Improved Dynamic Power Allocation Scheme for Massive Connectivity in NOMA System
Bibliographic record
Abstract
In a wireless network, the radio resources are allocated to the users based on the Orthogonal Multiple Access (OMA) technique. The performance of the network can be improved by introducing non-Orthogonal multiple access (NOMA) whenever the number of users increases tremendously. There is much scope in improving the outage probability, bit error rate (BER), high spectral efficiency and latency. To improve this, Non-Orthogonal Multiple Access (NOMA) technique has been discussed in this paper with fair power allocation technique. The effect of fixed power and dynamic power allocation has been discussed and compared, the Bit error rate (BER) and achievable rate have been analyzed and compared. The NOMA uses Successive interference cancellation (SIC) at the receiver for decoding the individual user’s data which is a complex process. The effect of imperfect SIC has also been evaluated and analyzed. In dynamic Power allocation technique higher power is devoted to the far users due to which the near user goes into outage. A new technique has been proposed to reduce the outage power while taking the target rate and SNR of each user into consideration. The results show an improvement in outage probability in comparison with the state of art technique.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".