A Two-Channel Medium Access Control Protocol for Mobile Ad Hoc Networks using Directional Antennas
Bibliographic record
Abstract
In recent years, the use of directional antennas in wireless networks has been widely studied. Since the medium access control (MAC) protocol of the IEEE 802.11 standard is designed for the use of omnidirectional antennas, it cannot perform efficiently when directional antennas are used. In this paper, we study the performance of an efficient two-channel MAC protocol for ad hoc networks when equipped with directional antennas. The proposed protocol utilizes the large throughput offered by directional antennas using two frequency division multiplexed channels. The first channel is used for control information and the second for user data transmission. Based on this, the proposed MAC protocol operates in two main modes, the omnidirectional mode where one antenna is used for the transmission of users' control frames, and the directional mode where antenna arrays are used for the transmission of data frames. The proposed protocol is assessed using computer simulations based on randomly generated network topologies reflecting the random movement of nodes in the network. Based on these random topologies, we present performance comparisons with the existing MAC protocols using different system parameters. In all cases, the proposed MAC protocol is shown to offer a significant throughput improvement relative to the existing protocols
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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 | 0.002 | 0.003 |
| 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 | 0.002 |
| 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".