On the Design of Opportunistic MAC Protocols for Multihop Wireless ; Networks with Beamforming Antennas
Bibliographic record
Abstract
Beamforming antennas promise a significant increase in the spatial reuse of the wireless medium when deployed in multihop wireless networks. However, existing directional Medium Access Control (MAC) protocols with the default binary exponential backoff mechanism are not capable of fully exploiting the offered potential. In this paper, we discuss various issues involved in the design of MAC protocols specific for beamforming antennas. Based on our discussion, we argue that the traditional binary exponential backoff mechanism limits the possible spatial reuse and aggravates some beamforming-related problems such as deafness and head-of-line blocking. To grasp the transmission opportunities offered by beamforming antennas, we design an Opportunistic Directional MAC (OPDMAC) protocol for multihop wireless networks. The OPDMAC protocol employs a novel backoff mechanism in which the node is not forced to undergo idle backoff after a transmission failure but can rather take the opportunity of transmitting other outstanding packets in other directions. This mechanism minimizes the idle waiting time and increases the channel utilization significantly and thereby enables OPDMAC to enhance the spatial reusability of the wireless medium and reduce the impact of the deafness problem without additional overhead. Through extensive simulations, we demonstrate that OPDMAC enhances the performance in terms of throughput, delay, packet delivery ratio, and fairness. To further improve its performance, we discuss and evaluate the benefits of carefully choosing some protocol parameters instead of using the default values commonly used by other directional MAC 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".