Intelligent NOMA-Based Wireless Backhauling for IoT Applications without End-Device CSI
Bibliographic record
Abstract
The article introduces an innovative wireless backhauling approach employing non-orthogonal multiple access (NOMA) and automatic repeat request (ARQ) mechanisms. In this novel scheme, power allocation follows a round-robin (RR) method, ensuring equitable performance among paired users. To address the potential packet loss afterARQ, an intelligent packet repair technique is incorporated to recover the dropped packets. A key feature involves storing dropped data packets for subsequent processing before forwarding to their respective IoT devices (IoDs). The proposed methodology hinges on recognizing that interference within a dropped packet may correspond to a packet retrievable in a forthcoming transmission, facilitating recovery through iterative successive interference cancellation (SIC). Significantly, the scheme enhances data reliability without necessitating an increase in the ARQ retransmission limit, which makes it particularly suited for certain Internet of things (IoT) applications. Empirical results confirm a substantial success rate in recovering dropped packets. Notably, the iterative interference cancellation (IIC) technique demonstrated a noteworthy reduction in the packet drop rate (PDR) from 10 −1 to 10 −3 , representing a 100-fold improvement. This implies the successful recovery of 99% of the packets initially dropped in specific scenarios, showcasing the efficacy of the proposed approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".