On Maximizing the Data Volume in a Wireless Sensor Network with Time-Varying Channels
Bibliographic record
Abstract
In this paper we present an analytical solution to the problem of jointly optimizing transmit power and scheduling for a star network of wireless energy-limited sensors around the base station. We have included factors which are usually neglected in modeling sensors and their communication channel. These include: accounting for power utilized for activities other than transmission, i.e. processing power, and time variations of the channel gains throughout the lifetime of the sensor. With an information theoretic approach to this problem, we have avoided the details of modulation and pulse shaping, and accounted for the mutual interference in simultaneous sensor transmissions. We employ the volume of data received at the base station over the lifetime of all sensors as the system performance measure and prove that in the optimal scheduling scheme sensors should avoid simultaneous transmissions. In addition, we show the optimal transmit powers that maximize the data volume can be determined via water-filling over the sensor's lifetime once the processing power and the single sensor transmission schedule are accounted for. In this work we have assumed prior knowledge of the channel gains throughout the lifetime of all sensors. Nevertheless, the solution will provide an optimistic value for the network's maximum data volume achievable by a causal scheduler.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".