Outage Balancing in Downlink NOMA Over Nakagami-<i>m</i> Fading Channels
Bibliographic record
Abstract
In this paper, we investigate joint optimization of power allocation factors (PAFs) and decoding order of users in the downlink of a non-orthogonal multiple access (NOMA) system over Nakagami- m fading channels. The objective is to guarantee fairness among the users in terms of outage probability (or its complement success probability). To this end, we maximize the minimum success probability (Max-MSP) among the users when only statistical channel state information (CSI) is available at the transmitter side. We solve such a problem by first proving that at the optimal solution, all the users have a common success probability (CSP), and then proposing an efficient algorithm for finding CSP of the users. The optimal PAFs and optimal decoding order are derived in closed form based on the CSP and statistical CSI of the users. It is proved that the proposed algorithm always converges to a unique optimal solution. Furthermore, we show that in contrast to Rayleigh fading channels, in Nakagami- m fading channels, the optimal decoding order not only depends on the average signal-to-noise ratio (SNR) of each user but also depends on the variance of the SNR and data rate of that user.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".