Ultra-Reliable Energy-Efficient Cooperative Scheme in Asynchronous NOMA With Correlated Sources
Bibliographic record
Abstract
Massive Internet-of-Things is proposed in fifth generation networks to serve the sporadic traffic generated by devices operating under tight resource constraints. In these applications the overhead for synchronization and control functions is comparable to the data size, hence the control/data ratio is very unfavorable. The proposed techniques in this paper take advantage of the fact that transmitters are privy to whole data in order to boost the spectral and power efficiency, while increasing the reliability. We propose identical content transmission over identical content transmission over NOMA (ICToNOMA) for transmitters with correlated sources, who cooperatively combine and transmit identical messages over consecutive data packets. Our proposed redundant transmission of data is not as straightforward as it seems, considering the asynchronous reception of the data streams at the receiver. Traditionally, it is believed that the substantial spectral efficiency (SE) achievements in nonorthogonal multiple access (NOMA) could be jeopardized in the asynchronous channels. We investigate the potency of successive interference cancellation (SIC), as the main block in current NOMA receivers, in asynchronous channels. By applying water-filling and geometric power allocation, we show that the SE degradation is caused by the nature of SIC. Moreover, we demonstrate that the SE is improved in asynchronous NOMA and ICToNOMA, by managing the channel's memory and correlation instead of canceling it. In addition, we propose our iterative joint detection and decoding (IJDD) receiver to outperform SIC in asynchronous NOMA receivers. Our extensive simulations show that ICToNOMA can outperform NOMA by providing a considerable boost in the channel reliability while increasing the spectral and power efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".