Delay Minimization for Massive Internet of Things With Non-Orthogonal Multiple Access
Bibliographic record
Abstract
Non-Orthogonal Multiple Access (NOMA) provides potential solutions for the stringent requirements of the Internet of Things (IoT) on low latency and high reliability. In this paper, we jointly consider user scheduling and power control to investigate the access delay minimization problem (ADMP) for the uplink NOMA networks with massive IoT devices. Specifically, the ADMP is formulated as a mixed-integer and non-convex programming problem with the objective to minimize the maximum access delay of all devices under individual data transmission demand. We prove that the ADMP is NP-hard. To tackle this hard problem, we divide it into two subproblems, i.e., the user scheduling subproblem (USP) and the power control subproblem (PCP), and then propose an efficient algorithm to solve them in an iterative manner. In particular, the USP is recast as a K-CUT problem and solved by a graph-based method. For the PCP, we devise an iterative algorithm to solve it optimally leveraging the standard interference function. Simulation results indicate that our algorithm has good convergence and can significantly reduce the access delay in comparison with other schemes.
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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) | 0.001 | 0.001 |
| 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".