Interference Management Using Cooperative NOMA in Multi-Beam Satellite Systems
Bibliographic record
Abstract
In this paper, we propose overlay coding scheme as the capacity achieving multiple access technique, i.e., transmitting over non-orthogonal channels. We employ the cooperative non-orthogonal multiple access (NOMA) in multi-beam satellite systems with dense frequency reuse. The overlay coding uses the cooperation of the strongest co-channel interference (CCI) as extra source of information, where the data intended for the target user is shared between the cooperating beams. The involved beams cooperate in jointly transmitting the data to the target user at the same time. Thus, the target user receives a signal containing the aggregate of the data streams from cooperating beams, similar to a multiple access channel (MAC). Our proposition is based on the duality theorem of MAC and broadcast channels (BC) capacity regions. Hence, by employing successive interference cancellation (SIC) both data could be recovered, as proposed in NOMA. In order to employ overlay coding in multibeam satellite systems, we propose an approach based on optimized user pairing strategies. We devise an information theoretic framework followed by simulation to compare different strategies by evaluating the aggregate data rate in the beam of interest. Being based on SIC, the existence of residual errors will degrade the spectral efficiency gain in overlay coding. We investigate the effect of channel signal to noise plus interference ratio (SNIR) estimation errors. Finally, it is verified by simulation that these issues can be overcome and overlay coding can reach expected data throughput very close to the cases with perfect channel estimation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".