreads users' symbols with spreading sequences that contain a low number of nonzero chips. This low-density structure allows for detection using the belief propagation-based message passing algorithm (MPA). MPA is a suboptimum detector that converges to the optimum maximum a posteriori (MAP) detector with reduced complexity, but the algorithm complexity is exponentially proportional to the number of interfering users, which can be prohibitive when the system is operating near peak load. We propose an alternative detector based on the minimum mean square error (MMSE) and parallel interference cancellation (PIC) detectors which offer complexity that is quadratic to the number of users. Simulations show with a system load of 150%, the number of multiplications, additions and exponentials are reduced by 81.8%, 67.8% and 97.9% respectively with a penalty of about 0.25 dB at an error rate of 10-3.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".