On Wireless Ad Hoc Networks with Directional Antennas: Efficient Collision and Deafness Avoidance Mechanisms
Bibliographic record
Abstract
Wireless ad hoc networks allow anywhere, anytime network connectivity with complete lack of central control, ownership, and regulatory influence. Medium access control (MAC) in such networks poses extremely timely as well as important research and development challenges. Utilizing directional antennas in wireless ad hoc networks is anticipated to significantly improve the network performance due to the increased spatial reuse and the extended transmission range. Nevertheless, using directional antennas in wireless ad hoc networks introduces some serious challenges, the most critical of which are the deafness and hidden terminal problems. This paper thoroughly explores these problems, one of which is discovered and reported for the first time in this paper. This paper also proposes a new MAC scheme, namely, directional MAC with deafness avoidance and collision avoidance (DMAC-DACA), to address both problems. To study the performance of the proposed scheme, a complete directional communication extension to layers 1, 2, and 3 is incorporated in the ns2 simulator. The simulation results show that DMAC-DACA significantly enhances the performance and increases the network throughput. This paper also reveals that deafness has a greater impact on network performance than the hidden terminal problem.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".