NOMA-Assisted On-Demand Transmissions for Monitoring Applications in Industrial IoT Networks
Bibliographic record
Abstract
In industrial IoT networks, the critical information of monitoring applications is required to be delivered with high reliability and low latency. Moreover, different monitoring applications usually have heterogenous requirements on the transmission performance, and sensors deployed in the field to collect and deliver information are generally powered with batteries. In order to alleviate the restriction on transmission reliability, transmission capacity and energy efficiency, this paper proposes a NOMA-assisted on-demand transmission scheme for monitoring applications in industrial IoT networks. Then, an constrained optimization problem is formulated to maximum the energy efficiency under constraints of heterogenous transmission requirements and limited spectrum resources. In the solution process, the proposed scheme determines the successive interference cancellation (SIC) decoding order by taking advantage of the heterogenous requirements of different applications, which significantly reduces the solution complexity caused by the tight coupling of decoding order, power control and channel assignment. Moreover, the pairwise matching based algorithm and the minimum cost flow based algorithm is designed to solve the formulated mixed integer programming problem effectively. Finally, simulation results demonstrate that the proposed scheme could meet the transmission requirements for heterogenous monitoring applications with limited spectrum resources and has advantages on improving the energy efficiency for the industrial IoT network with battery-powered sensors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".