Outage probability and capacity analysis of the collaborative NOMA assisted relaying system in 5G
Bibliographic record
Abstract
It is challenging for the base station (BS) to serve multiple cell-edge users concurrently with data rate guarantee due to limited power and spectrum resource. Using the relay enhances cell-edge user received signal strength by short-distance communication in low transmit power. Non-orthogonal multiple access (NOMA), a promising spectral efficient technology for the 5-th generation network (5G), enables the relay to transmit multiple messages to cell-edge users concurrently. In this paper, a Collaborative NOMA Assisted Relaying (CNAR) system for 5G is proposed with the collaboration of the source-relay (S-R) and relay-destination (R-D) NOMA link. The relay decodes its own message from the S-R NOMA signal and transmits the remaining part with adjusted power to cell-edge users in the R-D link. Then the exact expression of system outage probability is derived by analyzing the outage behavior in S-R and R-D links separately. To guarantee the data rate, the optimal power allocation among NOMA users is provided by minimizing the outage probability. To further characterize the system performance, the ergodic sum capacity in high SNR regime is approximated from discussions on the interference at cell-edge users. Simulation results validate our mathematical analysis, and show that the relaying system assisted by NOMA achieves lower outage probability and higher sum capacity than orthogonal multiple access (OMA).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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 | 0.000 |
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".