On the Asymptotic Analysis of Average Interference Power Generated by a Wireless Sensor Network
Bibliographic record
Abstract
Massive deployments of wireless sensor networks (WSNs) are expected in near future. In one of the most likely scenarios, these WSNs would share a licensed frequency band with a primary user. So, it is essential to understand the behavior of the interference generated by a WSN towards the primary user. This paper provides an asymptotic analysis of the average interference power generated by a WSN. The analysis is extended to a special but important shape of a sensor field. This shape can be used to provide an upper bound of the average interference power generated by any sensor field with an arbitrary shape. The paper shows that the expansion of the sensor field does not necessarily cause an increase in the average interference power. For most practical values of path loss exponent, the average interference power asymptotically approaches constant levels with the increase in the field size provided that the minimum distance from the field to the primary user is fixed. The paper provides expressions for these constants. Moreover, results indicate that a key parameter in determining the average interference power is the ratio of the radial depth of the field to the minimum distance from the field to the primary user. Also, this paper illustrates how a WSN can be equivalently represented by a single virtual node producing the same level of average interference power.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".