Non-Orthogonal Multiple Access: The Case of Improper Gaussian Signaling and Imperfect Successive Interference Cancellation
Bibliographic record
Abstract
This paper studies a two-user downlink non-orthogonal multiple access (NOMA) system that adopts an improper Gaussian signaling (IGS) strategy to compensate for the performance loss due to imperfect successive interference cancellation (SIC). Joint optimization problems are formulated to maximize the overall spectral efficiency and energy efficiency of a two-user NOMA system under minimum user-rate requirements and total power constraints. Sub-optimal solutions of IGS circularity coefficients and power allocation are obtained for the formulated problems. Furthermore, improper constellation diagrams are designed using widely linear transformation with predefined optimized IGS coefficients to study the impact of IGS on throughput and error performance. Simulation results show that the performance of IGS-based NOMA systems, where the IGS strategy is adopted by both users, outperforms that of the proper Gaussian signal (PGS)-based NOMA system under imperfect SIC.
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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 | 0.002 | 0.005 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".