Signal Superposition in NOMA With Proper and Improper Gaussian Signaling
Bibliographic record
Abstract
Recent studies of single-cell two-user networks have shown that a higher network throughput is achieved by using a common message to be decoded by both users and conveying partial information for both users, rather than using the common message to convey the entire information for one of the two users. The latter is essentially the conventional non-orthogonal multiple access (NOMA), which performs better than orthogonal multiple access (OMA) only under users' dissimilar channel conditions. Unlike NOMA, the former performs consistently better than OMA. This paper generalizes such a signaling strategy to a general multi-cell multiuser network, which leads to a new NOMA approach (called n-NOMA) in which each pair of users decodes a message that conveys partial information for one of them only. Unlike the conventional NOMA, whose performance is dependent on the users' pairing strategy, the proposed n-NOMA consistently outperforms both NOMA and OMA schemes. Both proper and improper Gaussian signaling is considered for all the concerned schemes and it is shown that the latter is clearly more advantageous than the former.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".