NOMA Receiver Design for Delay-Sensitive Systems
Bibliographic record
Abstract
Successive interference cancelation (SIC) has been considered widely for the detection of downlink nonorthogonal multiple access (NOMA) signals. However, the sequential detection inherent to SIC process may introduce additional time delay for certain users, making the SIC unsuitable for communication systems with time delay constraints such as wireless networks that utilize unmanned aerial vehicles or low earth orbit satellites. Therefore, this article considers the performance of NOMA systems using a joint multiuser detector (JMuD), which can detect the signals of all users simultaneously and, hence, reduce the detection time requirements. The performance of the JMuD is evaluated in terms of bit error rate (BER), computational complexity, and processing time and compared to the SIC detector (SICD). The exact BER of the JMuD is derived analytically using quadrature phase shift keying modulation where closed-form expressions are derived for the two- and three-user scenarios for the air-to-ground channel, which is modeled as a Rician fading channels with order statistics. The obtained analytical results corroborated by Monte Carlo simulation confirm that the BERs of the JMuD and SICD are identical; however, the processing time of the SICD is 51% more than the JMuD for several cases of interest.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".