Distributed Correlative Power Control Schemes for Mobile<i>Ad hoc</i>Networks Using Directional Antennas
Bibliographic record
Abstract
Medium access control (MAC) protocols simultaneously integrating transmission power (TP) control with directional antennas have the potential to enhance both energy savings and capacity throughput in wireless multihop Mobile Ad hoc NETworks (MANETs). In this paper, we present a model to calculate future interference in networks with directional antennas, and based on this model, we derive some relations that should exist between the required TP of RTS, CTS, DATA, and ACK frames for successful data packet delivery in MANETs based on the directional version of the IEEE 802.11 distributed coordination function. From these relations, we propose a distributed power control scheme. Furthermore, we show, via simulations, that the true potentials from the proposed control scheme cannot be shown due to the imperfection of the derived model. Based on these observations, we introduce another class of power control algorithm that instead deploys a prediction filter (Kalman or extended Kalman) to estimate future interference. Simulation experiments for different topologies are used to verify the significant throughput and energy gains that can be obtained by the proposed power control 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".